B I O L O G I C A L C O N S E RVAT I O N
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available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/biocon
Review
Potential landscape drivers of biodiversity components in a flood plain: Past or present patterns? Aude Ernoulta,*, Yves Tremauvilleb, Dominique Cellierc, Pierre Margeriea, Estelle Langloisa, Didier Alardd a
Universite´ de Rouen, Laboratoire dÕEcologie, UPRES EA 1293 ECODIV, Faculte´ des Sciences, F-76821 Mont Saint Aignan, France Muse´um dÕHistoire Naturelle de Rouen, 198 rue Beauvoisine, F-76000 Rouen, France c Universite´ de Rouen, Laboratoire de Mathe´matiques Raphae¨l Salem, UMR CNRS 6085, F-76821 Mont Saint Aignan, France d Universite´ Bordeaux I, UMR INRA 1202 BIOGECO, Ecologie des Communaute´s, F-33405 Talence, France b
A R T I C L E I N F O
A B S T R A C T
Article history:
Changes in landscape pattern under the control of agriculture intensification are consid-
Received 28 February 2005
ered to be an important driver of biodiversity and often a threat for conservation. The
Available online 22 September 2005
response of species to landscape changes is complex, including possible time lags, and depends on the taxonomic group. The search for surrogate species or surrogate data for
Keywords: Co-inertia analyses
biodiversity is confronted with this complexity. This study was conducted on two taxonomic groups (birds and vascular plants) for 20
GAM
sites each of 1 km2 equally distributed in the Seine valley floodplain. For plants, two habi-
Landscape history
tats were studied: grasslands and hedges. We used a generalised additive model (GAM) and
Species diversity
co-inertia analyses to determine whether present or past landscape attributes can best
Surrogate
explain the biodiversity components (structure and composition). This study confirms the major role of landscape pattern attributes for predicting some metrics of biodiversity, e.g. species richness. But this study shows that potential drivers of biodiversity come from both the past and present landscape patterns. The quality of surrogates for biodiversity is strongly dependent on understanding the importance of past conditions. This suggests the need for more functional surrogates, taking into account the situation of equilibrium or non-equilibrium between biodiversity and its drivers. 2005 Elsevier Ltd. All rights reserved.
Contents 1. 2.
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Study area . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2. Landscape variables . . . . . . . . . . . . . . . . . . . 2.3. Sample collection . . . . . . . . . . . . . . . . . . . . .
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* Corresponding author: Tel.: +33 02 35 14 67 71. E-mail address: Aude.Ernoult@univ-rouen.fr (A. Ernoult). 0006-3207/$ - see front matter 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.biocon.2005.07.008
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2 3 3 3 4
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B I O L O G I C A L C O N S E RVAT I O N
2.4.
3.
4.
1.
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Data analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.1. Biodiversity structure: species richness and distribution pattern . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.2. Relationships between landscape variables for the three dates and biodiversity structure . . . . . . . . . . . 2.4.3. Relationships between landscape variables for the three dates and species composition. . . . . . . . . . . . Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Bird community . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.1. Bird assemblages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.2. ‘‘Bocage’’ bird assemblages. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Plants in grassland habitat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3. Plants in the hedge habitat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix A. Correspondence between birds species abbreviations and species names . . . . . . . . . . . . . . . . . . . . . . . . . Appendix B. Correspondence between plants species abbreviations and species names . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Introduction
