PhD Research on Landscape Management by Jorge Rubiano

Mapping and modelling landscape stakeholders' visions in Sherwood Natural Area

Jorge EliĂŠcer Rubiano Mejia

Thesis submitted to the University of Nottingham for the degree of Doctor of Philosophy

Nottingham, United Kingdom, 2003

Dedicated to Luna For sharing with me her life and the fascinating road of knowledge

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Acknowledgements

Thanks must go to my supervisors Dr. Roy Haines-Young and Dr. Georgina Endfield for their guidance and support during the years it took to complete this research as well as to Dr. Charles Watkins for his comments and his willingness to act as a pilot test for several of the techniques used during the study. Special thanks to my wife, Liliana Hurtado, for her commitment in reading the numerous drafts of this report. Thanks must also go to those members of the organisations I approached during my time in the area of Nottinghamshire: Gordon Hewston (Sherwood Forest Trust), Andy Wicham (Nottingham County Council), Brian Clarke (National Farmers Union), Simon Bell (Forestry Commission), Ian Butterfield (English Nature), Nigel Hunston (Bassetlaw District Council), Peter Winstanley (Newark and Sherwood District Council), Shawn Galeguer (Nottinghamshire Wildlife Trust), Austin Brady (Forestry Commission) and to many others who directly or indirectly facilitated my work during my time in Nottinghamshire. Thanks too to Dr. Simon Cook (CIAT) for his guidance in testing the probabilistic models. Many thanks also to Christopher Pallaris for his help in correcting the misspellings natural to a foreigner. Special thanks to the UK - Forestry Commission and to the International Office of the University of Nottingham for the funds allocated to carried out this project.

TABLE OF CONTENTS

CHAPTER 1......................................................................................................................... 1 GENERAL INTRODUCTION ............................................................................. 1 1.1 Background to the research...................................................................... 1 1.2 Justification for the research .................................................................... 3 1.3 Objectives of this study............................................................................. 7 1.3.1 Main Objective ......................................................................................................... 7 1.3.2 Specific Objectives .................................................................................................. 7

1.4 Research problem and hypotheses.......................................................... 7 1.5 Methodology............................................................................................... 8 1.6 Outline of thesis ....................................................................................... 10 CHAPTER 2 ..................................................................................................... 12 THE SHERWOOD NATURAL AREA .............................................................. 12 2.1 Introduction .............................................................................................. 12 2.2 Location and boundaries......................................................................... 12 2.3 History....................................................................................................... 13 2.4 Natural Components ................................................................................ 17 2.5 Land use components ............................................................................. 20 2.6 Social and economic aspects ................................................................. 24 2.7 Conclusions.............................................................................................. 26 CHAPTER 3 ..................................................................................................... 30 'VISIONS': A REVIEW OF METHODS ............................................................ 30 3.1 Introduction .............................................................................................. 30 3.2 Capturing Visions..................................................................................... 31 3.2.1 What is a 'vision'? ..................................................................................................31 3.2.2 How to capture stakeholdersâ&#x20AC;&#x2122; visions?.................................................................33 v

3.3 Research approach .................................................................................. 34 3.3.1 Stakeholder Analysis .............................................................................................34 3.3.2 Soft systems ...........................................................................................................38

3.4 Research Techniques .............................................................................. 43 3.4.1 Documentation review and text analysis .............................................................43 3.4.2 Personal interviews ................................................................................................44 3.4.3 Qâ&#x20AC;&#x201C;methodology......................................................................................................45 3.4.4 Geographic Information Systems (GIS) and Remote Sensing ..........................48 3.4.5 Landscape Ecology and Ecosystem Management.............................................50 3.4.6 Integrated Models ..................................................................................................54 3.4.7 Bayesian Methods .................................................................................................55

3.4.7.1 Logistic Regression Modelling....................................................... 56 3.4.7.2 Weights of Evidence (WofE) ......................................................... 57 3.4.7.3 Bayesian Belief Networks (BBN)................................................... 60 3.5 Conclusion................................................................................................ 62 CHAPTER 4 ..................................................................................................... 65 BUILDING MODELS OF STAKEHOLDERS' VISIONS IN SHERWOOD NATURAL AREA............................................................................................. 65 4.1 Introduction .............................................................................................. 65 4.2 Methods .................................................................................................... 65 4.2.1 Text Analysis of institutional documents ..............................................................66 4.2.2 Graphical modelling...............................................................................................68

4.3 Results ...................................................................................................... 69 4.3.1 Text Analysis ..........................................................................................................69 4.3.2 Graphical modelling...............................................................................................71

4.4 Conclusions.............................................................................................. 72 CHAPTER 5 ..................................................................................................... 77 GATHERING EVIDENCE OF STAKEHOLDERS' LANDSCAPE PREFERENCES IN SHERWOOD NATURAL AREA...................................... 77 5.1 Introduction .............................................................................................. 77 5.2 Methods .................................................................................................... 77 5.2.1 Stakeholder selection............................................................................................77 5.2.2 The interviews ........................................................................................................78

5.3 Results and discussion ........................................................................... 80 5.3.1 The interview..........................................................................................................80 5.3.2 Landscape components and processes in Sherwood Forest............................81 5.3.3 Spatial analysis of preferred places .....................................................................84 vi

5.4 Conclusions.............................................................................................. 89 CHAPTER 6 ..................................................................................................... 93 BUILDING A GEOGRAPHICAL INFORMATION DATABASE FOR SHERWOOD NATURAL AREA ...................................................................... 93 6.1 Introduction .............................................................................................. 93 6.2 Methodology............................................................................................. 93 6.3 Results ...................................................................................................... 94 6.4 Discussion and Conclusions ................................................................ 102 CHAPTER 7 ................................................................................................... 105 PROBABILISTIC MODELS OF STAKEHOLDERS' VISION IN SHERWOOD NATURAL AREA........................................................................................... 105 7.1 Introduction ............................................................................................ 105 7.2 Methodology........................................................................................... 105 7.2.1 Model Building......................................................................................................106 7.2.2 Model Assessment ..............................................................................................110

7.2.2.1 Accuracy ..................................................................................... 110 7.2.2.2 Consistency and completeness................................................... 110 7.2.2.3 Model comparisons ..................................................................... 112 7.3 Results .................................................................................................... 112 7.3.1 The stakeholders' vision spaces.........................................................................112 7.3.2 Model Assessment ..............................................................................................114

7.4 Conclusions............................................................................................ 116 CHAPTER 8 ................................................................................................... 123 LANDSCAPE PATTERN ANALYSIS OF STAKEHOLDERSâ&#x20AC;&#x2122; VISIONS IN SHERWOOD NATURAL AREA .................................................................... 123 8.1 Introduction ............................................................................................ 123 8.2 Methodology........................................................................................... 123 8.3 Results .................................................................................................... 125 8.3.1 Landscape composition.......................................................................................125 8.3.2 Landscape Structure ...........................................................................................132

8.4 Discussion and conclusions ................................................................. 135 vii

CHAPTER 9 ................................................................................................... 143 RE-VISITING STAKEHOLDERS’ VISIONS IN SHERWOOD NATURAL AREA ....................................................................................................................... 143 9.1 Introduction ............................................................................................ 143 9.2 Methodology........................................................................................... 143 9.3 Results .................................................................................................... 146 9.4 Discussion and conclusions ................................................................. 151 CHAPTER 10 ................................................................................................. 154 SYNTHESIS................................................................................................... 154 10.1 The soft systems approach ................................................................. 154 10.2 The stakeholders’ visions.................................................................... 158 10.3 Revisiting the objectives ..................................................................... 159 10.4 Implications for further research and landscape management strategies of SNA ......................................................................................... 160

BIBLIOGRAPHY………………………………………………………………….. 162 Appendix A: Text analysis and Graphical Modelling…………………… 187 Appendix B: Logistic Regression………………………………………… 202 Appendix C: Landscape Patch Analysis Tables………………………… 205 Appendix D: Q-Analysis Output ……………………………………...…. 216

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TABLE OF FIGURES

Figure 1.1: Outline of thesis structure ..................................................................................... 11 Figure 2.1: Location of Sherwood Natural Area....................................................................... 13 Figure 2-2 Local areas in Sherwood Natural Area (Source NCC, 2000) .................................. 14 Figure 2.3: Hydrology of Sherwood Natural Area showing Nitrate Sensitive Areas (SA) (Source: MAFF, 2000 and BGS, 2000) .......................................................................................... 23 Figure 2.-4: Nottinghamshire – Population growth 1891-1991 (Source: Office for National Statistics – Census Division, 2001).................................................................................. 28 Figure 2.5: Graphic representations of NCC indicators (source: NCC, 2000)........................... 29 Figure 3.1: Summary of methods and their outputs when studying ‘visions’ as systems and their location in the current investigation.................................................................................. 31 Figure 3.2: The seven stages in the use of soft systems (Source Checkland, 1991 p163) ....... 40 Figure 3.0-3: The fundamental logic of IS (after Winter et al, 1995 p132) ................................ 41 Figure 3.4 Conceptual model of the landscape stakeholders' capturing vision system process.64 Figure 4.1: Concordance HTML consultation interface............................................................ 73 Figure 5.1: Detail of poster used for data collection showing the land cover around Sherwood Visitor’s Centre, Edwinstowe (Source NRSC, 1992)......................................................... 80 Figure 5.2: Location of places signalled by the stakeholders................................................... 90 Figure 5.3: Area signalled by the NFU .................................................................................... 91 Figure 5.4: Standard Distance Circles (their centres are the means) ....................................... 91 Figure 5.5: Median centres..................................................................................................... 91 Figure 5.6: Axes of Deviational ellipses................................................................................... 91 Figure 5.7: Points descriptors for the whole set of stakeholders .............................................. 92 Figure 5.8: Quadrat analysis................................................................................................... 92 Figure 6.1: Land use/cover classes in SNA (after NCC, 1992) ................................................ 97 Figure 6.2: Ancient woodland (English Nature, 1999a)............................................................ 99 Figure 6.3: Dense forest (after NCC, 1992)............................................................................. 99 Figure 6.4: Length of hedgerows (CIS, 1998).......................................................................... 99 Figure 6.5: SSSI's and historical gardens (English Nature, 1999 and NCC, 2001)................... 99 Figure 6.6: Roads, railways and urban areas (OS, 1999 and NCC, 1992) ............................. 100 Figure 6.7: Cycling paths (Cyclists’ Touring Club, 1997) ....................................................... 100 Figure 6.8: Golf courses (OS, 1999) ..................................................................................... 100 Figure 6.9: Unemployment (MIMAS, 2001) ........................................................................... 100 Figure 6.10: Population density (MIMAS, 2001) .................................................................... 101 Figure 6.11: Landscape features and the degree of human control upon them. ..................... 104 Figure 7.1: Summary of the steps for building probabilistic models of stakeholder visions. .... 108

Figure 7.2: Example of binary map calculation...................................................................... 111 Figure 7.3: Posterior probability of stakeholders' vision space (2nd run) ................................ 118 Figure 7.4: Stakeholders' vision space modelled with weights of evidence and logistic regression (Posterior probability < initial probability are in grey) ..................................... 119 Figure 7.5 Area of SVS defined by posterior probability > initial probability............................ 120 Figure 7.6: Areas of agreement/disagreement among the eight visions................................. 121 Figure 7.7: Overlay of SVSs indicating 'ideal' core areas in red and potential linking corridors in green as an input to an environmental management strategy for Sherwood................... 122 Figure 8.1: Relative land cover composition inside the eight SVSs in the SNA (last column represents the whole area)............................................................................................ 126 Figure 8.2: Relative land cover composition outside the eight SVSs in the SNA (last column represents the whole area)............................................................................................ 127 Figure 8.3: Absolute land cover composition inside eight SVSs in SNA with colours highlighting similarities and differences between stakeholders (Last column represents the whole area) ..................................................................................................................................... 128 Figure 8.4: Euclidean distances (Unweighted pair group centroid Method) using the absolute land cover composition of the eight SVSs ...................................................................... 129 Figure 8.5: Euclidean distances (Ward's Method) using the absolute land cover composition of the eight SVSs .............................................................................................................. 130 Figure 8.6: Land cover composition in nine areas at different levels of agreement (0=nobody agrees, 8=all agree) ...................................................................................................... 130 Figure 8.7: Absolute areas of land cover and level of agreement........................................... 131 Figure 8.8: Percentage of the area inside each stakeholders' vision as part of the eight levels of agreement ..................................................................................................................... 132 Figure 8.9: Fragmentation indices at the landscape extent for the eight stakeholders............ 133 Figure 8.10: Fragmentation indices for heathland and broadleaved plantations..................... 138 Figure 8.11: Fragmentation indices for coniferous plantations and farming............................ 139 Figure 8.12: TCA index at the landscape extent.................................................................... 140 Figure 8.13: TCA index for heathland, broadleaved and coniferous plantations and farming.. 140 Figure 8.14: IJI at the landscape extent ................................................................................ 141 Figure 8.15: IJI for heathland, broadleaved and coniferous plantations and farming .............. 141 Figure 8.16: Euclidean distances (Unweighted pair-group centroid) using four landscape indices ..................................................................................................................................... 142 Figure 9.1: Maps depicting hypothetical boundaries for Sherwood produced with LR modelling. Line below contains the stakeholders who selected each of them (* in between both options)......................................................................................................................... 144 Figure 9.2: Detail of maps shown to the stakeholders for Sherwood boundaries and composition identification using Ordnance Survey maps. .................................................................. 145

Figure 9.3: Land composition graphs showed to the stakeholders. The line below contains the names of those who selected them (* in between both options) ..................................... 147 Appendix A Figure A.1 Preliminary stakeholder modelling of several institutional documents…………..…194 Appendix B Figure B.1 The logistic transformation of P as a function of P………………………………….. 204

LIST OF PLATES Plate 2.1: Soils for crop production in the area of Sherwood (Photo: J.Rubiano, 2002)............ 17 Plate 2.2: River Meden about Thoresby Park (Photo: J.Rubiano, 2002) .................................. 19 Plate 2.3: Sherwood Forest SSSI â&#x20AC;&#x201C; public signs (Photo: J.Rubiano, 2002).............................. 20 Plate 2.4: Mix of oak woods, heathlands and unimproved grassland (Photo: J.Rubiano, 2002) 22 Plate 2.5: Statue of Robin Hood in Thoresby Hall (Photo: J.Rubiano, 2002)............................ 27 Plate 2.6 Thoresby Hall in the surroundings of Sherwood Forest (Photo: J.Rubiano, 2002)..... 27 Plate 2.7: Dead wood in ancient woodland areas (Photo: J.Rubiano, 2002) ............................ 28

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LIST OF TABLES Table 2.1: Landscape historical changes in the area of Sherwood (Sources: Windrum (1997), English Nature (1997), Watkins (1998)) ........................................................................... 15 Table 2.2: Land use in SNA, percentage (%) of area with data................................................ 22 Table 2.3: Assessment of economic, social and environmental indicators for SNA, regarding the county performance average, Percentage of counties area (%). ...................................... 26 Table 3.1: Tools for environmental management inquires (After van der Vorst, et al., 1999).... 34 Table 4.1: Organisations (rows) and partnerships (columns) actively involved in SNA. ............ 67 Table 4.2: Organisations considered in the documentary analysis listed by spatial extent and with a summary of their interest in SNA. .......................................................................... 68 Table 4.3: Landscape issues and their possible functions and services (after Antrop, 2001; Wascher, 2000; Wascher and Jongman, 2000; Bell and Morse, 1999; Daily, 1997).......... 70 Table 4.4: Graphical models of institutional visions – summary (Source: Documents referenced in Appendix A) ................................................................................................................. 75 Table 5.1: Organisations included in NCC and SFT partnerships (underlined were interviewed) ....................................................................................................................................... 79 Table 5.2: Reasons explaining the selected sites mentioned by the stakeholders (see section 5.3.2 for explanations) ..................................................................................................... 85 Table 6.1: Land cover classes present in the 1992 NCC land cover map ................................ 95 Table 7.1: List of variables used for the identification of vision space .................................... 109 Table 7.2: Logistic regression coefficients for the variables considered by the eight stakeholders in SNA (Variables signalled in grey were excluded in run 2)........................................... 113 Table 7.3: Logits (coefficients) transformed to odds .............................................................. 115 Table 7.4: Confidence (studentised contrast) for each of the explanatory variables (Half of the highest values in each SVS model are highlighted in bold)............................................. 115 Table 9.1 Q-statements used for the Q-samples addressing different landscape components, function and boundaries and the pattern found in the models discussed in previous chapters........................................................................................................................ 148 Table 9.2: Stakeholders' Q-Sorts for the statements in Table 9.1 identified with key words.... 149 Table 9.3: Correlation matrix between sorts .......................................................................... 149 Table 9.4: Unrotated factor analysis of matrix of Table 9.2 with eight centroid factors............ 150 Table 9.5: Factor loadings after rotation with an ‘X’ indicating a defining sort (The highest loadings in bold) ............................................................................................................ 151 Table 9.6 Factor Q-sorts values for each statement sorted by consensus vs. disagreement (Variance across normalised factor scores; distinguishing statements in bold, consensus statements in italics), see Table 9.1 for a full version of the statements.......................... 151

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Appendix A Table A.1 References of institutional documents…………………………………….…..… ….188 Table A.2 List of words used in Concordance by alphabetic order…………………….. ……189 Table A.3 List of words used in Concordance by frequency (16,200=100%) ……….…… .. 191

Appendix B Table B.1 Relationship between probability, ‘P’, odds, ‘O’, and logits, ‘Ln(O)’, the natural logarithms of odds ………………………………………..204 Appendix C Table C.1 Patch analysis statistics summary at landscape level …………………..…… ……..207 Table C.2 Patch analysis statistics summary at class level A……………………..…………….208 Table C.3 Patch analysis statistics summary at class level B……………………...…………….211 Table C.4 Patch analysis statistics summary C…………………………………….……..……….214

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LIST OF ACRONYMS AND ABREVIATIONS

BBN BDC BGS CA CIS DETR EA EMAGE EMDA EMRA EN FC GIS HAS HTML IJI IS LR

Bayesian Belief Network Bassetlaw District Council British Geological Survey Countryside Agency Countryside Information System Department of Environment, Transport and the Regions Environment Agency East Midlands Advisory Group of the Environment East Midlands Development Agency East Midlands Regional Assembly. English Nature Forestry Commission Geographic Information Systems Human Activity System Hyper Text Mark-up Language Interspersion and Juxtaposition Index Information System Logistic Regression

MAFF MIMAS MPS NCC NFU NP NRSC NSDC NWT ODA OS SA SDC SDE SDM SDSS SE SFT SNA SSM SSSI SVS TCA UNott WofE WWW

Ministry of Agriculture, Fisheries and Food. Manchester Information & Associated Services Mean Patch Size Nottinghamshire County Council National Farmers Union Number of Patches National Remote Sensing Centre Newark and Sherwood District Council Nottinghamshire Wildlife Trust Overseas Development Agency Ordnance Survey Stakeholder Analysis Standard Distance Circle Standard Deviational Ellipse Spatial Data Modeller Spatial Decision Support System Standard Error Sherwood Forest Trust Sherwood Natural Area Soft Systems Methodology Site of Special Scientific Interest Stakeholdersâ&#x20AC;&#x2122; Vision Space Total Core Area University of Nottingham Weights of Evidence World Wide Web

Abstract

Environmental management at the landscape extent implies the integration of a diverse set of biophysical, social and economic variables. It also requires consideration of the views of the stakeholders participating in decision-making processes. This is currently the case in the Sherwood Natural Area where a range of interests groups is concerned to restore the landscape of the area and revitalize the local economy. As a step towards this it has been suggested that a spatial decision support system (SDSS) to organise and plan future actions should be built. In order to allow for a more democratic and informed decision making process, however, this system would have to consider as one of its inputs the views and ideas of the multiple stakeholders working in the area. To develop this system, the current research has focused on identifying and developing methods to capture and map the stakeholders’ visions in the area. A ‘Soft system’ and a ‘Stakeholder Analysis’ were used as a methodological strategy to carrying out this work wherein qualitative and quantitative techniques such as text analysis, graphical modelling, spatial analysis, logistic regression, Weights of Evidence and Q-method were used in a sequential and complementary way. The research has yield a ‘vision capturing system’ which can be used to help structure and conceptualise ideas for building a SDSS. It uses as a basic input stakeholders’ landscape preferences and produces what was named here a ‘stakeholder vision space’ (SVS). This is an area in which it is highly probable one might find the idealised vision of each stakeholder, given the attributes each identify as significant. The analysis and comparison of these SVS’s allowed the depiction of differences and similarities between stakeholders. Using the similarities found in this process, landscape management strategies for the area were suggested. The system developed proved to be sensitive to the environmental character of the stakeholders selected for the study. It also confirmed the advantages of using an open approach such as soft systems when considering unstructured problems and for developing information systems applications.

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CHAPTER 1 GENERAL INTRODUCTION1 Environmental and landscape management implies the integration of a diverse set of biophysical, social and economical variables. This is in part due to the intrinsic complexities of the landscape uses and the need to include different stakeholders in decision-making processes. By understanding the frameworks that guide the preferences of the stakeholders in relation to landscape uses, a greater depth of knowledge can be gained to design landscape management strategies. The aim of this research is to explore how these stakeholder frameworks, or ‘visions’, can be identified and compared. Little research has addressed the issue of representing in spatial dimensions the ‘vision’ of different parties interested in managing landscapes. Most studies have focused on conceptual differences and few have gone on to examine the implications in spatial or geographical terms. The current research examines qualitative and quantitative methods that ‘capture’ and understand stakeholders’ visions for the management of Sherwood Natural Area (SNA) and implications for the development of management strategies within the region.

1.1 Background to the research The current research emerged as a proposal from a study devised by the School of Geography, University of Nottingham and the U.K. Forestry Commission (HainesYoung, 1998). It was argued that a new approach was necessary to overcome the limitations of available spatial decision support systems (SDSS), using ideas from environmental accounting and ‘soft systems’ methodologies. The 'soft systems' approach is a process for analysing and modelling complex systems that integrate environmental and social issues. Its key characteristic is that it recognises that problem

Parts of this study were published as:

Rubiano, J.E. 2002. Modelling stakeholder visions for the Sherwood Natural Area. In 'Trees are Company: Social Science Research into Woodlands and the Natural Environment’, O’Brien, E.A. and Claridge, J.N. (eds). Forestry Commission, Edinburgh. Rubiano, J.E. 2002. Incorporating Social and Ecological Values in Landscape Modelling, in Bell, Simon (ed) 'The Potential of Applied Landscape Ecology to Forest Design Planning', United Kingdom, The Forestry Commission (in press).

solving is essentially iterative, and that we discover more about a problem as we attempt to solve it (Clayton and Radcliffe, 1996; Checkland, 1991). It has been argued too, that current methods of environmental planning are inefficient in terms of the way in which they deal with and integrate the multiple services and values that nature provides for society. This is mainly due to the lack of ecological knowledge and the limitations of existing decision support tools (Kangas et al., 2000). The need to develop tools that allow us to assess the set of 'possible and alternative futures' and their consequences in terms of the goal of sustainability is one of the major challenges facing landscape ecology today (Haines-Young and Potschin, 2000; Potschin and Haines-Young, 2000). As a result, methodological research into the integration of various software technologies, knowledge acquisition tools, and specific user needs is required (Mikolajuk and Gar-On Yeh, 1999 p22; Underwood, 1998). The different tools and methods considered and developed in the current research are a contribution to fulfilling these needs. Geographical Information Systems (GIS) are often seen as key decision support tools for environmental planning (Lein, 1997). The problem we face in using them is that the construction of these systems is often regarded simply as an exercise in capturing and integrating data, rather than as one of characterising the multiple 'views' of the world held by the different stakeholders in an area. As a result, the application of GIS technology has often been criticised as promoting the views of the expert and of excluding key stakeholders from environmental debates and their views of what constitutes 'relevant knowledge' (see for example Pickles, 1995, and Schuurman, 2000). In this thesis it is argued that in developing more participatory applications of GIS, we must go beyond simply broadening the approach to include more 'local' forms of knowledge (cf, Harris et al. 1995). We must also develop ways of using these systems to represent the different and sometimes opposing visions that stakeholders might hold about the world and of dealing with the uncertainties attached to these visions. The motivation for this work is, therefore, the recognition of the need to devise new methods to identify stakeholders and involve them in the planning process (cf. Christensen et al, 1996). New types of information handling tools are needed to characterise

stakeholder

opinion,

and

manage

the

interactions

between

stakeholders and institutions involved in the planning process (Szaro et al, 1998; Yaffee, 1996; Christensen et al, 1996). This work takes as a premise that all decisions are made in a climate of uncertainty, where we cannot assume that the relationships 2

between variables are known, and that the integrated interpretation of data is rarely free of bias. Another assumption is that, in any planning debate there is rarely an optimal solution (cf. Hall et al, 1999). The essential job of any decision support tool is to enable users to explore the available ‘choice space’ and consider the trade-offs that might be required in selecting different options for the future (Haines-Young, 2000). The Sherwood Natural Area (SNA) was selected as the focus for study because it illustrates many of the issues associated with multifunctional landscapes typical of the English Lowlands. Farmlands, woodlands and heathland are widely distributed within the SNA. Processes like the closure of mines on the South Nottinghamshire coalfield, the increase in recreational and tourist activities and urbanisation are among the most significant pressures on the region. Historical and cultural assets are additional components represented in the legacy of Robin Hood and the estates known as the ‘Dukeries’. The current research addresses the issue of environmental characterisation of multifunctional landscapes in the area of Sherwood, where multiple stakeholders, with a diversity of values, are expressing their concern at different spatial and temporal scales.

1.2 Justification for the research Why is it important to understand stakeholders' 'visions'? The present driving forces of the world are hidden in the visions of people and groups in society. The current environmental, political and economic disharmony is a reflection of conflicting visions. It is recognised that action and change without an appropriate vision of the goal and analyses of the best methods to achieve it can be worse than counterproductive. Costanza (2000) argues that the major source of uncertainty in the destiny of the world is at the level of visions and worldviews, not in the details of analysis or implementation within a particular vision. "Recent work with business and communities indicates that creating a shared vision is the most effective engine for change in the desired direction, yet most effort in "futures modelling" has focused on extrapolating past trends rather than envisioning alternative futures" (Costanza, 2000). Given the accelerated rate at which information is generated, experts lack the capability to process this in coherent and logical ways before the decisions have to be taken. One way to cope with this is "to present complex issues in the form of a relatively small number of 'visions' which lay bare the conflicts and inconsistencies buried in the technical information" (Yankelovich, 1991). Slaughter (1996), Ringland 3

(1998) and PintĂŠr et al, (2000) suggest that thinking about the future may reduce the risk of unpleasant surprises and broaden the perceptions of both the general public and policy makers in at least the four following areas: -

Consequence assessment: assessing the implications of present actions, decisions, policies;

Early warning and guidance: detecting and avoiding problems before they occur;

Proactive strategy formulation: considering present implications of possible future events; and,

Normative scenarios: envisioning aspects of possible or desired futures.

As a result, we need methodologies and tools to capture and to perform systematic comparisons of visions allowing a wider participation of stakeholders in decisionmaking processes. Empowering people in the management of their environment has to be allowed by managers if both a deeper understanding by stakeholders and of eventually resolving conflicts are wanted.

Why is the Sherwood Natural Area important? The Natural Area is a key concept that allows an integrated approach to nature conservation, which takes into account both local and national priorities. Natural areas are a key reference for the implementation of local and national environmental plans throughout which the United Kingdom (UK) supports the 1992 Agenda 21. Sherwood Natural Area (SNA) is important because of the ecosystem goods and environmental services it provides to the people living in the area. â&#x20AC;&#x153;Ecosystems are sources of raw materials and food, and also provide a wide range of environmental and ecological services, which include maintenance of the composition of the atmosphere, maintenance of climate, operation of the hydrological cycle, waste assimilation, recycling of nutrients, generation of soils, pollination of crops, maintenance of biodiversity and landscape, and aesthetic and amenity servicesâ&#x20AC;? (Daily, 1997). See also Collados and Duane, 1999. Among these services, water is an important one. Sherwood is located upon an important aquifer that supplies the population of Nottingham and surrounding areas with potable water. The area is also relevant because of its diversity of landscapes: heathlands, grasslands, oak woods, and wetlands, which support a significant diversity of wildlife (English Nature. 1997). In 4

addition, the remnants of ancient woodland are important in terms of the biodiversity of insects and birds associated. The same can be said of heathland. The heathlands of the Midlands differ significantly in ecological terms from those found in Hampshire and Dorset, specifically in its flora composition (English Nature, 1997). In economic and social terms, SNA is also important for tourism. More than a million people visit the area every year (Nottinghamshire County Council 1997), largely as a result of its association with the legend of Robin Hood. In addition, SNA is receiving the attention of a large number of organisations which are forming partnerships for the development of landscape management strategies, thereby creating the opportunity to test and experiment with the development of stakeholdersâ&#x20AC;&#x2122; decision support tools.

Why is it necessary to insist on developing new strategies for environmental planning? Environmental management means different things to different people and this heterogeneity is considered beneficial, given the need to explore and test different ideas (Yaffee, 1999). Landscape ecology, environmental and ecosystem management have similar concerns. They seek to develop and test hypotheses about â&#x20AC;&#x2DC;how the world worksâ&#x20AC;&#x2122;. They are refined or reinforced through experiments and observation of a world that is dynamic. "Management goals, protocols, and directives should be viewed as hypothesis of ways to achieve clearly stated operational goals" (Christensen et al, 1996). See also Underwood, 1998. Current methods of environmental planning are, however, considered inefficient for the analysis of the integration of multiple services and values that nature and society provide. "Gaps in the available ecological knowledge, and the inefficiency of the planning methods and tools used often lead to vague planning processes" (Kangas et al., 2000). On the other hand, there is currently an under-utilisation of spatial analysis technologies and risk analyses (Kangas et al., 2000). For over a decade there has been increased acknowledgement that keeping traditional approaches to natural resource management is likely to result in further loses of biodiversity and ecosystem sustainability (Grumbine, 1992 cited by Szaro et al, 1998). The current research focuses on extending the application of spatial technologies to reveal the ways in which people understand and establish a relationship with the landscape, rather than the study of ecological processes per se.

How can we deal with the diversity of stakeholder interests and conflicts in a multifunctional landscape? 5

With the increase in population, the demand for ecosystem goods and environmental services will also rise. People then expect the environment to provide a broader and more diverse range of services and goods than before. In this sense, it is possible to talk about a ‘multifunctional landscape’ (Antrop, 2000), as a new challenge for environmental planning. “The multifunctional landscape refers to the different material, mental and social processes in nature and society that take place simultaneously in the landscape and interact accordingly” (Soini, 2001). In spite of the better availability of information, the uncertainty associated with the complexity of social and biophysical process and their relationships is a constant. Consequently, compensations can be achieved through research addressing this complexity with the use of information technologies. Because natural resource systems are usually independent of administrative and jurisdictional boundaries, their management requires dynamic interactions among and between stakeholders and institutions. A ‘system approach’ is required to solve these cross-jurisdictional problems, such as violation of governmental jurisdictional norms, leadership control or organisational culture. A ‘system approach’ to a problem takes a broad view, tries to take all aspects into account, and concentrates on interactions between the different parts of the problem (Checkland, 1993). "Ecosystem management is a political process, for it involves allocation decisions between different interests in society, and in practical terms, it will not occur without political concurrence" (Yaffee, 1996, p 726). A collaborative approach is thus urgently needed. There is also a need for improved access to information and information networks, promotion of organisational change and innovation, and movement towards educating people and building a sense of ownership to empower individuals (Yaffee, 1996). The extended dialogue derived from the outputs of the current research could contribute to implementing more sustainable environmental strategies that can be considered in the management of any of the other Natural Areas within England. Given the mismatch between natural areas and administrative boundaries, both the methodologies devised here and the information gathered for the SNA can be an example of the way in which information concerning the boundaries of natural areas can be organised and analysed.

1.3 Objectives of this study 1.3.1 Main Objective To develop a methodological framework to characterise stakeholders' visions in Sherwood Natural Area. 1.3.2 Specific Objectives To develop a method to capture and understand the institutional visions for the environment of Sherwood Natural Area. To identify the social and ecological values and their relationships that each of the Stakeholders assigns to the natural assets of Sherwood Natural Area. To integrate a set of spatial support tools which allows the representation of institutional visions related to the environmental management of the area.

1.4 Research problem and hypotheses The landscape in the SNA has a multifunctional role, according to the different values that different groups assign to it. It is an area in which there are multiple interests where environmental and socio-economic processes converge. In the area there are also on-going processes of biodiversity loss, pollution of water, air and land, a reduction in farming and the expansion of urban zones, unemployment due to the closure of coal mines and textile industries as well as rapid landscape change. Different institutions and partnerships at different geographical scales are currently carrying out multiple projects to mitigate the impact of these environmental and socioeconomic problems. The ideas of Agenda 21 have been included in local development plans and the environment has been defined as a common reference issue in many government policies. Concern for the environment is the subject of numerous efforts by local and regional institutions together with the private initiatives of non-governmental institutions and volunteers groups (Nottinghamshire County Council, 1996, p53) The Nottinghamshire County Council (NCC) is contributing to the co-ordination of these activities in a process that has been called the â&#x20AC;&#x153;Sherwood Studyâ&#x20AC;?, and considering ways to build common understandings of the visions and priorities for Sherwood (Nottinghamshire County Council, 1999). Nonetheless, given the complexity of the place and the processes occurring there, the current research considers as a basic assumption that a common vision is not at present feasible although it is not 7

necessarily impossible. Indeed, there may always be contradictory visions resulting from the characteristic dynamics of the area. The capturing and understanding of such differences, in addition to their implications for the environmental management of the area, constitutes most of the present research. In this sense, the first set of research questions concern the identification of stakeholder visions for Sherwood Natural Area. -

Who are the environmental actors in Sherwood Natural Area?

What visions and purposes do they have for the place?

How are the different natural assets of Sherwood valued by each of the groups?

What are the respective spatial and temporal scale contexts in which they are implementing their projects?

Conceptual models derived from the different visions have to enable the understanding of the stakeholders' activities and motivations in Sherwood.

Since stakeholders'

conceptual models are likely to develop in an iterative way, a further set of research questions concern the identification or development of methods to build these models: -

Can we build tools to capture and model stakeholder visions?

Is it possible to represent each individual vision as part of an operational physical or computer/based system?

1.5 Methodology The general methodological approach on which this research was developed is based on the ‘soft systems’ concept (Checkland, 1991). In this approach, special attention is assigned to the ‘fussiness’ of any problem. As mentioned above, the definition of the problem is an iterative process; the problem is continually refined, as those seeking solutions learn know more about the issues. Checkland (1993) presented a methodology for tackling unstructured problems made up of phases carried out in both the real world and systems thinking. Accordingly to the methodology, the framework suggested by Checkland (1993) starts in the real world, with the description or what he calls a rich picture of the problem situation rather than the problem itself -. This phase was partially carried out in a preliminary study (Haines-Young, 1998) and complemented in this research by means of literature review and interviews to a wider group of stakeholders. As a conclusion of this phase, 8

the need to develop an environmental and landscape management decision support systems (DSS) for the SNA was identified. This DSS had to be capable of capturing and processing the existing stakeholder’ visions. As part of the system-thinking phase a conceptual model was built to describe the “capturing vision system”. This system consisted of a set of activities required to capture the ‘visions’ and their characteristics. In a first step, a stakeholder analysis was carried out to identify their goals, missions and visions for the area of Sherwood in order to identify the key stakeholders. To deal with the large volumes of documentation, systematic text analysis was undertaken with the help of computer tools. Following this, graphical modelling techniques were used to prepare the information for use in modelling techniques such as Bayesian Belief Networks (BBN). "Belief Networks can be used to examine the impacts of potential management options on an environmental system as a whole" (Cain et al, 1999, p127). The information had to be translated into a geographical framework upon which the modelled visions can be tested and their impacts assessed. Individual interviews were undertaken with representatives of the key institutions in the area. From the interviews, geographical locations of current 'ideal places' or ‘preferred sites’ that corresponded to their visions were identified, together with the associated variables that defined them. The data collected were used as an input for the construction of geographical models of stakeholders' visions. Weights of evidence and logistic regression techniques were used to make models of what can be called the "vision spaces" of the stakeholders. The spatial models were validated using cross-validation techniques. Once the visions were represented spatially, the composition and configuration of the landscape embedded inside the 'vision space' were detected. A patch analysis of the land cover of those areas was carried out using a program for spatial pattern analysis called FRAGSTATS (McGarigal and Marks, 1995). This information was then used to highlight the landscape similarities and differences between the stakeholders' vision spaces. Finally, an additional technique was implemented both to validate the models obtained and as a ‘triangulation strategy’ to capture stakeholder visions by an independent method. It consisted of the application of a Q-sort algorithm. Q-sorts are part of Qmethodology, a technique that deals with perceptions, visions and subjective statements (Brown, 1999a). Through this technique, statements based on the findings of the previous methods were put to the consideration of the stakeholders' 9

representatives so that they may classify the main topics of stakeholder concern. In addition, the boundaries of the vision space and the conceptual relationships with other topics or elements of their visions were explored.

1.6 Outline of thesis Ten chapters, including this general introduction, compose the thesis. Each was written as a stand-alone document although all are sequentially and strongly linked (Figure 1.1). The first chapter describes the problem being investigated and why it was worth solving. The objectives of the project are presented and the scope of the work described. Chapter 2 describes the biophysical, social and economic characteristics of Sherwood Natural Area. An overview of historical facts that shaped the landscape of the area is given. Chapter 6 in which the variables selected for the modelling section were organised in a geographical database complements this section. Chapter 3 presents a review of the approaches and methodologies applied during the course of this project. In broader terms, soft systems and stakeholder analysis together with research techniques such as text analysis, graphical modelling, Q-methodology, Landscape Ecology and Integrated Models are discussed. Complementary details of the techniques are included in the chapters where each of them was used. Chapter 4 describes a qualitative approach to identifying the stakeholders' visions through the use of text analysis and graphical modelling. The outputs of this stage were useful for exploring the study site context and understanding the ill-defined phenomenon under consideration. The investigation of institutional documentation was an aid to clarifying concepts and generating more specific questions solved in later chapters. Chapter 5 presents a geographical analysis of stakeholders' preferences as a potential way of revealing their visions. This section corresponds to the stakeholders' interviews where evidence of landscape preferences was gathered. It also represents the primary data collected during fieldwork and was crucial for a more quantitative analysis (Chapter 7). At this stage variables were identified that required quantitative investigation. This lead to the construction of the geographical database for the Sherwood Natural Area (Chapter 6). In Chapter 7, the previous qualitative and quantitative studies were integrated through the use of probabilistic modelling. Weights of evidence and logistic regression were combined to model what was called the "stakeholderâ&#x20AC;&#x2122;s vision spaces". These models 10

could be considered as systems in which functions, boundaries, components and their relationships were identified. Their individual characteristics were then compared in a systematic way in Chapter 8, analysing the landscape composition and configuration of the patterns found in each model. Chapter 9 presents a final stage in the modelling process consisting of a validation/triangulation phase. After receiving feedback from the stakeholders, a Q-sort was applied in order to test the hypotheses expressed by the models in their similarities and differences. Chapter 10 offers a synthesis of the research and explains the use of soft systems as the approach used and as a strategy to systematise the development of the vision capturing methodology. A summary of the stakeholdersâ&#x20AC;&#x2122; visions with their implications for environmental management of the area is also presented. A discussion of limitations and ideas for further research are included.

CHAPTER 1 GENERAL INTRODUCTION

CHAPTER 5 INTERVIEWS - GATHERING EVIDENCE OF LANDSCAPE PREFERENCES

CHAPTER 8 LANDSCAPE PATCH ANALYSIS

CHAPTER 2

CHAPTER 7

CHAPTER 6 SHERWOOD FOREST NATURAL AREA

CHAPTER 3 'VISIONS' A REVIEW OF METHODS

STUDY AREA GEO-DATABASE

PROBABILISTIC MODELS OF STAKEHOLDERS' VISIONS

CHAPTER 4

CHAPTER 9

TEXT ANALYSIS - BUILDING MODELS FROM VISIONS

Q-SORTS - VALIDATING AND CLASSIFYING VISIONS

OBJECTIVES, STUDY AREA, LITERATURE REVIEW

DATA COLLECTION, RESULTS AND DISCUSSION

Figure 1.1: Outline of thesis structure

CHAPTER 10 SYNTHESIS

SUMMARY AND CONCLUSIONS

CHAPTER 2 THE SHERWOOD NATURAL AREA 2.1 Introduction This chapter describes the geographical and environmental characteristics of the Sherwood Natural Area (SNA) and shows how this area is a perfect example of a place on which people depend for the delivery of a range of ecosystem services. A review of the area's environmental history over the last thousand years is also presented. The current policies with their landscape implications are presented along with the description of the area. Additional geographical information is included in Chapter 6. Some pictures are included to illustrate the landscape found in SNA. The SNA is one of 120 natural areas into which England has been divided by English Nature. Each natural area represents a characteristic association of wildlife and natural features, land use and human history. The ‘Natural Areas approach’ provides a way of interpreting the ecological variations of the country (English Nature, 1999) and a framework for setting objectives for nature conservation (HMSO, 1995). Indeed, some organisations in the area have adopted these boundaries for environmental management purposes. The current landscape of Sherwood is highly populated and fragmented by roads. The predominant land use is now agriculture and conifer plantations, which were formerly native woodland, heathland and other semi-natural habitats.