The assessment and monitoring of biodiversity is a crucial issue that is still discussed by ecologists (Huston, 1993; Duelli and Obrist, 2003) especially as far as the problem of the accessibility of biological data is concerned. Recent studies have been developed in two main directions: (1) the difficulty of measuring the whole biodiversity in a natural system has led to the search for simple measurements on a few taxonomic groups that may be used as indicators of the overall diversity (Andersen, 1995; Balmford et al., 1996; Sauberer et al., 2004); (2) the possibility of using non-biological data to assess environmental conditions has questioned the potential of environmental metrics (e.g. landscape metrics) for inferring biodiversity levels in given areas (Faith and Walker, 1996; OÕBrien, 1998; Wohlgemuth, 1998; Moser et al., 2002; Shriver et al., 2004). This search for surrogate species or surrogate data is currently confronted with a major difficulty which is that all the species co-occurring in a given area are not likely to respond in a similar way to the same environmental variables or to the same range of variation of these variables (Noss, 1990; Prendergast et al., 1993; Lambeck, 1997; So¨derstro¨m et al., 2001). As a consequence, a given combination of environmental variables favourable to a high diversity of plants is not necessarily associated with a high diversity for another taxonomic group. Furthermore, even in this case, a time lag may be expected between environmental changes (the cause) and the dynamics of species of one taxonomic group (the effect) while other species may respond immediately. For example, when considering landscape metrics as possible surrogate data, key elements in looking for potential surrogates are: (1) the relevant spatial scale at which species may perceive their surrounding environment and its changes and (2) the time lag that may occur between landscape changes and species responses. There is a general consensus that landscapes are among key drivers of local dynamics of species and that landscape changes may be a threat to species diversity (Dale et al., 2000; Kerr et al., 2000; Wagner et al., 2000; Weibull et al., 2000; Sanchez-Zapata et al., 2003). But there are still considerable gaps in our knowledge of the spatial and temporal scale
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4 4 4 4 5 5 5 7 9 9 10 11 12 12 15
that we should take into account in order to understand the hierarchy of variables that drives species diversity in a changing landscape (Li and Wu, 2004). Studies of the role of landscape pattern on species diversity have mainly focused on the spatial dimension of the landscape, and have looked for the relationship that may exist between species richness and the structure of the landscape mosaic. The use of landscape metrics, which were developed in this context, has helped to demonstrate that species richness was generally positively correlated with landscape attributes linked to habitat size, habitat connectivity and landscape heterogeneity (Dauber et al., 2003; Krauss et al., 2004; Martinez-Morales, 2005). The temporal dimension has not led to similar developments, even if landscape dynamics or landscape history are supposed to be central factors in landscape ecology (Williams, 1989; Pedroli and Borger, 1990; Poudevigne and Baudry, 2003). At the same time, the development of historical ecology at site or habitat scale has underlined the importance of past land uses in understanding present patterns of species composition (Cousins and Eriksson, 2001). Landscape patterns may be seen as environmental filters deleting species in particular conditions and through specific traits (Keddy, 1992; Belyea and Lancaster, 1999). But this correlation between the pattern (the landscape) and the process (the biodiversity) is likely to be invalidated especially when the landscape has recently changed. In such conditions, biodiversity may reflect conditions inherited from past landscape patterns, even if these patterns are not still recognisable (Lindborg and Eriksson, 2004). Furthermore, similar landscape patterns in two sites may result from contrasting histories (Ernoult et al., submitted) and therefore reveal contrasting patterns of biodiversity. In this paper, our study of the biodiversity of a floodplain is based on a sampling design of 20 sites each of 1 km2. Our main purpose was to understand how the landscape features, that we examined over time, could explain the present patterns of biodiversity. We considered the species composition and the structure (species richness and distribution heterogeneity) of biodiversity for two taxonomic groups, i.e. birds and vascular plants, which are likely to respond differently to landscape changes because of their different mobility capac-
B I O L O G I C A L C O N S E RVAT I O N
ities. For plants, two habitats which were likely to present different patterns of variation over time in a given landscape were considered: (1) grassland, a ‘‘spatial habitat’’, being continuous or patchy, which is the main land cover and thus the main factor in terms of plant diversity in the area; (2) hedges, a ‘‘network habitat’’ of linear elements, whose role for maintaining biodiversity is well recognised (Burel and Baudry, 1995; Baudry et al., 2000).
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plain was mainly composed of wet grasslands surrounded with a well-developed hedge network constituting a typical ‘‘bocage’’ landscape. With agricultural intensification and the setting up of the drainage network, this flooded land dried up and landscape patterns became modified, with a switch to arable farming, an increasing field size, and a decrease in the hedge length.
2.2.
2.
Methods
2.1.
Study area
This survey was conducted in the lower Seine valley floodplain (Fig. 1) which extends from Rouen to Le Havre estuary. Twenty sites (each corresponding to a 1 km · 1 km square) representative of the floodplain landscape were more or less regularly spaced along the Seine river at altitudes of under 5 m above sea level. The climate is a temperate oceanic one (annual average temperature: 9.8 C), mainly influenced by Atlantic depressions. The rainfalls are heavy and regular (annual average precipitation: 785 mm). The landscape is dominated by rural activities and composed of a mosaic of habitats including wet grasslands, crops and orchards. These habitats are established on recent alluvia overlain by either gleysoils or fluvisoils (WRB, 1998). Like many European rivers, the Seine was almost entirely channelled at the beginning of the 20th century to enhance the agricultural use of the rich alluvial soils, to secure river navigation and to prevent flooding (Meybeck et al., 1998). The embankments, which prevent the river from flooding, have disturbed the hydrological functioning of the floodplain, which until the 19th century, was subjected to regular overflowing by the Seine. Its hydrology is now related to groundwater fluctuations (Fustec and Lefeuvre, 2000). This hydrological management has considerably reduced the wetlands area. Until the beginning of the 20th century, the flood-
3
Landscape variables
The landscape study was conducted on the twenty sites at three dates, in relation to changes in agricultural practices: 1963 (before the setting up of Common Agricultural Policy (CAP)); 1985 (after the setting up of the CAP) and 2000 (present-day landscape). Land cover was studied using aerial photographs for these three dates. Ten classes were identified: crops, orchards, poplar plantations, grasslands, forests, buildings, tree cultivation, copses, industries and open water. These land covers areas characterise the available habitats for species in the landscape and their influences on the biodiversity structure is demonstrated in many studies (Bennett et al., 2004; Radford et al., 2005). Additional data concerning the hedgerow structure completed the data set. The landscape structure was studied with the help of metrics widely used in landscape ecology. Metrics data were calculated for each site and for each date (1963–1985–2000). Most of these indices are significantly correlated and those that can be considered as highly redundant (r2 > 0.5) were removed from the analyses. The indices used in the analyses were chosen with respect to their potential influence on the biodiversity. We hypothesize that the indices that we selected were the most relevant drivers for explaining variation in diversity metrics. These indices were the number of patches (NP), the juxtaposition index (IJI), both two different aspects of the habitats fragmentation known to greatly influence species richness (Steiner and Kohler, 2003; Uezu et al., 2005), the connectivity index (CON) and the mean shape index (MSI) which measure
Fig. 1 – Localisation of the 20 sites over the Seine valley floodplain.
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the regularity of the plot, that both affect species fluxes, although the MSI index may also be considered as a predictor of the intensity of management (Moser et al., 2002), and then ShannonÕs evenness index (SEI) measured the heterogeneity of the landscape mosaic, which is likely to explain species diversity. The definitions of these indices were described in (McGarigal and Marks, 1995). All these landscape variables are calculated on each 1 km2 site. For birds, this intermediate spatial scale (mesoscale) falls within the range (from 500 · 500 m to 2 · 2 km) of sample size generally used in bird studies (Bo¨hning-Gaese, 1997; So¨derstro¨m et al., 2001; Suarez-Seoane et al., 2002; Heikkinen et al., 2004). This scale reveals the variations in habitats types and also captures many aspects of the landscape structure variations. At this scale, bird species distribution is likely to be the result of both the response to landscape features as well as biotic interactions (e.g. assembly rules). For plants, this scale of analysis is likely to reflect only the potential drivers of species distribution, because patterns due to species interactions are likely to emerge at a finer grain. Therefore, the species assemblages may have a different ecological meaning.
2.3.