2.2 Location and boundaries Twenty three percent of Nottinghamshire is within the SNA (53.4 km2), which is located to the north west of the County (Figure 2.1). The administrative boundaries of the following districts cross the Natural Area: Ashfield, Gedling, Newark & Sherwood, Nottingham, Mansfield and Bassetlaw. The ‘local areas’, as defined by the Nottinghamshire County Council (NCC, 2001a), included in SNA are: Worksop, Retford, Dukeries, Mansfield Woodhouse, Ravenshed, Sutton in Ashfield, Kirby in Ashfield, Newstead and Calverton, Arnold and Bestwood, Beeston, Stapleford, Carlton, Burton Joyce and Southwell (Figure 2.2).

Figure 2.1: Location of Sherwood Natural Area

2.3 History The name ‘Sherwood’ derives from the Old English meaning ‘the shire’s woods’. The addition of ‘Forest’ refers to status rather than composition (Gough, 2000). The current characteristics of the SNA cannot be understood without looking at the historical features that imprinted changes on the biophysical and social components of the region. There is extensive information describing historical land use change in Sherwood Forest. Most of the information comes from forest inventories carried out by forest authorities (Forestry Commission, no date). An extended review of this is included in the Countryside Profile of Sherwood (English Nature 1997); the last two Centuries are summarised by Watkins (Watkins, 1983) who provides a graphical representation of forest cover changes over the last two hundred years. Table 2.1 summarises the most important changes in the SNA's landscape in relation to socio-economic factors. After the last glaciation, Sherwood was covered by oak and birch woodland. Then, slash and burn agriculture and grazing took place in the forest with the arrival of humans. A more significant change was not seen until Roman times, when the woodland was largely cleared over the five Centuries of occupation. The 13

forest regenerated during the 5th to 10th Century, when the population declined. Heathland developed in places where grazing stopped the succession to woodland. Heathland is defined by The Nottinghamshire Heathland Forum as: ‘any site that includes areas with heather species in the vegetation; and/or areas of acid grassland with stands or scattered plants of oak, birch, bracken, gorse or broom’ (Fraser and O’Nions, 1997).

Worksop

Mansfield Woodhouse

Retford

Mansfield

Dukeries #

Sutton in Ashfield

# #

Kirkby in Ashfield

Southwell Ravenshed,Newstead,C #

Arnold and Bestwood

Burton Joyce #

Nottingham

Carlton

Stapleford # #

Beeston

Figure 2-2 Local areas in Sherwood Natural Area (Source NCC, 2000)

In the 11th Century, the landscape was characterised by wood-pasture grazed by pigs and sheep, sparse settlements and cultivated areas. The Norman Kings introduced laws to establish the ‘Royal Forest’, which preserved the land as such for almost three hundred years. Much of the forest passed in to monastic control in the 13th Century. Heathland was grazed and woodlands were managed for hunting and timber to satisfy an increasing demand, which led to the deforestation of large areas. With the dissolution of the monasteries in 1539 AD, the land passed into the hands of the aristocracy. Landscape vistas, ornamental lakes and large park areas were retained for 14

hunting, which characterised the area that became known as "The Dukeries". Thoresby Park, established in 1683 by the Earl of Kingston in the NE corner of the forest, was soon followed by Clumber Park. Later Dukes (Dukes of Norfolk and Leeds) led the way to introduce modern agricultural techniques. The legacy of aristocratic estates that preserved many wood pastures, as well as the coal mining industry, intensive farming, timber production and the urbanisation process, have shaped most of the current features of this natural area (Windrum, 1997, English Nature, 1997, Watkins, 1998). Table 2.1: Landscape historical changes in the area of Sherwood (Sources: Windrum (1997), English Nature (1997), Watkins (1998)) Date Ice ages

Location

Slash and Burn agriculture Grazing of domestic animals

Neolithic Roman Period 200 â&#x20AC;&#x201C; 300AD

Human influence

The whole area

Dunstans Clump, Menagerie Wood near Worksop, Chainbridge Road in Lound

Settlement of low status

Woodland clearing (except in Menagerie Wood)

Eastern margins in the Idle Valley of the Villages known Settlement of high status on the Magnesian Limestone to the west

More intensification in the north zone tracing a line between Warsop and Bevercotes until south of Yorkshire More woodland and heath produced by rough grazing surviving in the south.

End of Roman Empire 5th and 6th Centuries

Roman field systems abandoned

Mansfield, Ashfield, Farnsfield

Communities around riverâ&#x20AC;&#x2122;s margins used the landscape as grazing resource

9th and 10th Centuries

1086

Henry II Henry III

Land use/cover Forest Landscapes Thinning and clearings and pieces of heath

Sparsely settled Norman Kings introduce Forest Law consolidating royal right Woodland were managed for timber and game North of the Trent Only the countryside of woods and heaths of the Magnesian limestone and the Sherwood sandstone south of the Meden. North of the Meden

Soil exhaustion and erosion Woodland regeneration

Heath originated in areas of Roman clearance particularly around the margins of the south region Dense and not so dense Oak and Birch wood Birch and Oak in river valleys and in less arid soils Large and small tracts of sandland heath with gorse, ferns and grasswoods as a game reserve, sources of timber and small wood as fodder and grazing Low in arable land Woodland recorded as wood pasture

Forest Law extended

Cut back the Law

Hunting parks because land of low value

Forest officials preserve the royal woods and games New settlements and expansion of arable agriculture in response to population rise Creation of monasteries and nunneries

Date

Location

Human influence

Land use/cover

Large tracts of land passed into monastic control

12th and 13th Centuries 1350

14th Century

Expansion of existing settlements and creation of new ones Forests were given to the Church Bestwood Park Royal woods of Birklands and Bilhaugh Rufford Abbey's sheepwalk at Morton Grange in Babworth Dearth of Timber

Occasional control of Forest

Highly managed environment by the economic regime Maintenance of the traditional woodland and heathland resources against the pressure of till and grazing for animals Woodland for Timber and game Heath and grassland for grazing stock and deer Arable fields for food and fodder Animal husbandry (sheep raising) established in some monasteries estates Increase of clearance Woodland gradually eroded

Birklands, Bilhaugh, Roumwood, Mansfield, Clipstone, Bestwood

16th Century

Dukeries

17th Century

Only the core woods of the surviving royal estates remained. Landscape vistas and ornamental lakes Dissolution of monasteries and the estates surrounded great country houses of passed in to hands of a few aristocrats. The aristocrats area became known as "the Dukeriesâ&#x20AC;? Large areas retained for hunting Royal rights taken by the great landowners

Formal enclosures limited on the east Land owners became agriculturalist and invest in agriculture in the sandlands

18th Century

Enclosures of most of the open heath and commons in the region and the creation of new forms outside villages Agrarian revolution Large scale forest plantation scheme

Late 18th Century

Industrial development Chesterfield canal cut the region Advanced farming area of the country

1770 19th Century

20th Century

Worksop and Mansfield developed as commercial centres

During the First and Second World Wars, different agriculture commodities were produced Government and European policies supported intensive agriculture Timber production

1915, 1920, 1940, 1950 Present

Coal mining industry Railways and roads appear

Dukeries

No useful timber in the surviving woods Temporary enclosures of traditional agriculture for a limited number of years after which the enclosure was thrown down, the fields levelled and the exhausted soil allowed to revert to scrub, heath and grass Portions of all of the permanent arable land were enclosed Improving of crop rotation Creation of relatively small hedge fields Diversification of crops Introduction of new species Timber plantations Experimentation with fertilisers Enclosures geometrically laid out in field sizes larger than those of earlier enclosed areas Fields defined by fences or hedges, dominated by quickset hawthorn which are features kept until today Intensification of animal husbandry Cultivation of root crops and rotational grass Use of manure and early artificial fertilisers

Extensive arable fields Introduction of modern fertilisers led to the demolition of hedgerows and boundaries to create wide open spaces suited to manoeuvring large machinery Coal mines introduced lofty pithead buildings and structures and large-scale waste heaps into the landscape Spoil heaps Construction of new villages Corn and potato cultivation during war and livestock afterwards, Sugar beet replace turnips. Return to corn, then turnips disappeared Conifer plantations Forest areas important as a natural resource and tourist attraction

2.4 Natural Components Soils. The SNA is located on Bunter Sandstone (from the German "bright-coloured"), which forms an undulating landscape with limited surface drainage. The Bunter Sandstone is a sedimentary rock, which belongs to the Perm-Triassic geological period (Watkins, 1981). Three varieties of the sandstone are found in the area: white freestone, red sandstone and magnesian limestone (Dolomite). This geology is the reason why quarrying has been one of the main industrial and mining activities in the area since the 12th Century (Sister Cities Association, 2000). The soil is light, very well drained and poor in nutrients. It is therefore subject to wind erosion and has limited agricultural potential (Watkins, 1981).

Plate 2.1: Soils for crop production in the area of Sherwood (Photo: J.Rubiano, 2002)

Hydrology. The river Trent drains most of the Nottinghamshire with the Rivers Soar, Erewash and Idle as its main tributaries. In SNA rivers Rainworth, Maun, Meden and Poulter drain to the Idle (Figure 2.3). Along its course, water is extracted for power station cooling, agricultural irrigation and public water supply. In addition, it is a major regional resource for navigation, recreation, fisheries, nature conservation and effluent disposal. Up to 80% of the county's drinking water is obtained from the major aquifer located in the Sherwood Sandstone to the north of Nottingham, the supply is drawn through boreholes and wells from underground reserves. Levels of nitrates infiltrating 17

the major aquifer are high and rising (Environmental Agency, 1999); this has been attributed to the cumulative effect of post-war arable farming practices upon the poorer soils of the north and central Nottinghamshire (English Nature, 1997, p.21). There is also some evidence of localised pollution from past industrial activity (e.g. coal-gas and coke manufacture) and from the use of some pesticides. For this reason, most of the area has been designated by the UK Department of the Environment, Transport and the Regions (DETR) as a Nitrate Vulnerable Zone (Nottinghamshire County Council, 1996) (Figure 2.3). To reduce pressure on over-extraction in aquifers, water companies are forecasting a reduction in water availability over a large part of the region. To cope with the water demand, different development strategies are planned to take place. The main proposals are to increase abstraction from the River Trent, to import groundwater from Birmingham during periods of low flow and/or storage of surplus winter water for agricultural irrigation (Nottinghamshire County Council, 1996). This is forcing local authorities to protect water supplies from excess extraction and contamination. The Ministry of Agriculture, Fisheries and Food (MAFF), Agri-Environmental schemes such as Countryside Stewardship (CSS) and Nitrate Sensitive Areas (NSA) are addressing this problem to ensure ground and surface water are protected through conservation management and good agricultural practice. Within the East Midlands region there are 19,120 hectares of agricultural land under Nitrate Sensitive Areas management, representing 67% of the land in the scheme in England. A total of 7,955 hectares are located in SNA, which makes up 14% of the SNA (EMDA, 1999).

Plate 2.2: River Meden about Thoresby Park (Photo: J.Rubiano, 2002)

Biodiversity. The SNA contains a variety of landscapes and habitats, which are important for maintaining the biodiversity in the region. However, fragmentation of remaining natural sites, and their small size, threatens the viability of their animal and plant communities. The introduction of MAFF's Countryside Stewardship Scheme and English Nature's Wildlife and Reserves Enhancement Scheme in the 1990s has greatly assisted the management of a number of special sites through funding support and technical assistance (English Nature, 1999). According to Windrum (1997), a mix of oak woods, heathland, unimproved grassland and wetlands along the rivers support a significant diversity of wildlife. In the Sherwood Natural Area, woodland fragments contain old oak trees and birches. Alder and willow trees are part of wet woodlands and stag-headed oaks can be found in the parkland of the Dukeries. Heathland and wetland habitats, important for the regionâ&#x20AC;&#x2122;s biodiversity, have seen significant decline over the last 50 years mostly as a result of agricultural improvement and drainage. Fragmentation has put the remaining heathland areas further under threat and this concern is being tackled by the Sherwood Forest Initiative Re-creation and Enhancement Schemes (Hewston et al., 1998). Clumber Park in Bassetlaw and Birklands and Bilhaugh in Newark have been assigned in the SNA as Sites of Special Scientific Interest (SSSI), according to the Wildlife and Countryside Act (1981). There are over 4,000 SSSIs in England. They cover nearly 7% 19

of England’s total area. SSSIs are designated important because of their plants, animals, and geological features. Most SSSIs are privately owned or managed. Owners and occupiers of land within SSSIs are required to give English Nature written notice before carrying out any operation “likely to damage the special wildlife interest" (English Nature, 1999).

2.5 Land use components The land use of SNA is described by the Nottinghamshire County Council (1997) as “…well-wooded, and in places industrialised, characterised by semi-natural woodlands and heaths, historic country estates, large pine plantations, mining settlements and a planned layout of roads and fields”. The relative proportion of the main land uses is shown in Table 2.2.

Plate 2.3: Sherwood Forest SSSI – public signs (Photo: J.Rubiano, 2002)

Farmland is the predominant land use in Sherwood, covering about half of the natural area (49.2%). For the East Midlands region, the forest covers 4.8% compared with 8% nationally. Conifer plantations cover 10.02%, broad-leaved plantations (6.65%), wet woodlands along rivers, streams and lake margins and ancient or semi-natural woodland cover less than 4%. The best remaining examples of oak-birch woodland in 20

Nottinghamshire together with acid grassland and heath are located in Birklands and Bilhaugh. These are considered to be among the four most important places in United Kingdom for wildlife conservation purposes (Windrum, 1997). Other land uses and natural components of the landscape in SNA are pasture (10.6%) and heathland (2.9%). The main urban areas are Nottingham, Mansfield, Worksop, Retford, Warsop and Ollerton. According to projections for the East Midlands, 21,100 ha are likely to change from rural to urban uses to meet the area share of the expected growth of 4.4 million new households in England between 1991 and 2016. While it is important to safeguard the most productive land and soils as a non-renewable resource for future generations, this must be balanced with the need to safeguard poorer land, which has a recognised environmental designation when considering alternative development options (EMDA, 1999). It has been stated that the effect of urbanisation processes on biodiversity may be more devastating than previously suspected

(Thompson and Jones, 1999).

Quarrying has been one of the most important industrial activities in the area of Sherwood since the 14th Century. Even recently, for the construction of the M1 motorway at the end of the 1960s, 20,000 tonnes of road-stone per week were extracted. Coal mining, began in the area at the end of the 19th Century and brought with it a large growth in the local population (Sisters Cities Association, 2000). However, employment in the mining industry fell by 83% in the East Midlands Rural Area (EMRA) between 1991 and 1996 due to the closure of mines (EMDA, 1999).

Plate 2.4: Mix of oak woods, heathlands and unimproved grassland (Photo: J.Rubiano, 2002)

Table 2.2: Land use in SNA, percentage (%) of area with data

Land use Farmland Woodland Urban Pasture Coal mines Acid grass and heathland Water Total

Area (%) 49.2 20.5 13.3 10.6 3.1 2.9 0.5 100.00

Source: NCC (1997)

Figure 2.3: Hydrology of Sherwood Natural Area showing Nitrate Sensitive Areas (SA) (Source: MAFF, 2000 and BGS, 2000)

Cultural Heritage. 'Sherwood, in the minds of most people, is inseparably linked with the legend of Robin Hood and his companions' (Innes-Smith, 1984). The region has plenty of sites, parks and ancient monuments that reflect the richness in cultural heritage and use Robin Hood's name as an emblem. Sherwood has played a central part in English history from prehistoric times to the present day and currently these resources are expected to contribute to the need for increased public access and recreational facilities (EMDA, 1999). In spite of its importance, economic change, 23

development and the demands of modern agriculture continue to put the existence of these assets under pressure. Recently, the study of ancient woodlands in England has emerged as a topic of concern because of the implications in the loss of cultural heritage and biological resources. Within the East Midlands, Sherwood is one of 33 Character Areas recognised to posses a trend towards erosion of individual character and loss of distinctiveness (EMDA, 1999). Consequently, there are numerous initiatives addressing rural and environmental problems such as the Countryside Stewardship scheme, Greenwood Community Forest, and the Sherwood Forest Trust among others (The Countryside Agency, 1999). Actions to safeguard historic landscapes and provide sustainable resources for recreation and access are being taken by both the public and private sectors through initiatives such as Countryside Stewardship, the creation of long distance footpaths and cycle-ways and through access to the regionâ&#x20AC;&#x2122;s historic parks and sites.

2.6 Social and economic aspects The area is densely populated, higher than the UK average with a steadily growing population. The population of Nottinghamshire for the Census of 1991 was 993,872 inhabitants (MIMAS, 2001). Figure 2.4 shows the population trend for Nottinghamshire. Within a national and regional context, the NCC (2000) compiled a set of indicators to assess the condition of Nottinghamshire and to identify the social, economic and environmental challenges this county would face in the near future. Forty-four indicators, grouped into five main themes, were applied at county electoral division level and local areas level. The first theme highlights Economic Regeneration and draws together indicators related to employment, migration and the re-use of land for employment and enterprise performance. The second deals with Social Cohesion and Exclusion, which considers child protection, youth crime, poverty, education, the disabled, children and elderly in need, teenage pregnancy and democratic participation. The issue of Environmental Sustainability is third and takes into account re-use of land for housing and employment, sustainable travel to work, congestion, traffic flow, waste generation, water quality and biodiversity. The indicator used to measure biodiversity was based on water Voles, as they reflect the health of the aquatic ecosystem and the food chain that supports them. Quality of life is the fourth topic, including a variety of issues ranging from crime and mortality to the use of libraries, youth participation, housing stress and road injury accidents. The last group of 24

indicators deals with the ‘learning community’ and refers to education and academic performance. In order to characterise the SNA, information about the different indicators was extracted from NCC’s report (2000), in such a way that each local area of SNA was defined using a relative scale appraised as “above”, “equal” and “under” for every indicator, using the county average as a reference. For each of these five topics, the local areas received a final score compiled from these indicators. Thus, within each main theme, those areas with more than 30% of the indicators marked as “under”, were assessed as “worse”; those areas with no indicators marked as “under” were appraised as “better”; and the remaining were assigned with “medium”. Figure 2.5 depicts the results of this analysis. According to the results, southern areas like Southwell and Burton Joyce appeared to have better performance for the whole set of indicators in comparison to the northern areas, such as Worksop, Dukeries and Sutton in Ashfield. Table 2.3 shows that among the five groups of indicators, Environmental Sustainability was the best. More than half of the area in SNA (54%) had all the indicators above the county average. Other positive results were found for Quality of Life given that most of the area (77%) had most of the indicators above or equal to the county average and there were also fewer differences between the areas. Finally, Economic Regeneration and Community Learning demanded attention because of the contrasting performance of the local areas. On these issues, a third (35%) of the areas in the SNA were assessed as “better” while 62% and 55% were appraised as “worse” respectively. Other important components include the range of institutions working in the area; these are subject to detailed study in Chapter 4. The institutions constitute a diverse group in terms of objectives, methodologies and personnel targeting different locations and themes. They were considered the main focus of attention during this research given their interest in driving the recent and future development of the landscape in SNA and their ample knowledge and expertise acquired throughout the last twenty years.

Table 2.3: Assessment of economic, social and environmental indicators for SNA, regarding the county performance average, Percentage of counties area (%).

Set of Indicators

Economic Regeneration Social Cohesion and Exclusion Environmental Sustainability Quality of Life Learning Community

Worse

Medium

Better

14 23 55

32 70 10

54 7 35

Source: Data on indicators extracted from NCC (2000)

2.7 Conclusions The current landscape of SNA can be considered as the reflection of previous visions and/or driving forces, which shaped the features of the region. The proportion and arrangement of different land uses and their relative continuity in the landscape give an indication of the diversity of visions affecting the area during its history. The areas presently in conservation were originally preserved as part of the Dukeries Estates. Such areas, formerly owned by aristocrats, in some cases passed to public ownership and at present represent the main semi-natural vegetation assets and important recreational parks. Tourism and wildlife conservation activities have been objectives in preserving these areas, which contrast with industrialised and modern landscapes in other areas of SNA. Changes in the economic importance of coalmining as an industrial activity has left behind an exploited landscape. Nowadays, those places are being recovered and conserved by national and local authorities as well as members of the community. The existing pine tree plantations, ancient woodland and heathland areas in conservation use are an example and a consequence of this process. Agriculture and pig farming are other activities taking place however their importance is decreasing due to changes in markets and environmental policies. Farmland is, at the moment, the most extensive land use in the area and is the consequence of the need to increase food production during the mid of the last Century. The multiplicity of groups and organisations interested in shaping the future of the SNA will need to establish regeneration and maintenance plans to keep the ecosystem services and goods that the area has provided for centuries. A necessary step in this direction is to understand the different views and ideas of the groups both conceptually and spatially, hence the need for the work that follows. Chapter 3 presents a review of 26

methods for â&#x20AC;&#x2DC;capturing visionsâ&#x20AC;&#x2122; of the groups, followed by Chapter 4 in which a selected group of stakeholders were subject to study using this method.

Plate 2.5: Statue of Robin Hood in Thoresby Hall (Photo: J.Rubiano, 2002)

Plate 2.6 Thoresby Hall in the surroundings of Sherwood Forest (Photo: J.Rubiano, 2002)

Population change 1,200,000 1,000,000 800,000 600,000 400,000 200,000 0

Inhabitants

1891, April

1901,

1911, April 1921, June 1931, April 1939, Mid- 1951, April 1961, April 1971, April 1981, April 1991, April

5/6

March

2/3

19/20

26/27

year

8/9

23/24

25/26

5/6

21/22

445249

514059

603708

640693

712269

756528

840101

900904

974475

982900

984990

Figure 2.-4: Nottinghamshire â&#x20AC;&#x201C; Population growth 1891-1991 (Source: Office for National Statistics â&#x20AC;&#x201C; Census Division, 2001)

Plate 2.7: Dead wood in ancient woodland areas (Photo: J.Rubiano, 2002)

Learning Communities

Quality of Life

Environmental

Economic Regeneration Figure

2.5:

Graphic

representations

Social Cohesion of

NCC

indicators

(source:

NCC,

2000)

CHAPTER 3 'VISIONS': A REVIEW OF METHODS 3.1 Introduction As explained in the introductory chapter, one of the general characteristics of landscape and environmental management problems is their fuzziness. Given the multiple interpretations of landscape complexity, there is no well-defined problem on which to focus. In this sense, the current research is a learning process directed towards discovering what are the main concerns for the different stakeholders involved in SNA. To do this, different methods and techniques initially were suggested and some tested in the study. A combination of qualitative and quantitative methods were used to achieve a deep understanding of ‘visions’. These methods are explained in this section, although many additional details were incorporated in each chapter in which the methods and techniques were applied. To begin with, a review of what is understood by a ‘vision’ is presented and its meaning explained in the context of the management of landscapes (Section 3.2). Stakeholder analysis and ‘soft systems’ are discussed since these are the basis for the approach used in this project (Section 3.3).

This review suggested treating a ‘visions’ as a

system and, as such, each of the different techniques implemented tried to elucidate parts of them. A review of techniques used to ‘capture and represent visions’ during the research process is included (Section 3.4). These are alternatives methods to implement stakeholder analysis and supporting tools for the development of information systems. The first of these, ‘text analysis’, was used to obtain a broad picture of SNA in terms of functions and components suggested by each of the stakeholders in their visions. Given the complexity and uncertainty depicted in the stakeholder visions, it was necessary to use methods that accounted for these constraints. Graphical modelling was tested in an attempt to capture the relationships between components mentioned in the institutional documentation and as an introduction to the implementation of inferential methods with Bayesian Belief Networks (BBN). Subsequently, visions of the stakeholders were elucidated through interviews with representatives. This primary information was later incorporated into a geographical information system (GIS). Methods to manipulate these data include spatial analysis 30

and logistic regression and these are presented in Chapters 5 and 7 respectively. These analyses provide additional information concerning the boundaries, components and relationships of the stakeholder visions. Landscape analysis metrics were used to deduce more information about the function and relationships between landscape components within each of the ‘visions’. Finally, Q-methodology is summarised in Section 3.4.3, used to revisit the visions modelled with previous techniques as a validation/triangulation tool. Figure 3.1 use the same structure of Figure 1.1 but it summarises the relationships between methods and their position during the research process. The arrows indicate the sequence in the activity/method implementation. CHAPTER 1 GENERAL INTRODUCTION

CHAPTER 2 SHERWOOD FOREST NATURAL AREA: Components

CHAPTER 3 'VISIONS' A REVIEW OF METHODS

OBJECTIVES, STUDY AREA, LITERATURE REVIEW

CHAPTER 5 INTERVIEWS: Components, boundaries, relationships

CHAPTER 8 LANDSCAPE PATCH ANALYSIS: Structure

CHAPTER 6 STUDY AREA GEO-DATABASE: Components, structure.

CHAPTER 7 PROBABILISTIC MODELS: Relationships, boundaries, components.

CHAPTER 4 TEXT ANALYSIS and GRAPHICAL MODELLING: Functions, components, inputs, outputs.

CHAPTER 9 Q-SORTS - VALIDATING AND CLASSIFYING VISIONS: Relationships, boundaries, components.

DATA COLLECTION, RESULTS AND DISCUSSION

CHAPTER 10 SYNTHESIS

SUMMARY AND CONCLUSIONS

Figure 3.1: Summary of methods and their outputs when studying ‘visions’ as systems and their location in the current investigation

3.2 Capturing Visions 3.2.1 What is a 'vision'? A ‘vision’ is the ability to think about, or plan the future, with imagination or wisdom (Hornby, 1995). In strategic planning, the reasons and purpose for an organisation’s existence are set in its ‘vision’. The vision expresses the ideal state that the organisation aims to achieve. At the same time, it identifies major goals and performance objectives. The organisation’s philosophy is the framework used to define the vision and the mission, which are used as a context for the development of 31

strategies and criteria to evaluate an organisationâ&#x20AC;&#x2122;s performance (Hax and Majluf, 1996). The visions determine what goals and strategies an organisation pursues. The ways in which they select their goals and strategies depends on what they see in the future. The philosophy of an organisation is packed within the vision and mission. The strategy is based on what is established in the vision and mission precepts, which in turn has to be reflected in the plans, programmes and projects executed in the world of facts. "Future visions can help generate long-term policies, strategies, and plans, which help bring desired and likely future circumstances into closer alignment. The lack of vision promotes aimlessness and apathy, which, in turn, erodes the human resource base and increases waste of all kinds. Intelligent visions provide the backdrop or criteria for deciding what is more likely to be useful or useless in the future. " (Glenn, 1994 p2.). An example of how the vision transcends the intermediary layers before it is converted in action is the adoption of protected zones for biodiversity conservation by local authorities. Just after the Rio Convention, and moved by the concern for sustainable development expressed in Agenda 21, individual nations started including global policy in local biodiversity plans through 'Local Agenda 21' (Action Towards Local Sustainability, 1999). Vision and mission can be revealed through a critical review of projects and plans although clear limitations are found when those visions are only to be applied in the future and there are no existing projects to analyse. The concept of vision is a human construct and as such includes subjective elements, individually or socially built like wishes, purposes, values and ideals filtered by history and culture. In the words of Karl Popper, (1972, p. 76) this is â&#x20AC;&#x2DC;the world of subjective or personal experiencesâ&#x20AC;&#x2122; or 'third world'. In relation to landscape planning and management, it is suggested by Linehan and Gross (1998) that researchers and planners must go further in understanding landscapes. They must contribute in altering the values and directions of society; reassert the sense of vision and the meanings of the landscapes integrating knowledge, perceptions and practices to improve them (Linehan and Gross, 1998). This adaptive process expressed in broad goals and objectives must be translated into operational objectives and stated in terms of specific measurements that can be incorporated into monitoring programmes (Christensen et al, 1996). These operational objectives are called the 'desired future conditions', "although given the dynamic character of ecosystems, 'desired future behaviour' might better capture the objectives" 32

(Christensen et al, 1996, 680p). This is enforced by the fact that societal values often change more rapidly than ecological conditions (Lessard, 1998).

3.2.2 How to capture stakeholders’ visions? To achieve a broad understanding of the social, economic and political dimensions of a culture, social scientists use different methodologies and techniques. They gather qualitative or quantitative data that can be translated into information, in order either to understand human behaviour or as an explanation of facts and laws guiding human behaviour. Both approaches are complementary and their use depends on the research objectives, approaches and preferences of the researcher. The idea of capturing 'visions' is relatively new. There are no standard techniques or methodologies for capturing (people’s) visions. Studies focusing on similar topics, like capturing perceptions of the countryside or environmental public consultations, express their concern about the validity of their findings, because of the subjectivity associated with the degree of respondents’ knowledge and the involuntary impact upon them created by researchers (Speeding et al, 1988). Furthermore, the need to incorporate people's visions through participatory methods in the definition of the future of landscapes is widely recognised. "Experts, such as landscape ecologists and planners are seldom aware that their perceptions of the landscape differs from those of local residents" (Luz, 2000). The experience of the landscape is far more complex than previously thought (Purcell, 1994). This difficulty seems to be always present, which implies that visions must be the subject of continuous revision and analysis, to verify their congruence with changes in social demands and future expected needs (Linehan and Gross, 1988). It is in this sense that a stakeholder and soft systems approach is the more appropriate strategy to allow the incorporation of multiple perspectives and the systematic way to organise complex and diffuse information. Only by empirical and systematic exercises is it possible to adjust existing methods or develop new ones combined with the use of information management technologies, which are sufficiently flexible and adaptable. For the process of inquiry, it is important to distinguish between tools, techniques and approaches as suggested by van der Vost (van der Vorst, et al., 1999). Table 3.1 presents a classification of tools used in environmental enquiries that are of potential use for the elucidation of ‘visions’. In this classification, it is important to highlight that the tools are independent of the context.

Tools are not bad or good in themselves, although it is not always possible to use a particular tool in all situations. In the next section the stakeholder and soft system approaches used in the current research are explained followed by a description and discussion of each of the different techniques incorporated in this investigation.

Table 3.1: Tools for environmental management inquires (After van der Vorst, et al., 1999) Tool

Participant Observation

Ethnographic

Data Type predominance Qualitative

Collaborative Processes

Negotiation

Qualitative

The optimal solution is through consensus building.

Q-Method

Statistical

Quantitative

Documentation review and Text analysis

Concordance

Grounded Theory

Qualitative

Stakeholder Analysis

Several

Strategic planning

Qualitative

Several

Root definitions

Soft systems

Geographic Information Systems

Mapping

Hard systems

Quantitative

Computer Models

Mathematical Algorithmic

Systemic

Quantitative and Qualitative

Visions are subjective statements that reflect human behaviour. Institutional information reproduces societal or group interests. The identification and treatment of trade-offs between stakeholders holding different objectives is the clue for success. Through its application, the purpose of the system is clearly identified. The integration of spatial and nonspatial data sets allows a multidisciplinary approach to the ‘optimisation’ of a space. Landscape visions are captured by considering the integrated requirements of the present and future observers (regarding humans, plants or animals).

Interviews, rapid and participatory rural

Technique

Approach

Assumptions in capturing the ‘vision’ Those who live in a specific place are best in describing their own culture.

appraisal Workshops, panels, round tables, brainstorming sessions, carousels, focus groups, formal individual presentations Q-sorts

Qualitative

3.3 Research approach 3.3.1 Stakeholder Analysis One of the most revolutionary ideas produced in the area of business administration and management is the incorporation of stakeholder analysis (SA) within the scheme of modern corporations. There are different definitions of SA depending on the branch of knowledge which is being considered. However, Grimble and Wellard (1997) provide a generic definition of the systems approach based on the following reasons for carrying out stakeholder analysis: 34

Empirically: to discover existing patterns of interaction,

Analytically: to improve interventions;

As a management tool in policymaking, and,

As a tool to predict conflict.

They define SA as an holistic procedure for improving the understanding of a system through the identification of the key actors or stakeholders and the impact or changes that their interests can generate. SA originated in the areas of business and management during the 1970s as a means for organisations to interact with their customers and other interests groups in a constructive manner with the objective of guaranteeing the success of the enterprises. SA has also been related to the development of other approaches for decision analysis such as ‘decision theory, multicriteria

analysis,

environmental

impact

assessment,

outcome

measurement,

participatory appraisal, social actor approaches, and conflict resolution’ (Oudman et al, 1998; Grimble and Wellard 1997). Other sources place SA as one of many alternatives to the positivist paradigm in the search for knowledge associated with sustainable practices (Pretty, 1994). These more open approaches emphasise the effect that is produced by the framework of people’s knowledge, which is introduced in the findings of their enquiry. This is reflected in the change of goals every time a new individual or group tries to define or solve the problem. “If we believe in one absolute truth, disagreement can only mean negation” (Maturana, cited but not dated in Röling, 1994). Existing disagreements are treated by negotiation to rebuild the other’s different realities. Consequently, it has been considered essential to assure the participation of a wide group of stakeholders to include multiple perspectives on a given situation (Rölling, 1994). In this sense, SA could be considered as a ‘constructivist’ approach, where the focus is the identification of people’s intentions rather than achieving an agreement or consensus. This is referred to as the 'social actor' perspective, where the domains of the biophysical and economic sciences are complementary parts of the whole system', but not the only ones (Röling, 1994). This makes SA an appropriate strategy to deal with the subjectivity associated with the definition of a landscape and a description of visions for a landscape future. Grimble and Wellard (1997) reviewed the principles and methods of SA and evaluated its contribution to the issue of natural resource management and the environment. 35

They identify two approaches followed by the Natural Resource Institute (NRI) on the one hand, and by the Overseas Development Administration (ODA) and World Bank, on the other hand. The first one has an economic approach and this leads to inherent conflicts in natural resource management (NRM). The second one deals more with consensus building and the construction of common agendas between the parties involved. Many environmental organisations around the world include SA as one of the steps in the development of their projects. The method is being included as a key part of strategic planning followed by aid development project offices. SA is permanently being refined and included in different instances and at different levels of aid projects. One of the main reasons for doing this is the assumption that including not just the beneficiaries of the projects but the wider range of stakeholders potentially affected by the project or policies is more likely to lead to the achievement of project goals (Grimble and Wellard, 1997). In this sense, the purpose of SA in NRM is to provide a way of understanding environmental and development problems and considering the interactions between existing perspectives and visions of stakeholders at various spatial scales and hierarchical levels. Grimble and Wellard (1997, p. 175) relate the stakeholder term to a group of people, whether "organised or unorganised, who share a common interest or stake in a particular issue or system". It can be located at any level of the society or at different spatial scales. It may include both physical or tangible actors as well as abstract categories such as 'future generations', the 'historical heritage' and 'common welfare'. Depending on the decision to be taken, stakeholders can be classified in different ways. Those that are affected by the decision are called active stakeholders, and those who affect the decision called passive stakeholders. In Aid projects, they are sometimes called secondary and primary stakeholders respectively. In other cases, stakeholders are classified according to their relative influence and importance to the success of a project. Several steps are considered in the SA process. First, the stakeholders must be identified. Second, their goals and interests have to be analysed. Third, a comparison must be carried out searching for conflicts, trade-offs and similarities between their positions. The stakeholders who require more attention are those clearly affected and/or opposed to the project. "A central concern of SA in NRM is to highlight the trade-offs that have to be made by stakeholders between different objectives" (Grimble and Wellard, 1997, p. 181). Special cases are trade-offs involving macro-policies that 36

impact on the environment such as the use of nuclear energy, dam and motorway construction and genetically modified crops, because these require a rigorous appraisal of efficiency and equity, as well as a detailed assessment of environmental consequences. The output of the process is a set of tables indicating a detailed list of the stakeholders and their interests. These interests are classified into positive, negative, uncertain and unknown, depending on the coincidence with the project’s interest and the likely or actual impact of the project upon the stakeholders (Overseas Development Administration, 1995). The SA is simply a method to achieve specific goals that are, in a general sense, the identification of groups and the way in which they are involved in a problem. It is assumed that the critical concerns of the stakeholders are covered during the implementation of a project. However, this is not always the case because new ideas may emerge as the project develops. In addition, it is not clear what procedure to follow when analysing the information taken from the stakeholders (Grimble and Wellard, 1997). The identification of the positions of the different groups involved in the decision making process requires appropriate techniques that allow the most important and relevant parties to be identified. Stakeholder analysis is being adopted as one of many techniques to achieve this goal, although some limitations are imposed by the context whit in which the method has been applied. In spite of the benefits attributed to the SA, the method cannot be considered as the only one to manage the complexity of environmental problems. The method per se “cannot provide answers to problems or guarantee representation” (Grimble and Wellard, 1997, p. 189). Additional effort must be made to promote a more democratic process and the empowerment of the groups facing environmental problems. This additional task is outside of the domain of the SA method. A combination of methods and an improvement of the existing ones could highlight the critical and evolving factors that determine the sustainability of our environment (Grimble and Wellard, 1997). The way in which the stakeholder approach was used in this study, allowed the listing of a diverse group of stakeholders and the ruling out of those that were not relevant. During the rest of the study, the stakeholders’ positions were subjected to analysis, which focused on the similarities and differences between their visions.

3.3.2 Soft systems The soft system approach was developed as a methodology by staff working at Lancaster University and the Open University (Checkland, 1991). The method was developed because of the lack of techniques to manage the human aspects of organisational systems (Presley et al, 1998). The purpose of this approach is to develop behavioural models about the way human systems work through iterative comparisons between the proposed models and reality. This comparison allows for the identification of gaps between the way in which the system is operating and the way it should operate. The stakeholders involved in solving the problem, identify necessary changes in problem solving strategies during this process. This in turn allows the incorporation of ‘world views’ before the definition of objectives and implementation strategies reducing the risk of failure (Kirk, 1995). Kirk (1995) summarises that a soft system is characterised by having: - No agreement about the precise objectives of the system; - Qualitative rather than quantitative objectives; - No single solution, but a range of equally valid alternative solutions; and, - A need to involve all those affected by the system. SSM is a participative approach, in which the aim is to achieve an accommodation concerning what action should be taken (Vidgen, 1997). The soft system approach is considered a methodology for analysing and modelling complex systems that integrate technology and human groups. This is the reason why a stakeholder analysis becomes a fundamental stage in the use of SSM. The technology is considered the ‘hard’ component of the system, and people the soft part of the system. Checkland (1991) also calls this soft part, the Human Activity System (HAS). The main difference between these two systems is the subjectivity associated with the latter. The soft system methodology (SSM) is designed to understand problems with ambiguous characteristics and multiple solutions or outcomes introduced by the HAS. It is intended to address the different perceptions that multiple individuals have about a specific situation by attempting to ensure that the results of analysis are acceptable to the stakeholders concerned. SSM tries to improve the process of decision making rather than just achieving an optimal or exclusive solution. The SSM consists of a seven stage process as represented in Figure 3.2: 1) problem situation unstructured; 2) problem situation expressed; 3) root definitions of relevant 38

systems; 4) conceptual models; 5) comparison of conceptual models with the real world; 6) feasible, desirable changes; and 7) action to improve the problem situation. Stages 1 and 2 represent the identification and representation of the problem situation in terms of a “rich picture.” A rich picture is a representation of the problem situation, typically presented in the form of an abstract drawing, which describes the aspects of the system that are relevant to the problem definition (Presley et al, 1998). Stage 3 is the conceptualisation of root definitions. It consists of a short paragraph in which the stakeholder expresses the 'world view' and emergent properties of the system. In other words, it is the conceptualisation of the purpose of the system. Its construction is facilitated by contrasting the stated purpose against the mnemonic CATWOE (Platt and Warwick, 1995): C: customers (people affected by the system, beneficiaries or victims); A: actors (people participating in the system); T: transformation (the core of the root definition, the transformation carried out by the system); W: Weltanschaung (“world view”); O: ownership (the person(s) with the authority to decide on the future of the system); E: environment (the wider system). "World views are mental models about the very nature of reality, they define what is important, what questions can be asked, what goals are possible, what can and should be measured. World views not only give meaning to information, they actively screen information, only admitting what fits our preconceived models" (Meadows, 1998). The same author suggests that using indicators there is an opportunity to inquire into the models that produce discrepancy. Models are a tool for expanding, correcting and integrating worldviews. The root definition provides the analyst with the framework for ensuring that all points of view and interests are considered during the knowledge elicitation process (Finegan, 1994). The root definition uses verbs to describe activities and their logical dependencies. It makes sense in a particular context and is used in the construction of conceptual models that express the dispositions that root definition outlines (Checkland and Tsouvalis, 1998).

The fourth stage is the representation of the conceptual model and its comparison with formal systems or other systems thinking perspectives. The fifth stage is then the comparison of the model itself with the real world from which suggestions for changes can be established in Stage 6. Once the model has been modified in accordance with the desirables changes, it is put into action in Stage 7, in order to improve the problem situation.

Figure 3.2: The seven stages in the use of soft systems (Source Checkland, 1991 p163)

An important assumption in soft systems is that information is a socially constructed phenomenon rather than a concrete phenomenon. In other words, information is data plus the meaning attributed to it in the specific context by people (Holwell, 1989 cited by Winter et al, 1995). In the soft system approach, an information system is something that supports, or serves, purposeful action. Information supports an intention or purposeful action with knowledge and people use it to interact with the world. Given that computer-based information systems are systems that serve purposeful human action, the idea of â&#x20AC;&#x2DC;information systemâ&#x20AC;&#x2122; in the model being developed here is "one that 40

contains a ‘serving’ system (the information system) and a ‘served’ system which represents purposeful human action in organisations" (Winter et al, 1995). Once the information system is in use, the interactions between both components will lead to learning, which may change both. The model’s core concept then consists of two systems in a served-serving relationship as illustrated in Figure 3.3. The relationship between the served and the serving system has to be treated as dynamic process. Winter et al, (1995) mentions other concepts and tools, different from soft systems, which might be useful in managing this relationship, these include: service modelling concepts (Gronroos, 1990), organisational learning concepts (Senge, 1990; Lessem, 1993) and interpretative data analysis (Lewis, 1994). The sequence of activities in an SSM approach, to develop information systems is as follows (Checkland and Holwell, 1993 cited by Winter et al, 1995): 1. Meanings are attributed to their world by the people concerned; 2. Purposeful activity is identified; purposeful in the light of those meanings; 3. Information support is provided which is relevant to the people carrying out the activities; and, 4. Data structures and ways in which they could be manipulated to yield categories of information.

Figure 3.3: The fundamental logic of IS (after Winter et al, 1995 p132)

The methodology contains principles related to both systems (served and serving). In the served system, there are principles rooted in organisational analysis. In the serving system, we find those of information system engineering. With the objective of making sense of purposeful human action prior to information system design, it is essential to 41

establish the views or purposes of the organisations, which are relevant and meaningful to the project. To do this it is necessary to conceptualise the current activities of the organisations or interest groups by addressing the issue of concern and then discussing them with the stakeholders (Winter et al, 1995). The output of this debate is a set of clear conceptualisations about the way the organisations work (activities) and the types of information required for further analysis. The activities are examined to help derive the information requirements of anyone carrying out the activities. After identifying and defining information requirements, it is necessary to consider the data, which could provide the required information (Winter et al, 1995). Songkhla (1997) presents an example of the introduction of soft systems as a strategy for the development of information systems. Maciaschapula (1995) presents a case study in which SSM was used to identify the value, impact and barriers to information access and use, as related to quality of health care in Mexico. Using SSM, conceptual models were built at different levels of resolution representing the activities of the actors in the system. The results provided valuable insights for where to base the development of a model to continue research in the field. Checkland and Sholes (1992) present experiences in ten different sectors in which soft systems were applied: industry, UK National Health Service, Civil Service, Marketing and private business. After reviewing these ten case studies, the authors emphasise that SSM is systemthinking based, and that the process of enquiry is 'the system' itself. The system is not a part of the world that has to be engineered or optimised. Through the case studies they identified a range of possibilities for the use of the methodology. At one extreme, a formal use of the seven stages of the methodology was called SSM Mode 1 in which there is an intervention in the system in contrast with SSM Mode 2 in which use of the methodology is made through an interaction with the process. The use of soft systems in environmental management has recently emerged. It has highlighted the contribution of system thinking in environmental problem solving through the multiple values included by the human component. â&#x20AC;&#x153;Where the definition of the problem depends on the viewpoint adopted, it is important to make that viewpoint explicit, and then to work out the systematic consequences from that pointâ&#x20AC;? (Clayton and Radcliffe, 1997). Gough and Ward (1996), in the development of a decision support system for lake management in New Zealand, recommended the adoption of an informal soft system framework, concentrating on information gathering and consultation with affected parties before getting into the complexity of computer modelling and the establishing of computer support tools. Perhaps the main reason for 42

taking this option was that environmental decision-making is characterised by uncertainty at all stages of the decision making process. Uncertainty is merged in all the steps of the process, starting from problem definition and finishing with the occurrence of the outcomes (Gough, 1988). “A balance must be struck between complexity and quantity and quality of data. Ascertaining the appropriate information requirements in a particular circumstance is a difficult but crucial task. Environmental data are costly and time consuming to obtain and, therefore, the concept of ‘minimal’ data sets is important” (Gough and Ward, 1996). As mentioned in the introduction to this Chapter, the soft system approach was taken as a guideline for the development of a ‘vision capturing system’. The following Chapters present the implementation of several techniques to achieve this; in Chapter 10, a review of the use of soft system as a general approach for the current research is presented.

3.4 Research Techniques 3.4.1 Documentation review and text analysis Documentation review and text analyses are part of what is called 'qualitative analysis'. Even though these techniques have been used extensively in all kinds of research, they have gained importance recently due to the recognition that 'our post-modern culture is dominated by representation and images' (Aitken, 1997). The study of the use of these elements in human communication, both in language and by various nonlinguistic means, is called ‘semiotics’ (Hornby, 1995). The science of interpreting or explaining a text is called ‘hermeneutics’. Written text is only one form of written communication, other forms can include art, photography, film, music, among others. In parallel with semiotics and hermeneutics there are other theoretical approaches for the interpretation of a text such as Marxist, feminist, psychoanalytic and post-modern critique approaches. Each of these approaches uses different methods for the analysis of documentation. Pickles (1992), cited by Aitken (1997), suggested that hermeneutics is one of the most appropriate methods to analyse text. However, the same author adds that both semiotics (the study of signs and symbols) and hermeneutics have been criticised because their lack of theoretical basis, has allowed practitioners to invent structures rather than discover them. However, rigorous hermeneutic interpretations can keep the integrity of the text and the meaning attached to it. Interpreters are thus able to 43

discover the meaning the text had for those whom it was written, and can show what the text now means in the context of contemporary views, interests and prejudices. With the aid of computer systems, text analysis is less time consuming than previously and it is now possible to combine several techniques in the study of long documents in less time than before. It is extended the list of computer resources to execute tasks that were previously carried out manually (Tesch, 1990 and Kelle, 1995 in Aitken, 1997). One of the most popular strategies when working with long documents is to reduce the content using a categorisation process, in which segments of the text are codified and sequences and themes are identified. Those codes are then used to reconstruct the meaning of the text and to describe the subjective world views they contain (Huber, 1997). Another option to manage text is identifying word frequency, collocations and concordance, which was the alternative used for the analysis of institutional documentations. With word frequency established, it is possible to have a general sense of what the text is about as well as highlighting the more important words in the meaning of a text. Collocations are a way of relating word-forms to meanings, thereby signalling the words that tend to be together, allowing us to find the linguistic environment of specific words. With the latter, it is possible to see every place where a word is used, to detect patterns of usage and meaning and to locate evidence for an argument. The use of computers in text analysis is a systematic way of dealing with specific searches and the revision of long documents (King's College London, 2000). Text analysis was selected in the current research as a strategy to deal with the large volume of stakeholders’ documentation, and as an entrance point to know the official ‘world view’ of the different stakeholders. Chapter 4 presents the application of this technique in detail. 3.4.2 Personal interviews Investigation by interview is part of a group of techniques generally referred to as the 'ethnographic method’, which is a process of describing a culture from the perspective of those living it. Sociologists, anthropologists and psychologists have used this method extensively. The techniques are flexible in the sense that their use depends on the specific situation to gather “behavioural and attitudinal data, which will allow the understanding of how the groups function, the world view of their members and, importantly, how they are reacting or may react to change” (Furze et al, 1996).

Ethnographic methods include interviews and rapid and participatory rural appraisals. Within each of these, there is a diversity of methods that can be adjusted to the needs, resources and circumstances of the case study. Within interviews, there are formal interviews, semi-structured interviews, and group interviews. For rapid and participatory rural appraisal, there are complementary techniques like secondary data reviews, direct observation, key informants, community groups, stories and portraits by informants, diagrams and workshops. Chambers (1992) presents a detailed review of all these techniques. Semi-structured and narrative interviews were applied to representatives of the selected stakeholder groups in SNA to obtain a closer look of what they were ‘visioning’ for the landscape in the SNA. 3.4.3 Q–methodology Q–methodology is a statistical procedure developed by the British physicistpsychologist William Stephenson (1953) directed towards the study of behaviour and specifically towards the identification of subjective perceptions of groups or people looked at from the point of view of the groups or people being observed. It is based on the assumption that classical quantitative methods cannot deal with information like “aesthetic judgement, poetic interpretation, perceptions of organisational role, political attitudes, appraisals of health care, [or] perspectives on life and the cosmos, […]” (Brown, 1996, p.1). In other words, the assumption of this technique is that the subjectivity of people’s views can be systematically analysed making possible the identification of different approaches at the core of a group. Subsequently, the approaches can be compared to permit an appropriate selection of courses of action. Differences in patterns of subjective perspectives between individuals are the concern of Q-methodology. It tries to reveal how different individuals perceive an issue. Instead of generalising about it, it searches for the variability between individuals or with groups (stakeholders) (Steelman and Maguire, 1999). Q-methodology has been used in different areas of research. Durning (1999) reported a review made by Peritore (1990 p.11) of the existence of more than 2000 papers using the Q-method. This method has been used in research in psychology, sociology, political sciences and public administration (Durning, 1999). It is rooted in social psychology methods, which make use of a group’s dynamics to get deep into people’s attitudes and behaviour to capture normative influences (e.g. rules, norms or payoffs) and explain collective action in social conflicts. 45

In the environmental context, the Q-method was used in a case study at watershed scale in Chattooga County, Georgia (Steelman and Maguire 1999). The Q-method was combined with other techniques like surveys, contingent valuation, focus groups and multi-attribute utility analysis. The study found three different philosophies in the Chattooga watershed: (1) Management for wildlife and Habitat: minimising human disturbance; (2) Managing for Timber production and wildlife: active management; and (3) Traditional use: maintaining access and minimising impacts. The method identified the differences and similarities in opinions between the participants. All three groups thought that water, hardwood and mixed forest tree species were important to the residents of the Chattoga watershed. A parallel study of the interviews using hierarchical coding analysis was used as a means of validation of the findings with Qmethod. In both cases water was identified as the main issue. There is an increasing use of the technique with the expansion of democratic processes and participatory enquiries. Donors are now supporting research where conflicts of interest are the main concern. For example, Brown (1999) reported the use of Q methodology in the identification of community perspectives on radioactive and chemical waste contamination supported by the National Institute for Environmental Health Sciences to work with the Cherokee community near Gore and Vian, Oklahoma. Swaffield and Fairweather (1996) reported the use of this technique for identifying attitudes towards land use change in New Zealand. McKeown and Thomas (1988), Brown (1991) and Durning (1999) highlight the general steps in the application of the Q-Methodology. First, it is necessary to create a selection of relative statements (not facts) about the topic under discussion from a preliminary list. They are subject to opinions or affected by values. The statements can be generated using interviews, previous questionnaires, institutional profiles or a broad random topic selection. This is usually called the Q-Sort. Second, a person or groups of interest to the researcher (selected for theoretical or practical reasons) are asked to rank each of the statements from agree to disagree in a scale that is usually between â&#x20AC;&#x201C; 5 to +5 (ie. a quasi-normal distribution). It is assumed that there are no good or bad answers and the analysis can be executed independent of the number of cases. Third, a correlation matrix is built using the scores obtained. Fourth, the correlation matrix is used in a factor analysis with the purpose of identifying cluster groups of participants who classified the statements in a similar way. In Q-method, the sorts criteria are the variables, and the observations are the Q-statements. In some cases, statistical variation is used to differentiate the clusters more clearly. Finally, the weighted average 46

sorts of the different groups of participants are examined with the aim of identifying the characteristics of each group. A description of their attitudes, similarities and differences is then carried out. It is often said by the advocates or participatory methods that the decision-making process lacks the inclusion of people’s goals. However, people’s goals are usually a mixture of objective and subjective statements. Perhaps the great advantage of Qmethodology is that it allows for the incorporation of this subjectivity in the decision making process – information that is normally lost with the use of formal techniques. As Steelman and Maguire (1999) note: “Q-methodology can be a tool to make more explicit the expectation and beliefs held by the public with respect to planning and how the public should be involved in planning". An example of this is presented in the study of the Monongahela National Forest Planning Process (Steelman and Maguire 1999). As a product of the application of the Q-method carried out in this case, the implications for management and public involvement were elucidated. There were the identification of sections within the forest agency, the definition of internal viewpoints and perceptions, the provision of guides to the planner and planning staff about their roles, the outlining of areas of consensus and conflict, and the development of a common view towards planning and public involvement. However, the authors highlighted the need to have some prior conceptualisation about the public involvement or the relationship between decision-maker and the public, when using Q methodology. The authors stated that “the analyst knows the type of information that will be yielded from the exercise and how that information will be used to facilitate decision making" (Steelman and Ascher, 1997). This can be considered a bias towards the values, preferences or expertise of the analyst during the feedback process to the public or users. Even though Q-method makes explicit valuable information for policy analysis and decision making, there are no guarantees of its success and the advantages against other methods have not yet been clarified (Weimer, 1999). Its implementation looks promising when combined with complementary techniques. This was the case in the current research where the stakeholders were subjected to Q-sorts, produced with the findings of preliminary techniques such as text analysis and geographical models. As Chapter 9 shows, it also allowed the incorporation of subjective and sometimes contradictory stakeholders’ statements about the landscape in the SNA into the analysis.

3.4.4 Geographic Information Systems (GIS) and Remote Sensing Following the review of methods and techniques used to capture 'stakeholder visions', the use of information systems must be considered in detail in the context of representing ‘desired future conditions ‘or’ ideal situations. They are a systematic and presumably less subjective approach to defining the "ideal situation" of the terrain in terms of landscape characteristics. GIS, defined as a digital structure for managing spatial databases, allows the integration of information from different sources: textual, numerical, and graphical as well as from other audiovisual media. These can be organised at different levels of detail (scale and thematic resolution). In addition, data can be collected and used at different temporal resolutions to represent specific processes. This versatility for representing complex biophysical and socio-economic processes occurring in the environment is based on conceptual assumptions about the way nature and humans operate and act (Curry, 1995). The risk of subjectivity is supposed to be reduced through the implementation of quantitative measurements, which are applied to each step in the system and validated through comparisons with the process in the real world. Then, ecological and socioeconomical events can be examined, modelled and tested in virtual computer scenarios to produce (pre-defined) stakeholders’ desired outputs. The outputs can be defined in terms of a specific policy or development approach. If this specific scenario represents an ideal sustainable path, GIS could be considered a potential tool to represent the objective desired situation for sustainable landscapes. They are pivotal both to the original design stage and to the revision and testing of subsequent modifications to the basic framework (Selman, 1993). However, subjectivity is introduced during the process of selection and classification of real life elements of the landscapes. The incorporation of the real world into a database is a process of simplification in which space is split into categories, each of which is arranged in continuous or discrete values. The effect of this process implies a loss of information and in some cases the emergence of illusory relationships between the elements of the external real world. In this sense, the conceptual framework, which defines the landscape elements and its relationships, becomes the most important part of the subsequent analysis and the conclusions that are derived from a landscape geographical information system. It is in this conceptual framework and in the structure of the GIS where one finds the designer(s) vision and purpose of the landscape subject to analysis.

An example of the GIS contribution to monitoring landscape change in the UK is presented by Bird et al (1994). The main argument for the use of GIS in this case is the magnitude of the information and the kind of operations necessary to identify landscape changes in the National Parks. Landscape was regarded in this preliminary study as a land cover feature. Cultural and socio-economic features of the landscapes were not included. The restrictions in defining a landscape imposed by GIS are discussed in the light of the kind of features that can be represented in a spatial database (for example lines, polygons and points and the way in which field information is collected and classified). Brabyn (1996, p 295) concluded that â&#x20AC;&#x153;GIS can be used to classify the important characteristics of landscapesâ&#x20AC;?. Using National databases in New Zealand, the author develops a landscape classification at different levels of perception. The author found several difficulties like the inability of the system to cope with the spatial composition of landscape as well as the incorporation of seasonal changes. In spite of these, it is suggested that landscape classification is the first step in developing landscape theories, which through subsequent validation can improve the understanding of its nature and develop new theory. Among remote sensors, aerial photographs have been used since the 1920s in the classification of terrain attributes (Howard, 1970). They have been the base of the first maps and the main source of ancillary information in field studies in the areas of biology, geology, hydrology and civil engineering. Young (1994) employed aerial photographs for landscape mapping using a technique called land systems mapping. The method is highly dependent on the abilities of the photo-interpreter, who should have experience in aesthetic judgement as well as a good knowledge of the countryside. The method is rooted in the 'visual' and has a degree of realism, apparently absent in multivariate analysis from image derived data sets. The author compares this technique with the current method adopted by the Countryside Commission in the Countryside Character Programme emphasising its reduced costs in resources and time in the UK. The method used by the Countryside Agency is now the basis for national guidelines in landscape appraisal.

With the purpose of evaluating the impact of development

projects on nature distinctiveness, quality and local diversity, The Countryside Agency have developed a framework consisting of a sequence of landscape character descriptions and environmental capital evaluation. Countryside character is the description of a place considering systematically the characteristics and locally 49

distinctive features of an area. Environmental capital is “what matters in the landscape” and the reason for its importance (DETR, 2000). The potential of remote sensing as a tool for landscape classification was evaluated by Cherrill (1994). This research compared three landscape classification systems, two of them based on satellite images (SPOT and LANDSAT 4 and 5) and the third one using land cover data from a field survey. The last was used to validate the information derived from remotely sensed land cover maps. Great correspondence was found between the three classifications systems, which used independent data sets and different levels of environmental and ecological organisation. "It was concluded that the use of remotely sensed land cover in the production of landscape classification has many advantages over previous approaches" (Cherrill, 1994). Geographical information systems were used in the current research as a necessary platform to illustrate spatially the different ideas given by the stakeholders. Its implementation and the subjectivity associated with the interpretation of landscape features are discussed in Chapter 6 and 7. 3.4.5 Landscape Ecology and Ecosystem Management Landscape ecology is considered to be a coherent scientific basis upon which to reconstruct functional new landscapes (Countryside Commission, 1993). Its use in countryside planning or land use planning is acquiring interest among environmental institutions due to scientific research findings about ecological principles and, in the UK, inspired by policies for environmental sensitive areas like the Countryside Stewardship Scheme and ‘Community Forests’. Given the multiple existing definitions of landscape it is necessary to establish a reference frame with respect to the term. It is an abstract concept, with no precise boundaries and refers to concepts such as scenery, system and structure (Antrop, 2000). Landscape is used as an abstract entity representing a subset of the physical world that includes evolving interactions with other (external and internal) elements that can be biological. When humans are included in this interaction, specific features of their culture are embodied on it and no single function can be attributed to it. Landscape and ecosystem management generally focus on delivering goods and services rather than on sustainability of structures and processes (Christensen et al, 1996). The identification of these services and goods at a local scale is currently part of the intention of environmental organisations and it is expected that they will adapt with time and changes in societal vision. In terms of ecological services, there is general 50

concern about the need to maintain biological, physical and chemical processes that occur in natural or semi-natural ecosystems (Brussard et. al., 1998). “Visions” in landscape management can be understood as the desired future conditions of these ecosystems’ structure and functions that can be accomplished with current management tools (City of Boulder, 1999). In the United States, it has been called the 'ecosystem approach', "…a method for sustaining or restoring natural systems and their functions and values. It is goal driven, and it is based on a collaboratively developed vision of desired future conditions that integrates ecological, economic, and social factors. It is applied within a geographic framework defined primarily by ecological boundaries" (The Interagency Ecosystem Management Task Force, 1995). An example of the application of Landscape Ecology in expressing a vision was the Landscape Planning Unit Guide developed in British Columbia, Canada (Ministry of Environment and Forest, 1999). After classifying land resources based on social and biophysical attributes, the analysts overlaid the layers in a GIS to obtain landscape units. These are "areas of land and water for long-term planning of resource management activities with an initial priority for biodiversity conservation" (Ministry of Environment, Lands and Parks, 1999, p 21). Objectives and strategies for landscapelevel biodiversity were created using these units and for managing other forest resources. "Landscape unit objectives are statements of desired future conditions for a forest resource or resource use. They apply to specific geographic areas and are measurable, either directly or indirectly, as a basis for monitoring the effectiveness of a plan". (Ibid., p 61). This geographical specificity of a landscape ecological approach is clearly recognised by Yaffee (1999) in an essay in which the term is contrasted with other approaches in order to clarify the confusion in the objectives of ecosystem management. He achieves this by classifying the meanings ascribed to ecosystem management in three different 'faces': (a) environmentally sensitive, multiple-use management, (b) ecosystem-based approaches to resource management and (c) eco-regional management or landscape management. The perspective from which each of the faces considers the environment is distinctive. The first has an anthropocentric inclination where human needs and values are the important objectives. The second has a bio-centric view in which ecosystems have the complexity and dynamics of ecological systems together with scale and boundary issues. The third emphasises landscape scale management as a fundamental goal. The author acknowledges the existing debate in the sense that a

diversity of approaches is desirable to deal with the difficulties of what is being asked of ecosystem management. Specifically, he asks: -“How do we balance the time required to understand ecosystem complexity with the need to make timely management decisions? - How can a desired future state be articulated to guide management when ecosystems are inherently dynamic and provide no absolute guidance as to what that future should be? - How do we define appropriate management boundaries when various problems and processes are organised differently spatially and temporally? - How do we deal with the need for collaboration among diverse interest groups and yet confront the reality that real value differences separate many of those groups?” Landscape units are considered appropriate boundaries for ecosystem management (Brussard et. al., 1998). However, an early consensus on how to achieve ecosystem management is unlikely because there are no easy answers (Yaffee, 1999, p 722). Lessard (1998) suggests the use of the hypothesis formulated by Everett et al, (1994): 1. "Human values and expectations can be incorporated into ecosystem management by identifying landscape patterns that are representative of these values”, and 2. “Sustainable ecosystems can be achieved by integrating people's expectations with the ecological capacities of ecosystems". A very logical and concise framework of implementing ecosystem management is presented by Brusssard et al (1998). They suggest the following seven steps: 1. To delineate the ecosystem to be managed. 2. To define strategic management goals. 3. To understand the ecosystem to be managed. 4. To determine the relationships among ecological conditions and human activities. 5. To link ecosystem data and socio-economic data into a meaningful model. 6. To implement experimental management options. 7. To monitor the management actions. With the emergence of systems theory and its subsequent application within ecology, progress has been made in the stratification of the real world to understand how 52

particular components of the environment work and the ways in which they are linked. The application of system theory concepts within ecology has permitted the division of ecosystems into functional sub-components facilitating their study and the development of methodologies for detailed inspection of their structure (Odum et al, 1960). Classification procedures emerged as part of the work with an interest on the organisation and analysis of abstract entities found in the physical world. Biologists, ecologists and scientist theories split the rural world into agro-ecosystems and natural systems. Within the former, different types of virtually independent structures, such as plantations or subsistence mixed crops, can be isolated to measure their exchange of information and energy with the rest of the system. At their interior, fluxes of resources are subject to analysis, opening an unlimited research field for social and biophysical scientists. Other agro-systems subject to detailed study proliferate in research including mono-cropped systems, pastures and agro-forestry systems. Within natural systems the diversity of subsystems is more extensive. The classification of tropical forests, savannahs, deserts and then numerous sub-classes allows the incorporation of knowledge derived from other physical and natural sciences as part of the explanation of evolution and current state. In some of these classes, considered at the first sight â&#x20AC;&#x2DC;naturalâ&#x20AC;&#x2122;, the influence of humans has been recently accepted as the most important of the sculpturing forces. In this sense, as noted by Farina (1998), three different perspectives in landscape ecology can be distinguished: -

Human: where the space is grouped by functional entities with a meaning for human life.

Geobotanical: where the environmental requirements of specific species or communities fulfil their adaptation, colonisation and survival processes.

Animal: as in the human-perceived landscape but restricted to the animal species-specific scale.

The three aforementioned approaches share a concern for space and the spatial arrangement of processes and patterns. Landscape ecology incorporates the interdependence between the human world and nature by trying to place a pattern or a process in space at the correct scale. It puts humans within the analysis considering the dimension of their current influence upon nature and emphasises strengthening theoretical and experimental verification of the meaning of space, time, components

and processes. As Farina (1998) states, there is a human landscape, a plant landscape and an animal landscape. The real world is however, not as simple as this. The hierarchy in which scientists arrange the world is just an abstraction, an arrangement of compartments for research purposes. Perhaps the biological and ecological hierarchies are ‘nested’ and can be expressed in spatial terms, but human-organised hierarchies like governments and corporations are ‘not nested’ (Odum, 1997, Simon, 1998). Social hierarchies and even biological and physical hierarchies could be arranged by observing who interacts with whom; the hierarchy explained in terms of intensity of interaction (Simon, 1998). One concept rooted in landscape ecology is that of "Holism” which holds that “the whole is more than the sum of the composing parts" (Antrop, 2000). Holism allows the integration of landscape ecology and perception. It also explains the interaction between structure, functioning, and their dependence on the scale of observation. Perception determines the way in which humans identify structures, patterns and functioning, and it might be considered as a subjective and complex learning process. Thus, "landscape observation is primarily subjective and can be understood only relative to the characteristics of the observer" (Antrop, 2000, p 19). "Landscape is Multifunctional" (idem p.23), it is considered an asset of the whole society, subject to the use of landowners, and temporary visitors, tourists and neighbours. The subjectivity and the multifunctional characteristics of landscapes is what this research attempt to elucidate. In Chapter 8, the different stakeholders’ visions are analysed and compared using pattern analysis techniques in current use in quantitative landscape ecology. 3.4.6 Integrated Models One of the challenges in all these attempts to merge sustainability concepts at the landscape dimension is the integration of biophysical and socio-economic parameters (Buuren, 1990, Ahern, 1991, Selman and Doar, 1992, Selman, 1993, Thackray, 1999 and Allen, 1999). The idea of integrated studies has been extended to include additional elements of the physical world. New outputs are being incorporated as part of the objectives and functions of the existing systems because of society’s new demands. Following these premises, landscape visions, together with other methodologies, can be captured considering the integrated requirements of the present and future observers, these being human, plant or animal. Integrated ecological models are emerging and their developers are expressing the necessity of involving decision-makers in the understanding of scientific information. 54

There is a trend towards the development of computer friendly interfaces to facilitate public access and a wider participation of the public in the management of our environment (Theobald et al., 2000; Grant and Thompson, 1997; Engelen et al., 1995; Holling, 1978). The National Institute of Public Health and Environment in the Netherlands is advancing this goal. They have developed an 'Environment Explorer', which is a spatial-interaction model that simulates the future use of space. It is composed of a set of models, which forecast the distribution of populations and economic activity and its effect on land use in urban and rural areas. "The primary goal of the model is to explore the effects of (alternative) policy options on the quality of the physical environment and, with this information, to stimulate and facilitate discussion prior to decision-making" (RIVM, 1998). Argent et al (1999), presented the main problems and needs in achieving success using integrated models, exploring the technical and social aspects of their development. The authors conclude that if model builders want their creations to be used in integrated environmental management, they must undertake a process that "fully involves stakeholders and potential users, that exploit current knowledge, and that illustrates the influence of uncertainty in the technical knowledge" (Argent et al., 1999, p.699). In spite of the advantages of following what Argent calls 'open modelling, problems emerge when considering timetables and the imperatives of policy makers. An example that considers the complex integration of social process as environmental conflict resolution is the Graph Model for Conflict Resolution (GMCR II). The model is designed to understand environmental disputes, advise decision-makers and forecast compromise solutions. It uses a graphical technique one step ahead of game theory and conflict analysis techniques. The tool has been used in international environmental disputes in North America (Hipel et. al. 1997). The integration of biophysical and socio economic landscape components is another aim of this research. As Chapter 7 shows, the techniques used allow the integration of diverse source of data, despite the fact that exact relationships between components is a more complex issue. Hence the Bayesian methods discussed below. 3.4.7 Bayesian Methods The Bayesian paradigm offers a natural and consistent way for framing a problem, data integration and for developing methodological solutions (Herriges and Kling, 1998). Bayesian methods were considered because â&#x20AC;&#x153;â&#x20AC;Śthe patterns of reasoning involved in assessing

relative

likelihood

familiar 55

events,

thinking

under

hypothetical

assumptions, judging the relevance of information sources, processing causal relationships, and combining contextual and stimuli clues in perceptual tasks, show a remarkable agreement with the rules of probability calculus” (McClelland and Rummelhart, 1981, in Pearl, 1990, p.342). Three methods, ‘Weights of Evidence’ (WofE), ‘Logistic Regression (LR) and Bayesian Belief Networks (BBN), have been extensively applied in environmental research and the health sciences (Spiegelhalter et al, 2000). Early in the nineties, Aspinall (1992) applied Bayesian modelling to the analysis of the winter habitat relationships of red deer in northeast Scotland. The inductive modelling procedure was also similar to the WofE method presented by Bonham-Carter using binary maps for prediction of potential gold sites in Nova Scotia (Bonham-Carter et al, 1988; Agterberg et al, 1990; Bonham-Carter 1994). Similar research has been carried out in western United States (Mihalasky, 1997), Iran (Asadi and Hale, 1999) and in The Philippines (Carranza and Hale, 1999). Corner (1999), developed an application called 'Expector’, in which probability theory was used to combine diverse data sets to produce maps showing the probability that a range of soil properties occur at specified levels. In a recent paper, Gritzner et al (2001) using topographic attributes and a wetness index applied a Bayesian probability model in a GIS to generate maps of relative landslide hazard in an Idaho catchment. The conditional probabilities were derived by comparing attributes of the cells containing landslide with attributes of cells without landslides. At the time of writing, there had been no use of Bayesian techniques for the study of landscape preferences. In the following sections, these three techniques are explained. Additional and complementary information about Logistic Regression is included in Appendix B. 3.4.7.1 Logistic Regression Modelling Logistic regression is one of a set of multivariate analysis techniques developed to model dichotomous dependent variables. Since the 1990s, logistic regression has become a standard method to describe the relationship between a response variable and one or more explanatory variables (Hosmer and Lemeshow, 2000). It can be used when the explanatory variables are not conditionally independent like in many geographical problems and its use can be extended to situations when variables are multi-class or continuous (Agterberg et al, 1993). Dichotomous variables, like binary maps, are simpler to deal with in multivariate analyses than multi-class maps, where the number of possible overlap conditions is large and unwieldy. 56

As with other modelling techniques, the goal in logistic regression is to find the best fit and most parsimonious model to describe the relationship between an outcome and a set of independent variables. The most common example of modelling is the linear regression model, where the outcome variable is assumed to be continuous (Hosmer and Lemeshow, 2000 p.1). While the method of least squares is usually adopted when fitting linear regression models, the most widely used general method of estimation for logistic regression is the maximum likelihood estimator technique (MLE), which maximises the probability of getting the observed data, given the fitted regression coefficients. A consequence of this is that the goodness of fit and the overall significance statistics used in logistic regression are different from those used in linear regression (Lea and Hinde, 2001). MLE is an iterative algorithm that starts with an initial arbitrary "guesstimate" of what the logit 2 coefficients should be. The MLE algorithm determines the direction and size change in the logit coefficients, which will increase log likelihood (LL). After this initial function is estimated, the residuals are tested and a re-estimate is made with an improved function. The process is then repeated (usually about a half-dozen times) until convergence is reached (that is, until LL does not change significantly) (Garson, 2001). The formulation for LR models is presented in Appendix B. 3.4.7.2 Weights of Evidence (WofE) Before defining what WofE is exactly, it is necessary to clarify some concepts in order to clarify how the technique works as a method for inferential mapping. The heart of Bayesian technique lies in the inversion formula:

P(H / e ) ?

P(e / H ) P(H ) P(e)

This formula states that the probability we accord with a hypothesis H upon obtaining evidence e can be computed by multiplying our previous belief P(H) by the likelihood P(e/H) that e will materialise if H is true. P(H/e) is sometimes called the posterior probability, and p(H) is called the prior probability (Pearl p 32, 1988). Prior probability (Jeffrey, 1983) is called 'initial probability' by Good (1983), which is the probability of a hypothesis H before some experiment is performed. The prior probability can be successively updated with the addition of new evidence, so that the posterior probability, by adding one piece of evidence can be treated as the prior for 2

Logits are the natural logarithms of odds.

adding a new piece of evidence (Bonham-Carter, 1994, p.302,). The calculation of the posterior probabilities of desired environmental conditions D, given the presence (B) and absence ( B ) of a predictor pattern is defined by:

P(D / B) ?

P( D ) P( B / D ) P( B)

Equation 1

P(D / B ) ?

P ( D) P ( B / D) P( B )

Equation 2

If there are two predictor patterns B1 and B2, from probability theory it can be shown that the conditional probability of D given the presence of two predictor patterns is:

1 2 ? D ? P( D )P( D )P( D ) ?? ? P?? B ? B P( B1 ) P(B2 ) 2 ? ? 1

Equation 3

Probability can be expressed as odds or vice versa, using the relation O = P/(1 - P). Odds values less than 1 correspond to probabilities less than 0.5 and very small probabilities are nearly the same as odds. Logits are the natural logarithms of odds. The logit scale is therefore centred, about 0, corresponding to a probability of 0.5 with negative values for odds less than 1/1 and positive values for odds greater than 1/1. For the notation considered here:

P(D / B) ?

P(D / B) Equation 3.A P(D / B)

If Equation 1 and 2 are expressed in odds terms, both sides are divided by P( D /B) leading to:

P(D / B) P( D )P( B / D) ? P(D / B) P( D / B)P( B)

Equation 4

But from the definitions of conditional probability:

P(D / B) ?

P( D ? B) P( B / D ) P( D ) ? P( B) P( B)

Equation 5

Replacing this expression for P( D /B) into the denominator of the right side of Equation 4 and rearranging terms yields the following:

P( D / B) P( D) P( B) P( B / D ) ? P( D / B) P( D ) P( B) P( B / D )

Equation 6

And from Equation 3A, cancelling leads to the expression:

O ( D / B) ? O (D )

P( B / D ) P( B / D )

Equation 7

Where O(D/B) is the conditional (final) odds of D given B, O(D) is the initial odds of D and P(B/D)/P(B/ D ) is known as the sufficiency ratio LS. Similar algebraic manipulations lead to the derivation of an odds expression for the conditional probability of D given the absence of the indicator pattern, resulting in:

O ( D / B ) ? O (D )

Here the term

P( B / D) P( B / D )

Equation 8

P( B / D) is called the necessity ratio LN. LS and LN are also called P(B / D )

likelihood ratios. In weights of evidence, the natural logarithm of both sides of Equation 7 and 8 are taken, and Loge LS is the positive weight of evidence W+, and Loge LN is the negative 59

weight of evidence W . W+ would be negative and W- positive for the case where fewer points occur on the pattern than would be expected due to chance. If the desired conditions are independent of whether the pattern is present or not, then W+ = W = 0 and the posteriors will be equal to the priors. When several maps are combined, the calculation of weights is made from each map independently, and then combined in a single equation. This requires an assumption of conditional independence, which is surpassed by combining it with LR (Agterberg et al, 1993). 3.4.7.3 Bayesian Belief Networks (BBN) "A Bayesian Network is a model of a problem domain, representing (causal) relations among domain variables, and is used for calculating probability distributions of the unobserved variables given the observed variables" (KjĂŚrulff and Jensen, 1996). It allows the construction of more complex inferential structures than the LR and WofE. Bayesian networks are also called belief networks, causal networks, qualitative Markov networks or constraints networks. The principle of networking nodes representing conditional, locally updated probabilities is a key characteristic of these techniques (Varis, 1997). "Bayesian networks provide an approach to holistic formulation of management plans by supporting mathematically based analysis of environmental systems while not excluding a more descriptive approach. Also, they allow uncertainty in decision making to be explicitly accounted for and their superficial simplicity facilitates the participation of a wide variety of people" (Cain et al., 1999). Recent development of probabilistic graphical models, such as Bayesian networks (Pearl, 1988) and closely related influence diagrams (Howard and Matheson, 1984), has caused a considerable interest in applying probability theory and decision theory in intelligent systems (Druzdzel and Diez, 2000). The way in which pieces of the real world are simplified using graphical representation of a domain, allows the interactive abstraction of system for the construction of models through participatory processes. It is also possible to quantify functional relationships established between the components (Druzdzel and Diez, 2000 and Druzdzel and Simon, 1993). Druzdzel and Simon (1993) using structural simultaneous equations as a way to construct BBN, propose that the structure of a BBN is the same as causal graph of the system that it represents. An influence diagram is a belief network extended with decision variables and a utility function, which allows the representation of scenarios, particularly those consisting of a predefined sequence of actions and observations. Decision variables 60

represent actions that are under the full control of the decision maker" (Jensen, 2000 and Jensen et al. 1994). Historically, BBN models were developed to represent a subjective view of a system elicited from a decision maker or a domain expert (Howard and Matheson, 1984). Although there are several empirically tested model-building heuristics, there are no formal foundations and the process is still essentially an art (Druzdzel and Simon, 1993). "No scientist will claim that a model he or she has proposed is the true model of the real-world system and, in that sense, the causal structure explicated by the procedure of causal ordering is to a certain extent subjective. It is as good as the current state of knowledge, as the physical, chemical, or social laws, and as good as the real-world measurements that it is based on and the approximations that the scientist was willing to make. This subjectivity seems to be an irreducible property of models, but luckily a property that is comparable to the subjectivity of science" (Druzdzel and Simon, 1993). Bayesian Belief Networks (BBNs) have been used in different fields like medicine, artificial intelligence and recently in agriculture and environmental studies (Kangas et al, 2000; Varis, 1998; Varis and Kuikka, 1997; Jensen, 1996; Dittmer and Jensen, 1996; Stassopoulou et al, 1996). Kangas et al, (2000) integrated Bayesian techniques together with GIS and expert knowledge to build alternative forest plans in a forest state in Finland. Cain et al (1999) present the application of BBN in water management in Sri Lanka and in India. The authors emphasise the potential of the technique to investigate the consequences of potential management options and to resolve conflicts between stakeholder groups, as a product of the transparency in the inclusion of individual's perception into the conceptual models. A general guide for the construction of a BBN model is presented by Druzdzel and van der Gaag, (2000) and Jensen (2000) as follows: 1. To identify the relevant variables involved in the process to be modelled. This step allows the incorporation in a participatory fashion, of several points of view about a specific problem. 2. To identify the states (or classes) of each variable and their relationships expressed in a graphical structure. This is considered a critical step in comparison with the assignments of probability values. The extent to which inaccuracies in its numbers influence the output of a probabilistic network can be studied by investigating the extent to which deviations from the numbers 61

affect the output (sensitivity analysis). However, if the way in which the understanding of the modelled process does not correspond with the graphical representation, the outputs will be distorted. 3. To estimate the probabilities assigned to each state from statistical data, literature or human expertise. When databases are used, they should provide reliable probability assessments, and care has to be taken when combining different sources of information (Druzdzel and Diez, 2000). Information from literature must be used with care, to avoid the use of reversal probabilities (contradictions), assigned to the network. Human expertise in the application domain is necessary when there are few or no reliable data available.

A description of preliminary steps in the construction of a BBN is in Chapter 4. Graphical models were devised to study the relationships between components considered by the stakeholders accordingly to the results of the text analysis. Use of LR and WofE to analyse spatially the landscape components obtained through the stakeholders interviews is described in Chapter 7.

3.5 Conclusion Our environment is in a state of permanent change. The trajectory of change is not fixed, but rather a dynamic response to human actions on the environment. Consequently, our future actions will be in response to the new states that the environment achieves. In this sense, environmental management is dynamic too. It is restricted by our current knowledge of ecosystem functioning and by the uncertainty associated with the way we understand and abstract its complexity. Systems, models and data developed and produced by experts are biased by simplification per se, and limited in validity by the environmental dynamic. Additionally, individual interests, values and degrees of understanding of the way the world works affect our 'visions', especially those of the societal institutions created to deal with environmental problems. A flexible and dynamic framework will serve as a basis to generating new shared futures. It has to consider spatial and temporal scales of ecosystem processes as well as the degree to which institutions can affect them. The study of â&#x20AC;&#x2DC;landscape visionsâ&#x20AC;&#x2122; is implemented through out the methods and techniques summarised in this Chapter. The complexity of the problem forces the 62

combination of these methods. Limitations and constraints are explained in each section where the method/techniques are applied. The conceptual model of the landscape stakeholders' capturing vision system is the â&#x20AC;&#x2DC;framework to characterize stakeholdersâ&#x20AC;&#x2122; visions proposed as the main objective of this research. This framework, which is an output of this research and summarised in Figure 3.4, presents a method to capture and understand the visions in a spatial context. Each subcomponent is explained throughout the following chapters and a synthesis is clearly stated in Chapter 10. The system works for the different stakeholders such as those institutions considered in this study. Basically, these are environmental and planning organisations that have a good knowledge of the area and a clear vision of what they prefer for the landscape. Due to the fact that in Nottinghamshire there are more stakeholders than those considered here, the analysis presented in this research would not necessarily apply to those with a restrictive knowledge or having limited information of the area.

STAKEHOLDERS' INTERVIEWS

LANDSCAPE PREFERENCES

EVIDENTIAL THEMES OR EXPLANATORY VARIABLES

GIS LAYERS

GEOGRAPHICAL LOCATION OF PREFERENCES

LANDSCAPE BOUNDARY DEFINITION

DATABASE

STATISTICAL AND INFERENTIAL DATA ANALYSIS (LOGISTIC REGRESSION OR BAYESIAN BELIEF NETWORKS)

ANALYSIS OF AND BETWEEN STAKEHOLDERS' VISION SPACES (LANDSCAPE PATCH ANALYSIS)

STAKEHOLDERS' VISION SPACES

SCENARIO VALUATION

Figure 3.4 Conceptual model of the landscape stakeholders' capturing vision system process.

CHAPTER 4 BUILDING MODELS OF STAKEHOLDERS' VISIONS IN SHERWOOD NATURAL AREA 4.1 Introduction As stated in Chapter 3, a ‘vision’ expresses the ideal situation that the stakeholder aims to achieve. By understanding the stakeholders interested in Sherwood Natural Area (SNA), i.e. their goals, assumptions and processes directed to achieve this ideal state, it is assumed that their visions can be modelled. The method devised here attempted to achieve this by integrating the following: an understanding of the environmental processes occurring in the area and the relationships between elements of the landscape and the decision-making process as expressed by each of the selected stakeholders. For the purpose of analysis, the organisations working in the area are considered as ‘the stakeholders’. Their ‘institutional documentation’ and the direct interaction with them formed the material used for the analysis presented here. Tabulated information and graphical models showing the inputs, relationships and outputs of the ‘stakeholders’ activity systems’ were the main product of this analysis. They were considered as a preliminary step in the construction of Bayesian Belief Networks (BBN) prior to the construction of scenarios expressing the modelled visions. Preliminary models were obtained and a rich picture of what was 'going on' in the area was obtained. This allowed the identification of available data and methodological strategies for further research.