Sample collection
For the two taxonomic groups, we used different standardized data sampling protocols. For birds, the point count method (Bibby et al., 1992) was used to record the presence and abundance of birds. Each site was divided into four squares of 500·500 m. A point count was made at the centre of each square. All points were visited twice for 20 min, once in the early breeding season (May), once in the middle of the breeding season (June). The bird surveys were all performed by the same ornithologist (YT) to exclude any between-observer variation. At each point, all bird species detected acoustically were recorded. In this work, we considered both (1) the whole set of recorded species, considering in the same assemblages species that may have different mobility capacities and territory sizes; (2) a sub-set of species determined by the help of a correspondence analysis, and including a set of species that are positively associated. This group concerned mainly species characteristic of the ‘‘bocage’’ landscape which are expected to exhibit similar dispersal abilities and resource requirements, although some may be associated with this group for other reasons. Such a distinction was made in order to verify if different taxonomic resolutions (species level vs ‘‘ecological group’’) may detect the same drivers (or not) of species richness at the landscape scale. For plants, two sampling methods were used. For grassland habitat, 20 records were generally made for each site. Each record is an estimate of the relative abundance of vascular plants within 16 quadrats (0.25 · 0.25 m) regularly spaced in a 4·4 m grid. For hedges, 20 records were collected, corresponding to 10 hedges for each site. For each record, the relative abundance of vascular plants was estimated at 10 points on one side of the hedge, the distance between two points being 10 m. Consequently, two records were made for each hedge, one record on each side. Although all the 20 sites were surveyed for birds, a few sites were only partially (i.e. with less than 20 records) or not surveyed for plants, either owing to the absence of the
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habitat (hedges) or more frequently to problems of legal accessibility to fields (grasslands). The analyses on plants were therefore performed on 15 sites for grasslands and 17 sites for hedges.
2.4.
Data analyses
2.4.1. Biodiversity structure: species richness and distribution pattern For each site, we assessed the total species richness for birds and for the two distinct habitats for plants (i.e. grasslands and hedges). We also calculated two indices accounting for heterogeneity in intra-site and inter-site distribution. These indices are based on the formula S = 1 D2 where S is the Sorensen similarity index (Gower and Legendre, 1986) and D is the distance between two records in terms of its species composition calculated by the help of ADE-4. S varies from 0 (which indicates that two samples are totally different) to 1 (which indicates that two samples are identical). These indices were based on the 4 samples for birds and generally 20 samples for each plant habitat. The intra-site index accounts for distribution heterogeneity at the landscape scale (site scale) and inter-site index accounts for heterogeneity at the floodplain scale.
2.4.2. Relationships between landscape variables for the three dates and biodiversity structure We used multiple-regression analyses to investigate combinations of landscape variables for each date in explaining the variability in the dependent variables (richness, inter-site heterogeneity index and intra-site heterogeneity index). Our goal was to determine with greater realism which landscape variables or combination of landscape variables at a given date explained most of the biodiversity variation in the structure indices. The Generalised Additive Model or GAM (Hastie and Tibshirani, 1990) was chosen because it is a flexible method in which both linear and smoothing methods, as well as a mix of the two, can be applied. For studying these dependent variables we used (i) Poisson error and Log identity for species richness because when richness consists of counts, a Poisson distribution and log link is assumed (McCullagh and Nelder, 1989) and (ii) a normal error and an identity relationship for the two heterogeneity indices. These distributions were tested with the Kolmogorov–Smirnov test. The forward–backward stepwise regression procedure was used to choose landscape variables that have the strongest relationships with biodiversity structure indices with the aim of identifying the best subset model. All regression analyses were performed using S-Plus version 2000 (Venables and Ripley, 2002).