4.2 Methods A number of organisations and partnerships interested in the landscapes of the SNA were identified (Table 4.1). The list was built using written reports of some of the organisations listed, and by information provided by staff of key agencies and other key informants. Based on published and available documents related to those organisations, a group of these stakeholders was chosen for further analysis. The selection was made using criteria such as the spatial extent of their interest or responsibility and their participation in partnerships devoted specifically to the management of SNA. Different criteria were possible for use in the selection of the stakeholders, however, the important point here is the methodological process devised 65

here to analyse different points of view. Not all the existing stakeholders in the SNA were included, so there is a possibility that the methods tested here do not apply completely to the diversity of stakeholders. The organisations listed in Table 4.2 were the ones selected. 4.2.1 Text Analysis of institutional documents The first task was to collect and create a database with each of the stakeholdersâ&#x20AC;&#x2122; published information, which contained their visions, goals, purposes, projects and future actions. A search for official policy and vision statement documents of each institution was carried out in offices located in the Nottinghamshire County Council (NCC), local District Councils and through the Internet, between November of 1999 and July of 2000. Due to the volume of material, the use of a text handling software was considered necessary to organise and analyse the materials. Several commercial applications were reviewed and assessed. The application for text analysis finally selected was "Concordance ÂŠ" (WATT, R.J.C, 1999, 2000) which gives easy and fast access to the documentation. "Concordance in its simplest form is an alphabetic listing of the words in a text, together with the context in which they appear" (Ball, 1996). It is assumed that when a word in a text is used more times than another, it is partially reflecting the concern of the writer for the referential or conceptual meaning the word expresses. With text analysis the "aim is to understand the participants' categories and to see how these are used in concrete activities" (Silverman, 1993 p.10). The following aspects were identified and extracted by reading 17 documents: 1. To identify the expressed vision of the organisation. 2. To identify the aims, goals and objectives of the organisation. 3. To identify which elements or components of the landscape are considered by each organisation in order to obtain their goals. 4. To identify the means, methods or strategies used to manage the elements identified in 3. 5. To identify the particular driving forces that are shaping the problemâ&#x20AC;&#x201C;situation and re-directing the organisation goals, e.g. Agricultural Policy as driver of reduction or increase in cropped areas.

Ashfield District Council Bassetlaw District Council British Tourist Authority British Trust for Conservation Volunteers Broxtowe Borough Council Country Landowners Association Countryside Agency East Midland Development Agency East Midlands Advisory Group on the Environment East Midlands Regional-Local Government Association Eastern Generation English Heritage English Nature Environmental Agency Farming and Wildlife Advisory Group Forestry Commission Gedling Borough Council Greenwood Community Forest Partnership Groundwork Trusts Mansfield District Council National Association of Local Councils, Nottinghamshire National Farmersâ&#x20AC;&#x2122; Union National Lottery Charities Board National Trust Newark and Sherwood District Council Nottingham City Council Nottingham Friends of the Earth Nottingham Green Partnership Nottingham Trent University Nottinghamshire Biodiversity Action Group Nottinghamshire County Council Nottinghamshire Green Network Nottinghamshire Inter-Authority Green Working Group Nottinghamshire Rural Community Council Nottinghamshire Wildlife Trust Royal Society for the Protection of Birds Rushcliffe Borough Council Sherwood Forest Trust Sir Andrew Buchanan, Lord Lieutenant Sport England The Woodland Trust

v v

NOTTINGHAMSHIRE GREEN NETWORK

v v

v v v v v

NOTTINGHAMSHIRE INTER-AUTHORITY WORKING GROUP

NOTTINGHAM GREEN PARTNERSHIP

NOTTINGHAMSHIRE BIODIVERSITY ACTION GROUP

TREES OF TIME AND PLACE

v v

SHERWOOD FOREST TRUST

GREENWOOD COMMUNITY FOREST

ORGANISATION/PARTNERSHIPS

SHERWOOD STUDY ADVISORY GROUP NCC

Table 4.1: Organisations (rows) and partnerships (columns) actively involved in SNA.

v v

v v v v v v v

v v

v v v

v v

v v v

v v

v v v

Table 4.2: Organisations considered in the documentary analysis listed by spatial extent and with a summary of their interest in SNA. Spatial Extent 1. Great Britain The Forestry Commission Department of Environment Transport and Regions (now DEFRA) 2. England Countryside Agency Environmental Agency English Nature Country Landowners Association 3. Regional (East Midlands) East Midlands Advisory Group on the Environment East Midlands Development Agency East Midlands Regional Assembly 4. Sub regional (Nottinghamshire County) Nottinghamshire County Council 5. Local (Sherwood Area) English Nature Nottinghamshire County Council

General focus of Interest for SNA To expand forest cover and native woodland Environmental legislation

Rural population, economy, access, recreation Water monitoring Biodiversity, heathlands, native woodlands Farming Competitiveness and regeneration Economy and environmental quality Economic development Conservation of farmland, historic landscape and biodiversity Semi-natural habitats, conservation schemes Partnership and farming, forestry, tourism and recreation in harmony

4.2.2 Graphical modelling An open modelling approach was used to represent the vision of each organisation as the basis for the construction of a BBN,. The modelling exercise was undertaken using the guidelines of Eden and Ackerman (1998) for building cognitive maps. Cognitive mapping is a causal-based mapping technique (Brightman, 2000), which encourages users to look for a 'hierarchy' in the ideas that are being mapped. The hierarchy is one of cause and effect, means and ends, how and why, which works towards identifying desired and undesirable outcomes. It is based on the ‘Personal Construct Theory’ (Kelly, 1955) in which ideas are referred to as 'concepts' expressed in short phrases rather than as single words. In the context of strategic management interventions, this technique is used for systematic problem identification (Brightman, 2000). The tool used to build concept maps was Decision Explorer ©, which is a qualitative data management and analysis tool. The analysis started by selecting words associated with the vision of the organisation for SNA. Words such as ‘vision’, ‘mission’, ‘aims’, ‘goals’, ‘objectives’, ‘future’, ‘scenarios’, ‘alternatives’, ‘choices’ and ‘foresight’ among others, were read in context identifying their relationship with

landscape components such as landform, water, vegetation, soil, communities, habitats, infrastructure, etc. In summary, the focus points for modelling visions were: 1. The central environmental areas or issues of concern within the SNA; 2. The driving forces, which have most impact on outcomes; 3. The interrelationships of topics of concern or particular problems; 4. The identifications of those relationships which, if changed, would have a significant impact on the nature of the issue, and, 5. The identification of feedback loops. In building a BBN, the identification of cause and effect is usually a problematic task because of potential inconsistencies in the arrowheads of the graphs. The basic idea was to link driving forces, components and outputs in a logical sequence.

4.3 Results 4.3.1 Text Analysis Seventeen documents of ten different organisations were prepared for analysis in the text analysis tool (Appendix A). The documents in digital format were converted into ‘ASCII text’ files, and those only available in printed versions were digitised using scanners and object character recognition tools. Once the documents were in the same format they were all introduced into ‘Concordance’, where it was possible to calculate the frequency of words and their context. The total number of word occurrences (tokens) in the whole documents was 94,600, from which 6,781 were different words. In order to simplify the analysis and to focus on landscape issues considered in the documentation, it was necessary to create a list of predefined words. This list was created combining the most frequent 10% of words of each document with terms and concepts used in landscape literature. Firstly, the most frequent 10% of words was extracted and compiled in a single file. In order to filter the obtained list, it was then classified into the following groups: a) No meaning; b) Verbs; c) Nouns and related words; d) Adjectives; and, e) Gerunds. Secondly, nouns and related words were complemented with terms used in landscape literature found in a review of the terminology used in the description of European 69

Landscapes (Wascher, 2000; Wascher and Jongman, 2000; Bell and Morse, 1999; Daily, 1997). This list included terminology about landscape functions and indicators of ecosystem and landscape assessment studies in Western European landscapes (Table 4.3).

A review of a publication about the language used by landscape

ecologists and planners compiled by Antrop (2001) was also considered. The final list contained 367 different words that were used as a guide to look for their use in the 17 documents. These 367 words were used 16,200 times and corresponded with 16.6% of the total in tokens (Appendix A). The output was then incorporated into an HTML interface that allows permanent access and consultation. Figure 4.1 presents an example of this. The list of word is organised alphabetically in the left column of this Figure. The context in which each word was used is called by signalling with the cursor on one of them. When signalling with the cursor on a specific context, the lower window is positioned in the section of the full text in which the context belongs. Finally, the selected words were read in their context in each of the 17 documents. Having in mind the incorporation of the outputs of this phase in a geographical information system (GIS), special attention was given to the spatial elements or features located on the landscape mentioned in the documentation. A compilation of the results for each document is presented in Table 4.4. Table 4.3: Landscape issues and their possible functions and services (after Antrop, 2001; Wascher, 2000; Wascher and Jongman, 2000; Bell and Morse, 1999; Daily, 1997) LANDSCAPE ISSUES

LANDSCAPE FUNCTION AND SERVICES Coherence Diversity (visual) Cultural identity Singular attractors Recreation Accessibility Education Biological diversity Water related qualities Soil and landform qualities Air related qualities Naturalness Site-adequate Land use Food production Employment opportunities Fuel and energy Raw material for industry

SOCIAL (Perception + culture)

ENVIRONMENTAL (Ecological)

ECONOMIC

4.3.2 Graphical modelling During the reading of the documents, graphical models representing the activity system of each stakeholder were drawn following the guidelines explained in section 4.2.2. In building the graphs, systemsâ&#x20AC;&#x2122; elements were extracted from the documentation. These were the goals, driving forces, physical components, strategies, processes, services, objectives and functions. Each of these elements are summarised below. An example of one of these graphs is presented in Figure 4.2. Each of the documents was built into a World Wide Web interface for further and permanent consultation. The whole set of graphical models produced in parallel with the text analysis method is presented in Appendix A, summarised in Table 4.4 and explained in more detail as follows: Goals and Functions: The Forestry Commission expressed interest in woodland and making aware people of its value. English Nature showed interest in wildlife and natural features of the landscape. The Environmental Agency had a more focused interest in anticipating risks associated with human impacts on the environment.

The Nottinghamshire County

Council (NCC) presented an interest in retaining certain features of the countryside with a multifunctional approach: farmland, biodiversity and tourism among others. The protection of special features was expressed also by DETR. The goals of EMAGE, EMDA and EMRA, are closely linked; and all were interested in both the protection of the environment together with business economic growth and policies. Regarding the function of these stakeholders, it seems that the most of them are trying to play a role in the achievement of sustainable development. Components and Services: These two columns in Figure 4.4 illustrates what physical or abstract elements are considered part of a landscape and are relevant to the work of the organisations and the type of services they are offering society. Again, those organisations directly involved with activities in the countryside such as the Forestry Commission, English Nature, the Environmental and the Countryside Agencies appear to be dealing with different types of habitats, their biodiversity and their environmental condition. â&#x20AC;&#x2DC;Peopleâ&#x20AC;&#x2122; were also considered by emphasising their role in the management of those habitats. The NCC had a wider range of components and its action was confined to the management and conservation of services. The Country Landowners Association addressed peoples' rights and their organisation as their main subject and service. The

remaining organisations were more interested in dealing with policies and business regulation. Driving Forces: The Government and its policies determined the efforts of most of these organisations. In the cases of EN and NCC, modern farming methods and their intensification were identified as two driving forces they are trying to regulate. For those organisations interested in the economic aspects, consumers and commercial pressures were the regulating forces of their actions. Visions: This column in Figure 4.4 quotes fragments of the text in which each organisationâ&#x20AC;&#x2122;s visions were expressed. It also summarises how they would like the area to be managed. The Forestry Commission made reference to a diversified landscape with emphasis on woodland management. English Nature focused on the conservation of wildlife and natural features of the landscape. Nottinghamshire County Council shared some of these elements but included the role of people and the economy of communities. The Country Landowners Association expressed its concern for the performance of farming and the rural development including recreational and job opportunities. The regional associations devote their attention to the role of the East Midlands in the UK economy and were general when describing the environment they were looking for.

4.4 Conclusions A general picture of the â&#x20AC;&#x2DC;problem situationâ&#x20AC;&#x2122; (using soft system terminology) in the SNA was obtained through a modelling approach using policy and vision statement institutional documentation as a data source. The main concerns of a wide group of organisations with activities in the area were identified. Graphical models were produced to represent the inputs, relationships and outputs of each stakeholder activity system. This introductory step allowed the identification of a subset of stakeholders that were subjected to further study in the following chapters. Two different techniques, text analysis and graphical modelling, were used to build systems or models of stakeholders visions. These were used to produce an initial picture of institutional concerns and the raw material to build graphical network models of potential use in Bayesian analyses. However, the information they provided was not 72

sufficient to incorporate the geographical nature of their purposes and preferences. On the other hand, the classes that discriminate each of the variables (states) required for modelling purposes were not recognised. The identification of stakeholders' visions by means of text analysis resembled a process of 'discourse analysis' (Lemke, 2001). In other words, building models from stakeholders’ visions implies a rediscovering of their assumptions and their understanding of 'how the environment works'. However, the ideas expressed in the documents considered were very general and did not support the detailed modelling required. These preliminary findings suggested that a more accurate system to capture stakeholders’ visions was needed. In the following Chapters, complementary methods whit more quantitative approach accounting for the spatial aspects of the ‘visions’ as well as their relationships are used to resolve this constraint. .

Figure 4.1: Concordance HTML consultation interface

Figure 4.2: Example of a graph modelling the activity system of one of the stakeholders (numbers signal the occurring times in text).

Table 4.4: Graphical models of institutional visions – summary (Source: Documents referenced in Appendix A) ORGANISATION FORESTRY COMMISSION STRATEGY FORESTRY COMMISSION WWW

FUNCTION

DOMAIN

MAIN GOAL

Sustainable Development

Forest Management

Increase of quality and quantity of woodlands

Biodiversity

Research

Protect and expand Britain's forests and woodlands and increase their value to society and the environment To maintain, enhance and restore the natural wildlife and geological exposures of each Natural Area Sustain and improve the wildlife and natural features of England

ENGLISH NATURE Sherwood Natural Area Profile

Habitat management

ENGLISH NATURE WWW

Sustainable Development

Programmes and plans

ENVIRONMENT AGENCY

Sustainable Development

Sites management.

NCC Structural Plan Review

Plan and proposals.

To anticipate risks and encourage precaution, particularly where impacts on the environment may have long-term effects The objective is to retain a clearly identifiable countryside for the conservation of farmland, characteristic and historic landscape, biodiversity and for the provision of appropriate leisure/tourism uses.

COMPONENTS

SERVICES

DRIVING FORCES

VISION

Countryside, Woodlands, People Timber Forest Woodland, Trees

Forest management. Public Benefits Surveys Information Management

Agricultural Policy

Sites, wildlife, species, populations, habitats

Habitat management

Modern farming methods

English Nature will sustain and improve the wildlife and natural features of England for everyone

Habitats, SSSI, wildlife, species

Information, management, biodiversity Site management, floods, conservation

Government

English Nature will sustain and improve the wildlife and natural features of England for everyone

Government

A better environment in England and Wales for present and future generations

Historic assets, landscapes, woodland, land, countryside, urban, habitats

Management, conservation

Intensification of farming practices, economic changes of the last decade

Environment state, land and water

NCC Sherwood Forest Study

Conservation, Quality of life and environment

Resources

Resources, communities, woodland, wildlife

Transport, management, public services

Countryside Agency 2020

Sustainable Development

Quality of life

Business, land, farmers, agriculture, people

Quality of life, access, transport

Our vision is of a great variety of well-managed woodlands

Policy

Policy, Government, traffic

A distinctive landscape which is rich in wildlife, enjoyed and understood by local people and visitors alike, with communities befitting from a healthy economy that is supported by the forest's natural and cultural heritage Diverse character and outstanding beauty • prosperous and inclusive communities • economic opportunity and enterprise • sustainable agriculture • transport that serves people without destroying the environment • recreational access for local people and visitors

ORGANISATION

FUNCTION

DOMAIN

MAIN GOAL

COMPONENTS

Countryside Agency - Sherwood

Landscape Character

Land

Land, Industry, forest, rivers, soils

Countryside Landowners Association

Development

Countryside development.

Countryside, environment, water, agriculture, land

DETR

Public access

Common land

To secure the future of properly registered common land, and village greens, to ensure that the special features of such land are protected for future generations to enjoy To encourage the adoption of environmental best practice by small and medium sized firms in the East Midlands

Common Land

EMAGE

Legislation

EMDA

Regional Strategy and Economy

Social progress which recognises the needs of everyone, effective protection of the environment, prudent use of natural resources, maintenance of high and stable levels of economic growth and employment

Business and people

Strategies.

Integrating action on economic, social, environmental and spatial issues

Regional policies and strategies for social, economic, environmental and spatial issues

EMRA

Sustainable Development

Large Businesses, Education Institutions, Training and consultancy organisations, Local Authorities and TECs and Business Links

SERVICES Landscape, amenity and wildlife enhancement. Rights, organisation

DRIVING FORCES

VISION

The Sherwood Sandstone, coal industry

Habitat restoration, heathlands, broadleaved woodland, pasture and hedgerow trees

Legislation

Farming become more competitive, a countryside which is clean, attractive and rich in wildlife, historical features and recreational opportunities, to develop rural economies

Legislation and regulation, consumer and commercial pressures and supply chain pressures The East Midlands is particularly sensitive to conditions that affect manufacturing and agriculture

To have the East Midlands recognised as a region where enhanced environmental performance and competitiveness are integral to its business culture and economic activity

Ownership, information, protection, management

By 2010, the East Midlands will be one of Europe's top 20 regionsâ&#x20AC;Ś a place where people want to live, work and invest, because of our vibrant economy, our healthy, safe, diverse and inclusive communities and our quality environment

The East Midlands will be the most progressive region in Europe, recognised for its high quality of life, vibrant economy, rich cultural and environmental diversity and sustainable communities

CHAPTER 5 GATHERING EVIDENCE OF STAKEHOLDERS' LANDSCAPE PREFERENCES IN SHERWOOD NATURAL AREA 5.1 Introduction A rich picture of the ‘problem situation’ 3 in the SNA was obtained through a modelling approach using institutional documentation as a data source. It was possible to identify the main concerns of a wide group of organisations with interests in the area. Graphical models were produced to represent the inputs, relationships and outputs of each stakeholder ‘activity system’ 4. However, this information was not sufficient to locate in spatial terms the vision expressed, in terms of the value of the states of each variable considered in the models. Consequently, the aim of this chapter is to investigate methods to represent the stakeholders’ visions in a spatial context. Given the uncertainty associated with the identification of human preferences, Bayesian methods were considered as strategy to fill this gap systematically. This chapter describes the process of gathering the evidence of stakeholders' landscape preferences as an additional step in the definition of their visions. A spatial analysis of the location of stakeholders’ preferences represented in points spread on a map was made to explore individual and collective evidence as a potential source of physical boundaries for the SNA.

5.2 Methods 5.2.1 Stakeholder selection After reviewing the documentation for the diverse group of stakeholders considered in Chapter 4, and having approached some of the existing partnerships, it was clear that some stakeholders were more involved in the area than others. In order to address those stakeholders likely to possess the most extensive knowledge of the area, a new selection was made following the recommendation of coordinators of the NCC and Sherwood Forest Trust (SFT) partnerships. Three lists were used to select the sample: a list of regular participants in the Sherwood Study supplied by the Nottingham County

In soft systems it is defined as “a nexus of real-world events and ideas which at least one person perceives as problematic” (Checkland, 1993). 4 Intellectual constructs according to soft systems terminology.

Council (NCC), a list of Trustees and Advisors given by the Sherwood Forest Trust (SFT) and the list of stakeholders considered in Chapter 4 (Table 4.1). Both the NCC and the SFT have initiatives specifically concerned with Sherwood Forest. These were closely linked to the various partnerships with activities in the area. The lists contained members of the organisations included in Table 5.1. Representatives of NCC, SFT, National Farmers Union (NFU), English Nature (EN), Bassetlaw District Council (BDC), Newark and Sherwood District Council (NSDC) and Nottinghamshire Wildlife Trust (NWT) were contacted by telephone during the months of May, June and July of 2001, with the purpose of arranging meetings to present the project and collect data. These stakeholders, underlined in Table 5.1, were subject to the exercise described in the next section. An additional stakeholder outside these lists was included, this being an academic member of the University of Nottingham (UNott) Who has carried out extensive research in the area. He was considered â&#x20AC;&#x2DC;the Pilot Caseâ&#x20AC;&#x2122; and during the preliminary trials of the research, his comments were valuable not only for testing and refining the methods, but also for providing a contrasting vision rich in content and useful for the comparative analysis of visions carried out in this and following sections. 5.2.2 The interviews Stakeholders' narrative interviews (Mayers, 2001) were conducted to collect primary information to feed the model devised for visioning representation and analysis. First, a summary of the project was presented using the document prepared for the 'Social Forestry' meeting organised by the Forestry Commission (Rubiano and Haines-Young, 2002). Second, a mosaic of aerial photographs of the area was shown as a poster, size 1.2m x 0.8m, to confirm the skills of the interviewee in locating places in the study area. Features like the SNA boundary, SSSI's, wards boundaries and its names were included in the poster as a guide to the respondents. A detail of the poster is presented in Figure 5.1 (guiding lines in this image are not present due to scale). Third, the respondents were asked to highlight on the map those places that for their organisation or institution represented or fulfilled the characteristics of the ideal landscape conditions for the whole area of Sherwood; in other words, places that they considered ideal or truly representative of Sherwood. The location of such places was marked with the aid of adhesive colour 'stickers', which were then numbered each time an interview took place. Respondents were then asked to justify their choice. It was expected that the reasons for explaining the place of interest would flow naturally, by expressing characteristics of the place, criteria for its selection, landscape features and processes associated with it. If any encouragement was required to express this 78

information, open questions like ‘Why?’, ‘What is found in this area?’ and ‘What is happening there?’ were asked to produce a smooth and continuous flow of information (Valentine, 1999 p. 111). All the information was recorded in a notebook. Once the places were indicated on the poster, a short explanation of the use of these data was given, and an acknowledgement of the confidentiality of their names in the data analysis process was expressed. The session was then brought to a close. The places signalled in the poster were then transferred to a geographical information system as layers of points or polygons accordingly.

Table 5.1: Organisations included in NCC and SFT partnerships (underlined were interviewed) Name Ashfield District Council Basetlaw District Council (BDC) Countryside Agency (CA) Countryside Landowners' Association (CLA) English Heritage English Nature (EN) Environment Agency (EA) Farming and Wildlife Advisory Group Forestry Commission (FC) Gedling District Council Highways Agency Mansfield District Council Minister of Agriculture, Fisheries and Food National Farmers Union (NFU) National Trust Newark and Sherwood Forest District Council (NSDC) Nottingham City Council Nottinghamshire Association of Local Councils Nottinghamshire County Council* Nottinghamshire Rural Community Council Nottinghamshire Wildlife Trust (NWT) Sherwood Forest Trust

Figure 5.1: Detail of poster used for data collection showing the land cover around Sherwood Visitorâ&#x20AC;&#x2122;s Centre, Edwinstowe (Source NRSC, 1992)

5.3 Results and discussion The location of preferred places and the characteristics associated with them are presented in this section. To make it more comprehensible, the information obtained from the interviews is presented first, followed by a geographical analysis of the locations signalled by the stakeholders. 5.3.1 The interview The process of gathering information about places with ideal characteristics as expressions of stakeholders' preferences was easily understood by most of the respondents. The attractiveness of the aerial photograph helped promote an instantaneous immersion in the area. The establishment of spatial/physical relationships between places was a consequence of the recognition of known places. This effect facilitated the location of different features in the poster. In some cases, the 80

extent of the place to be located meant that more than one sticker was used, although not all the stakeholders acted in this way. It is important to note that the sticker identifying a place represented the point itself and its surroundings as defined its boundaries, e.g. Clumber Park or by the feature being represented, e.g. a forest. The number of points representing a place had a significant influence in subsequent analyses. It was a methodological issue that required individual based adjustments. This is explained in detail in section 5.3.3. As expected, the description of each place was fuelled by unconscious criteria. The people interviewed seemed to be surprised with their own response to the exercise, in the sense that re-discovering their institutional vision expressed through their preferred places was an interesting and challenging experience. When signalling places, some interviewees found conflicts between their personal preferences and the institutional visions. In addition, they were also interested in to know other stakeholders’ places. During the meetings the respondents were invited to signal those ‘ideal places’ even if they felt these were outside the SNA boundaries. However, there was considerable consensus in selecting places inside the existing boundaries. In the cases of the District Councils and the NWT, the institutional responsibilities seemed to drive the location of sites. In other words, the planning activities in the Districts and the biodiversity concern in the NWT had an important effect in their selection of area subject to planning and wildlife conservation. Interviews lasted between 45 minutes and two hours. After closing the meeting any other further complementary printed documentation produced by the institution was collected. 5.3.2 Landscape components and processes in Sherwood Forest The reasons provided by the stakeholders when justifying the selection of the sites were often associated with physical features of the landscape or with social or physical indicators. In other cases, their reasons related to specific activities of the community living in the area. Table 5.2 presents a list of the reasons 5 mentioned by those interviewed. The positive or negative contribution to the 'ideal condition' was inferred from the context in which these reasons were presented. In other words a characteristic was included even when this had a negative connotation for the respondent. These attributes were then marked on the table as ' + ' or ' - ' respectively. 5

Within the text, the words ‘features’, ‘components’ and ‘processes’ were used to make reference to the 'reasons' or explanatory variables.

For most of the interviewees, the optimum landscape consisted of a place with a mosaic of ancient broadleaved woodland with acid grassland, heathland in different stages of ecological succession, open spaces created by farmland and livestock with cattle and sheep. The presence of coniferous plantations was controversial. Six of eight of the stakeholders mentioned this component, specifically three of them in the context of the habitat having a positive role and the other three as being a negative component in Sherwood. Heathland was mentioned in all of the interviews as a characteristic feature that has been depleted throughout the years and which needs to be expanded in order to maintain its intrinsic biodiversity. Lakes, rivers and wetland sites were mentioned as places with a positive role within Sherwood except for the NCC and the SFT. However, for these two stakeholders the wildlife associated with these places, as represented by birds and aquatic fauna, was considered important. The history of the area played an important role for almost all the stakeholders. With the exception of the representative of the farming community (NFU); the Dukeries Estates and the historical gardens were emphasised in one way or another as key features necessary for maintaining the character of the landscape, especially the remaining ancient woodland in the area. The mythology represented in the legend of Robin Hood was mentioned only twice and as having a dual role: an attraction for tourists and a feeling of disappointment after their visit. Two other components with a controversial character were the presence of coalmines and housing. However, as an important feature in the restoration process, mineral sites such as coal pits and quarries were mentioned in eight of the nine interviews as positive or negative components but stakeholders generally expressed the view that there was an opportunity to reclaim land and restore heathland and woodland on them. In spite of being artificially created, it was mentioned that lakes, water meadows and old railways were valuable elements in wildlife preservation. The vision presented by the representative of the farming community was a special case. The representativeâ&#x20AC;&#x2122;s contribution was more personal than institutional. As optimum landscape, he also suggested an approximate area instead of single places or isolated spots. For him, the current arrangement of components enclosed in the area, highlighted in Figure 5.3, represents the singularity of Sherwood. He placed greater emphasis on the role of the population in shaping this landscape. Specifically the role of grazing management in the re-creation of heathland and the role of farming in the creation of jobs in the area rather than the physical elements that composed it. This

interviewee was the only one who mentioned â&#x20AC;&#x2DC;jobsâ&#x20AC;&#x2122; as a key component in the landscape. Features with a recreational role such as cycle paths, golf courses, historic gardens and parks always had a positive character but they were not key features for the NCC and the UNott stakeholders. For the English Nature representatives, it was clear that at present there is no ideal or satisfactory landscape. Nor were they clear on when or how to achieve it. There was a general recognition of the trade-off between conservation objectives and public access to places. In contrast, development, housing and land ownership were generally valued negatively. In summary, heathland was a feature common to all interviews, due in part to the fact that one of the partnerships from which the stakeholders were selected has its restoration as one of their main objectives. Ancient woodland, coniferous and broadleaved plantations, wildlife, mineral sites and recreation followed heathland in order of importance. River corridors, wetland, cycling paths, Dukeries Estates, farming and public involvement can be considered as a third category. Finally the remaining components listed in Table 5.2 including characteristics such as the density of forest, the presence of hedgerows, accessibility, commercial management, jobs, site management and population density. Although this list can give some insight into the factors considered important by the stakeholders, there are, however, some constraints. Some components were included in other categories or are inter-related with each other and some were mentioned with either a positive or negative connotation, e.g. degree of development was seen as a detrimental feature by some of the interviewees. It is important to notice in Table 5.2 the differences and similarities between different stakeholders in terms of components and the way in which they are perceived. There are agreements but not necessarily consensus. Moreover, there are clear contradictions in features like coniferous forest, mineral sites and farming. All existing land uses in the SNA were mentioned and concepts that integrate several vegetation types, such as the openness of space and density of the forest, were also mentioned. Features that in one or another way represent a recreational asset were differentiated as cycling paths, golf courses, parks and historic gardens. From a management and economic point of view, farming, livestock, jobs, commercial management, degree of development, grazing, funding schemes and site management were also mentioned. 83

With respect to processes occurring in the area, public involvement in conservation activities, grazing or site management, land ownership, funding schemes and employment were the kind of social activities and processes of interest for the stakeholders sampled. With respect to natural processes, the expansions of ancient woodland and the heathland succession cycle were also mentioned. Representing these processes is problematic, they are more dynamic than the previous 'fixed' characteristics and clearly do not refer to specific spatial locations. A more complex treatment of the explanatory variables supplied by the stakeholders is given in Chapter 6 and 7, where a more precise spatial analysis was carried out with the point data gathered during the interviews. 5.3.3 Spatial analysis of preferred places In order to explore the use of preferred locations as a potential source for the definition of a physical boundary of a SNA ‘vision’, three standard spatial statistical analyses were used with each group of stakeholders’ points (Lee and Wong, 2001). In addition, the whole set of points was merged in a single geographical layer and subjected to the same analyses. Figure 5.2 shows the location of the places pointed out by the stakeholders against the background of boundaries of SNA and Nottinghamshire. The analysis presented here refers only to the geographical location of these ‘ideal places’ that stakeholders considered to depict the ‘best of’ Sherwood Forest, without considering the variables associated with them. The objective here was to describe the distribution of points marked as preferred places using statistical descriptors, to explore their potential use as a technique for stakeholder visioning. In addition, they were used to allow comparisons and to establish relationships among the points representing their preferences.

Table 5.2: Reasons explaining the selected sites mentioned by the stakeholders (see section 5.3.2 for explanations) STAKEHOLDER

NCC

UNott

SFT

BDC

NFU

Broadleaved plantation Coal mine sites remains and gravel extraction Commercial management

Coniferous presence

+ +

Cycle paths presence

Dense forest Dukeries Estates presence Farming presence

+ + -

+ + + +

+ + +

+ + + +

+ + -

+ + + + + +

+ + -

+ -

Funding Schemes Golf courses Good grazing management Heathland presence Hedgerows presence

+ +

+ + +

Historic gardens Housing

+ + + + -

Jobs

Lakes

Land ownership

Livestock

Low people presence Openness of space

Parks

+ +

Public involvement Recreation River corridors Robin Hood mythology

+ +

+ + +

+ +

+ + +

Site management SSSI's Wetland site/meadows Wildlife â&#x20AC;&#x201C; wilderness

Acid grass

Degree of development

NSDC

Accessibility

Ancient woodland

NWT

+ +

+ + +

+ +

+ + +/+ + + +

In any data collection exercise, there are always potential errors associated with the process. Firstly, the interviewers could have failed to locate the 'ideal place' in the right position. Secondly, the respondentâ&#x20AC;&#x2122;s own bias could have produced an omission of a 85

place important for the organisation he/she was part of. Thirdly, there may be a possible misplacement in the transcription of the data from the map to the computer. Double-checking the transcription of points reduced this last potential source of error, but caution must be taken in considering these points as fixed preferred places. The representative of the farming community drew an area rather than a set of points. To make this data fully compatible with the rest of the stakeholders, a random group of points was generated inside the area drawn (Figure 5.3). Similar adjustments were required for several of the stakeholders whom marked only one point when referring to a well-defined and wider area. The criteria used to overcome this methodological issue was to increase the total number of points for each stakeholder case up to the number required to make the weights of evidence and logistic regression techniques statistically operative (see Chapter 7). The spatial statistical descriptors used were: 1. Central tendency Central tendency measurements are used to summarise a set of numerical data to give an indication of the average or representative value. The mean is the average value in a data set and the median is the middle value when those are arranged in ascending or descending order. The extension of this concept when analysing observations distributed in space received the name of 'centre'. Given the nature of the problem explored, the method used to calculate the mean and median centre can be different (Lee and Wong, 2001). a) The mean centre was found here calculating the sum of the x and y coordinates of each point and divided them by the number of points. The mean centre represents the geographical centre of each set of observations (Figure 5.4). b) The median centre is the centre of minimum travel. In other words, the total distance from each of the points to the median centre is the minimum. Mathematically, median centre, (u,v) satisfies the following objective function: n

Min?

( xi ? u ) 2 ? ( y i ? v ) 2

i ?1

where x i and yi are the x and y coordinates, respectively of point pi. The median of each stakeholder is represented in Figure 5.5. Most of the medians are close to what is called the core of Sherwood Forest around Edwi nstowe, with the exception of the response of BDC which tended to put it further north and more central, 86

with respect to the Bassetlaw district boundaries. When considering the eight set of points as a single stakeholder, both the total mean and total median were located very close to each other. It is important to clarify that the whole set of points identified by the stakeholders is not geographically biased but rather represents the full geographic extent of the SNA (Figure 5.7). Coincidentally, they were located at just 1,200 and 800 meters respectively from the Major Oak, in the Sherwood Forest Visitorâ&#x20AC;&#x2122;s Centre. 2. Dispersion and orientation Two descriptors were considered here, the standard distance and the deviational ellipse. The more dispersed a set of points is around a mean centre, the largeer the standard distance circle and the larger the standard deviational ellipse. a) Standard distance is the spatial analogy of standard deviation in classical statistics (Lee and Wong, 2001). It indicates how points in a distribution deviate from the mean centre and can provide the visual comparison of the extent of spatial spread among the different stakeholder 'ideal places'. The standard distance of a point distribution can be calculated by using the following equation: n

SD ?

f i ( xi ? x mc ) 2 ? f i ( y i ? y mc ) 2

i ?1

where f i is the weight for point (xi, y i).

Figure 5.4 shows the standard distance circles (SDC) of each group of preferred points. The centre of each SDC corresponds to its own mean. With the exception of the SDC of Bassetlaw District Council, the rest of the stakeholders SDC's were centred towards the Sherwood Forest Visitorâ&#x20AC;&#x2122;s Centre in Edwinstowe, and their ancient woodland designations in the surrounding areas. The SDC of the Nottingham Wildlife Trust was the biggest and that one of the Newark and Sherwood District Council the smallest. This pattern reflects the wider range of the NWT activities in the nature reserves throughout Nottinghamshire. By contrast, the NSDC action inside Sherwood Natural Area is limited to the area of the district council. A similar situation was found for Bassetlaw District Council. The SDC of English Nature was confined to the SSSI's around the Major Oak and the ancient woodland designations. Similar extent and location of the SDC's were found in the cases of UNott, Sherwood Forest Trust and

Nottinghamshire County Council. The SDC for the whole set of points is shown in Figure 5.7. b) Standard Deviational Ellipse (SDE): Sometimes the distribution of points can be better represented in a deviational ellipse, in which the directional bias of the maximum and minimum spread of points is captured. The method to calculate it is explained by Lee and Wong (2001). Figure 5.6 shows the major and minor axis of this descriptor. Again the stakeholders highlighted and focused their attention on the surroundings of the Major Oak and following a long axis of the shape of the SNA. The BDC's and NFU SDEsâ&#x20AC;&#x2122; were located further north than the others. This also reflects the selection of sites inside the boundaries of the District Council. In the case of the representative of the farming community, it corresponded to an area in between the Dukeries Estates and the ancient woodland, leaving outside the coniferous plantations. The SDE for the whole set of points is shown in Figure 5.7. It is important to note that when using all the points as a single group, around 50% of SNA was covered by the statistical descriptors of the stakeholders' preferences. The southern part of SNA, corresponding to the city of Nottingham, and the most of the BDC part of SNA, were outside the boundaries highlighted by the spatial descriptors. These could be considered as zones that do not represent Sherwood Natural Area.

3. Pattern Lee and Wong (2001), also present three standard techniques used to detect pattern from

point

distributions.

They

are

quadrat,

nearest

neighbour

and

spatial

autocorrelation analyses. The first two are more concerned with detecting pattern and comparing them with other known patterns. The third considers the attributes of the points, in addition to their location. In the current case, quadrat analysis was selected to evaluate how the density of a point distribution changes over space. A theoretical random pattern is built and compared with the measured density to see if this is more clustered or more dispersed than the random pattern. To do this, the study area was overlaid with a regular grid. To define an optimal grid size the following calculation is suggested (Griffith et al., 1991, p.131 cited by Lee and Wong, 2001):

Quadrat _ size ?

2 ? Area No. _ points

The square size width can then be calculated by: 88

square _ size _ width ?

2 ? Area No. _ points

For the layer of stakeholder points, the optimal cell size was 3,607.92m. A grid of 16 x 8 cells produced a cell size of 3,577.37m (Figure 5.8). A frequency distribution table was created with the number of points in each cell. Using this table is possible to infer if the pattern of the point distribution under consideration is close to a clustered, random or dispersed distribution. A Kolmogorov-Smirnov (K-S) test is used to compare the statistical differences between the observed and theoretical distributions. The K-S D statistic was 0.519. When compared with a critical value of 0.087 obtained from K-S Dstatistic table (at 0.05% level of confidence). This allows the rejection of the hypothesis that there is no significant difference between a dispersed pattern of 241 points and the distribution formed by the 241 places signalled by the stakeholders. In other words, the whole set of points is not distributed in a dispersed manner, which is confirmed also by visual inspection of Figure 5.8. It is clustered around the middle of SNA. This uncovers the existence of certain agreement between the different stakeholders about the location of the â&#x20AC;&#x2DC;ideal placesâ&#x20AC;&#x2122; in the Sherwood Natural Area.

5.4 Conclusions The method used here to gather evidence of stakeholders landscape preferences allowed a systematic location of points, areas and other places for further geographical comparisons and analyses. The method combined qualitative and quantitative techniques that made it flexible enough to explore new criteria considered by people when identifying landscape preferences. This was the case with criteria like public participation, land management strategies and availability of funding schemes. These are characteristics that change relatively quickly in spatial and temporal dimensions and are not easily represented in layers of spatial information.

A similar situation

occurred with biological processes such as the succession or expansion of specific vegetation types (e.g. ancient woodland or heathland). The method requires that respondents have a good knowledge of the area but does not necessarily mean they are capable of reading maps. Places can be described by interviewees and located later in a geographical database. There were clear differences between the representatives of the stakeholders in the location of their preferred places although there is a trend in the distribution towards the area surrounding the Major Oak, leaving the northern and southern part outside of what is known as Sherwood Natural Area. The spatial descriptors used to analyse the pointâ&#x20AC;&#x2122;s distribution confirmed this. 89

Heathland was a common natural landscape component of concern for all the stakeholders and there were clear contradictions about the presence of coniferous plantations, mineral sites and farming areas. Socio-economic aspects like employment, commercial management, degree of development and funding schemes seemed to have an important role in the definition of landscape preferences for individual stakeholders. A limitation of the spatial analysis presented here is that differences in the reasons attributed to each of the places were not taken into account. Thus, analysis must go further to consider such additional information with the aim of identifying which are the most important landscape features that defined each stakeholdersâ&#x20AC;&#x2122; vision.

This is

addressed in Chapter 7. As a preliminary step to achieve this, it is necessary to organise a geographical database with the variables representing the reasons presented by the interviewees. The following chapter explains how this database was organised and a discussion of the constraints found in this process is also presented.

Figure 5.2: Location of places signalled by the stakeholders

Figure 5.4: Standard Distance Circles Figure 5.3: Area signalled by the NFU

(their centres are the means)

Figure 5.6: Axes of Deviational ellipses Figure 5.5: Median centres

Figure 5.7: Points descriptors for the

Figure 5.8: Quadrat analysis

whole set of stakeholders

CHAPTER 4CHAPTER 6 CHAPTER 5BUILDING A GEOGRAPHICAL INFORMATION DATABASE FOR SHERWOOD NATURAL AREA 6.1 Introduction In Chapter 5 the location of a set of ‘preferred sites’ in the SNA was considered as evidence of stakeholders' visions and were subjected to a geographical analysis. As noted earlier, the stakeholders argued different reasons when justifying the selection of those sites. The reasons listed in Chapter 5 were, in some instances, related to physical features of the landscape and social or physical indicators, or in other cases specific community activities. This chapter describes the way in which these reasons were interpreted and translated into geographical layers for further processing in a geographical information system (GIS). As stated in Chapter 5, it was necessary to go from the point representation towards a more comprehensive spatial version of stakeholders’ visions. The way in which those visions are interpreted and represented has implications for the outputs of this theses project, since understanding how these processes take place is essential. The construction of a database to support any project is relatively straightforward when a research or management problem is clearly defined. In comparison, the time and effort involved in building databases with reference to a fuzzier problem can be considerable. As mentioned in the introductory chapter, for the development of the methodology, it was necessary to consider the data that were available. This chapter describes the data gathered to fulfil the methodological requirements and the assumptions behind each of the variables considered. The resulting database complements the description of the SNA given in Chapter 2.

6.2 Methodology The creation of SNA database was carried out in several stages. First, a preliminary search for geographical information was made in relevant organisations of Great Britain and Nottinghamshire such as The Ordnance Survey, English Nature, Nottinghamshire County Council and the University of Nottingham. This gave an initial picture of the GIS data available. It was also evident that data were available in a diversity of formats and varied in extent, quality and resolution. The initial task therefore, consisted of 93

standardisation and conversion of files into a common format. ArcView 3.2a and ArcInfo were used to convert all data into ESRI coverage, shape and grid formats. In a second phase of work, an additional data search was carried out, based on the needs arising from the stakeholder interviews. Once the set of reasons was listed (Table 5.2), layers of GIS data were allocated to each of them by looking for the best descriptor for each reason. The variables and corresponding data sources are explained next, together with a brief description of content and quality issues.

6.3 Results The representatives of the stakeholders justified their choice based on characteristics of the landscapes, which explained the ideal sites corresponding with their â&#x20AC;&#x2DC;visionsâ&#x20AC;&#x2122;. Some of the reasons were vegetation cover such as broadleaved or coniferous plantations. A variety of land uses such as farming, housing and coalmines; physical elements like roads, cycle paths and lakes were also mentioned. The state of social indicators like employment, land ownership, degree of development and public involvement were also included. These landscape features were depicted as GIS layers; for example, variables representing land cover were available at the School of Geography, University of Nottingham, taken from a 1992 Nottinghamshire County Council land cover map (NCC 1992 map). This was originally in a vector map format, covering most of the SNA, with the exception of the urban area of Nottingham after conversion available as IDRISI raster files at 25 and 50 meters pixel resolution. The 25m grid was chosen for this study. A visual inspection of 1998 aerial photographs available for the area was carried out to evaluate a more recent and detailed land cover classification. As a result, since negligible changes were detected and the 25 meter resolution raster file was selected due to its pixel size, the extent of its coverage and its compatibility with the national land cover classification system. This file was converted to ESRI GRID format using the IDRISI image import utility for ArcView developed by Cederholm (1999). The land classes of this file are summarised in Table 6.1 and are depicted in Figure 6.1. Some classes were merged, considering the requirements of the LR and WofE statistical analysis. Table 6.1 also presents the new classes of land use. The other landscape features are explained below.

Table 6.1: Land cover classes present in the 1992 NCC land cover map NEW CLASS Acid grassland and heathland

No.

LAND COVER (NCC 1992 map)

Rough grass with scrub

Bracken grass heathland

Bracken with heather

Bracken with scrub

Broadleaved

Broadleaved woodland

Coniferous

Coniferous woodland

New plantations woodland

Cultivated arable land

Allotments

Permanent horticultural crops

Permanent pasture with mature trees

Rough grass with mature trees

Mixed woodland

Bracken with mature trees

Permanent pasture and meadow

Permanent pasture with scrub

Rough grass including marshland

Amenity grassland

Mineral

Mineral workings

Water

Urban

Grassland

New improved grassland

Farming

Other forest

Other pasture

Accessibility was represented using the roads of the area from an OS map at 1:50.000 scale. Motorways, A and B roads, and those that are more than 4m wide were digitised. Railways were also digitised also but not used due to the fact that the main access via railways is through train stations, which are located near roads or in urban areas (Figure 6.6) – but used for development. Acid grass and heathland was represented as the union of ‘bracken grass heathland’, ‘bracken with heather’, ‘bracken with scrub’ and ‘rough grass with scrub’ from the 1992 Nottinghamshire land cover map (Figure 6.1). Ancient woodland was depicted using the English Nature’s downloadable files for the area of interest available as MapInfo and CAD files Ordnance Survey (SK tile). These 95

boundaries were originally digitised at 1:25,000 scale and include areas that have been woodland since at least 1600AD and that are larger than 2ha. These areas include, firstly, ancient semi-natural woodland and, secondly, ancient replanted woodland. The first refers to sites that have retained the native tree and shrub cover (that have not been planted), although it may have been managed by coppicing or felling and allowed to regenerate naturally. The second class includes ancient woodland sites where the original native tree cover was been felled and replaced by plantation (usually with conifers) mainly during the 20th Century. After CAD to shapefile conversion, a 25m grid was created (Figure 6.2). Broadleaved plantation corresponded to the class of the 1992 Nottinghamshire land cover map named broadleaved woodland (Figure 6.1). Coalmines and gravel extraction areas were represented using the class of the 1992 Nottinghamshire land cover map corresponding to mineral sites (Figure 6.1). Commercial management was mentioned as the integration of nature conservation activities with the strengthening of business in the local economy. It was an attribute mentioned for some of the places, but it was not represented due to lack of information for the whole area. Coniferous

plantation

was

represented

using

the

classes

the

1992

Nottinghamshire land cover map corresponding to coniferous woodland and new plantation woodland. At the time of the study, there was no available information to discriminate the new plantations into more detailed plantation types (Figure 6.1). Cycle paths were drawn from a list of bike rides compiled by members of Pedals and Nottinghamshire District Association, Cyclists' Touring Club for 1997 (Figure 6.7). Degree of development was mentioned as a negative attribute and referenced to processes of urbanisation and road expansion. It was represented using the union of urban, road and railway buffer features (Figure 6.6). Dense forest was represented using the classes of the 1992 Nottinghamshire land cover map corresponding to broadleaved woodland, coniferous woodland, new plantations woodland, permanent pasture with mature trees, bracken with mature trees, rough grass with mature trees and mixed woodland (Figure 6.3). The presence of mature trees and tree plantations were used as the criteria to represent dense forest. However, the definition of a dense forest is controversial.

Figure 6.1: Land use/cover classes in SNA (after NCC, 1992)

The Estates of the Dukeries were represented as the local area defined by the Nottinghamshire County Council as 'The Dukeries' in its report about 'Conditions of Nottinghamshire' (NCC, 2001) (Figure 2.2). Precise boundaries of the Estates in the area were not mapped. Farming was represented using the classes of the NCC 1992 map corresponding to allotments, cultivated arable land and permanent horticultural crops (Figure 6.1). 97

Funding schemes alluded to the support offered by the UK Government such as stewardships intended for promoting specific land use management in certain areas. It was not considered due to lack of spatial information. Golf courses were represented using polygons digitised from 1:25.000 OS Explorer and Landranger maps of the area (Figure 6.8). Grazing management was represented using the class of the 1992 Nottinghamshire land cover map corresponding to new improved grassland and renamed as grassland (Figure 6.1). However, there are different management strategies throughout the area that were impossible to identify and hence illustrate. Hedgerows were depicted based on the Countryside Information System (CIS) database. Lines of trees/scrub and relict hedge were calculated for each environmental zone from the 1998 sampling (ITE, 1998). The CIS file of environmental zones 1 and 2 were converted to a grid and then the values for each zone assigned. Two classes were found with 28,400 and 27,300 km respectively. The resolution of the grid was of 1km. (Figure 6.4). More detailed information was not found. Historic gardens were represented using the list reported in â&#x20AC;&#x2DC;wildlife and wildernessâ&#x20AC;&#x2122; at the end of this list (p 101) (Figure 6.5) from a list supplied by the NCC. Housing was represented using the class of the NCC 1992 map corresponding to urban settlements (Figure 6.1). Nottingham City was added using OS maps as a source (25 meters pixel size). Jobs information was drawn from unemployment figures in the area. The local rate of unemployment was calculated by dividing the number of male and female unemployed persons by the total population (Figure 6.9). The data source for this was the 1991 Census for Wards in England supplied by Manchester Information & Associated Services (MIMAS, 2001). Lakes were represented using the class of the NCC 1992 map corresponding to inland water. Additional new lakes in the area of 'Centre Parks' were added using the latest (2001) 1:25.000 OS maps (Figure 6.1). Population density was represented using the 1991 Census for Wards in England supplied by MIMAS. Population density was calculated by dividing the total number of people by the area of the wards in square kilometres, followed by the conversion of boundary shapefiles into grids of 25 meters pixel size (Figure 6.10).

Figure 6.2: Ancient woodland (English

Figure 6.3: Dense forest (after NCC,

Nature, 1999a)

1992)

Figure 6.4: Length of hedgerows (CIS,

Figure 6.5: SSSI's and historical gardens

1998)

(English Nature, 1999 and NCC, 2001)

Figure 6.6: Roads, railways and urban areas (OS,

Figure

1999 and NCC, 1992)

Touring Club, 1997)

Figure 6.8: Golf courses (OS, 1999)

Figure 2001)

100

6.7: Cycling paths (Cyclistsâ&#x20AC;&#x2122;

6.9:

Unemployment

(MIMAS,

Figure 6.10: Population density (MIMAS, 2001)

Public involvement was referenced as the connection between communities and adjacent natural assets like ancient woodland or heathland areas. This information was not compiled in any source, and it was not considered in the analysis. Recreation was represented as the union of bike rides, historic gardens, parks and tourist sites. The Rivers file was created using images taken from the British Geological Survey web site. They were grabbed and colour extracted using graphical software (Jasc Software, Inc 2001, Paint Shop Pro). After using its source coordinates, a georeferenced file was assigned. A final grid file of 12.5metres pixel size was created using the image to grid conversion function in ArcInfo (Figure 2.3). The â&#x20AC;&#x2DC;Robin Hood mythâ&#x20AC;&#x2122; was represented using the places that currently have an association with the name of Robin Hood such as 'The World of Robin Hood', 'Sherwood Forest Country Park & Visitor Centre' and 'Sherwood Pines Forest Park'. The Soils file was created using the same systems as with rivers but maintaining the resolution of the grabbed image (25 meters). The original source was a 1:625.000 scale map of England showing the Solid Geology. The area corresponding to the Bunter sandstone was used as the Nitrate Vulnerable Zone presented in Figure 2.3. 101

SSSI's were represented using the Special Sites of Scientific Interest designated by English Nature and available in MapInfo and AutoCAD formats from its web site. Site management, as in the case of grazing management, made reference to land use practices but extended to other type of land covers. It was not represented due to lack of precise information. Wetland sites were represented as the union of streams and lakes. Lakes were taken from the NCC 1992 map and updated using OS maps (Figure 6.1). Due to the relatively small area in marshland and meadows they were assigned to the class named ‘other pasture’. Wildlife and wilderness were represented using the English Nature designated Special Sites of Scientific Interest and available in MapInfo and AutoCAD formats. In addition, a list of Historic Parks and Gardens in the area of Nottinghamshire with importance and attractiveness for wildlife purposes was created and geo-referenced (Figure 6.5). The listed places were: Babworth Hall, Thoresby Park, Wellbeck Abbey, Wollaton Hall, Bestwood Pumping Station, Papplewick Pumping Station, Felley Priory, Hodsock Priory, Holme Pierrepont and Thrumpton Hall, Patching Farm and Greenwood Gardens (NCC, 2001). The circled spots in Figure 6.5 are not proportioned with the map scale.

6.4 Discussion and Conclusions The stakeholders mentioned in total 34 landscape components that characterised their ‘ideal’ sites. These components are, in a high or low degree, a by-product of human interventions upon the ‘natural’ or original characteristic of the landscape. However, some of these components are more controlled by human actions or managed by a function of the population than others. This is the case, for example, of ‘housing,’ a component determined according to human needs. Alternatively compared this to ‘rivers’, which in addition to human needs also consider ecological requirements of aquatic and other wildlife species. When displaying the 34 landscape components in a range from high (+) to low (-) degree of human dependency, the results look like Figure 6.11. For this Figure, drawing a line separating biophysical and socio economic components was not a straightforward task. Most of the components mentioned by the stakeholders appeared to be human related (approximately 75%). Groups 1 and 2 on Figure 6.11 contain land 102

use/cover biophysical related features whereas groups 3 and 4 are more related to human/social features. With an increase in the degree of human control there is an increase in the difficulty of representing these variables spatially and graphically. Six of these features (18%) were not included in the later analyses because of difficulties in depicting them or due to a lack of information. These were commercial management, funding schemes, site management, openness of space, land ownership and public involvement in environmental management activities. There were some limitations in the collection of geographical data that conditioned their use: there was a lack of spatial data for the extent of SNA; the information provided in the Sherwood Natural Area Profile concentrates mainly on its biophysical attributes; and the social characteristics always corresponded with the administrative boundaries in Nottinghamshire making difficult to contrast/overlap them with biophysical data. In this sense, further work is necessary to cover this gap with a more flexible data structure available for the public, researchers and decision-makers interested in the human characteristics of natural areas. As already mentioned, there were some variables that were not available due to their singular characteristics. A good example of a feature that says more about the quality of a landscape rather than its physical appearance or layout is site management. To represent this variable, specific information is required at plot level. The interpretation of the multiple alternatives for management strategies is perhaps more complex and controversial. Additional research is needed if some attributes are to be considered by those who seek to qualify and classify the landscape. A similar case occurred with the representation of wilderness. Wilderness was assigned to the SSSI’s and to historic gardens, but they are not necessarily the best for representing this attribute as they are not the only sites where wilderness is present. Another issue that emerged in the process of creating the geographical database was conditional dependence between several of the landscape components. This is better explained with an example such as ‘accessibility’. If it is represented with the layers depicting roads, rails and waterways, some stakeholders could argue that these features also express a ‘degree of development’. A similar problem was found when representing wilderness. This is strongly associated with areas making up of the Dukeries Estates and the historic parks and gardens. In order to deal with this constraint, the multivariate analysis technique presented in Chapter 7 (Logistic Regression) was selected. This can be used when the variables are not conditionally independent, as in many geographical problems. 103

4 Approximate boundary between biophysical and socioeconomic components

1 River Corridors Acid Grass Lakes Ancient Woodland Wetland / Meadows Dense Forest Wildlife / Wilderness Openness of space

Farming Grazing Management Livestock Coniferous Broadleaved Plantations Heathland Hedgerows

Golf Courses Parks Historic Gardens Dukeries Estates Cycle Paths Housing Coal mine sites and gravel extraction Accessibility

Land Ownership Commercial management Funding Schemes Robin Hood Mythology SSSI's Jobs Low People presence Public Involvement Degree of Development Recreation Site Management

- Degree of Human control +

Figure 6.11: Landscape features and the degree of human control upon them.

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CHAPTER 6CHAPTER 7 CHAPTER 7PROBABILISTIC MODELS OF STAKEHOLDERS' VISION IN SHERWOOD NATURAL AREA 7.1 Introduction One interpretation of a ‘stakeholder vision’ is what is called here ‘conceptual scheme’ produced with the analysis of text presented in Chapter 4. In this chapter a deeper investigation of these visions is made. The stakeholders’ reasons for selecting the preferred sites were described in Chapter 5 and 6, and these were used as guidelines for the construction of a geographical information system. The points signalled by their representatives (respondents) were used in this system as training points to help in defining the boundaries and significant variables characterising the landscapes enclosed in what is named the stakeholders’ vision space (SVS) 6. The technique used to obtain the SVSs was a combination of ‘Weights of Evidence’ and the Logistic Regression multivariate analysis. While building the model it was identified the relative importance of the explanatory variables (evidence) as suggested by the stakeholders. This technique solved the problem of going from the points presented in Chapter 5 to a more concise map. The main output of this phase was a series of probability maps, representing the preferred landscapes envisioned by the stakeholders.

7.2 Methodology Two complementary methods based on Bayesian inference were used to model the visions expressed by the stakeholders: Weights of Evidence (WofE) and Logistic Regression (LR). WofE and LR are different techniques based on the loglinear model for multivariate analysis. Using these techniques it is possible to obtain a probabilistic distribution of the occurrence of modelled events, and reduce the uncertainty and sample space for further inquiries. An advantage of a Bayesian approach is that it allows explicit recognition of multiple perspectives (Spiegelhalter et al., 2000). It answers questions like 'How should a piece of evidence change what we currently 6

An alternative name used through the text is ‘probabilistic models of stakeholders preferences’.

105

believe?' (Spiegelhalter et al., 1999). A Bayesian approach was chosen to allow explicit identification of multiple perspectives (Spiegelhalter et al., 2000). In addition, the Bayesian paradigm offers a natural and consistent way of framing a problem and achieving data integration and developing methodological solutions (Herriges and Kling, 1998). These methods have not been used before with the purpose of landscape visioning. For the purpose of 'vision modelling', the method adopted was as follows: stakeholders were asked to locate on a map or to mention the name of places where one was likely to find the ideal situation or desired conditions representing their 'vision' for Sherwood; in other words, 'what Sherwood Forest â&#x20AC;&#x153;shouldâ&#x20AC;? be like'. Then, the stakeholders were asked to mention the reasons explaining their selection and in this way a list of criteria and explanatory variables for the selected sites was collected and transformed into spatial layers of information. The characteristics of these places were treated as evidence and represented by layers of geographical data. As anticipated in Chapter 6, the geographical location of these 'ideal sites' (also called the dependent or response variable), together with the multiple criteria for their selection (evidential or explanatory variables), were used to build probabilistic models. These models were then used to estimate the location of sites similar to those expressed by the evidence (Figure 7.1). 7.2.1 Model Building The tool used for the spatial analysis was 'Arc-Spatial Data Modeller (Arc-SDM)' (Kemp et al., 1999). It is an add-in extension to ArcView 3.2a (ESRI, 2001), which was the selected geographical information system. This extension was developed originally for mapping mineral potential but it can also be used to predict the probability of occurrence of point objects in other domains. Before running the model, four general steps were required: 1. Points layers preparation: The places mentioned by the respondents (training points) were represented in layers of points in ESRI shapefile format (Figure 5.2). 2. Explanatory variable preparation: The full set of explanatory variables mentioned by the stakeholders is presented in Table 7.1. The variables were represented as explained in Chapter 6 but before running the model, a conversion to a binary classification was required. Collet (1991) suggests that the replacement of a continuous variable by a binary one implies a loss of information and so this procedure should only be used when the circumstances demand it. Some of the continuous 106

variables here were converted to binary ones due to the reduced number of preferred sites mentioned by some of the stakeholder cases. The reduction of continuous variables to binary ones was also considered necessary in order to reduce the number of unique conditions in the multivariate analysis. In the case of the variables 'population' and 'unemployment', three different continuous classes were kept instead of two. Figure 7.2 presents an example of the binary map calculation from buffered variables using the contrast C as the cut-off for the variable. When LR is combined with WofE, a basic assumption considered in the analysis is that the probability of occurrence of an 'ideal place' decreases exponentially with distance from a class 'x' of an explanatory variable. In order to incorporate this exponential decrease in the logistic model, new representations of the explanatory variables were created assigning distance buffers to the original ones. To select the cut-off of the original explanatory variable, weights and contrast for each distance corridor and its variation with distance were calculated for each variable measuring the weights of evidence. The optimum cut-offs for classifying patterns into binary presence/absence for the jth pattern was determined from:

W j? ?

? ? ? ln ? p( j d )? W j? ? ? ? ? ? p( j d )

ln ?p( j d )? ?

p( j d )

Under normal conditions, the maximum value of the weight (W+ - W ) gives the cut-off at which the predictive power of the resulting pattern is maximised but the best binary predictor map corresponds with the cut-off class in which the contrast (C) is higher (Bonham-Carter et al, 1988). The contrast C, is the absolute difference between W+ and W- and measures the correlation strength between the evidential points and the binary pattern (Agterberg et al., 1993). However, â&#x20AC;&#x153;the process of converting multiclass maps to a binary form can either be carried out subjectively, using expert judgement, or statistically, so as to determine the threshold that maximises the spatial association between the resulting binary map pattern and the point pattern" (Bonham-Carter, 1994 p.319).

107

Figure 7.1: Summary of the steps for building probabilistic models of stakeholder visions.

3. Setting the parameters of the model: Once the binary variables for each stakeholder case were calculated, it was necessary to set up the parameters for the logistic regression calculation. They were: a) The study area: In this study the area corresponded to the Sherwood Natural Area, with an extent of 534 square kilometres. b) The unit area (Sq. km) of analysis: The unit area was calculated using the following equation: Suggested Value = (total study Area / total training points) / 40 This value is a guideline but typically the unit area should be about the same or smaller than the suggested unit area and the important thing is to select a unit cell that is relatively small relative to the study area and possibly meaningful for the training sites (Raines, G. 2001, personal communication).

108

c) Initial Probability: When setting the initial parameters, the initial probability was calculated by dividing the total number of points by the total study area in units. This value will be the reference to compare to the posterior probabilities obtained after running the model. Table 7.1: List of variables used for the identification of vision space

Landscape Component Accessibility Ancient woodland Broadleaved Coal mines Coniferous Dense forest Dukeries Estates Farming Golf courses Heathland Historic gardens Housing Jobs Lakes Parks Recreation River corridors SSSI's Wetland Wildlife

UNott NCC SFT BDC NFU NWT NSDC EN ? ? ?

? ? ? ?

? ? ?

? ? ? ?

? ?

? ? ? ?

? ? ?

? ?

? ? ?

? ?

? ? ? ? ?

? ? ?

4. Running the model: The list of evidential themes (binary maps) was given as an input to calculate the posterior probability response themes, which show the probability of finding a place throughout the whole area such as those signalled by the points. The whole process was repeated twice, in the first run (run 1) the variables weakly associated with the evidential points were identified and excluded for the second run (run 2). The posterior probability maps were then compared with the initial probability values and the areas with posterior probability lower than the initial were masked. The remaining areas composed what was called the SVS 5. Model assessment: Once the model was formulated, an attempt was made to obtain information regarding its reliability. The questions that were answered were: Did the model explain the observed data? Which of the models worked best and why? 109

Were the models consistent and complete? In the following paragraphs these questions are answered. 7.2.2 Model Assessment 7.2.2.1 Accuracy Due to the limited number of evidential sites, it was not possible to omit a subset of observations for posterior validation from the modelling process. To overcome this problem, a cross validation test was carried out. â&#x20AC;&#x153;Cross Validation is based on a leaving out one object at a time- technique. This means that with N objects N models are made with N-1 calibration objects, and the prediction ability is tested on the object omitted. This will give an estimate of the average prediction ability for the N modelsâ&#x20AC;?. Thus, a comparison was made between the observed probabilities found with the full model (all observations included) and the predicted set of values (estimated probabilities) calculated for each of the locations where the observations were excluded from the modelling process. These two sets of data were subject to the Kolmogorov-Smirnov (K-S) test. This is a frequency test to determine whether two samples are drawn from the same or different populations (Norcliffe, 1977, p103.). Some deviation between the two samples as a result of sampling variation is expected; hence rejection of the null hypothesis depends upon the deviation being large. Ho is rejected when the maximum difference, D, is equal or greater than the critical value, Da, given in K-S tables. The sampling distribution of test statistic, D, is closely approximated by a X2 square with two degrees of freedom. For v = 2 and a =.05, the critical value D a from Tables is 5.99. 7.2.2.2 Consistency and completeness Consistency is achieved when the model is not overloaded with an excessive number of parameters; an unnecessary number of parameters may lead to contradictory conclusions. On the other hand, completeness guarantees that the model is not under specified; lack of information will affect the results (Pearl, 1990).

110

PREFERRED SITES (EVIDENCE)

BUFFERED MAP WITH MULTIPLE CLASSES ROADS - ACCESS (EXPLANATORY VARIABLE))

TABLE OF WEIGHTS AND CONTRAST IN EACH BUFFER CLASS

SITES

? CLASSES highest contrast 900 m from roads

BINARY MAP OF ACCESS

New Class

Value

0 - 900m > 900m

1 0

Figure 7.2: Example of binary map calculation

In order to identify the meaningful variables in the model, attention was given to the coefficients. The estimated coefficients represent the slope (i.e., rate of change) of a logit function of the dependent variable per unit of change in the independent variable. The meaning of the coefficients is better understood when transformed into Odds. The ratio between the odds of the outcome being present among individuals with x=1 and 111

x=0 is defined as the odds ratio. This is a measure of association that approximates how likely (or unlikely) it is for the outcome to be present among those with x=1 than among those with x=0. A positive association is found when the values are greater than 1, close to 1 indicates independence, and lower than 1 means a negative association. Nevertheless, when looking at contrast (C) values, also used to quantify spatial association, the higher the C values the stronger the association between the response and the variables explaining the model. The studentised C value for each of the coefficients, which is the contrast divided by its standard deviation. Studentized C values lower than 2 are considered not significant at 97.5% of confidence. Changing this level of confidence allows the inclusion or exclusion of other variables into or from the model. 7.2.2.3 Model comparisons After producing the SVSs, they were overlaid in a GIS to identify areas of different degrees of consensus. A detailed comparison of the SVSs produced here was one of the key materials for further analysis in Chapter 8.

7.3 Results 7.3.1 The stakeholders' vision spaces The SVSs were drawn after running the model twice. In the first run redundant variables were identified. After eliminating these variables, the model was run again and the obtained response maps are shown in Figure 7.3. The initial probability values were used as a threshold to locate those areas in which the posterior probabilities were lower. After masking these areas in a GIS, the remaining areas were defined as the Stakeholder Vision Spaces (SVSs). In other words, white areas in Figure 7.4 represent the location in which it is highly probable to find places with similar characteristics to those signalled by the training points. The size of the areas is presented in Figure 7.5. Looking in detail at the SVSs of the UNott, NWT, EN, NCC and NSDC it is clear that a regular pattern in the shape and location of three core areas can be identified. The SVS of NSDC showed this pattern more clearly. In the cases of SFT and NFU cases, the SVSs were more scattered but still contained the three blocks identified. Different degrees of fragmentation were revealed in the SVS maps. A deeper analysis of the composition and configuration of the patterns found on these SVSs is the subject of Chapter 8. 112

The logistic regression coefficients for the second run are presented in Table 7.3 and the transformation to Odds is shown in Table 7.4. There were differences in the values among stakeholdersâ&#x20AC;&#x2122; cases. Even so, the strongest positive association were related to the farming and recreation coefficients, and the most negative association were those of wildlife and historic gardens. Table 7.2: Logistic regression coefficients for the variables considered by the eight stakeholders in SNA (Variables signalled in grey were excluded in run 2).

Landscape features Accessibility Ancient woodland Broadleaved Coal mines Coniferous Dense forest Dukeries Estates Farming Golf courses Heathland Historic gardens Housing Jobs Lakes Parks Recreation River corridors SSSI's Wetland Wildlife

UNott SFT

NWT

Stakeholder BDC EN NFU

NCC

NSDC

0.112 -0.708 0.487 -0.655 -3.641 -0.687 -2.291 -1.351 -1.385 0.111 -0.478 -1.233 -0.010 -2.935 -1.360 -0.952 -0.526 -0.417 -0.194 -0.489 -1.444 -0.663 -1.158 -2.046 -0.250 -1.158 1.796 1.887 1.445 -0.944 -0.602 0.200 -2.855 -1.555 -0.201 0.361 -0.814 -0.795 -3.062 0.088 -0.573 -2.004 -0.251 1.381 -1.037 0.886 -1.096 -0.123 -3.058 -0.672 -0.387 -1.276 -2.120 -0.962 -3.482 -3.179 -1.062 -1.060 -0.101

The studentised C contrast values for each of the coefficients are displayed in Table 7.4. This table shows the half-highest values of C highlighted in bold and these could be considered as the most relevant characteristics in each SVS. Comparisons among the absolute values of different visions are meaningless because the calculations of the models were made with their own parameters. However, a relative comparison shows that for more than half of the stakeholders considered, C values were high for broadleaved plantations, heathland, wildlife and Dukeries Estates. On the other hand, C values were low for the most of the stakeholders when referring to coniferous plantations, accessibility, farming and golf courses. Considering the final set of variables that were part of the models (but without giving importance yet to the coefficient values) it was possible to distinguish three different 113

groups: the first, EN and NWT, were among the few stakeholders that included the SSSIâ&#x20AC;&#x2122;s and wetlands in their models; the second group, UNott, SFT and BDC shared most of the first variables in Table 7.4; and the third, a dissimilar group made up of NFU, NCC and NSDC. The overlay of SVSs allowed the identification of agreement zones (Figure 7.6). The characteristics of the land cover inside the areas for each category are presented in Table 7.5. In Figure 7.6 the overlay of the eight SVS produced the pattern of the three core areas mentioned above. The areas corresponded with the surroundings of Clumber Park and Welbeck Estate in the north. Around the middle, the Birklands and Bilhaugh sites in Sherwood Forest Country Park. To the south part, the west of Sherwood Forest Pines Park near Rainworth. As Table 7.4 shows, the core areas are mainly heathland, broadleaved and coniferous plantations (Column 8). At the other extreme, farming and urban areas were generally excluded from the eight SVS. 7.3.2 Model Assessment Cross validation analysis allowed the hypothesis nule (Ho) to be accepted stating that there was no significant difference between the paired samples. Table 7.6 presents the goodness of fit arranged from the best (first) to the worse (last). It can be seen that different models had different levels of disagreement, i.e. different K-S values. This suggests that SFT and BDC models worked better than the rest even at a = .98. They were followed by NCC and NSDC models (a = .70), then NWT and NFU models (a = .30) and finally the UNott model (a =.50). The reasons why these differences emerged can be inferred from the number of variables included in each of the models, the number of evidential sites mentioned by each stakeholder and the correspondence of individual variables with the features expressed during the interviews. In relation to the consistency and completeness assessment, Table 7.3 lists the evidential theme variables of the eight SVSs' identified. Those spaces masked in grey in this table signal the variables that were not significant in the first run supported by the analysis of the studentised C value. The excluded variables were less spatially associated (positive or negative) with the points marked as preferred places than those variables maintained in the second run. Comparatively, the coal mines (4 out of 7 cases) and recreation variables (3 out of 6 cases) were those most often excluded from the SVSs. Heathland and ancient woodland were excluded only once.

114

Table 7.3: Logits (coefficients) transformed to odds

UNott SFT 0.90

0.26 0.31 6.03 1.22

NWT

1.12 1.63 0.10

0.52 0.26 0.29 0.59

0.39

Stakeholder BDC EN NFU 0.49 0.50 1.12

0.03 0.25 0.99 0.66

0.62 0.61 0.24

0.78 6.60 0.21

NSDC

0.05 0.82

0.13 0.39 0.06

NCC

0.55 1.44

0.82

0.44

0.52 0.14 4.24 0.45 0.05 1.09

0.56 0.13 3.98

0.12

0.35

0.33 0.88 0.51 0.03

0.38

0.05 0.68 0.04

0.78 2.43

0.28 0.35

0.35

Table 7.4: Confidence (studentised contrast) for each of the explanatory variables (Half of the highest values in each SVS model are highlighted in bold)

UNott

SFT

3.33

3.28 2.40 5.03

5.48

3.78

4.56 3.41

5.30

2.23

NWT

Stakeholder BDC EN

4.29 4.62 4.17 4.06

7.14 7.74 3.69 4.09

2.65 9.77 5.81

NFU

1.82 4.66

5.53

1.68 3.32

4.93 3.42

2.02 8.04

8.19

2.11

NCC NSDC

6.43 9.49

2.71

3.09 3.57 2.11 3.27 2.53 2.02

1.78 7.57 4.69

6.04

7.49

2.40 3.88 4.64 7.56

115

6.54 13.48 4.75 13.48

1.00 1.63

1.29 3.42

2.52

7.4 Conclusions In this Chapter, a systematic method was presented to help organised the fuzzy information collected, regarding stakeholders’ landscape preferences. It has been assumed that the stakeholders’ preferences contain the whole of what can be conceived as their visions. In addition, a ‘vision’ was conceptualised as a system in which boundaries, components, functions and relationships can be depicted. In this chapter, some of these elements were identified. In defining a system, the definition of the boundary is the initial step and for the current case the boundaries of the visions were the stakeholders’ vision spaces. The raw material to identify these boundaries were the presence of landscape features considered important by each of the stakeholders. Given that the distribution of these features/components is qualitatively and quantitatively complex, a probabilistic approach was chosen to model their relationships. The translation of these components into geographical representations required simplification, but care was taken in giving the appropriate weight to the value of the information, by means of the use of ‘weights of evidence’ technique. As a result, boundaries, components and relationships started to be uncovered. Trends were revealed in the similarities between the stakeholders, and groups emerged that shared common visions. Current places symbolising ‘Sherwood’ and some neighbouring areas were part of what was conceived by the stakeholders as their vision. The main components associated with the definition of these areas were the presence of ancient woodland, broadleaved and coniferous plantations, heathland, farming and wildlife. However, they both differed in importance and in their meaning between visions. The accuracy of the models was checked statistically and the tests demonstrated their stability and consistency. Nonetheless, further research is needed to confirm and validate these results. This task is the subject of the following two chapters. The analysis of stakeholders differences and similarities represented in the maps could also be used to devise landscape management strategies, although the implications derived from the analysis carried out here have to be considered with caution because of the 'pilot' character of the implementation of the technique. However, it is important to recognise that this method has potential for a more thorough and detailed implementation in specific cases in Sherwood or similar areas. In the present research, the recommendations can guide environmental policy makers, technicians and

116

promoters by enriching their knowledge of how other stakeholders perceive the area and point to how to achieve consensus for the management of specific places. The stakeholders’ vision spaces are also useful in identifying those core areas throughout Sherwood that are susceptible to integrate with the existing areas in conservation (Figure 7.7). This new areas could receive a similar environmental management and be also part of ‘Sherwood Forest’. This would increase the extent of what is currently perceived as Sherwood Forest and integrate surrounding areas in tourism activities. Table 7.5: Proportion of land cover (%) inside the overlay of the SVS Land Cover \ Level of agreement

4.85

7.12

5.21

6.23

No data

15.14 20.56 7.96

7.74

5.71

Acid grass and heathland

0.00

0.23

3.47

6.48 18.41 28.63 32.67 31.73

Broadleaved plantation

0.09

3.88 11.61 13.18 11.58 33.31 22.56 34.50 42.24

Coniferous plantation

0.21

0.75 15.92 39.34 55.29 22.98 18.09 5.82 14.89

Water

0.00

0.25

0.60

0.52

0.55

1.61

3.48

7.86

1.04

Coal mines

0.00

3.89

8.80

4.84

3.67

8.45

3.41

6.31

1.10

Farming

57.16 46.70 30.99 8.58

2.64

1.16

0.50

1.28

0.21

Grazing management

2.75

4.72

2.90

4.94

1.75

1.13

0.43

0.04

0.00

Other forest

0.71

3.22

8.39

9.01

7.62

4.76 12.30 3.88

1.77

Other pasture

6.80

6.40

5.47

4.99

2.84

2.12

2.82

2.42

0.31

Urban

17.14 9.39

6.20

3.38

1.86

1.22

0.66

0.01

0.48

1.15

Table 7.6: Maximum differences (D) and chi square calculated values in KolmogorovSmirnov test applied to the observed and estimated (cross validation values)

D value X2

Stakeholder Vision SFT BDC NCC NSDC UNott NWT NFU EN

0.068 0.090

0.154 0.166 0.117 0.147 0.172 0.288

117

0.27 0.36 0.61 0.66 0.94 1.47 1.72 9.76

UNott

SFT

NWT

BDC

NFU

NCC

NSDC

Figure 7.3: Posterior probability of stakeholders' vision space (2nd run)

118

University of Nottingham (UNott)

Sherwood Forest Trust (SFT)

Nottinghamshire Wildlife Trust (NWT)

Bassetlaw District Council (BDC)

English Nature (EN)

National Farmers Union (NFU)

Nottinghamshire County Council (NCC)

Newark and Sherwood District Council (NSDC)

Figure 7.4: Stakeholders' vision space modelled with weights of evidence and logistic regression (Posterior probability < initial probability are in grey)

119

Figure 7.5 Area of SVS defined by posterior probability > initial probability

120

Figure 7.6: Areas of agreement/disagreement among the eight visions.

121

Figure 7.7: Overlay of SVSs indicating 'ideal' core areas in red and potential linking corridors in green as an input to an environmental management strategy for Sherwood

122

CHAPTER 8 LANDSCAPE PATTERN ANALYSIS OF STAKEHOLDERS’ VISIONS IN SHERWOOD NATURAL AREA 8.1 Introduction In addition to the application of quantitative and qualitative methods in stakeholders' landscape research, one of the main objectives of the current study was to discover the relationship between a particular stakeholder’s vision and the landscape. The study of landscape function and change has, as a prerequisite, characterisation of its structure. A composition and configuration analysis of the patchiness of the landscapes enclosed in the SVSs identified in Chapter 7 was carried out by means of a tool to quantify landscape structure. The results were analysed focusing on the similarities and differences between the stakeholders and suggests a typology of three different visions.

8.2 Methodology A review of the meaning of landscape metrics is presented in order to clarify concepts before detailing the way in which the landscapes obtained from the SVS’s were analysed. A discrete element of a landscape is called a “patch”. Together with other patches it creates a mosaic that must be considered as a descriptive attribute of a landscape (Farina, 2000 p.46). The patch approach for interpreting complexity of the landscape started with Island Biogeography Theory (MacArthur and Wilson, 1967). According to this theory the isolation and the size of the island are the driving factors for colonisation and extinction. Patchiness may be synonymous with heterogeneity, which strongly influences those processes, or organisms that depend on multiple patch types and are controlled by a flow of organisms, water, air, or disturbance among patches (Chapin III et al, 1998 in Farina, 2000 p. 74). Heterogeneity is also responsible for the different patch quality as appreciated by living organisms; there are more suitable and less suitable habitat patches (Farina, 2000. p.75). The “patch” approach is, in general, more pattern oriented than functional, and mainly represents the effect of other non-spatial processes e.g. the disturbance of a forest. 123

When the spatial configuration of patches appears narrow like a road, it receives the name of a corridor. These are linear habitats that have a connectivity function. They have an important role in the dispersion and movement of plants and animals. Connectivity is one of the first attributes of a land mosaic to be reduced by fragmentation caused by human intrusion with infrastructure elements like roads, intensive fields, urban and industrial settlements (Farina, 2000, p. 90). Fragmentation is a process that dramatically reduces habitats and biodiversity worldwide (Saunders et al. 1991) increasing the risk of extinction (Burkey, 1995) and, in some cases, modifying the social and economic structure of human populations (Skole et al. 1994). Fragmentation is one of the processes that affects the integrity of a landscape (Farina, 2000, p125). It can be measured as the level of patch dispersal or in terms of dynamic processes. Patch size and shape, patch spatial arrangement, distance between patches and hostility of the inter-patch environment are the main attributes usually used to measured fragmentation. Several authors have discussed the limitations of patch indices in describing multiple qualities of a landscape (Haines-Young and Chopping, 1996; Gustafson, 1998; Tischendorf, 2001). The emergence of patterns in the landscape does not necessarily mean functional organisation (Sanderson and Harris, 2000). However, there is considerable evidence supporting the argument that spatial land use and land cover patterns are associated with both ecological and human processes like hydrology, nutrient cycling, fire regimes and plant and animal biodiversity. In optimal cases, these measures of spatial pattern and composition can be used to describe the function of landscapes (Forman and Gordon, 1986). Their use is acknowledged when comparing alternative landscape configurations as products of different scenarios (Gustafson, 1998) as in the current study. A criterion for evaluating a landscape is the measure of the basic matrix structure by using attributes characteristics such as dominance of a cover type, diversity of the cover type, aggregation, diversity in patch size, shape of the patches, length of the edges, level of contagion, and spatial distribution of patches (Farina, 2000 p. 118). â&#x20AC;&#x153;Landscape metrics quantify the structure of the landscape within designated landscape boundaries onlyâ&#x20AC;? (McGarigal and Marks, 1995). In this study, those boundaries correspond with the SVSâ&#x20AC;&#x2122;s. In order to characterise the landscapes of each SVS and to address environmental concerns in the SNA such as fragmentation of biotypes (e.g. heathland, ancient forest and their loss of biodiversity), a selected group of indices was used to analyse the 124

visionsâ&#x20AC;&#x2122; spaces. It is important to emphasise that the analysis of the patterns found in the SVSs aimed to discover hidden elements of the stakeholdersâ&#x20AC;&#x2122; visions in relation to the type of landscape they preferred and envisioned. The land cover theme of the SNA was initially intersected with the 'vision space' of each stakeholder. This left a series of patches of different land covers whose properties were quantified and analysed. In a first step, the proportion of each land cover inside the SVS was measured. Secondly, a more detailed quantification of these patches was made using a landscape analysis tool called 'Patch Analyst 2.2' (Elkie et al., 1999). Patch Analyst 2.2 is an extension to ArcView 3.2 that facilitates the spatial analysis of landscape patterns and the assignment of values to patch types based on attribute modelling of patch characteristics. It incorporates the program called FRAGSTATS developed by McGarigal and Marks (1995) to quantify landscape structure. With this tool, a series of standard metrics were computed including area, diversity, contagion and interspersion metrics.

8.3 Results The ten different land classes in which the SNA was initially classified were found in all the SVSs. They differed both in the area of each class (composition) and in the location and configuration of the patches (structure). 8.3.1 Landscape composition Landscape composition refers here to the land cover found in the area delimited by the boundaries of each SVS. Figure 8.1 shows the relative landscape land cover composition inside the eight SVS in the SNA. The composition found in SVS of the SFT and the farming representative, were very similar. Farming, coniferous and broadleaved plantations together represented more than 60% of the land cover in the SVSs. The SVSs of UNott, BDC and NCC also showed similarities; acid grass and heathland, coniferous and broadleaved plantations sum to more than 60% of the total land cover for each of these stakeholders. The UNott was the stakeholder who appears to give most importance to the urban areas as part of the landscape. The remaining stakeholders were very dissimilar; EN had the highest proportion in heathland and broadleaved plantations (+/-70%). A more balanced composition, but one with a big proportion of broadleaved plantation, was found in the case of NWT, which came second in the importance given to heathland. In the case of NSDC, more balanced 125

distribution in terms of the extent assigned to each type of land cover. The last column of Figure 8.1 shows the proportion of land cover types for the whole extent of SNA. This represents the current proportion of land cover types found in what is known as SNA.