2.4.3. Relationships between landscape variables for the three dates and species composition Data were treated by multivariate analysis with ADE-4 software (Thioulouse et al., 1997). Co-inertia analysis (Dole´dec and Chessel, 1994) was used to couple the two data tables. In this study, the co-inertia analysis was performed on two matrices: the species matrix (71 species · 20 sites for the whole bird community, 20 species · 19 sites for the bocage bird community, 160 · 15 sites for grasslands and 183 · 17 sites for hedges) and the landscape variables matrix (12 · 20
B I O L O G I C A L C O N S E RVAT I O N
sites for birds, 12 · 15 sites for grasslands and 12 · 17 sites for hedges). In total three co-inertia analyses (1963, 1985, and 2000) were conducted for each assemblage. This is a two-tables ordination method based on a covariance matrix (species · landscape variables). It provides a simultaneous projection, at the same scale, of the two previous analyses on the same co-inertia factorial plane. Co-inertia analysis was conducted to establish a hierarchy of independent factors (here landscape variables) from the species composition. Species-landscape variables relationship was measured by the RV coefficient, which fluctuates between 0% and 100% (100% when the correlation between the two tables is total). A Monte-Carlo permutation test was conducted on the RV coefficient to investigate the statistical significance of the species-landscapes variables relation (number of permutations: 100 000). The analysis was completed with a clustering by hierarchical clustering (Roux, 1991) using WardÕs method (Ward, 1963) to identify groups of species associated together in the floodplain landscapes. A co-inertia analysis followed by clustering was conducted for each group (whole bird community, bocage birds, grasslands and hedges).
3.
Results
We recorded 70 species of birds and 157 plant species in grasslands and 282 plant species in hedges from the sites of the Seine floodplain.
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The construction of the multivariate GAM models of birds, grasslands species and hedges species biodiversity indices (richness, inter-site heterogeneity index and intra-site heterogeneity index) for each date is shown step-by-step in Tables 1–4. All variables selected were highly significant (p < 0.05). The results of the co-inertia for each date are summarised in Table 5. The first two axes of each co-inertia analysis explained more than 65% of the inertia. The low eigenvalues of both axes 3 and 4 compared to axis 2 meant that only the first two axes of each co-inertia analysis could be interpreted. For each analysis, clustering was conducted on the species scores and sites scores. For each group, only the best model between indices and landscape feature are studied in details.
3.1.
Bird community
3.1.1.
Bird assemblages
The results showed that whatever the date studied, the variations of different measures of diversity were all explained by the landscape pattern (Tables 1 and 5). Nevertheless, depending on the index studied, it was not always the same date which best explained the variations. The ‘‘present-day’’ explained particularly well the variations of the inter-site heterogeneity index. The present-day landscape model explained 89% of variations of the inter-site heterogeneity index compared to 66% for 1985 and 80% for 1963. This model included two landscape variables: the mean shape index and then the inter-juxtaposition index.
Table 1 – Results of the GAMs on the indices of global birds biodiversity structure in relation to the past and present landscape attributes Global birds assemblage 2000 Richness Copses Grasslands CON Inter-site heterogeneity index MSI IJI Intra-site heterogeneity index MSI 1985 Richness SEI Inter-site heterogeneity index SEI Intra-site heterogeneity index SEI 1963 Richness Copses MSI Inter-site heterogeneity index MSI Intra-site heterogeneity index NP MSI
Null deviance 51.43
0.114
0.345
51.42
Residual deviance 12.82
0.012
0.108
12.39
% of explanation
<0.0001 <0.001 <0.005
10.49 4.91
<0.01 <0.05
6.82
<0.005
31.11
<0.0001
8.52
<0.005
3.39
<0.05
18.82 11.36
0.001 <0.05
6.91
<0.005
4.44 3.87
<0.05 <0.05
68.53
75.89 66.72
0.345
0.201
41.67
73.11
0.114
0.022
80.80
0.345
0.063
81.60
Bold values indicate the strongest correlations.