Figure 8.1: Relative land cover composition inside the eight SVSs in the SNA (last column represents the whole area)

When analysing the land cover composition outside the SVSs (Figure 8.2), the picture is very different. There were remarkable similarities among the eight stakeholders and there were no noticeable differences with the current state of SNA when considered fully (last column in Figure 8.2). Figure 8.3 shows the absolute land cover composition for each SVS. In spite of the differences in size, the representation allows comparison of similarities and differences between the SVSs composition. In Figure 8.3, the eight stakeholders were arranged in three different groups according to a similar pattern of land cover composition, highlighted with three different colours. The first group in blue consists of the stakeholders that contained in their preferred landscape areas cover by Heathland, Broadleaved and Coniferous plantations. The group in yellow, included large areas of farming in addition to those of the first group. The third group in green, included in a more balanced proportion, all types of land cover. 126

Figure 8.2: Relative land cover composition outside the eight SVSs in the SNA (last column represents the whole area)

In order to quantify the similarities and differences, a cluster analysis was carried out to compute Euclidean distances (ED) between the land cover classes for each stakeholder. ED is the geometric distance between objects (stakeholders) in a multidimensional space (land cover classes). Once several objects have been linked together, there are a number of alternatives (amalgamation or linkage rules) to determine the distances between the new clusters. Two different linkage rules were used: Unweighted pair-group centroid and Wardâ&#x20AC;&#x2122;s Method. In the first, a â&#x20AC;&#x2DC;centroidâ&#x20AC;&#x2122; of a cluster is computed, which is the average point in the multidimensional space defined by the dimensions. It is the centre of gravity for the cluster. Using this method, the distance between two clusters is determined as the difference between centroids (StatSoft, Inc., 1995). Thus, the computed clusters show the normalised distances between stakeholders (Figure 8.4). The UNott and the NCC formed a cluster that was similar to a group formed by NWT, NSDC, EN and BDC. These two groups were then linked with SFT, which in turn was linked with the NFU. The SNA closed the clusters.

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Figure 8.3: Absolute land cover composition inside eight SVSs in SNA with colours highlighting similarities and differences between stakeholders (Last column represents the whole area)

Ward's method uses an analysis of variance approach to evaluate the distances between clusters. The clusters in this case incorporate the size effect of the land cover classes. Again a group was formed by the UN and the NCC vision spaces that were linked to a group composed of SFT and the NFU. A third group with NWT, NSDC, EN and BDC was linked to the two previous groups. The whole set of stakeholders was then more remotely linked with the SNA (Figure 8.5). Figure 8.6 shows a more complex, but not necessarily more complicated, analysis of the eight SVSs. It represents the proportions of land cover types found in each agreement level of Figure 7.6, which is an overlay of visions. The proportion of land cover types in the column identified as â&#x20AC;&#x2DC;0â&#x20AC;&#x2122; represents the landscape composition that none of the stakeholders considered as inside their vision space. The land classes found on it were farming (67.4%), urban (20.2%) and pasture areas (8%). In contrast, 128

at the opposite extreme the column identified as '8' represents the landscape composition of the area that all stakeholders considered as their 'ideal sites'. It is composed of broadleaved plantation (45%), heathland (33.8%) and coniferous (15.9%). In the intermediate columns the proportion of land cover types fluctuated; when approaching total agreement, the trend is of an increase in heathland, broadleaved plantations and water cover types. There is also a decrease in farming, urban and pasture areas. Coniferous plantations and coal mine sites had a low proportion towards the ‘0’ and ‘9’ (extreme) agreement levels and were high towards the middle. Other forest fluctuated along the 0 to 8 level scales without a defined pattern. Unweighted pair-group centroid Euclidean distances

UN NCC NWT NSDC EN BDC SFT NFU SNA

Order of Amalgamation (distances are non-monotonic)

Figure 8.4: Euclidean distances (Unweighted pair group centroid Method) using the absolute land cover composition of the eight SVSs

Figure 8.7 shows the absolute areas of land cover and level of agreements. On the right side (in cyan colour) the total area of each level of agreement is represented. Around 25,000 ha did not concern any stakeholder, and only 5,000 ha were of concern to at least two stakeholders. Single land covers, urban, other pasture and farming classes had extensive areas in agreement levels at one, two and three. Coniferous and broadleaved plantations behaved differently from the remaining classes. 129

Ward`s method Euclidean distances

UN NCC SFT NFU NWT NSDC EN BDC SNA

5e7

1e8

1.5e8

2e8

2.5e8

3e8

3.5e8

4e8

Linkage Distance

Figure 8.5: Euclidean distances (Ward's Method) using the absolute land cover composition of the eight SVSs

Figure 8.6: Land cover composition in nine areas at different levels of agreement (0=nobody agrees, 8=all agree)

130

As mentioned, Figure 7.6 shows the overlapping areas of the eight SVS. In this Figure, those areas suggested by the whole group of stakeholders were in the ‘level of agreement number 8’; while areas suggested by none of the stakeholders was labelled ‘level of agreement number 0’. For each stakeholder, the proportion of their vision inside the level of agreement zones was quantified and represented in Figure 8.8. In this way it was possible to compare the percentage of areas that the stakeholders had in a specific level of agreement. For instance, EN had 88% of its SVS as part of the level of agreements 6, 7 and 8. Thus, if the EN vision were to be implemented, it would have the support of six other stakeholders – up to 88% of its suggested area. By contrast, BDC had 21.3% of its vision space in the same levels (6, 7 and 8) so this stakeholder would have support for only 21.3% of its suggested area (Figure 8.8). In addition, BDC had 47.1% of the area of its SVS that no one else was interested in. Looking at these figures in a different way, were a total agreement (level 8) wanted, less than 4% of the area (constrained by the contribution of the NFU vision at level 8) would be selected.

Figure 8.7: Absolute areas of land cover and level of agreement

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8.3.2 Landscape Structure Landscape configuration is defined by the way in which the different patches of land cover are arranged spatially. When looking at the configuration of the landscape found in the eight SVSâ&#x20AC;&#x2122;s, the NFU seems to be the most fragmented of the visions. But when focusing on specific land cover classes, things looked completely different. There was no consistency between the results found at the landscape and class extents. Four patch indices at the landscape and class extents were selected to investigate the fragmentation, habitat availability and heterogeneity of the landscape. The contrast between stakeholders rather than the meaning of absolute values were emphasised. The explanation of figures in these terms highlights the most relevant differences or similarities between stakeholdersâ&#x20AC;&#x2122; indices.

Figure 8.8: Percentage of the area inside each stakeholders' vision as part of the eight levels of agreement

Mean Size (MPS) and Number of Patches (NP): These two indices are perhaps the most basic aspects of landscape pattern that affect multiple processes. â&#x20AC;&#x153;Briefly, as habitat is lost from the landscape (without being fragmented), at some point there will be insufficient area of habitat to support even a single individual and the species will be extirpated from the landscape. This area relationship is expected to vary among species depending on their minimum area 132

requirementsâ&#x20AC;? (McGarigal and Marks, 1995. p24). In this sense, a large number of patches and small mean patch size for a specific land cover class would make it more fragmented. As Figure 8.9 shows, for the landscape as a whole, the most fragmented SVS corresponded to the NFU and the least fragmented was the EN landscape. In relation to these extreme conditions, the rest of the stakeholders could be considered as having an intermediate level of fragmentation. The District Councils were similar to the EN condition followed by the NCC, SFT and the UNott, which had intermediate values for these two indices. At the scale of individual classes, the size and number of patches of four different land cover classes (heathland, broadleaved and coniferous plantations and farming), were computed and represented in Figures 8.10 to 8.11. Special attention was paid to these classes given that they represented more than 60% in all the SVS. To assert that a land cover class is in a state of fragmentation depends on the ecological requirements of each species or communities associated with the class. However, it is important to remember that the analysis was focused on contrasts rather than on absolute values.

Figure 8.9: Fragmentation indices at the landscape extent for the eight stakeholders

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In the case of heathland (Figure 8.10A), the more fragmented landscapes were those of EN, NSDC and BDC and the less fragmented those of NFU, NWT, SFT and UNott. The NCC had an intermediate level of fragmentation for this vegetation type. For broadleaved plantations (Figure 8.10B), the less fragmented was the NFU, while the rest of the other SVSs had a highly fragmented land cover class. The coniferous plantation class (Figure 8.11C) had a less fragmented value in the NFU landscape than in the NSDC and SFT landscapes. A similar situation was found in the case of farming land cover class (Figure 8.11D) and together with the NFU landscape, the BDC, EN and NWT had also a low fragmentation for the farming land cover class. In summary, the NFU case seems to be the least fragmented landscape when looking at individual land cover classes. With the exception of heathland, SFT had the most fragmented land cover classes.

Total Core Area (TCA): The TCA Index quantifies the core area for the entire class or landscape as a percentage of total class or landscape area, respectively. The core area was defined as the interior of a patch excluding a buffer zone 25m from the edges of each patch. For organisms strongly associated with patch interiors, core area may provide a measure of habitat availability. It integrates into a single measure the effects of patch area, patch shape, and edge effect distance, quantifying habitat fragmentation without being affected by landscape configuration. For this reason, this metric at the class extent may be useful in the study of habitat loss and fragmentation (McGarigal and Marks, 1995. p. 43). At the landscape extent, there were no substantial differences between the TCAâ&#x20AC;&#x2122;s, but the lower value was found in the NFU case (Figures 8.12). Figure 8.13 show this index for the four previously mentioned land classes. The lowest value in TCA index for the heathland land class was found in the NFU case. The rest of the stakeholders had similar scores for this land class. There were no substantial differences in the TCA of the broadleaved land class. Coniferous land class had its lowest value in the EN case while the rest had similar values. In the farming land class, there were more noticeable differences; BDC, EN and UNott had the lowest TCA values while the highest were found for the SFT, NSDC and the NFU cases. The NCC and the NWT cases had an intermediate position.

134

Interspersion and Juxtaposition (IJI) Index: The IJI measures the configuration of the landscape or the extent to which patch types are interspersed but not necessarily dispersed. Higher values result from landscapes in which the patch types are equally adjacent to each other whereas lower values characterise landscapes in which types are disproportionately distributed. At the landscape extent, the IJI did not present contrasting differences (Figure 8.14). At the class extent (Figure 8.15), the IJI had its highest values for the NSDC and NFU cases in most of the four land classes. The NWT had the lowest values for this index, with the exception of the coniferous land class where the UNott was the lowest. The four indices computed at the landscape extent were subjected to a cluster analysis as in the land composition analysis to identify the similarities and differences between the stakeholders. The Euclidean distance was calculated with the Unweighted pair group centroid method only (Figure 8.16). There were also three main groups. One represented by the NFU exclusively, the second formed by EN, NSDC and BDC. The SVSs of NCC and UN together with the SFT and NWT composed the last group.

8.4 Discussion and conclusions Through the analysis of the landscape composition and structure of what was contained inside the SVS it was possible to obtain information regarding the stakeholders’ characterization of the SNA. The results obtained here allowed the comparison of stakeholders’ visions in a more quantitative and concrete way than with the analysis of institutional documentation presented in Chapter 4. From the landscape composition it is inferred that those interested in nature conservation presented more emphasis on specific land cover classes as valued for its biodiversity. Such was the case of heathland and broadleaved plantations that are currently receiving special attention from stakeholders such as EN and the NWT. In contrast, NFU presented a clear interest in having the farming land cover class as a dominant feature of the landscape, as was expected. Intermediate positions were generally found in the local authorities, District and County Councils and in the UNott. The SFT had also an intermediate position with a trend towards supporting the NFU vision. The NFU composition was the most similar to the current composition of the SNA. SNA is, in a certain way, a by-product of previous ‘visionaries’. It could be argued that the landscape has been shaped with the vision of previous stakeholders like those being part of the current NFU. 135

These observations are supported by the quantification of similarities and differences between the SVSs land cover composition obtained with the cluster analysis. From this analysis, the eight stakeholders can be separated in three groups: 1) SFT and NFU; 2) UNott and NCC; and 3) EN, NWT, NSDC and BDC. In group 3, BDC was slightly different from the rest. When the areas outside of the SVSs were analysed, there was strong agreement on what was considered non-ideal for SNA in terms of its composition. The average composition of these ‘outside areas’ was very similar to the land composition of the current situation in SNA. This forms, perhaps, one of the conditions for the existence of partnerships working towards the improvement of the landscape in Sherwood. However, when looking at the agreement of areas inside the SVSs, the consensus area was very small showing discrepancies between stakeholders about what is currently considered ‘ideal’ for Sherwood. The stakeholders agreed on only 6,000 ha out of a total of 534,000 ha, in which at least 60% of the Sherwood landscape should contain heathland, broadleaved plantations and a certain amount of coniferous and water bodies. With respect to the landscape structure found in the SVS, three out of four indices (NP, MPS and TCA) used at the landscape extent revealed that the NFU had ‘in theory’ a more fragmented landscape. Alternatively, EN, NSDC and BDC had the least fragmented landscapes. NCC, UNott and SFT were in an intermediate group. The existence of these three different landscape structure groups was also confirmed with the cluster analysis. The patterns found with these indices when calculated for land classes such as heathland, broadleaved, farming and coniferous, showed differences with respect to those at the landscape extent. It could be said from this that a more fragmented landscape does not necessarily mean that specific individual land classes are also more fragmented. These results revealed in part the existence of at least two extreme visions: the first composed by EN, NWT, BDC and NSDC. Their vision of the landscape for SNA is of an area with a high content of semi-natural vegetation such as broadleaved plantations, heathland, water bodies and coniferous plantations arranged in solid blocks to avoid fragmentation and to allow habitats availability due to their extensive core areas.

136

The second vision was represented by the NFU. Its vision of the landscape for SNA is of an area where no more than 50% is devoted to conservationist activities while the rest is committed to farming and other activities. The way in which the patches inside this landscape are arranged is highly fragmented due to their small size and high number. Between these two positions the other stakeholders assumed intermediate positions. The most central positions were those of NCC and UNott. The remaining stakeholders were sometimes close to one of the extreme positions; by way of example, the similarity between SFT and NFU in the landscape composition could be explained by the fact that this partnership has to negotiate the interest of both farmers and environmental organisations like EN. The vision of the NCC was also intermediate because it is one that has to acknowledge the interest of both farmers and District Councils. In summary, the analysis of the composition and structure of the landscapes enclosed in the â&#x20AC;&#x2DC;vision spacesâ&#x20AC;&#x2122; revealed fundamental differences between the stakeholdersâ&#x20AC;&#x2122; visions.

137

Figure 8.10: Fragmentation indices for heathland and broadleaved plantations

138

Figure 8.11: Fragmentation indices for coniferous plantations and farming

139

Figure 8.12: TCA index at the landscape extent

Figure 8.13: TCA index for heathland, broadleaved and coniferous plantations and farming

140

Figure 8.14: IJI at the landscape extent

Figure 8.15: IJI for heathland, broadleaved and coniferous plantations and farming

141

Unweighted pair-group centroid Euclidean distances

NCC UN SFT NWT BDC NSDC EN NFU

Order of Amalgamation (distances are non-monotonic)

Figure 8.16: Euclidean distances (Unweighted pair-group centroid) using four landscape indices

142

CHAPTER 9 RE-VISITING STAKEHOLDERS’ VISIONS IN SHERWOOD NATURAL AREA 9.1 Introduction In the previous chapters several techniques were used to identify and characterise stakeholders’ visions about the SNA. The response patterns previously found with those techniques were used as a hypotheses, to be tested with the additional methods presented here; Q-sample sorting, which is the technical component of Q-methodology, was applied to the representatives of the organisations involved during this study. This was used at ‘the soft system stage’ of the research in which “a comparison was made between what exists there and what is in, or suggested by, the models of ‘visions’” (Checkland, 1993). The method allowed the validation of the information obtained during the research process regarding components, boundaries, structure and functions of the stakeholders’ visions.

9.2 Methodology The Q-samples consisted of 20 statements referring to landscape components (1 to 12), six with alternative landscape functions (13 to 18) and two statements (19 and 20) regarding the boundaries and structure of the system (Table 9.1). The statements were written with information derived from the text analysis, the logistic regression modelling and patch analysis described in Chapters 4, 7 and 8. These statements were put to the representatives of the environmental organisations selected for this study. Additionally, four alternative maps (Figure 9.1) were used to re-identify stakeholders’ preferences of landscape extent and structure for Sherwood. These contained three selected SVSs maps modelled with logistic regression and the full extent of the SNA. A detail from the maps is shown in Figure 9.2. Details from Ordnance Survey maps at a scale of 1:50000 were included inside the SVS to guide the respondents in the selection of the map that matched their ideal image of boundaries and extent for Sherwood. Three complementary graphs (in pie and bar formats) showing three land cover distributions (Figure 9.3) were also subjected to selection. The source of the maps and figures was not supplied to the respondents to avoid bias in their selection.

143

SNA

NFU

EN*

EN*,

NSDC,

NWT,

SFT

NFU, BDC and SFT

None

NCC and UNott. Figure 9.1: Maps depicting hypothetical boundaries for Sherwood produced with LR modelling. Line below contains the stakeholders who selected each of them (* in between both options)

Instructions to the respondents were given following the guidelines for the collection of Q-samples suggested by McKeown and Thomas (1988) and Brown (1991). An initial instruction was given prior to the sorting exercise. The representatives of the institutions were asked to organise the statements according to the vision of Sherwood promoted by the organisation they belonged to. The sorting exercise consisted of organising the statements from left to right in three piles titled ‘agree’, ‘neutral’ and ‘disagree’. Once the statements were distributed, those located in the middle were recorded and both the left and right piles were subsequently distributed into two groups according to the degree of agreement or disagreement respectively. In total, five groups of statements were conformed to the following categories: ‘strongly agree’, ‘agree’, ‘neutral’, ‘disagree’ and ‘strongly disagree’. The values assigned to each of these categories were +2, +1, 0, -1 and –2 respectively. Once all the statements were arranged, the completed Q-sort was recorded by writing the item numbers on a score sheet reproducing the Q-sort distribution. Then, the different maps and graphs were displayed in a table and the respondents were asked

144

to select from them the map and the figure that best represented their extent and land composition for SNA. The number of the selected figures was then recorded.

Figure 9.2: Detail of maps shown to the stakeholders for Sherwood boundaries and composition identification using Ordnance Survey maps.

As a result, a list of numbers identifying the category in which each stakeholder classified each statement was produced (Table 9.2) together with the number of the preferred map and land composition figure. The Q-sorts were processed in an MS-DOS version of PQMethod (2.09a), developed by Peter Schmolck (2000) for personal computers. It computes correlations among Qsorts, which allowed the identification of similar response patterns between stakeholders. Brown (1993), states that it is rarely the case that the correlation matrix is of interest, since attention is usually on the factors to which the correlations lead, so its analysis can be considered exploratory. Therefore, the correlation matrix is used for a factor-analysis with either the Centroid or Principal Component analysis. Factors represent points of view and these can be rotated either analytically (Varimax or orthogonal analytical rotation), or judgmentally with the help of two-dimensional plots in order to distinguish groups of similar stakeholders. In most conventional factor analysis, rotation proceeds according to statistical principles, but â&#x20AC;&#x153;in Q-methodology rotation may be guided by the judgements of the investigator using factor analysis not 145

as a passive finder of Nature’s ‘truths’, but as a probe into Nature’s possibilities” (Brown, 1993). The association of each respondent with each point of view is indicated by the magnitude of his or her loading on that factor, as summarised in a factor loadings table. Finally, factor scores are computed for each statement in each factor. The factor scores help in the interpretation of the factors, indicating the extent to which the 20 statements characterise them. In summary, the analysis of these tables produces groups of stakeholders that share common perceptions as explained by some of the 20 statements under consideration.

9.3 Results Figures 9.1 and 9.3 contain the information regarding the maps and figures that were selected by the stakeholders. In the case of the maps, only the representatives of EN selected a very different map for that produced with its model. The rest of the stakeholders selected either the NFU or the SFT as the best representation of their vision for the SNA.

With respect to the figures representing the land cover

composition, the most of the stakeholders selected the one produced by the EN model. The exception was the farming representative whom selected the SFT land cover composition. Table 9.3 shows the correlation matrix between sorts that summarises which of the Qsorts were similar to, or different from, one another. Following Brown (1993), correlations are generally considered to be statistically significant if they are approximately 2 to 2.5 times the standard error. The standard error (SE) is given by the expression 1/(N1/2), where N is the number of statements (N=20 in this case). The value is therefore 1/201/2 = 0.22, i.e., somewhere between 2(0.22) = 0.44 and 2.5(0.22) = 0.56 (irrespective of sign). Thus in Table 9.3, values above 0.44 indicate statistically significant correlations between stakeholders. In Table 9.3 it is clear that the farming representative (sort 1) did not correlate with any of the other stakeholders. To the contrary, EN (sort 8) had a correlation with all the other stakeholders. BDC (sort 3) had correlations only with EN and SFT.

146

Sherwood Natural Area

None

English Nature

Sherwood Forest Trust

UNott, BDC, NCC, NSDC, NFU and EN* NWT, SFT and EN*

Figure 9.3: Land composition graphs showed to the stakeholders. The line below contains the names of those who selected them (* in between both options)

As a second step, a factor analysis of the correlation matrix was carried out to determine how many different families or groups of correlated or uncorrelated members there are. These families are called factors and its number is purely empirical and wholly dependent on how the Q-sorters actually performed. In the current case, a factor indicates a different perception of SNA by those stakeholders sharing a common factor. Table 9.4 presents the initial set of â&#x20AC;&#x2DC;factor loadingsâ&#x20AC;&#x2122; for each of the eight Q-sorts. PQmethod produces by default a set of eight factors. As said before, the loadings express the extent to which each Q-sort (stakeholder) is associated with each factor (point of view). According to Brown (1993), factor loadings in excess of 0.50 (either + or -) can be considered significant (as noted before, approximately 2.5 times the SE). In

147

Table 9.4 only the first three factors contained significant loadings and these showed the same pattern found in the correlation matrix. Table 9.1 Q-statements used for the Q-samples addressing different landscape components, function and boundaries and the pattern found in the models discussed in previous chapters. No.

Issue

Statement

Pattern in models

Accessibility

Access to Sherwood Forest is a priority

Ancient woodland

Broadleaved

Heathland

Coal mines

Coniferous

Golf courses

Farming

Dukeries Estates

10 11

Wetland, river corridors, lakes Wildlife

Jobs

The ancient woodlands are the main characteristic of Sherwood Forest. Mixed broadleaved woodland are the key feature of Sherwood Forest The expansion of heathland in Sherwood must go further than that found in the parklands of the Dukeries The relics of the former coal industry diminish the 'ideal' for Sherwood Coniferous forest is an important landscape component in Sherwood Forest Golf courses must be extended to increase the recreational facilities in Sherwood Forest The area of Sherwood Forest without farming is unthinkable The conservation of the Dukeries Estates are a good strategy to enhance biodiversity The abundance of narrow river corridors and wildlife found along them makes Sherwood unique The wildlife in Sherwood does not warrant the conservation efforts The area of Sherwood Forest lacks job opportunities

EN and SFT were the only ones considering this important NCC, NFU and NSDC models did not include this component Important for EN and NWT mainly

Function Function1

Function2

Function3

Function4

Function5

Function6

Boundaries B1

Recreation and conservation are contradictory assets in Sherwood Forest Agricultural development within the Sherwood area is more important than conservation The natural environment must be conserved and safeguarded from development proposals Sustainable development is a feasible goal for Sherwood Forest Ensuring that people in the Sherwood area have adequate access to housing is more important than the work in biodiversity conservation It is more important that people in the Sherwood area have a good standard of living than the area be conserved The current Sherwood Forest is a set of places in Nottinghamshire rather than a single unified area around the Major Oak Sherwood Forest is a single area clearly delimited

Considered important by all except by NSDC NWT and BDC models included coal mines All models included this feature except NCC and NSDC SFT and EN included this Important component for NCC, BDC and UNott Dukeries was found associated with biodiversity Only important for NWT Important for all, except BDC and NFU Jobs were considered important by the NFU only Controversial NFU tends to support this statement Controversial Not defined Controversial Controversial

All models look fragmented landscapes Opposed to N. 19.

The significance of a factor is usually determined by the eigenvalue criterion, whereby its importance is estimated by the sum of its squared factor loadings. By convention, factors with eigenvalues greater than 1 are considered significant. However, the importance of a factor cannot be determined by statistical criteria alone, it must take into account the theoretical setting to which the factor is connected (McKeown and

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Thomas, 1988). In the current case, the first three factors in Table 9.4 were selected for further investigation.

Table 9.2: Stakeholders' Q-Sorts for the statements in Table 9.1 identified with key words STATEMENTS (key word)

NFU UNott BDC NCC NSDC NWT SFT EN -1 0 2 2 1 2 1 0 Ancient woodland 2 -1 -1 2 1 2 2 -1 Broadleaved 2 -1 2 1 1 2 1 2 Heathland 2 1 2 2 2 2 2 -2 Coal mines 0 -1 -2 0 -2 -1 -2 0 Coniferous 1 2 0 1 1 -2 -1 -2 Golf courses -2 -2 -1 -1 -2 0 -2 -1 Farming 2 -1 1 0 2 -1 1 0 Dukeries Estates 2 2 0 1 2 1 2 0 Wetland, river corridors, lakes 2 -2 2 -2 -2 -1 -1 -2 Wildlife -2 -2 -1 -2 -2 -2 -2 -2 Jobs 2 1 -2 0 1 1 -1 0 Recreation vs. conservation -2 -2 1 -2 -2 -1 -2 -2 Agriculture vs. conservation -2 -1 -2 -2 -2 -2 -2 -1 Conservation vs. development -1 2 1 2 2 2 2 2 Sustainability 2 1 2 2 2 2 2 2 Urban Development vs. conservation 0 0 -1 -1 0 -1 -1 -1 Standard of living vs. conservation 2 0 0 -1 0 -2 0 -2 Fragmented landscape 1 1 2 2 2 2 2 1 Solid landscape -1 0 0 -1 1 -1 1 -1 Accessibility

Table 9.3: Correlation matrix between sorts

1 2 3 4 5 6 7 8

SORTS NFU UNott BDC NCC NSDC NWT SFT EN

1 2 3 4 5 6 7 8 100 22 100 22 21 100 27 67 31 100 42 74 38 86 100 21 48 43 81 68 100 35 53 46 85 88 75 100 8 50 45 77 67 86 67 100

Factor rotation enables us to take advantage of other information or to make guesses about the stakeholders that could be use as a support to re-arrange the factors’ table. “The purpose of rotation is to maximise the purity of saturation of as many variates (Qsorts) as possible on one or the other of the m factors extracted initially” (McKeown and 149

Thomas, 1988, p.52). After several rotations a new set of factors is produced (Table 9.5). With this re-arrangement of factors, the differences between the NFU and EN (factor 3) were maintained. The NWT also had its highest loading as part of factor 3. NSDC and SFT were added to the NFU group (factor 1). BDC and UNott were segregated from the rest with the BDC as the stakeholder characteristic of factor 2. The statements associated with each of the factors were identified through the calculation of factor scores. A factor score is the average score for each statement associated with a specific factor in the Q-sorts (Brown, 1993). Table 9.6 indicates the extent to which each of the 20 statements characterised each of the three factors. In this table the statements were listed from consensus to disagreement and organised from top to bottom. The disagreement statements were highlighted in bold and the consensus statements in italics. As Table 9.6 shows, the two statements that caused disagreement in factor 1 were ancient woodlands and Dukeries Estates; in factor 2 the statements were wetland/ rivers, jobs and recreation and conservation; and in factor 3, statement five referring to coalmines and their role in the â&#x20AC;&#x2DC;ideal Sherwoodâ&#x20AC;&#x2122;.

Table 9.4: Unrotated factor analysis of matrix of Table 9.2 with eight centroid factors

1 2 3 4 5 6 7 8

SORTS NFU UNott BDC NCC NSDC NWT SFT EN Eigenvalues % expl.Var.

Factors 1 2 0.376 0.883 0.718 0.038 0.522 0.078 0.932 -0.093 0.924 0.148 0.874 -0.229 0.905 0.059 0.842 -0.361 4.941 1.004 62 13

3 0.046 -0.440 0.767 -0.186 -0.188 0.124 0.031 0.129 0.886 11

4 -0.199 0.493 0.362 -0.105 0.087 -0.269 -0.105 -0.143 0.536 7

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5 -0.188 -0.194 0.016 0.069 0.177 -0.179 0.375 -0.248 0.344 4

6 0.017 -0.075 -0.022 -0.050 0.147 -0.226 -0.039 0.240 0.141 2

7 0.011 -0.039 0.047 0.266 -0.071 -0.099 -0.090 -0.018 0.098 1

8 -0.024 -0.055 0.015 0.009 0.153 0.066 -0.125 -0.063 0.051 1

Table 9.5: Factor loadings after rotation with an â&#x20AC;&#x2DC;Xâ&#x20AC;&#x2122; indicating a defining sort (The highest loadings in bold)

1 2 3 4 5 6 7 8 %

QSORT NFU UNott BDC NCC NSDC NWT SFT EN expl.Var.

1 0.7939X 0.4516 0.6462 0.6531 0.7643X 0.6258 0.7704X 0.5252 44

Loadings 2 3 -0.0134 -0.555 -0.6697 0.1819 0.5207X 0.388 -0.5269 0.4614 -0.5034 0.255 -0.2322 0.6334 -0.3021 0.3806 -0.2283 0.7315X 18 23

Table 9.6 Factor Q-sorts values for each statement sorted by consensus vs. disagreement (Variance across normalised factor scores; distinguishing statements in bold, consensus statements in italics), see Table 9.1 for a full version of the statements. Factor Arrays 1 2 Sustainability 2 1 Urban Development vs. conservation -1 -1 Fragmented landscape 1 1 Agriculture vs. conservation -2 -1 Broadleaved 1 1 Golf courses -1 -2 Farming 1 0 Wildlife -2 -1 Accessibility 1 0 Conservation vs. development 0 0 Coniferous 0 0 Heathland 0 0 Dukeries Estates 2 0 Standard of living vs. conservation 0 0 Solid landscape -1 0 Coal mines -1 -2 Ancient woodland* 2 -1 Jobs* 0 -2 Recreation vs. conservation* -2 0 Wetland, river corridors, lakes* 0 1 Variance= 1.6 St. Dev.= 1.265 Issues/Statements

3 1 0 1 -2 1 -2 0 -2 1 1 -2 1 0 -2 1 0 0 0 -2 -2

9.4 Discussion and conclusions The selection of land cover graphs provided evidence of the existence of an idealised composition that is better described by the EN land cover content. It seems that the farming representative was the only interested in farming, although there was 151

recognition by EN of its importance of farming within the landscape. On the other hand, the selection of maps showed that there is an agreement about what spatially is ideally cover by what must be called ‘Sherwood’. It is not a compact area and is distributed towards the centre of the current boundaries of the SNA. With the use of Q-methodology, components, boundaries and functions of the landscape visions were presented to the respondents in the form of controversial statements to identify their individual and relative perspective with respect to its own vision for Sherwood. From this inquiry three stakeholders’ groups were found. The first group consisted of NFU, NCC, NSDC and SFT. The groups was typified by their agreement with the statements referring to ancient woodlands as the main characteristic of Sherwood and The Dukeries Estates as the basis for a good strategy to enhance biodiversity. The UNott and the BDC comprised the second group. This group strongly disagreed with the statement referring to a lack of jobs in Sherwood Forest, agreed with the importance of river corridors and wildlife in Sherwood, and had a neutral position with respect to the potential contradiction between recreation and conservation in SNA. The ‘jobs’ issue led to a paradoxical result, given that the BDC is one of the District Councils most affected with the loss of job opportunities through the closure of coal mines and other industries. The third group, which consisted of the EN and NWT, was characterised by its neutral position with respect to the diminishing role of coal mines in Sherwood. However, they strongly disagreed with the statements referring to water bodies, coniferous plantations and, when considering the statements referred to contradictory goals, such as recreation and standard of living versus conservation. Members of all three groups regarded Sherwood as a set of places rather than a single unified area. They also agreed that the wildlife found there deserved conservation efforts and that sustainable development is a feasible goal for Sherwood Forest. Members of all three groups also disagreed with the propositions of giving more importance to agricultural development than to conservation and with increasing the golf courses as recreational facilities. The information about the groups provided with the use of Q-method was less descriptive than that offered with the combination of the previous techniques. However, in relation to the patterns found in previous models as summarised in Table 9.2, there were few cases in which the Q-method results confirmed those findings. The understanding of Sherwood as a fragmented place is one of the clearest concepts. 152

Inside these areas the wildlife associated with ancient woodland and the Dukeries Estates were the most important components together with the benefits that society derives from them. In functional terms Sherwood is regarded as a multifunctional landscape that supplies the needs of the human population living there while also offering extensive areas in conservation and recreation. The three groups discriminated with Q-methodology revealed a similar pattern found with the previous techniques, landscape composition and configuration analysis. By implementing the Q-methodology, it was confirmed the fact that EN appeared as a distinctive stakeholder. Given that EN works in nature conservation the link to the NWT is also understandable. The NFU was again linked with the SFT, NCC and NSDC.

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CHAPTER 10 SYNTHESIS Prior to this research, it was thought that the complexity of environmental issues in Sherwood required the development of a spatial decision support system (HainesYoung, 1998). It was also argued that the construction of such a system needed the incorporation of new conceptual and technological elements that reduce the rigidity of traditional decision support systems, and at the same time, allowed the incorporation of stakeholder concerns and visions about the landscape in the area. By taking into account stakeholders’ preferences, it would be possible to produce scenarios of future events while making comparisons of alternatives that would lead to the selection of the most sustainable options. Thus, investing in understanding what the users want is an economic and reasonable strategy before getting into the construction of any type of decision support system. The way to build such support systems was the focus of this thesis which has developed methods for ‘capturing’ people’s preferences and representing them in a spatial context. The synthesis presented in this Chapter considers two complementary aspects: a) the methodological process of modelling visions and the use of soft systems as a framework/approach during the research process; and b) the stakeholders visions and the findings regarding their meaning, similarities and differences, together with the implications for the management of the Sherwood landscape. Limitations and further research are included in the discussion of each of these.

10.1 The soft systems approach It was assumed that knowing the way in which different ‘actors’ in a region understand the environment was a key element in advancing towards a more efficient and sustainable strategy for landscape management. This was the reason why a select group of stakeholders was considered by the research. The stakeholders chosen belong to the ‘Sherwood Forest Trust’ and ‘The Sherwood Study’ partnerships - both of whom are closely related to the management of the landscape in the area. A ‘soft systems’ approach was taken as a guide in the construction of the methodology, due to the nature of the problem and the uncertainty associated with the multiple interpretations that the stakeholders have about the Sherwood landscape. Soft

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systems are used when the problem is ill-structured as was the case when referring to building a DSS for Sherwood Forest. In accordance to the guidelines of Checkland (1993), as summarised in Chapter 3, the process of developing a ‘soft systems’ analysis is made up of seven steps. The first stage deals with identifying an unstructured problem. For SNA this was done by Haines-Young (1998). In stage two, in order to obtain an expression of the problem, a wide range of perceptions of the issues were collected through the use of institutional documentation and their systematic analysis (Chapter 4). As a result, many views were uncovered regarding ‘the problem’ in Sherwood Forest. Their variety seems to be due to differences in principles, objectives, extent and methods of work that these stakeholders have. All are interested in modifying the current conditions but doing so in different ways. The differences appear to be rooted in the complexity of topics they are addressing such as landscape management, environmental accounting, sustainability, rural development, conservation and development, recreation, institutional coordination and public participation, among others. The spatial and temporal nature of these topics is an issue that increases the unmanageability of the complete picture. This stage also lead to the selection of relevant systems to be engineered and it was decided to choose and analyse a system for capturing and modelling stakeholders’ visions. It is important to clarify at this point, that two types of system are discussed in the course of this synthesis. The first is the model/system used to capture visions; the second is the stakeholders’ own visions that are ‘world views’. These were also treated as systems. The first system refers to the model designed to capture visions. The second type of system is built to characterise and understand each of the SVSs. This is because a ‘serving system’ (i.e. the system to capture visions) cannot be defined and modelled unless the ‘served system’ (i.e. stakeholders’ vision) is defined and modelled. The next step concerned the ‘root definition’ of the selected system. In the terminology of soft systems, the system is described by a root definition as a human activity using the elements of the CATWOE (Chapter 3). The root definition of the ‘capturing and modelling system’ is presented as follows: The ‘capturing and modelling vision system’ of this study is an initiative of the University of Nottingham and the UK Forestry Commission (Owners of this idea), related to the first stage of the development of a DSS for the environmental management of SNA, under the multiple presence of institutions and groups which are trying to improve the conditions of the Sherwood landscape (Environment). The system 155

makes it easier to understand the multiple stakeholders visions and contrasts them in a geographic manner by taking into account the stakeholder preferences (Inputs), and converting them into what was called here ‘stakeholders’ vision spaces’ (Outputs). This process is realised by means of the following major activities (Transformation): -

To carry out Stakeholders’ interviews (Chapter 5).

To organise spatial databases (Chapter 6).

To process and map stakeholders’ preferences by means of logistic regression techniques (Chapter 7).

To analyse and compare the stakeholders’ vision spaces using multivariate statistics and landscape composition and configuration tools (Chapter 8).

To validate the output models throughout alternatives techniques such as Qmethodology. (Chapter 9)

The transformation process is carried out by the author of this research (Actor), and directly affects the stakeholders considered in this study (Customers). The world view (W), which makes this transformation meaningful, states that subjective information concerning landscape visions and the uncertainty of our incomplete knowledge about environmental management practices can also be systematically analysed and incorporated into the debate that allows society to take more informed decisions. Stakeholders’ visions are systems, and as such can be combined with what traditionally has been considered ‘objective knowledge’, usually represented as pure data. The fourth step consists of building and testing a conceptual model based on the root definition of the system. Representing its structure graphically is a way of conceptualising the system. Figure 3.4 is a model of the system showing its components, inputs, outputs and the sequence to carry out the transformation of peoples ‘preferences’ into more concrete ‘objects’, which are later represented in a spatial context.

The model should be compared with a formal general conceptual

model for human activity systems or alternatively with other systems designed to capture visions in order to make the conceptual picture more consistent. Since there were no other systems, which could be used for comparison, in the current research the strategy was to test the validity of the ‘capturing system model’ using its own outputs. For this purpose, the SVSs were tested for their accuracy by comparing the outputs of the model with original data. This was done with the cross validation 156

technique applied to the stakeholders’ vision spaces presented in Chapter 7. Only the EN model presented significant differences with the observed data, which suggests caution when interpreting the meaning of its ‘vision space’ and the comparative analysis with other stakeholders. The remaining stakeholder models can be considered reliable and consistent. The fifth stage consists of comparing the conceptual model with reality, as was the case in Chapter 9 with the application of Q-sorts to verify model results. It was considered an independent method although influenced also by the subjectivity of the interviewees. It was an opportunity to present the conceptualised model and its findings to the potential users of the system. In this comparison stage, the results provided with the use of Q-methodology confirmed the existence of three different groups of views among the eight stakeholders. The composition of these groups was practically the same as that of the groups obtained with the use of the ‘capturing system’: EN and NWT on one hand, NFU on the other extreme, and the remaining organisations, SFT, NSDC, BDC, NCC and UNott maintaining an intermediate position. The variables that characterise each of the groups were different and less descriptive than those found with the use of the ‘capturing system’. It could be said that the ‘capturing system’ when compared with the findings of Q-methodology only, partially fitted reality. Nevertheless, as mentioned before, Q-methodology itself is another model that does not necessarily capture all the details of the ‘real’ world. The success or failure of this comparison is relative. In the current example some preliminary findings were confirmed, such as the stakeholders groups, while other issues appeared contradictory, like the controversial support of jobs and coal mines as explanatory variables. But more relevant than this, is that new information was produced and made available to enrich the discussion about ‘what is going on’ in Sherwood. A noticeable result is that even using organisations that are similar in their concerns regarding the management of the landscape in Sherwood, the system was able to identify differences between stakeholders’ visions. It could be said that it is sensitive enough and that major differences could be identified with the ‘capturing systems’ if other types of stakeholder were subjected to the same analysis. It is economic in the use of information, very efficient in combining complex information (biophysical and social) and innovative in incorporating the spatial representation of stakeholders’ ideas.

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From the comparison of model and reality, some suggestions can be derived for future enhancements to the system. Refining the representation methods and developing more accurate databases could improve the way in which the evidence themes listed by the stakeholders are represented in digital geographical layers. The subjectivity of the researcher when transferring the interviews outputs into layers of data, together with the lack of appropriate data, are constraints that influence the outputs of the system. Additional computer work could allow the integration of all the components of the system into a single or stand alone piece of software that could be linked afterwards with economic and ecological models. This could be a way to process the outputs of the current system and value the different stakeholdersâ&#x20AC;&#x2122; visions obtained here. The outputs of the capturing system explained here also have some constraints. They are, like scenarios, an output of a systematic and quantitative analysis, but sensitive to the nature of the method itself (data dependent), data collection, biases of the interviewee and the assumptions and restrictions imposed by the statistics used along this research. On the other hand, the intrinsic dynamic of institutions such as those considered here, change, as do the physical and social processes they are dealing with. Thus, visions evolve focusing their attention in different landscape components or from a different perspective. Rather than using the model once, it would be optimal to use it again to trace how a vision has changed. This is the essence of the soft system methodology. Additional work is required in circulating the findings reported here among the stakeholders and partnerships in Sherwood. This will offer an opportunity to contribute in enriching the debate that leads toward a more informed environmental management process.

10.2 The stakeholdersâ&#x20AC;&#x2122; visions The way in which the Stakeholders value what is found in Sherwood was uncovered step-by-step through the use of the system designed and explained above. From early on in the documentation review, positions were found ranging from those environmentalist ideas towards more interventionist positions with a trend towards those considered more sustainable. At the end of the process those differences were clearer and groups of stakeholders were defined.

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Those coming from a more conservationist perspective like EN and NWT are more concerned about landscape features representing natural assets like ancient woodland, broadleaved plantations, heathland, rivers, SSSI’s, wetland, wildlife and coniferous. Features such as coalmines and accessibility are considered because these can threaten the ecological interest or because of the opportunities they offer to enhance those assets. On the other hand, stakeholders like the farming representative, with more interventionist features, are concerned with the economic and social benefits derived from the countryside. Agricultural production and farming are obviously their concern, but they are also open to more friendly practices of production favouring other benefits derived from the countryside such as jobs, access, recreation and biodiversity conservation. However, the farming raison d'être is still evident in the landscape they preferred, namely more devoted to farming and less to conservation. Other stakeholders were situated between the two groups, in a conciliatory role trying to shorten the distances between these contrasting positions and visions. They are trying to achieve a balance between interventionist activities and conservation, a balance that could be considered more sustainable. The perception of the idealised landscapes for Sherwood for all stakeholders was presented in detail in Chapters 7, 8 and 9. Although it was not the objective of this study to investigate the roots of these different positions, it could be said that recent global pressures for the construction of a more sustainable society have triggered the concern of the stakeholders for the conservation of natural assets like biodiversity, wildlife and the character of the countryside. The assimilation of these relatively new ideas differs in accordance to the context in which the institutions carry out their work. After analysing each of the stakeholders’ visions, it is clear there is a commitment to listening to others and to the need for cooperation. Existing partnerships and the inclusion of criteria that consider sustainability issues are the best evidence of this. Because the different spatial extents in which the institutions work do not necessarily match the extent at which environmental processes occur, there is a need for integrate their efforts in order to cover the network of relationships across a landscape as complex as Sherwood.

10.3 Revisiting the objectives The conceptual model of the landscape stakeholders' capturing vision system presented in Figure 3.4 is the ‘framework to characterize stakeholders’ visions in a spatial context proposed as the main objective of this research. The system was designed using the eight selected stakeholders. 159

The social and ecological values that are associated with the different stakeholders’ visions were identified using as a proxy, the characteristics of the places associated with the preferred places.

Their relative importance was estimated by means of

Bayesian statistical techniques presented in Chapter 7. An approximation to the complexity of relationships between those values was obtained with the graphical models devised in Chapter 4, but they were also demonstrated with a simpler structure as described in Chapter 7 with the logistic regression technique. Another important contribution of this research was the integration of ‘soft’ data such as the landscape stakeholders’ preferences with ‘hard’ biophysical and social data into a spatial decision support tool. This integration was facilitated considering ‘visions’ as systems. This is in essence the ‘world view’ that is characterized by soft systems as explained above.

10.4 Implications for further research and landscape management strategies of SNA The system capturing methodology presented here uncovers the differences between stakeholders’ visions, such as those of organisations with conservationists or interventionists views, but also highlights the similarities. These are perhaps more important than the differences in the sense that they offer a starting point for building projects around those thematic and physical areas in which they share common goals and perspectives. The common concerns for heathland and ancient woodland are one example just as the areas in which their preferred landscapes coincided with each other offer a geographic opportunity. The analysis of these differences and similarities could also be used to devise landscape management strategies, although the implications derived from the analysis carried out here have to be considered with caution because of the 'pilot' character of the implementation of the technique. However, it is important to recognise that this method has potential for a more thorough and detailed implementation in specific cases in Sherwood or similar areas. In the present research, the recommendations can guide environmental policy makers, technicians and promoters by enriching their knowledge of how other stakeholders perceive the area and point to how to achieve consensus for the management of specific places. The stakeholders’ vision spaces are also useful in identifying those core areas throughout Sherwood that are susceptible to integrate with the existing areas in conservation. This new areas could receive a similar environmental management and 160

be part of â&#x20AC;&#x2DC;Sherwood Forestâ&#x20AC;&#x2122;. This would increase the extent of what is currently perceived as Sherwood Forest and integrate surrounding areas in tourism activities. The carrying capacity for visitors to the area will also be higher. The consensus areas could be used to identify the shortest links between those core areas that would allow the improvement of the ecological relationships between different protected zones. Linking the current areas of heathland and forest with corridors will reduce the effects of fragmentation allowing wildlife communities a broad range of movement. Figure 7.7 shows in red the areas perceived by at least one of the eight stakeholders as areas with a landscape that deserve to be called Sherwood Forest. Some arrows in green indicate potential corridors to link these core areas. These virtual links are also a guide in defining environmental impacts of other type of projects. Expansion of the road network, new settlements or similar projects in these areas could block forever the possibility to create a wider Sherwood Forest. New funding schemes for nature conservation could be assigned to those farms currently part of potential corridors zones. The next step in this research could be the integration of the techniques presented here in a single application in order to simplify their use for a wider group of stakeholders. The system design as part of this thesis is the basis for a further development as software applications for supporting the decision-making processes in this or other areas. Alternatively, the outputs of this pilot system could be part of an ecological and economic model that value the visions and obtains vital information for decision makers to select sound alternatives for the future of Sherwood Forest. Each of the components considered by the different stakeholders can also be value and quantified in order to weight the different visions. Using this systems several times or with different people can also help in identifying the dynamics of institutional changes through the time.

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Watkins, C., 1998. A solemn and gloomy umbrage: Changing interpretations of the ancient forest oaks of Sherwood Forest. In: C. Watkins, ed. European Woods and Forests: Studies in Cultural History. London: CAB INTERNATIONAL, 1998.

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APPENDIX A

Table A.1: References to institutional documents

Forestry Commission. 2000. Website [Online] available at: http://www.forestry.gov.uk/ [2000, May]. Forestry Commission. 1998. Native Woodlands of Scotland: a vision for the future. [Online] Available at: http://www.forestry.gov.uk/ [2000, May]. Department of the Environment, Transport and the Regions. 2000. Greater Protection and Better Management of Common Land in England and Wales. [Online] Available at: <http://www.detr.gov.uk> [2000, February]. The Countryside Agency. 1999. Tomorrow's countryside – 2020 vision. [Online] Available at: http://www.countryside.gov.uk/index.htm [2000, May]. Environment Agency. 2000 An Environmental Strategy for the Millennium and Beyond. [Online] Available at: www.environment-agency.gov.uk [2000, May]. English Nature – Review 1997/98 [Online] Available at: http://www.englishnature.org.uk [2000, March]. Country Landowners Association. 2000. http://www.cla.org.uk/abouin5.htm [2000, May].

Website

[Online]

available

at:

East Midlands Advisory Group on the Environment. 2000. Progress Report. [Online] Available at: http://www.emnet.co.uk/emage/BES/ [2000, May]. East Midlands Development Agency. 2000. Prosperity through people: Economic development strategy for the East Midlands – 2000-2010 East Midlands Regional Assembly. 2000. Integrated Regional Strategy Summary Document, Consultation Draft. April 2000. Nottinghamshire County Council. 1996. Structure Plan Review, 1996. Director of Planning and Economic Development Nottinghamshire County Council November 1996, Chapter 3. Brennam, D. 1999. Greenwood Action Plan 1999/2000 Greenwood Community Forest. Growing Success. Newark and Sherwood Local Plan Adopted – March 1999, Chapter 2 Pages 7 - 13. Countryside Agency. 2000. Countryside Character Initiative: Character Areas CCIVolume 4 – East Midlands 49. Sherwood. [Online] Available at: http://www.countryside.gov.uk/what/cci/em/1_em_049.htm [May 2000]. English Nature 1997 Sherwood Natural Area Profile. Andrew Windrum. Natural Areas Project Officer East Midlands Team. Sherwood Study Advisory Group. A Vision for Sherwood Forest, October 2000.

187

Table

A-2

A ACCESS ACCESSIBILIT Y ACCOUNT ACTIVITIES ADVISORY AGENCY AGENDA AGREEMENT S AGRICULTUR E AIMS ANIMAL ANIMALS ANTECEDENT APPLICATION S APPROACH AQUIFER ARABLE AREA AREAS ARRIVAL ASSEMBLY ASSETS ASSOCIATION AUTHORITIES AUTHORITY AWARENESS B BASIS BENEFIT BENEFITS BIODIVERSIT Y BIRDS BOUNDARIES BOWLAND BUILDINGS BUSINESS-ES C CANAL CAPITAL CARBON CAUSE CHANGES CHARACTER CHARCOAL CLIMATE COAL COAST COHERENCE COLLIERY COLONISATIO N COMBINATIO N COMMONS COMMUNICAT IONS COMMUNITIE S COMMUNITY COMPANIES

List

words

CONCERNS CONDITIONS CONECTION CONIFER CONSEQUENCE CONSERVATION CONSOLIDATION CONSULTATION CONSUMER CONTRIBUTION CORPORATE CORRIDORS COUNCIL COUNCILS COUNTRY COUNTRYSIDE COUNTY CREATION CROPS CULTURE CYCLE D DECISIONS DEER DEMANDS DEPENDENCE DERELICT DEVELOPERS DEVELOPMENT DISPOSAL DISTRICT DIVERSITY DUKERIES E ECONOMIC ECONOMY EDGE EDUCATION EFFECT EFFECTS EMISSIONS EMPLOYMENT EMPOWERMENT END ENERGY ENTERPRISE ENVIRONMENT ESTATE ESTATES EXTENT EXTINCTION F FACILITIES FACTORS FARM FARMERS FARMLAND FARMS FAUNA FEATURES FIELD FIELDS FILTER FISCAL FISHERIES

used

Concordance

FLOOD FLORA FOOD FORESIGHT FOREST FORESTRY FORESTS FRAGMENTATION FRAMEWORK FREEDOM FRESHWATER FUNDS FUTURE G GENERATIONS GEOLOGY GLOBALISATION GOAL GOVERNMENT GRANTS GRASSLAND GREENS GREENWOOD GROUNDWATER GROUP GROUPS GUIDANCE H HABITATS HEALTH HEATH HEATHERLAND HEATHLAND HEATHLANDS HEATHS HEDGES HERITAGE HISTORIC HISTORY HOSPITAL I IDENTITY IMPACTS IMPLICATIONS IMPORTANCE INCENTIVES INDICATORS INDIVIDUALS INDUSTRY INFORMATION INFRASTRUCTURE INITIATIVE INITIATIVES INLAND INTEREST INTERESTS INVESTMENT INVOLVEMENT ISLAND ISSUES J JOBS

188

alphabetic K

KEY KNOWLEDGE L LAKES LAND LANDFORM LANDOWNERS LANDSCAPE LANDSCAPES LARCH LEGISLATION LEVELS LIFE LINKS LIVE LOCATIONS LOWLAND M MANAGEMENT MARKET MARKETS MATERIAL MEANS MEASURES MEMBER MEMBERS METHODS MIDLAND MIGRATION MOORLAND MOTIVE MOVEMENT N NATIVE NATURE NETWORK NON-LAND NOTTINGHAMSHIRE NUTRIENT O OBJECTIVE OBJECTIVES OPPORTUNITIES ORGANISATION ORGANISATIONS ORIGIN OSPREYS OUTCOME OWNER OWNERSHIP P PARK PARKLAND PARKLANDS PARKS PARTICIPATION PARTNER PARTNERS PARTNERSHIP

order.

PARTNERSHI PS PASTURE PATCHES PATTERN PEOPLE PINE PLACE PLACES PLAN PLANS PLANT PLANTATIONS PLANTS POLICIES POLICY POLLUTION PONDS POPULATION S POWER POWERS PRACTICES PREDATION PRINCIPLES PRIORITIES PRIORITY PRIVATE PROBLEMS PROCESSES PRODUCTION PRODUCTS PROFILE PROGRAMME PROGRAMME S PROJECTS PROPOSAL PROPOSALS PROTECTION PUBLIC PURPOSE PURPOSES Q QUALITY R RECREATION REGENERATI ON REGION REGIONS REGISTRATIO N REGULATION REGULATION S RELATIONS REMOVAL REPERCUSSI ON REPRODUCTI ON REQUIREMEN TS RESEARCH RESERVES RESOURCE RESOURCES

RESPONSIBILITIES RESTORATION RESULT RIGHTS RIVER RIVERS ROADS ROLES ROUTES ROYAL S SANDSTONE SCALE SCHEME SCHEMES SCHOOLS SCRUB SECTION SEED SEQUENCE SERVICE SERVICES SETTLEMENT SETTLEMENTS SHELTER SHERWOOD SITE SITES SKILLS SOCIAL SOILS SOLUTIONS SOURCE SOURCES SPATIAL SPECIES SSSIS STAFF STANDARDS STAR STATE STATION STATUTORY STOCK STRATEGIES STRATEGY STREAMS STRUCTURE SUGAR SURROUNDINGS SURVEYS SUSTAINABILITY SUSTAINABLE SYSTEM SYSTEMS

TRANSPORT TREE TREES TREND TRIBUNAL TRUST U UPLAND URBAN URBANISATION V VALLEYS VALUE VIEWS VILLAGE VILLAGES VISION VISITORS W WAR WASTE WASTELAND WATER WAYS WETLAND WILDLIFE WOOD WOODLAND-S WOODS

T TARGETS TEAM TECHNOLOGY TENURE TERMS THEMES TIMBER TIME TIMES TOURISM TOWN TOWNS TRAFFIC

189

Table A.3: List of words used in Concordance by frequency (16200 = 100%) WORD

No.

WORD

No.

LAND

564

3.481

PLANS

0.531

DEVELOPMENT

515

3.179

WATER

0.525

AREAS

328

2.025

RESEARCH

0.5

STRATEGY

274

1.691

COMMUNITIES

0.5

ENVIRONMENT

257

1.586

RESOURCES

0.494

COUNTRYSIDE

252

1.556

APPROACH

0.481

MANAGEMENT

237

1.463

GREENS

0.475

CONSERVATION

179

1.105

OPPORTUNITIES

0.475

PLAN

174

1.074

ORGANISATIONS

0.475

SITES

174

1.074

TIME

0.469

AREA

173

1.068

PARTNERSHIP

0.469

REGION

172

1.062

AGRICULTURE

0.463

POLICY

171

1.056

FRAMEWORK

0.451

GOVERNMENT

162

GUIDANCE

0.444

FOREST

155

0.957

HERITAGE

0.444

QUALITY

155

0.957

URBAN

0.438

PUBLIC

152

0.938

BIODIVERSITY

0.438

NATURE

150

0.926

SPECIES

0.432

PEOPLE

150

0.926

INVESTMENT

0.426

ECONOMIC

149

0.92

ASSEMBLY

0.414

INFORMATION

148

0.914

STRATEGIES

0.414

SUSTAINABLE

146

0.901

SCHEMES

0.407

COMMUNITY

138

0.852

SOCIAL

0.407

COMMONS

136

0.84

SHERWOOD

0.395

BUSINESS

134

0.827

WOODLANDS

0.389

AUTHORITIES

133

0.821

STATE

0.383

WOODLAND

132

0.815

AUTHORITY

0.383

RIGHTS

130

0.802

EDUCATION

0.383

COUNTY

126

0.778

LEGISLATION

0.383

AGENCY

123

0.759

SKILLS

0.377

ACCESS

120

0.741

ECONOMY

0.377

PROTECTION

119

0.735

PARTNERS

0.377

POLICIES

116

0.716

REGENERATION

0.377

ISSUES

115

0.71

TARGETS

0.37

OBJECTIVES

108

0.667

CHARACTER

0.37

REGISTRATION

106

0.654

PLACE

0.364

WILDLIFE

105

0.648

FORESTRY

0.364

GREENWOOD

102

0.63

HISTORIC

0.364

COUNCIL

101

0.623

SERVICES

0.364

FUTURE

100

0.617

VALUE

0.358

SECTION

100

0.617

VISION

0.358

PROPOSALS

0.605

INTEREST

0.358

TRANSPORT

0.599

ACTIVITIES

0.358

LANDSCAPE

0.586

WASTE

0.346

PROPOSAL

0.568

INDUSTRY

0.346

BUSINESSES

0.543

RECREATION

0.346

KEY

0.537

TREES

0.333

HABITATS

0.537

CHANGES

0.333

190

WORD

No.

WORD

No.

BUILDINGS

0.333

OBJECTIVE

0.204

PRIORITIES

0.327

SETTLEMENTS

0.204

CONSULTATION

0.327

MEANS

0.198

MEMBERS

0.321

RESULT

0.198

MEASURES

0.315

PURPOSE

0.198

EMPLOYMENT

0.315

PRIORITY

0.198

SITE

0.309

CONDITIONS

0.198

BENEFITS

0.309

TECHNOLOGY

0.198

FEATURES

0.309

CONTRIBUTION

0.198

NOTTINGHAMSHIRE

0.309

LINKS

0.191

PROGRAMME

0.302

HEALTH

0.191

DISTRICT

0.296

THEMES

0.191

PROJECTS

0.29

ACCOUNT

0.191

POLLUTION

0.29

ANIMALS

0.191

LIFE

0.284

CLIMATE

0.191

BENEFIT

0.284

NETWORK

0.191

VILLAGE

0.284

RESOURCE

0.191

APPLICATIONS

0.284

INTERESTS

0.191

STATUTORY

0.278

STANDARDS

0.191

AIMS

0.259

SUSTAINABILITY

0.191

SERVICE

0.259

PARKS

0.185

IMPORTANCE

0.259

POWERS

0.185

GROUPS

0.253

KNOWLEDGE

0.185

PROBLEMS

0.253

OWNER

0.179

VILLAGES

0.253

TOWNS

0.179

OWNERSHIP

0.253

PARTNER

0.179

STRUCTURE

0.253

PRIVATE

0.179

PROGRAMMES

0.253

FACILITIES

0.179

TOURISM

0.247

FARM

0.173

VIEWS

0.241

TERMS

0.173

SCHEME

0.241

COMPANIES

0.173

COUNTRY

0.241

FISHERIES

0.173

INDICATORS

0.241

FOOD

0.167

BASIS

0.235

FARMERS

0.167

SSSIS

0.235

LANDOWNERS

0.167

METHODS

0.235

REGULATION

0.167

CREATION

0.235

SPATIAL

0.167

AGENDA

0.228

POWER

0.16

EFFECTS

0.228

TRUST

0.16

INITIATIVES

0.228

MARKET

0.16

TOWN

0.222

REGIONS

0.16

GROUP

0.222

COUNCILS

0.16

STAFF

0.222

RIVER

0.154

SYSTEM

0.222

ARABLE

0.154

ASSOCIATION

0.216

FORESTS

0.154

ENERGY

0.21

AGREEMENTS

0.154

HEATHLAND

0.21

IMPLICATIONS

0.154

ENTERPRISE

0.21

ORGANISATION

0.154

INITIATIVE

0.21

INFRASTRUCTURE

0.154

LEVELS

0.204

GENERATIONS

0.148

191

WORD

No.

WORD

No.

INDIVIDUALS

0.148

PRACTICES

0.099

PARTNERSHIPS

0.148

BIRDS

0.093

JOBS

0.142

PLACES

0.093

TEAM

0.142

CULTURE

0.093

REGULATIONS

0.142

SYSTEMS

0.093

TREE

0.136

DISPOSAL

0.093

TRAFFIC

0.136

EMISSIONS

0.093

LIVE

0.13

LOCATIONS

0.093

FLOOD

0.13

ASSETS

0.086

STOCK

0.13

ROUTES

0.086

EFFECT

0.13

PRODUCTS

0.086

RIVERS

0.13

RESERVES

0.086

PRODUCTION

0.13

FRESHWATER

0.086

COAL

0.123

INCENTIVES

0.086

WAYS

0.123

LANDSCAPES

0.086

SCALE

0.123

WOOD

0.08

EXTENT

0.123

CROPS

0.08

SOLUTIONS

0.123

SOILS

0.08

PRINCIPLES

0.123

ESTATE

0.08

INVOLVEMENT

0.123

HEDGES

0.08

RESTORATION

0.123

CONCERNS

0.08

PARK

0.117

TIMBER

0.074

MARKETS

0.117

PASTURE

0.074

SCHOOLS

0.117

REMOVAL

0.074

DERELICT

0.117

SURVEYS

0.074

PURPOSES

0.117

COLLIERY

0.074

DECISIONS

0.117

DEVELOPERS

0.074

RESPONSIBILITIES

0.117

POPULATIONS

0.074

END

0.111

ROADS

0.068

GRANTS

0.111

DEMANDS

0.068

SOURCES

0.111

PROFILE

0.068

FARMLAND

0.111

DUKERIES

0.068

VISITORS

0.111

CYCLE

0.062

CORPORATE

0.111

FARMS

0.062

DIVERSITY

0.111

FIELDS

0.062

SANDSTONE

0.111

MEMBER

0.062

COMMUNICATIONS

0.111

ESTATES

0.062

EDGE

0.105

HISTORY

0.062

FIELD

0.105

LOWLAND

0.062

FUNDS

0.105

HEATHLANDS

0.062

FACTORS

0.105

CAUSE

0.056

IMPACTS

0.105

COAST

0.056

PATTERN

0.105

TIMES

0.056

ADVISORY

0.105

WOODS

0.056

BOUNDARIES

0.105

CARBON

0.056

REQUIREMENTS

0.105

SOURCE

0.056

NATIVE

0.099

STATION

0.056

PLANTS

0.099

COMBINATION

0.056

CAPITAL

0.099

PARTICIPATION

0.056

AWARENESS

0.099

GOAL

0.049

192

WORD

No.

HEATH

0.049

PLANT

0.049

ROYAL

0.049

HEATHS

0.049

UPLAND

0.049

CONIFER

0.049

MISSION

0.049

VALLEYS

0.049

CONSUMER

0.049

CORRIDORS

0.049

GRASSLAND

0.049

PARKLANDS

0.049

PROCESSES

0.049

PLANTATIONS

0.049

PINE

0.043

ROLES

0.043

STREAMS

0.043

LANDFORM

0.043

PARKLAND

0.043

SEED

0.037

LARCH

0.037

IDENTITY

0.037

SETTLEMENT

0.037

FLORA

0.031

LAKES

0.031

MATERIAL

0.031

RELATIONS

0.031

GROUNDWATER

0.031

SURROUNDINGS

0.031

ACCESSIBILITY

0.031

FRAGMENTATION

0.031

DEER

0.025

FAUNA

0.025

ANIMAL

0.025

INLAND

0.025

AQUIFER

0.025

FREEDOM

0.025

WETLAND

0.025

EMPOWERMENT

0.025

CANAL

0.019

SCRUB

0.019

TREND

0.019

HOSPITAL

0.019

MOORLAND

0.019

MOVEMENT

0.019

TRIBUNAL

0.019

16200

100

TOTAL

193

Figures A.1: Preliminary stakeholder modelling of several institutional documents

7 GOVERNMENT 55 FORESTRY 170 POLICY

21 INTEGRATED APPROACH 26

17 COUNTRYSIDE 34

3 WOODLAND 121 WOODLANDS 143

6 LAND 59

10 STRATEGY 49

20 PARTNERSHIP 27 14 OPPORTUNITIES 38

23 PEOPLE 24 24 PROGRAMMES 24

11 WOODS 49 22 TIMBER 25

13 ACCESS 38

16 PUBLIC 35 BENEFITS 50

8 FORESTS 52 TREES 36

4 FOREST 66 MANAGEMENT 41

19 SUSTAINABLE 29 DEVELOPMENT 62

Legend

FORESTRY COMMISSION STRATEGY

194

16 POLICY 6

3 FOREST 25 FORESTS 13 WOODLANDS 6 12 SURVEYS 7 13 ENTERPRISE 6 ESTATE 6

1 RESEARCH 29

11 INFORMATION 7 17 TREE 6

8 STATION 9

7 METHODS 9

10 MANAGEMENT 8

6 BIODIVERSITY 10

FORESTRY COMMISSION (WEB)

30 MODERN FARMING METHODS

1 HABITATS 21 MANAGEMENT 10

11 WOODLAND 8

9 SHERWOOD 9 NATURAL AREA 15 FEATURES

24 FARMLAND 4 5 WILDLIFE 11 SPECIES 14 POPULATIONS 5

13 SITES 7

10 WATER 8 FRESHWATER 4 STREAMS 5

14 HEATHLAND 6 20 LOWLAND 5 26 GRASSLAND 4

3 PROMOTE NATURE CONSERVATION 15 QUALITY 7

27 INFORMATION 4

ENGLISH NATURE (SHERWOOD NATURAL AREA PROFILE)

195

10 GOVERNMENT 25 21 ENVIRONMENT AGENCY 13 18 TARGETS 16 23 PARTNERS 13 15 PROGRAMME 19 PLANS 15

20 PUBLIC 14

6 SITES 32 SSSIS 34

16 HERITAGE 17 4 WILDLIFE 37 SPECIES 32 12 HABITATS 24

14 INFORMATION 19 1 NATURE 77 CONSERVATION 68

3 MANAGEMENT 41 17 BIODIVERSITY 16

13 SUSTAINABLE 24 DEVELOPMENT 25

ENGLISH NATURE REVIEW

20 GOVERNMENT 12

2 AGENCY 41

1 ENVIRONMENT 75 STATE 19

22 PLANS 12 STRATEGY 22

3 WATER 31

15 CONSERVATION 13

21 LAND 12 17 FLOOD 13

16 FISHERIES 13

18 RESEARCH 13

12 INFORMATION 17

196

4 POLLUTION 30

23 SITES 12 MANAGEMENT 24

19 SUSTAINABLE 13 DEVELOPMENT 18

ENVIRONMENT AGENCY

5 WASTE 25

13 RESOURCES 17 QUALITY 23

3 COUNTY 100 COUNCIL 53

14 LANDSCAPE 36

25 URBAN 23

4 COUNTRYSIDE 63

6 POLICY 59

12 NATURE 38 5 LAND 61 16 HISTORIC 34 ASSETS

20 PLAN 27 PROPOSALS 24

22 HABITATS 24

15 BUILDINGS 34

9 ENVIRONMENT 47 PROTECTION 31 QUALITY 29

13 WOODLAND 38

19 WILDLIFE 29

2 DEVELOPMENT 104 7 CONSERVATION 54

21 MANAGEMENT 26

NOTTINGHAMSHIRE COUNTY COUNCIL (STRUCTURAL PLAN REVIEW)

12 SHERWOOD 4 FOREST 5 VISION 3

19 PUBLIC 3 SERVICES 5

7 RECREATION 5

20 TRANSPORT 3 10 TOURISM 5

8 RESOURCES 5

3 HERITAGE 7 16 LANDSCAPE 3

17 MANAGEMENT 3

15 ECONOMIC 3 DEVELOPMENT 7

4 COMMUNITIES 7

6 LIFE/ENVIRONMENT QUALITY 5

14 CONSERVATION 3

NOTTINGHAMSHIRE COUNTY COUNCIL (SHERWOOD STUDY)

197

23 WOODLAND 3

22 WILDLIFE 3

1 COUNTRYSIDE 82 AGENCY 19

22 GOVERNMENT 8

24 TRAFFIC 8

23 POLICY 8 20 ECONOMIC 9

17 AGRICULTURE 9

2 PEOPLE 47

11 STRATEGY 11

13 LAND 10 21 BUSINESS 8 6 COMMUNITIES 15

25 FARMERS 7 9 QUALITY 12 LIVE 10

15 PUBLIC 10 SERVICES 13 10 ACCESS 11

5 TRANSPORT 16

18 CHARACTER 9 16 SUSTAINABLE 10 DEVELOPMENT 17

COUNTRYSIDE AGENCY (VISION 2020)

22 DEVELOPMENT 7

19 INDUSTRY 8

4 SHERWOOD 18

14 COAL 10

6 LANDSCAPE 16 CHARACTER 10

9 VILLAGES 13 8 LAND 14

15 RIVER 10 16 HEDGES 9

18 FOREST 8 12 HEATHLAND 11

21 PASTURE 8

10 SOILS 12

2 WOODLAND 23 7 SANDSTONE 15 3 ARABLE 21 FARMLAND 17 DUKERIES 8 PARKS 7 8

COUNTRYSIDE AGENCY (SHERWOOD FOREST)

198

1 GOVERNMENT 42

21 COUNTRY 11 ASSOCIATION 10

23 APPROACH 10 7 POLICY 22 POLICIES 10

11 ENVIRONMENT 18

26 ECONOMY 10

12 CONSULTATION 15 15 LEGISLATION 13

14 INDUSTRY 13 2 MEMBERS 36 25 ECONOMIC 10 18 BUSINESSES 12

20 RIGHTS 12

5 COUNTRYSIDE 25 16 PUBLIC 13 4 ACCESS 26

3 LAND 27

22 WATER 11

9 AGRICULTURE 20

10 PROPOSALS 20 19 ORGANISATION 12

8 DEVELOPMENT 21

COUNTRYSIDE LANDOWNERS ASSOCIATION

19 LEGISLATION 30 STATE 34 AUTHORITIES 73 REGISTRATION 103 AUTHORITY 44

25 POWER 20

17 PROPOSALS 33

23 COUNTRYSIDE 24 VILLAGE 30 GREENS 76

2 COMMONS 129 LAND 362

14 INFORMATION 37

13 PUBLIC 39

15 OWNERSHIP 35

3 RIGHTS 115

10 PROTECTION 52

9 MANAGEMENT 68

24 ACCESS 20

DEPARTMENT OF ENVIRONMENT TRANSPORT AND REGIONS (DETR)

199

6 GOVERNMENT 18

19 GROUPS 7 ORGANISATIONS 20 17 AUTHORITIES 7

20 REGULATION 7

11 LEGISLATION 12

18 ECONOMIC 7

4 ENVIRONMENT 21 MANAGEMENT 14

9 COMPANIES 16 BUSINESS 37 BUSINESSES 17 REGION 23

21 RESOURCES 7

16 SUSTAINABLE 8 DEVELOPMENT 21

EAST MIDLANDS ADVISORY GROUP OF THE ENVIRONMENT (EMAGE)

1 REGION 99 STRATEGY 80 PLAN 25

9 REGENERATION 36

7 SKILLS 42

10 ENVIRONMENT QUALITY 33

16 PRIORITIES 27 18 PARTNER 25

14 PEOPLE 31 COMMUNITIES 28

20 ECONOMY 22

13 SUSTAINABLE 32 DEVELOPMENT 69

EAST MIDLANDS DEVELOPMENT AGENCY (EMDA)

200

4 BUSINESS 61 5 ECONOMIC 53 BUSINESSES 37 INVESTMENT 46

6 ASSEMBLY 39 4 POLICY 46 POLICIES 30 5 REGION 45

15 TRANSPORT 27

19 SPATIAL 20

7 STRATEGIES 38

16 ENVIRONMENT 23

10 SOCIAL 31

9 FRAMEWORK 32 SUSTAINABLE 30 DEVELOPMENT 98

EAST MIDLANDS REGIONAL ASSEMBLY (EMRA)

201

8 ECONOMIC 34

APPENDIX B LOGISTIC REGRESSION

Introduction Given n binomial observations of the form y 1 / n i, i = 1,2,…,n, where E (yi) = n I p

the success probability corresponding to the ith observation. The linear logistic model for the dependence of pi on the values of the k explanatory variables x1i, x2i, ….xki, associated with that observation, is logit (pi) = log (pI / (1-pi)) = ? 0 + ? 1x1i + ? 2x2i + … + ? kxki On some rearrangements, pi = exp (? 0 + ? 1x1i + ? 2x2i + … + ? kxki) / 1 + exp (? 0 + ? 1x1i + ? 2x2i + … + ? kxki) or writing pI = e

? i = ? j? jxji,

/1+e

The relationship between p and x is sigmoidal, whereas logit (p) is linearly related to x. A graph of this function is presented in Figure B.1. Interpreting the coefficients As its equivalent in ordinary least squares (OLS), the estimated coefficients for the independent variables represent the slope or rate of change of a function (logit in logistic regression) of the dependent variable per unit of change in the independent variable. Odds and the odds ratio When two sets of binary data are to be compared, a relative measure of the odds of success in one set relative to the other is the odds ratio. The odds of success are defined as the ratio of the probability of success to the probability of a failure (Collet, 1991 p.35). This idea of probability and odds was applied to the relationship between two binary map patterns by Bonham-Carter (1994) for the study of geological patterns and the estimation of mineral potential. Probability can be expressed as odds, or viceversa, using the relation O = P / (1 - P). Odds values less than 1 correspond to probabilities less than 0.5; very small probabilities are nearly the same as odds. Logits are the natural logarithms of odds. The logit scale is therefore centred about 0, 202

corresponding to a probability of 0.5, with negative values for odds less than 1/1 and positive values for odds greater than 1/1 (Table B.1). Logits are used in logistic regression models and for Bayesian weights of evidence modelling (Bonham-Carter, 1994, p250).

Table B.1: Relationship between probability, P, odds, O and logits, Ln(O), the natural logarithms of odds (From Bonham-Carter, 1994. p.249)

P .0 .1 .2 .4 .5 .6 .8 .9 1.0

O 0 1/9 1/4 2/3 1/1 3/2 4/1 9/1 ?

Ln(O) -? -2.20 -1.39 -0.41 0 0.41 1.39 2.20 ?

The odds ratio is always positive, being greater than 1 for variables that are positively associated, 1 if the two patterns are independent and less than 1 if they are negatively associated. Transforming to a logit scale, by taking the natural logarithm of the odds ratio, produces a closely related index of association called the contrast, C. The contrast is 0 when the patterns overlap only by the amount expected due to chance is positive for positive associations and negative for negative associations. The contrast is used in weights of evidence and in weighted logistic regression to quantifying spatial associations between binary map patterns and for predicting modelling. The contrast can be expressed as the difference in the natural logarithms of the conditional odds by the relation C = ln O(BÂŚA) - ln O(BÂŚA). B and A being the two binary patterns present and A (the no presence of pattern A).

203

Figure B.1: The logistic transformation of p as a function of p

Testing the coefficients significance After estimating the coefficients, the next step is to determine whether the independent variables in the model are 'significantly' related to the outcome variable. The guiding principle with logistic regression is to compare the observed values of the response variable to predicted values obtained from models with and without the variable in question. It is carried out using the likelihood function and is based on the following expression:

? likelihood .of.the.current.model ? D ? ? 2 ln ? ? likelihood.of.the.saturated.model ??

The saturated model is one that contains as many parameters as there are data points. The quantity inside the large brackets is called the likelihood ratio. The reason for using the -2log is mathematical and it is necessary to obtain a quantity whose distribution is known and can thus be used for hypothesis testing purposes. The statistic, D, the 204

deviance, plays a central role in some approaches to assessment of goodness of fit. For purposes of assessing the significance of an independent variable we compare the value of D with and without the independent variable in the equation. The change in D due to including the independent variable in the model is obtained as follows: G = D (for the model without the variable) - D (for the model with the variable) Under the hypothesis that the coefficient ? 1 is equal zero, the statistic G will follow a chi-square distribution with 1 degree of freedom. The calculation of the log likelihood and the likelihood ratio test are standard features of any good logistic regression package. The likelihood ratio test is obtained by multiplying the difference in these two values by -2. This makes it possible to check for the significance of the addition of new terms to the model as a matter of routine. After running logistic regression for the overall and nested models, subtract the deviance of one model from the other and let df = the difference in the number of terms in the two models. Look in a table of chisquare distribution and see if dropping the variables from the model significantly reduced model fit. An additional test to evaluate the coefficients is the Wald test statistic:

Wj ?

?j SE( ? j )

Under the hypothesis that an individual coefficient is zero, this statistic will follow the standard normal distribution. Thus, the value of these statistics may give us an indication of which of the variables in the model may or may not be significant. When combining WofE and LR interactively, it is common practice to identify a significant studentised value of C as a threshold in order to exclude some of the variables from the model or to increase the number of evidential points. A value of C = 2 is used for a 97.5% of confidence but this selection depends on the type of modelling and the risk implied in the decisions derived from the model (Raines, G, 2001 personal communication). In this way, one can first define a confidence level that is appropriate for the specific model and then see if the training sites will produce results that meet that confidence level.

205

Appendix AAPPENDIX C LANDSCAPE ANALYSIS

Table C.1: Patch analysis statistics summary at landscape level

Edge density (m/ha)

Edge metrics

Total edge (m)

Patch size standard deviation (ha)

Number of patches

Mean patch size (ha)

Patch size coefficient of variance (%)

Patch density, size and variability metrics

Largest patch index (%)

Total Landscape Area TLA (ha)

Name

Area metrics

95.2

2053

26.0 4313.5 1123.3

800825

15.0

53430

96.9

1350

39.6 3558.7 1408.5

609225

11.4

NFU

53430

67.0

10353

5.2 6817.8 351.9

3783875

70.8

NCC

53459

82.2

5085

10.5 5864.4 616.5

2229625

41.7

NSDC

53459

88.4

2733

19.6 4620.5 903.8

1175775

22.0

NWT

53459

88.6

3949

13.5 5563.9 753.2

1935725

36.2

SFT

53459

80.3

5025

10.6 5693.1 605.7

2001025

37.4

UNott

53459

78.0

5091

10.5 5568.6 584.7

2413200

45.1

Area weighted mean patch fractal dimension

Mean patch fractal dimension

Area weighted mean shape index

Mean shape index

Landscape shape index

Name

Shape metrics

Nearest-neighbour metrics

Contagion and interspersion metrics Interspersion juxtaposition index (%)

53462

Mean nearest neighbour distance (m) Mean proximity index

BDC

8.7

1.3

5.3

1.0

1.2

166.1

76244.7

68.5

6.6

1.3

4.1

1.1

156.6

70030.0

64.0

NFU

40.9

1.3

15.1

1.1

1.3

103.1

48900.5

71.6

NCC

24.1

1.4

11.7

1.1

1.2

117.3

81695.5

64.2

NSDC

12.7

1.4

5.1

1.1

1.2

128.3

61318.1

77.0

NWT

20.9

1.4

13.5

1.1

1.3

151.1

90007.8

62.5

SFT

21.6

1.4

7.9

1.1

1.2

94.6

44354.9

71.1

UNott

26.1

1.4

13.0

1.1

1.3

108.7

79886.8

62.9

206

Number core areas

Mean core area per patch (ha)

Mean core area index (%)

Disjunct core area standard deviation (ha)

Disjunct core area coefficient of variation (%)

Total core area index (%)

Core area standard deviation (ha) Core area coefficient of variation (%)

Mean core area per disjunct core (ha)

Core area density (#/100ha)

Total core area (ha)

Name

Core area metrics

BDC

51310.1

1.0

92.8 2124.5

8500.3

96.0

4414.7

1103.4

5.5

25.0

51864.7

0.7

134.7 2600.0

6767.5

97.1

3617.5

1389.8

5.2

38.4

385

NFU

42406.6

6.6

12.0 556.3 13581.4

79.4

7928.3

324.8

6.8

4.1

3527

NCC

47119.4

4.0

22.1 911.2

9832.9

88.1

6365.0

589.8

8.3

9.3

2130

NSDC

50265.8

1.8

51.3 1484.1

8068.9

94.0

4831.2

888.6

8.7

18.4

979

NWT

47999.4

3.1

28.8 1105.2

9092.3

89.8

5906.8

718.0

7.2

12.2

1666

SFT

47855.9

3.2

27.6 1000.0 10500.5

89.5

6169.9

587.6

6.8

9.5

1734

UNott

46406.0

3.9

22.2 864.7

86.8

6076.1

553.9

8.2

9.1

2088

9485.9

Modified Simpson's evenness index

Simpson's evenness index

Patch richness density (#/100ha)

Patch richness (#)

Modified Simpson's diversity index

Shannon's evenness index

Shannon's diversity index

Name

Diversity metrics

BDC

0.3

0.1

0.02

0.1

0.0

0.2

0.1

0.02

0.1

0.0

NFU

1.2

0.5

0.7

0.02

0.5

0.3

NCC

0.8

0.3

0.4

0.02

0.3

0.2

NSDC

0.6

0.2

0.02

0.2

0.1

NWT

0.5

0.2

0.02

0.2

0.1

SFT

0.8

0.3

0.4

0.02

0.4

0.2

UNott

0.9

0.4

0.02

0.4

0.2

BDC

229.06

BDC

MSI

AWMSI MPFD

Mean patch fractal dimension

Area weighted mean shape index

PSSD

Mean shape index

PSCoV

Edge density (m/ha)

NumP MPS

Total edge (m)

TLA

0 51031.62 53461.7

Patch size standard deviation (ha)

Number of patches

CA BDC

Patch size coefficient of variance (%)

Total Landscape Area (ha)

Mean patch size (ha)

Class area (ha)

Class

Name

Table C.2 Patch analysis statistics summary at class level A

804

63.47

2826.86

1794.27

630500

11.79 1.16

5.42

1.03

53461.7

2.44

373.7

9.11

80400

1.5 1.47

3.47

1.06

674.38

53461.7

306

2.2

349.97

7.71

263500

4.93 1.53

3.33

1.07

636.62

53461.7

116

5.49

291.88

16.02

133850

2.5 1.44

2.73

1.06

15.5 53461.7

1.41

164.22

2.31

7500

2.15

1.07

207

0.14

1.5

553

MSI

AWMSI MPFD

Mean patch fractal dimension

PSSD

Area weighted mean shape index

PSCoV

Mean shape index

Edge density (m/ha)

Total edge (m)

NumP MPS

Patch size standard deviation (ha)

TLA

Patch size coefficient of variance (%)

Number of patches

Mean patch size (ha)

Total Landscape Area (ha)

Class area (ha)

Class

Name

CA BDC

156.75

53461.7

19.59

185.04

36.26

17150

0.32 1.49

1.8

1.07

BDC

54.44

53461.7

370

0.15

169.3

0.25

62800

1.17 1.15

1.35

1.03

BDC

107.88

53461.7

3.17

199.09

6.32

31250

0.58 1.44

1.88

1.06

BDC

212.88

53461.7

2.6

176.16

4.57

62400

1.17 1.39

1.69

1.06

BDC

140.31

53461.7

115

1.22

243.16

2.97

59600

1.11 1.34

1.87

1.06

BDC

202.25

53461.7

113

1.79

422.6

7.56

56850

1.06 1.28

1.78

1.05

2170.55

2370.36

470450

8.81 1.16

4.1

1.03

0 51981.88 53429.5

476 109.21

564.94

53429.5

149

3.79

371.85

14.1

186150

3.48 1.62

4.33

1.08

456.81

53429.5

238

1.92

272.53

5.23

168500

3.15 1.39

2.52

1.06

87.06

53429.5

1.1

415.36

4.58

33150

0.62 1.25

2.5

1.04

112.38

53429.5

5.11

191.49

9.78

33950

0.64

7.19

53429.5

0.33

143.27

0.47

45.56

53429.5

163

0.28

161.93

0.45

1.69

53429.5

0.19

73.7

112.06

53429.5

1.65

168.54

56.62

53429.5

112

0.51

3.31

53429.5

NFU

0 37375.94 53429.5

NFU

935.19

NFU

3913.69

NFU

1.8

3.05

1.1

5950

0.11 1.27

1.5

1.05

41550

0.78 1.26

1.59

1.05

0.14

1900

0.04 1.21

1.38

1.04

2.78

41950

0.79

1.4

1.64

1.06

252.7

1.28

36600

0.69 1.31

1.68

1.06

0.28

216.08

0.6

2400

0.04 1.11

1.43

1.02

5042

7.41

6798.92

504 2669200

49.96 1.19

20.13

1.03

53429.5

471

1.99

396.27

395400

7.4 1.57

3.42

1.08

2763.5 53429.5

1174

2.35

359.1

8.45 1061900

19.87 1.55

3.28

1.08

53429.5

447

8.76

293.66

25.71

820800

15.36 1.67

3.49

1.08

156

53429.5

117

1.33

339.24

4.52

72200

1.35

1.5

2.71

1.08

216.62

53429.5

2.78

258.78

7.19

60700

1.14 1.39

1.97

1.06

NFU

5202.5 53429.5

1057

4.92

417.09

20.53 1188750

22.25 1.44

3.7

1.06

NFU

563.44

53429.5

205

2.75

254.41

6.99

159050

2.98 1.39

2.19

1.06

NFU

988.56

53429.5

414

2.39

255.57

6.1

305550

5.72 1.39

2.17

1.06

NFU

824.12

53429.5

785

1.05

215.56

2.26

403450

7.55 1.38

1.06

NFU

489.94

53429.5

563

0.87

228.29

1.99

234850

4.4 1.29

1.68

1.05

NCC

0 44545.81 53459.3

2894

15.39

5308.6

817.12 1699250

31.79 1.19

13.44

1.03

NCC

930.81

53459.3

293

3.18

348.79

11.08

334950

6.27 1.69

3.55

1.09

NCC

1943.62

53459.3

767

2.53

301.9

7.65

791750

14.81 1.63

3.33

1.08

NCC

3369.38

53459.3

300

11.23

254.23

28.55

675400

12.63 1.77

3.43

1.09

NCC

53459.3

2.39

252.26

6.03

31550

0.59 1.65

2.42

1.09

NCC

408.31

53459.3

9.72

233.5

22.7

58900

1.1 1.48

1.89

1.07

NCC

229.06

53459.3

2.52

151.89

3.82

79500

1.49 1.49

1.87

1.07

NCC

137.06

53459.3

2.54

126.77

3.22

49550

0.93 1.53

1.81

1.08

NCC

1210

53459.3

280

4.32

212.8

9.2

329050

6.16 1.53

2.33

1.08

NCC

284.56

53459.3

198

1.44

220.2

3.16

129650

2.43 1.47

2.22

1.07

NCC

314.69

53459.3

130

2.42

316.75

7.67

83700

1.57 1.34

1.75

1.06

0 47574.44 53459.3

1707

27.87

4102.77

1143.45

801100

14.99 1.19

5.41

1.04

NSDC

208

7.87

MSI

Mean patch fractal dimension

PSSD

Area weighted mean shape index

PSCoV

Mean shape index

Edge density (m/ha)

Total edge (m)

NumP MPS

Patch size standard deviation (ha)

TLA

Patch size coefficient of variance (%)

Number of patches

Mean patch size (ha)

Total Landscape Area (ha)

Class area (ha)

Class

Name

AWMSI MPFD

NSDC

639.06

53459.3

142

4.5

320.77

14.44

214150

4.01

1.8

4.04

1.1

NSDC

751

53459.3

292

2.57

215.8

5.55

293750

5.49 1.63

2.68

1.08

NSDC

818.81

53459.3

9.3

191.6

17.83

173000

3.24

1.7

2.72

1.09

NSDC

130.31

53459.3

4.34

208.73

9.07

41050

0.77 1.78

2.98

1.1

NSDC

1432.25

53459.3

29.84

139.44

41.61

137750

2.58 1.54

1.84

1.07

NSDC

1236.75

53459.3

14.55

141.83

20.64

214500

4.01 1.71

2.41

1.09

NSDC

87.06

53459.3

3.11

147.79

4.6

24550

0.46 1.47

1.44

1.07

NSDC

351.81

53459.3

3.59

194.76

6.99

103450

1.94

1.5

2.28

1.07

252.5 53459.3

NSDC

112

2.25

201.89

4.55

95550

1.79 1.54

2.26

1.08

53459.3

103

1.8

354.87

6.38

56700

1.06 1.33

1.85

1.06

NWT

0 47791.19 53459.3

1429

33.44

3743.01

1251.8 1579300

29.54 1.17

14.75

1.03

NWT

1196.06

53459.3

414

2.89

334.94

9.68

457200

8.55 1.69

3.48

1.09

NWT

1992.88

53459.3

713

2.8

365.63

10.22

758200

14.18 1.63

3.68

1.08

NWT

535.75

53459.3

138

3.88

263.67

10.24

143500

2.68 1.52

2.64

1.07

NWT

210.5 53459.3

117

1.8

314.91

5.67

83150

1.56 1.52

2.75

1.08

NWT

496.56

53459.3

13.42

201.95

27.1

66500

1.24

1.6

2.21

1.08

NWT

572.19

53459.3

477

1.2

316.09

3.79

259350

4.85 1.53

2.09

1.08

NWT

58.06

53459.3

104

0.56

182.91

1.02

39850

0.75

1.4

1.8

1.07

NWT

219.12

53459.3

122

1.8

179.46

3.22

82000

1.53 1.49

1.66

1.08

NWT

259.38

53459.3

281

0.92

197.8

1.83

152550

2.85 1.51

2.17

1.08

NWT

127.62

53459.3

117

1.09

312.1

3.4

53850

1.01

1.4

1.67

1.07

SFT

0 43681.62 53459.3

3168

13.79

5530.98

26.81 1.22

8.99

1.04

SFT

53459.3

382

3.11

327.95

10.21

445900

1.7

3.55

1.09

SFT

1523.5 53459.3

541

2.82

297.4

8.37

590300

11.04 1.64

3.34

1.08

SFT

53459.3

149

14.78

235.86

34.86

410100

7.67 1.78

3.65

1.08

SFT

107.5 53459.3

2.62

234.92

6.16

36100

0.68 1.62

2.44

1.09

SFT

253.56

53459.3

11.02

190.69

21.02

34150

0.64 1.45

1.91

1.07

SFT

3168.31

53459.3

150

21.12

240.32

50.76

412750

7.72 1.64

3.2

1.07

SFT

260.38

53459.3

5.42

157.73

8.56

63800

1.19 1.55

2.15

1.07

SFT

576.38

53459.3

158

3.65

239.09

8.72

169750

3.18 1.53

2.41

1.08

SFT

303.38

53459.3

203

1.49

223.15

3.33

134300

2.51 1.48

2.23

1.08

SFT

193.06

53459.3

162

1.19

272.15

3.24

75550

1.41 1.28

1.59

1.05

UNott

0 42905.06 53459.3

2846

15.08

5186.56

781.9 1927750

36.06 1.19

15.4

1.03

UNott

1318.62

53459.3

447

2.95

322.86

9.52

505450

1.7

3.41

1.09

UNott

1240.69

53459.3

449

2.76

309.86

8.56

486600

9.1 1.63

3.44

1.08

UNott

4503.75

53459.3

370

12.17

258.82

31.5

867300

16.22 1.77

3.53

1.09

UNott

128.56

53459.3

2.92

248.11

7.25

46200

0.86 1.71

2.96

1.1

UNott

546.31

53459.3

21.01

163.51

34.36

57700

1.08 1.52

1.9

1.07

UNott

15.69

53459.3

0.24

166.32

0.4

15650

0.29 1.24

1.6

1.04

UNott

492.25

53459.3

6.48

174.76

11.32

112850

2.11 1.62

2.21

1.08

185.31

1189.44 2202.19

209

762.63 1433350

8.34

9.45

MSI

AWMSI MPFD

Mean patch fractal dimension

Area weighted mean shape index

Mean shape index

PSSD

Edge density (m/ha)

PSCoV

Total edge (m)

NumP MPS

Patch size standard deviation (ha)

TLA

Patch size coefficient of variance (%)

Number of patches

Mean patch size (ha)

Total Landscape Area (ha)

Class area (ha)

Class

Name

CA UNott

705.69

53459.3

170

4.15

223.41

9.27

187450

3.51 1.53

2.17

1.08

UNott

560.94

53459.3

329

1.7

208.87

3.56

233250

4.36 1.48

2.17

1.07

UNott

1041.75

53459.3

268

3.89

501.73

19.5

190200

3.56 1.32

2.16

1.05

CAD

MCA

TCA

Total core area index (%)

Mean core area (ha)

IJI

Core area coefficient of variation (%)

Core area density (#/100ha)

MPI

Core area standard deviation (Ha)

Interspersion juxtaposition index (%) Total core area (ha)

AWMPFD MNN

Mean proximity index

Mean nearest neighbour distance (m)

Area weighted mean patch fractal dimension

Class

Name

Table C.3 Patch analysis statistics summary at class level B

CASD

CACoV

TCAI

BDC

1.17

35.65

194628.48

82.92

50014.81

0.1

980.68

6933.17

706.97

98.01

BDC

1.18

283.95

101.26

56.12

109.19

0.11

1.85

6.55

353.75

47.67

BDC

1.18

122.45

67.58

59.71

288.19

0.37

1.46

5.37

367.02

42.73

BDC

1.15

148.78

102.17

49.52

410.25

0.12

6.51

15.16

232.76

64.44

BDC

1.14

2959.8

9.56

57.99

4.62

0.01

0.58

1.25

215.59

29.84

BDC

1.09

652.39

25.12

65.36

126.69

0.02

14.08

30.34

215.52

80.82

BDC

1.06

115.85

0.87

45.07

2.69

1.34

0.59

44.19

4.94

BDC

1.1

837.53

25.87

53.58

60.56

0.04

2.63

5.95

225.89

56.14

BDC

1.09

492.56

15.77

65.96

117

0.09

2.54

4.05

159.36

54.96

BDC

1.11

273.86

13.59

65.86

55.44

0.1

1.07

2.79

261.59

39.51

BDC

1.09

441.07

23.7

69.98

120.69

0.08

2.81

9.8

349.04

59.67

1.14

35.58

198529.61

76.17

51204.69

0.07

1347.49

8176.54

606.8

98.5

1.21

175.76

150.77

56.13

275.88

0.23

2.21

8.71

394.76

48.83

1.14

126.98

55.41

59.09

210.81

0.23

1.69

4.21

249.51

46.15

1.14

169.76

5.64

46.04

39.88

0.02

3.99

6.61

165.66

45.8

1.17

750.61

64.17

62.5

60.44

0.04

2.52

5.91

234.63

53.78

1.09

106.71

2.6

47.49

0.81

0.41

0.28

69.23

11.3

1.1

136.68

2.4

55.22

2.94

0.01

0.59

0.52

88.14

6.45

1.07

1301.84

0.16

48.74

1.09

432.2

26.15

63.34

53.06

0.07

1.4

2.44

174.79

47.35

210

MCA

Core area standard deviation (Ha)

CAD

CASD

Total core area index (%)

TCA

Core area coefficient of variation (%)

IJI

Mean core area (ha)

MPI

Core area density (#/100ha)

Interspersion juxtaposition index (%) Total core area (ha)

Mean proximity index

Mean nearest neighbour distance (m)

Area weighted mean patch fractal dimension

Class

Name

AWMPFD MNN

CACoV

TCAI

1.1

179.11

6.14

62.56

15.44

0.03

0.91

1.8

198.75

27.26

1.07

1862.66

0.06

66.19

0.75

22.64

NFU

1.3

37.63

100329.95

81.66

33618.56

0.56

112.44

1907.38

1696.4

89.95

NFU

1.18

220.91

56.09

77.75

360.62

0.6

1.12

5.16

462.39

38.56

NFU

1.17

102.45

70.35

69.8

1186.06

1.65

1.35

5.78

429.2

42.92

NFU

1.17

140.2

203.01

54.75

2522.56

0.84

5.63

18.58

329.99

64.45

NFU

1.16

346.19

30.57

58.42

59.31

0.1

1.1

3.87

352.11

38.02

NFU

1.11

399.42

7.63

63.94

122.12

0.09

2.54

6.46

253.84

56.38

NFU

1.17

98.52

152.39

55.9

3248.62

1.08

5.63

18.74

332.89

62.44

NFU

1.12

396.48

30.79

66.99

318.88

0.2

2.93

6.17

211.02

56.59

NFU

1.12

244.65

34.05

75.84

521.62

0.45

2.17

4.95

227.96

52.77

NFU

1.12

141.23

14.07

77.69

273.25

0.65

0.79

1.8

229.38

33.16

NFU

1.09

214.85

11.81

72.17

174.94

0.37

0.87

1.89

215.59

35.71

NCC

1.26

34.23

143477.21

76.59

42180.19

0.32

243.82

3188.54

1307.76

94.69

NCC

1.18

226.87

96.96

72.54

429.94

0.45

1.78

7.18

404.4

46.19

NCC

1.17

124.33

78.43

56.06

786.81

1.3

1.13

4.54

400.83

40.48

NCC

1.17

168.56

265.6

41.48

2215.75

0.7

5.96

18.94

317.95

65.76

NCC

1.15

303.79

16.9

67.06

0.04

1.91

5.06

264.79

48.84

NCC

1.09

592.56

48.54

63.25

310

0.07

8.61

20.61

239.39

75.92

NCC

1.11

327.05

24.22

54.66

106

0.15

1.34

2.65

197.48

46.28

NCC

1.1

839.43

18.26

60.21

0.09

1.31

2.35

178.92

45.96

NCC

1.12

259.45

64.35

52.74

690.31

0.51

2.53

6.39

252.62

57.05

NCC

1.13

225.63

21.09

72.13

105.06

0.23

0.87

2.14

246.55

36.92

NCC

1.09

438.48

16.57

65.79

190.38

0.13

2.76

8.48

307.19

60.5

NSDC

1.17

38.86

98119.11

89.46

46512.38

0.08

1107.44

7082.95

639.58

97.77

NSDC

1.2

146.49

176.51

71.3

310.88

0.29

1.98

7.95

401.65

48.65

NSDC

1.15

80.53

81.41

79.86

325.44

0.46

1.33

3.48

261.73

43.33

NSDC

1.15

181.06

105.24

57.88

521.56

0.19

5.02

11.94

238.07

63.7

NSDC

1.17

717.41

55.96

76.13

68.25

0.06

2.28

5.7

250.42

52.37

NSDC

1.09

798.05

160.41

23.06

1193.19

0.1

21.31

34.9

163.77

83.31

NSDC

1.13

280.99

171.05

68.28

870.06

0.18

8.88

14.84

167.21

70.35

NSDC

1.06

1244.53

18.97

73.58

49.56

0.04

2.25

3.98

176.74

56.93

NSDC

1.12

391.6

53.27

74.79

196.5

0.17

2.21

4.92

222.97

55.85

NSDC

1.13

314.84

28.06

82.39

113.62

0.16

1.32

3.23

244.78

NSDC

1.1

309.35

19.24

76.01

104.38

0.09

2.09

7.03

336.8

56.32

NWT

1.27

35.32

248641.75

76.64

45314.44

0.31

272.98

3491.57

1279.07

94.82

NWT

1.18

203.18

79.52

47.3

512.44

0.67

1.44

5.93

413.15

42.84

NWT

1.18

120.34

102.91

42.06

878.12

1.12

1.47

6.55

446.03

44.06

NWT

1.14

254.17

30.26

57.85

304.31

0.18

3.24

7.94

245.33

56.8

211

MCA

Core area standard deviation (Ha)

CAD

CASD

Total core area index (%)

TCA

Core area coefficient of variation (%)

IJI

Mean core area (ha)

MPI

Core area density (#/100ha)

Interspersion juxtaposition index (%) Total core area (ha)

Mean proximity index

Mean nearest neighbour distance (m)

Area weighted mean patch fractal dimension

Class

Name

AWMPFD MNN

CACoV

TCAI

NWT

1.16

458.48

31.34

76.75

96.62

0.1

1.76

4.98

283.55

45.9

NWT

1.11

509.52

87.81

63.18

390.06

0.04

16.96

26.02

153.44

78.55

NWT

1.12

135.84

17.78

48.21

254.69

0.26

1.85

4.69

254.19

44.51

NWT

1.11

335.27

5.28

49.19

12.94

0.04

0.68

0.71

104.61

22.28

NWT

1.09

284.45

15.2

68.82

103.19

0.13

1.54

2.9

188.16

47.09

NWT

1.14

249.78

10.69

68.06

0.22

0.61

1.32

216.96

27.37

NWT

1.09

549

3.75

61.56

0.06

1.92

4.76

247.36

48.24

SFT

1.22

38.04

70288.1

81.31

41826.31

0.26

305.3

3545.52

1161.32

95.75

SFT

1.18

181.1

92.27

53.07

518.69

0.68

1.44

6.07

422.25

43.61

SFT

1.17

108.79

84.57

66.25

651.69

0.93

1.31

5.06

385.93

42.78

SFT

1.18

160.22

408.34

60.07

1491

0.39

7.24

22.2

306.77

67.71

SFT

1.15

359.78

31.46

70.29

55.88

0.04

2.94

5.45

185.18

51.98

SFT

1.09

577.64

51.25

61.55

198

0.03

13.2

20.18

152.86

78.09

SFT

1.16

208.92

325.79

65.63

2445.75

0.22

20.9

45.33

216.85

77.19

SFT

1.12

473.75

88.64

76.35

157.94

0.08

3.59

6.02

167.58

60.66

SFT

1.12

249.97

45.71

73.91

308.19

0.27

2.13

5.82

273.88

53.47

SFT

1.13

201.65

22.75

81.93

114.69

0.22

0.96

2.28

239.04

37.8

SFT

1.08

250.38

7.23

76.99

87.75

0.14

1.2

3.61

300.58

45.45

UNott

1.27

34.97

142823.93

76.63

40102.69

0.41

183.12

2664.15

1454.89

93.47

UNott

1.18

218.35

79.92

47.95

563.56

0.76

1.38

5.75

416.24

42.74

UNott

1.17

109.7

87.36

66.9

525.19

0.74

1.32

5.24

395.84

42.33

UNott

1.17

164.54

270.45

32.65

3014.25

0.81

6.96

21.48

308.59

66.93

UNott

1.17

381.57

34.59

66.72

62.69

0.05

2.16

5.41

250.19

48.76

UNott

1.09

286.21

94.59

66.5

445.94

0.04

21.24

31.19

146.86

81.63

UNott

1.09

324.78

3.29

58.71

0.5

0.38

6.37

UNott

1.12

302.39

84.05

57.72

307.5

0.14

3.99

8.2

205.29

62.47

UNott

1.11

269.85

33.52

60.77

406.25

0.29

2.64

6.91

261.92

57.57

UNott

1.13

243.72

25.32

65.2

225.31

0.39

1.07

2.47

230.93

40.17

UNott

1.11

192.13

100.4

59.85

751.62

0.26

5.49

23.21

423.14

72.15

212

BDC

BDC BDC

95.45

453.1

5.26

0.43

463.44

4.36

1.26

BDC

328.89

11.63

1.19

BDC

260.11

1.09

0.03

BDC

200.44

31.74

BDC

1483.64

BDC

BDC BDC

LSI

MCAI

Number core areas

LPI

Mean core area (ha)

Double log fractal dimension 93.55

Mean core area index (%)

ZLAND C_LAND DLFD

1762.46

Landscape shape index

Percent of landscape (%)

CASD1

2833.2

Largest patch index (%)

Disjunct core area standard deviation (ha)

CACV1

Core of landscape (%)

Disjunct core area coefficient of variation (%)

Class

Name

Table C.4 Patch analysis statistics summary C

MCA1

NCA

1.49

95.22

6.94

0.31

62.21

0.2

1.4

0.15

2.99

9.83

1.16

0.54

1.43

0.18

4.94

9.36

0.94

197

0.77

1.29

0.25

3.56

16.02

3.54

0.01

1.43

0.01

2.2

6.86

0.42

0.29

0.24

1.13

0.21

2.3

55.04

15.84

0.11

0.1

0.01

1.5

0.01

2.79

0.24

0.01

283.22

5.04

0.2

0.11

1.3

0.07

2.43

21.7

1.78

230.43

3.29

0.4

0.22

1.22

0.05

2.78

21.7

1.43

404.29

1.95

0.26

0.1

1.31

0.05

2.74

9.25

0.48

580.03

6.19

0.38

0.23

1.24

0.14

2.72

10.57

1.07

2174.28

2338.93

97.29

95.84

1.4

96.89

5.11

0.82

107.57

433.21

8.02

1.06

0.52

1.42

0.26

4.13

10.96

1.85

125

357.18

3.16

0.85

0.39

1.34

0.09

3.93

11.52

0.89

125

534.6

2.7

0.16

0.07

1.38

0.06

2.48

3.24

0.5

222.78

6.12

0.21

0.11

1.39

0.06

2.49

13.86

2.75

390.8

0.14

0.01

1.39

2.18

2.02

0.04

754.48

0.14

0.09

0.01

1.47

0.01

2.57

0.68

0.02

1.55

2.14

250.13

1.95

0.21

0.1

1.29

0.03

2.57

15.85

0.78

562.25

0.77

0.11

0.03

1.39

0.02

2.51

3.65

0.14

331.66

0.21

0.01

1.28

2.14

2.78

0.06

NFU

6977.56

465.24

69.95

62.92

1.53

66.99

29.2

0.65

6.67

299

NFU

562.45

4.31

1.75

0.67

1.42

0.26

6.39

10.03

0.77

323

NFU

498.8

5.04

5.17

2.22

1.4

0.26

13.52

12.04

1.01

881

NFU

329.59

18.6

7.32

4.72

1.34

0.39

10.98

22.21

5.64

448

NFU

529.42

2.68

0.29

0.11

1.44

0.08

2.9

5.71

0.51

NFU

333.1

5.22

0.41

0.23

1.24

0.11

2.77

19.56

1.57

NFU

459.69

14.13

9.74

6.08

1.31

0.63

14.84

13.76

3.07

577

NFU

304.22

4.73

1.05

0.6

1.27

0.11

3.82

15.74

1.56

109

NFU

311.27

3.92

1.85

0.98

1.27

0.15

5.4

16.04

1.26

240

NFU

362.28

1.26

1.54

0.51

1.35

0.06

6.46

8.45

0.35

348

NFU

385.99

1.2

0.92

0.33

1.28

0.05

4.65

8.42

0.31

200

NCC

5363.47

781.73

83.33

78.9

1.58

82.24

18.5

0.46

14.58

173

NCC

447.34

6.56

1.74

0.8

1.45

0.26

5.73

13.32

1.47

242

NCC

422.31

4.33

3.64

1.47

1.43

0.27

10.63

14.43

1.03

695

213

Mean core area index (%)

Mean core area (ha)

Number core areas

Double log fractal dimension

Landscape shape index

Percent of landscape (%)

ZLAND C_LAND DLFD

Largest patch index (%)

Disjunct core area standard deviation (ha) CASD1

Core of landscape (%)

Disjunct core area coefficient of variation (%)

Class

Name

CACV1

LPI

LSI

MCAI

MCA1

NCA

NCC

282.12

20.84

6.3

4.14

1.35

0.35

9.41

29.53

7.39

372

NCC

347.98

4.06

0.16

0.08

1.4

0.05

2.46

9.67

1.17

NCC

261.77

19.32

0.76

0.58

1.21

0.23

2.75

28.07

7.38

NCC

215.5

2.51

0.43

0.2

1.31

0.04

2.98

22.49

1.16

NCC

193.04

2.25

0.26

0.12

1.34

0.04

2.64

24.55

1.17

NCC

256.33

6.32

2.26

1.29

1.28

0.18

5.67

28.55

2.47

273

NCC

325.32

1.73

0.53

0.2

1.38

0.05

3.51

11.43

0.53

121

NCC

432.01

6.33

0.59

0.36

1.26

0.12

3.01

13.91

1.46

NSDC

4125.76

1124.19

88.99

87.01

1.58

88.4

8.83

0.33

27.25

NSDC

380.73

8.34

1.2

0.58

1.45

0.26

4.43

16.36

2.19

157

NSDC

289.07

3.22

1.4

0.61

1.41

0.09

5.27

15.75

1.11

245

NSDC

215.45

12.77

1.53

0.98

1.32

0.19

3.99

31.16

5.93

104

NSDC

250.42

5.7

0.24

0.13

1.39

0.06

2.56

13.34

2.28

NSDC

146.84

36.5

2.68

2.23

1.15

0.28

3.55

56.29

24.86

NSDC

151.4

15.5

2.31

1.63

1.27

0.22

4.39

46.43

10.24

NSDC

206.11

3.65

0.16

0.09

1.16

0.05

2.37

32.17

1.77

NSDC

236.12

4.73

0.66

0.37

1.27

0.11

3.23

27.69

2.01

NSDC

284.71

2.89

0.47

0.21

1.35

0.05

3.14

14.76

1.01

NSDC

494.24

5.01

0.35

0.2

1.28

0.1

2.73

10.79

1.01

NWT

3762.92

1193.24

89.4

84.76

1.48

88.55

17.19

0.89

31.71

166

NWT

446.7

5.53

2.24

0.96

1.45

0.26

7.05

13.04

1.24

357

NWT

489

6.02

3.73

1.64

1.42

0.27

10.26

13.42

1.23

598

NWT

305.03

6.73

0.57

1.31

0.16

3.67

15.72

2.21

NWT

426.98

3.53

0.39

0.18

1.41

0.08

3.01

6.98

0.83

NWT

209.68

22.1

0.93

0.73

1.18

0.25

2.83

28.45

10.54

NWT

497.89

2.66

1.07

0.48

1.41

0.09

4.91

6.36

0.53

138

NWT

323.47

0.4

0.11

0.02

1.44

0.01

2.55

4.46

0.12

NWT

269.59

2.28

0.41

0.19

1.26

0.05

16.66

0.85

NWT

356.47

0.9

0.49

0.13

1.43

0.03

3.76

6.67

0.25

117

NWT

500.28

2.63

0.24

0.12

1.34

0.05

2.69

7.43

0.53

SFT

5604.26

739.92

81.71

78.24

1.6

80.31

15.6

0.36

13.2

137

SFT

435.03

5.91

2.22

0.97

1.45

0.26

6.92

13.41

1.36

361

SFT

403.75

4.86

2.85

1.22

1.42

0.27

8.46

15.19

1.2

497

SFT

255.54

25.57

4.12

2.79

1.35

0.35

6.55

31.2

10.01

206

SFT

292.54

3.99

0.2

0.1

1.36

0.05

2.51

9.63

1.36

SFT

202.88

17.47

0.47

0.37

1.17

0.13

2.49

28.94

8.61

SFT

251.21

40.96

5.93

4.57

1.27

0.62

6.56

27.11

16.31

117

SFT

177.61

5.84

0.49

0.3

1.28

0.07

2.81

30.91

3.29

214

Mean core area index (%)

Mean core area (ha)

Number core areas

Double log fractal dimension

Landscape shape index

Percent of landscape (%)

ZLAND C_LAND DLFD

Largest patch index (%)

Disjunct core area standard deviation (ha) CASD1

Core of landscape (%)

Disjunct core area coefficient of variation (%)

Class

Name

CACV1

LPI

LSI

MCAI

MCA1

NCA

SFT

287.46

5.61

1.08

0.58

1.28

0.18

3.95

26.4

1.95

145

SFT

321.83

1.82

0.57

0.21

1.36

0.05

3.57

11.41

0.56

120

SFT

461.19

2.5

0.36

0.16

1.25

0.07

2.93

11.13

0.54

UNott

5256.18

740.64

80.26

75.02

1.54

78.04

21.01

0.67

14.09

219

UNott

436.78

5.51

2.47

1.05

1.45

0.26

7.56

14.12

1.26

408

UNott

422.52

4.94

2.32

0.98

1.42

0.27

7.35

14.97

1.17

397

UNott

282.7

23.03

8.42

5.64

1.35

0.39

11.48

28.26

8.15

433

UNott

316.46

4.51

0.24

0.12

1.43

0.06

2.62

8.15

1.42

UNott

170.54

29.25

1.02

0.83

1.14

0.23

2.72

40.47

17.15

UNott

711.07

0.11

0.03

1.55

0.01

2.29

0.68

0.02

UNott

203.64

8.24

0.92

0.58

1.3

0.11

3.32

30.06

4.05

UNott

277.07

6.62

1.32

0.76

1.26

0.17

4.14

27.89

2.39

154

UNott

297.9

2.04

1.05

0.42

1.35

0.05

4.62

13.31

0.68

211

UNott

599.84

16.82

1.95

1.41

1.24

0.48

4.15

13.42

2.8

137

215

APPENDIX D Q-ANALYSIS Correlation matrix between sorts SORTS

1 NFU

100

2 UNOTT

100

3 BDC

100

4 NCC

100

5 NSDC

100

6 NWT

100

7 SFT

100

8 EN

100

0.0108

-0.0240

Unrotated factor matrix Factors SORTS

1 NFU

0.3760

0.8833

0.0457

-0.1994 -0.1884 0.0174

2 UNOTT

0.7175

0.0379

-0.4396 0.4928 -0.1939 -0.0749 -0.0389 -0.0546

3 BDC

0.5217

0.0780

0.7665

0.0157

-0.0224 0.0465

0.0149

4 NCC

0.9317

-0.0928 -0.1858 -0.1046 0.0686

-0.0496 0.2657

0.0091

5 NSDC

0.9241

0.1477

0.1468

6 NWT

0.8735

-0.2286 0.1238

-0.2687 -0.1790 -0.2263 -0.0985 0.0660

7 SFT

0.9046

0.0591

-0.1051 0.3752

8 EN

0.8421

-0.3608 0.1291

-0.1427 -0.2484 0.2398

-0.0175 -0.0634

Eigenvalues

4.9409

1.0041

0.8856

0.5358

0.3441

0.1406

0.0976

0.0513

% expl.Var.

0.3621

-0.1879 0.0865

0.0312

0.1774

Rotating angles used between factors

216

-0.0711 0.1528

-0.0385 -0.0901 -0.1253

FTR#1 FTR#2 ANGLE 1

25.

24.

76.

-3.

-2.

-10.

-3.

Generated By PQROT [17:41, 12/6/2001]

Factor matrix with an ‘X’ indicating a defining sort Loadings QSORT

1 NFU

0.7939X

-0.0134

-0.5550

2 UNOTT

0.4516

-0.6697

0.1819

3 BDC

0.6462

0.5207X

0.3880

4 NCC

0.6531

-0.5269

0.4614

5 NSDC

0.7643X

-0.5034

0.2550

6 NWT

0.6258

-0.2322

0.6334

7 SFT

0.7704X

-0.3021

0.3806

8 EN

0.5252

-0.2283

0.7315X

% EXPL.VAR.

Free distribution data results Q-SORTS

MEAN

ST.DEV.

0.400

1.569

-0.250

1.517

0.100

1.373

0.200

1.542

217

0.300

1.658

0.050

1.669

0.150

1.631

-0.250

1.585

Rank statement totals with each factor Factors No. Statement

No.

1 statement 1

0.71

-0.07

0.79

2 statement 2

1.05

-0.80

-0.47

3 statement 3

0.81

1.38

1.42

4 statement 4

0.15

0.66

1.42

5 statement 5

-1.15

-1.53

0.16

6 statement 6

0.05

-0.07

-1.10

7 statement 7

-0.72

-1.53

-1.10

8 statement 8

1.04

0.66

0.16

9 statement 9

1.28

-0.07

0.16

10 statement 10

-0.35

1.38

-1.10

11 statement 11

-1.71

-0.80

-1.10

12 statement 12

0.33

-1.53

0.16

13 statement 13

-1.71

0.66

-1.10

14 statement 14

-1.43

-0.80

-1.10

15 statement 15

0.43

0.66

1.42

16 statement 16

1.28

1.38

1.42

17 statement 17

-0.45

-0.80

-0.47

18 statement 18

0.34

-0.07

-1.10

19 statement 19

1.00

1.38

0.79

20 statement 20

-0.96

-0.07

0.79

218

Correlations between factors 1

1.0000 0.4218 0.5490

0.4218 1.0000 0.4474

0.5490 0.4474 1.0000

Normalised factor scores â&#x20AC;&#x201C; For factor 1 No. Statement

No.

Z-SCORES

9 statement 9

1.276

16 statement 16

1.276

2 statement 2

1.048

8 statement 8

1.038

19 statement 19

0.995

3 statement 3

0.810

1 statement 1

0.715

15 statement 15

0.434

18 statement 18

0.345

12 statement 12

0.334

4 statement 4

0.154

6 statement 6

0.054

10 statement 10

-0.348

17 statement 17

-0.454

7 statement 7

-0.724

20 statement 20

-0.962

5 statement 5

-1.147

14 statement 14

-1.428

11 statement 11

-1.708

13 statement 13

-1.708

219

Normalised factor scores â&#x20AC;&#x201C; For factor 2 No. Statement

No.

Z-SCORES

3 statement

1.384

10 statement

1.384

16 statement

1.384

19 statement

1.384

8 statement

0.656

13 statement

0.656

15 statement

0.656

4 statement

0.656

9 statement

-0.073

6 statement

-0.073

1 statement

-0.073

18 statement

-0.073

20 statement

-0.073

14 statement

-0.801

11 statement

-0.801

17 statement

-0.801

2 statement

-0.801

12 statement

-1.530

5 statement

-1.530

7 statement

-1.530

Normalised factor scores â&#x20AC;&#x201C; For factor 3 No. Statement

No.

Z-SCORES

3 statement

1.419

4 statement

1.419

15 statement

1.419

16 statement

1.419

220

1 statement

0.788

19 statement

0.788

20 statement

0.788

8 statement

0.158

9 statement

0.158

12 statement

0.158

5 statement

0.158

2 statement

-0.473

17 statement

-0.473

14 statement

-1.104

11 statement

-1.104

10 statement

-1.104

13 statement

-1.104

18 statement

-1.104

6 statement

-1.104

7 statement

-1.104

Descending array of differen ces between factors 1 and 2 No. Statement

No.

Type1

Type2

Difference

12 statement

0.334

-1.530

1.864

2 statement

1.048

-0.801

1.850

9 statement

1.276

-0.073

1.349

7 statement

-0.724

-1.530

0.806

1 statement

0.715

-0.073

0.788

18 statement

0.345

-0.073

0.418

5 statement

-1.147

-1.530

0.383

8 statement

1.038

0.656

0.382

17 statement

-0.454

-0.801

0.347

6 statement

0.054

-0.073

0.126

221

16 statement

1.276

1.384

-0.108

15 statement

0.434

0.656

-0.221

19 statement

0.995

1.384

-0.389

4 statement

0.154

0.656

-0.502

3 statement

0.810

1.384

-0.574

14 statement

-1.428

-0.801

-0.626

20 statement

-0.962

-0.073

-0.889

11 statement

-1.708

-0.801

-0.907

10 statement

-0.348

1.384

-1.732

13 statement

-1.708

0.656

-2.364

Descending array of differences between factors 1 and 3 No. Statement

No.

Type1

Type3

Difference

2 statement

1.048

-0.473

1.522

18 statement

0.345

-1.104

1.449

6 statement

0.054

-1.104

1.157

9 statement

1.276

0.158

1.118

8 statement

1.038

0.158

0.880

10 statement

-0.348

-1.104

0.756

7 statement

-0.724

-1.104

0.380

19 statement

0.995

0.788

0.207

12 statement

0.334

0.158

0.176

17 statement

-0.454

-0.473

0.019

1 statement

0.715

0.788

-0.074

16 statement

1.276

1.419

-0.144

14 statement

-1.428

-1.104

-0.324

13 statement

-1.708

-1.104

-0.604

11 statement

-1.708

-1.104

-0.604

3 statement

0.810

1.419

-0.609

222

15 statement

0.434

1.419

-0.985

4 statement

0.154

1.419

-1.265

5 statement

-1.147

0.158

-1.305

20 statement

-0.962

0.788

-1.751

Descending array of differences between factors 2 and 3 No. Statement

No.

Type2

Type3

Difference

10 statement

1.384

-1.104

2.488

13 statement

0.656

-1.104

1.760

6 statement

-0.073

-1.104

1.031

18 statement

-0.073

-1.104

1.031

19 statement

1.384

0.788

0.596

8 statement

0.656

0.158

0.498

11 statement

-0.801

-1.104

0.303

14 statement

-0.801

-1.104

0.303

3 statement

1.384

1.419

-0.035

16 statement

1.384

1.419

-0.035

9 statement

-0.073

0.158

-0.231

2 statement

-0.801

-0.473

-0.328

17 statement

-0.801

-0.473

-0.328

7 statement

-1.530

-1.104

-0.426

15 statement

0.656

1.419

-0.764

4 statement

0.656

1.419

-0.764

1 statement

-0.073

0.788

-0.861

20 statement

-0.073

0.788

-0.861

5 statement

-1.530

0.158

-1.688

12 statement

-1.530

0.158

-1.688

Factor Q-sort values for each statement

223

Factor Arrays No. Statement

No.

1 statement

2 statement

-1

3 statement

4 statement

5 statement

-1

-2

6 statement

-2

7 statement

-1

-2

8 statement

9 statement

10 statement

-2

11 statement

-2

-1

-2

12 statement

-2

13 statement

-2

14 statement

-2

-1

-2

15 statement

16 statement

17 statement

-1

18 statement

-2

19 statement

20 statement

-1

Variance = 1.600 St. Dev. = 1.265 Factor Q-sort values for statements sorted by consensus vs. disagreement (Variance across normalised factor scores) Factor Arrays No. Statement

No.

16 statement

17 statement

-1

19 statement

224

14 statement

-2

-1

-2

3 statement

7 statement

-1

-2

8 statement

11 statement

-2

-1

-2

1 statement

15 statement

6 statement

-2

4 statement

9 statement

18 statement

-2

20 statement

-1

5 statement

-1

-2

2 statement

-1

12 statement

-2

13 statement

-2

10 statement

-2

Factor characteristics Factors

No. of Defining Variables

Average Rel. Coef.

0.800

Composite Reliability

0.923

0.800

S.E. of Factor Scores

0.277

0.447

225

Standard errors for differences in normalised factor scores (Diagonal entries are S.E. within factors) Factors

0.392

0.526

0.632

0.526

0.632

Distinguishing statements for factor 1 (P < .05 ; Asterisk (*) Indicates significance at P < .01) Both the Factor Q-sort value and the normalised score are shown. Factors 1 No. Statement

No.

RNK SCORE

9 statement

1.28

-0.07

0.16

2 statement

1.05*

-1

-0.80

-0.47

Distinguishing statements for factor 2 (P < .05 ; Asterisk (*) Indicates significance at P < .01) Both the factor Q-sort value and the normalised score are shown. Factors 1 No. Statement

No.

RNK SCORE RNK SCORE RNK SCORE

10 statement 10

0 -0.35

1 1.38* -2 -1.10

13 statement 13

-2 -1.71

0 0.66* -2 -1.10

12 statement 12

0 0.33

-2 -1.53*

Distinguishing statements for factor 3 (P < .05 ; Asterisk (*) Indicates significance at P < .01) Both the factor Q-sort value and the normalised score are shown.

226

0 0.16

Factors 1 No. Statement

No.

5 statement 5

RNK SCORE RNK SCORE RNK SCORE

-1 -1.15

-2 -1.53

0 0.16

Consensus statements â&#x20AC;&#x201C; Those that do not distinguish between ANY pair of factors All listed statements are non-significant at P>.01, and those flagged with an * are also non-significant at P>.05. Factors 1 No. Statement

No.

RNK SCORE RNK SCORE RNK SCORE

1* statement

1 0.71

0 -0.07

1 0.79

3* statement

1 0.81

1 1.38

1 1.42

4 statement

0 0.15

0 0.66

1 1.42

6 statement

0 0.05

0 -0.07

-2 -1.10

7* statement

-1 -0.72

-2 -1.53

-2 -1.10

8* statement

1 1.04

0 0.66

0 0.16

9 statement

2 1.28

0 -0.07

0 0.16

11* statement

-2 -1.71

-1 -0.80

-2 -1.10

14* statement

-2 -1.43

-1 -0.80

-2 -1.10

15* statement

0 0.43

0 0.66

1 1.42

16* statement

2 1.28

1 1.38

1 1.42

17* statement

-1 -0.45

-1 -0.80

0 -0.47

19* statement

1 1.00

1 1.38

1 0.79

QANALYSE was completed at 17:59:

227