22.19 19.88 12.96 89.42
0.038
13.82
p
75.07
0.114
51.42
F
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Table 2 – Results of the GAMs on the indices of bocage birds biodiversity structure in relation to the past and present landscape attributes Bocage birds assemblage 2000 Richness IJI Buildings Inter-site heterogeneity index MSI CON Intra-site heterogeneity index SEI 1985 Richness SEI Crops Inter-site heterogeneity index SEI Intra-site heterogeneity index
1963 Richness NP Others IJI Inter-site heterogeneity index Grasslands Intra-site heterogeneity index Crops SEI
Null deviance 49.39679
0.1127055
0.4322656
49.39679
0.1127055
Residual deviance 8.98
0.014
0.145
5.46
0.031
% of explanation
F
p
18.00 10.25
<0.0005 <0.05
10.05 4.16
<0.005 <0.05
4.65
<0.05
20.95 12.70
<0.005 <0.01
9.12
<0.005
14.21 12.54 11.74
<0.005 <0.01 <0.01
5.32
<0.05
4.56 3.75
<0.05 <0.05
81.80
87.33
66.43
88.96
71.61
0.4322656 No correlation
49.39679
5.77
88.31
0.1127055
0.038
65.96
0.4322656
0.067
84.41
Bold values indicate the strongest correlations.
Table 3 – Results of the GAMs on the indices of grasslands biodiversity structure in relation to the past and present landscape attributes Grasslands assemblage 2000 Richness CON Orchards lenght of hedges Inter-site heterogeneity index Intra-site heterogeneity index Others Buildings 1985 Richness Copses MSI Inter-site heterogeneity index Intra-site heterogeneity index SEI 1963 Richness Buildings Inter-site heterogeneity index
Null deviance 29.66
Residual deviance 3.29
% of explanation
0.013
54.21
0.114
0.005
95.13
No correlation 0.114
29.66
5.63
0.034
12.04
p
35.5 8.23 7.98
<0.0001 <0.05 <0.05
88.90
0.028
29.66
F
3.82
<0.05
9.5 8.2
<0.05 <0.05
12.05 8.48
<0.01 <0.05
7.12
<0.01
80.99
69.99
59.38 19.54
No correlation Intra-site heterogeneity index No correlation Bold values indicate the strongest correlations.
<0.0005
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Table 4 – Results of the GAMs on the indices of hedges biodiversity structure in relation to the past and present landscape attributes Hedges assemblage
Null deviance
2000 Richness Graslands SEI Inter-site heterogeneity index Buildings Intra-site heterogeneity index
40.35
Residual deviance
% of explanation
11.76
0.025
F
p
70.84
0.012
9.11 7.98
<0.05 <0.05
3.52
<0.05
10.32 8.45
<0.05 <0.05
14.48 6.23
<0.01 <0.05
14.52
<0.005
7.29 5.46
<0.05 <0.05
51.01
No correlation 1985 Richness Orchards SEI Inter-site heterogeneity index Grasslands IJI Intra-site heterogeneity index
40.35
10.76
0.025
73.33
0.002
90.82
No correlation 1963 Richness Grasslands Inter-site heterogeneity index Grasslands Copses Intra-site heterogeneity index
40.35
18.82
0.025
53.36
0.004
83.23
No correlation
The landscape pattern in 1985 was well correlated with the birds species richness. This model explained 76% of the variance compared to 75% for the present-day landscape and 73% for 1963. The species richness was associated only with one variable in 1985: the Shannon diversity index. Finally, the landscape pattern in 1963 explained both the variations of the intra-site heterogeneity index and the species composition. The landscape variables in 1963 explained 81% of the variance of the intra-heterogeneity index. The variables the most correlated were the number of patches and the mean patch shape index. For the variations of the bird species composition, it was the landscape pattern in 1963 which best explained the bird assemblage in the Seine floodplain (59% for 1963 compared to 54% in 2000 and 56% in 1985 (Table 5). This analysis demonstrated that three types of bird community principally occur in the Seine floodplain landscapes and that their occurrences are well correlated with the landscape pattern in 1963. The first assemblage grouped together species such as Parus palustris, Locustella naevia and Luscinia megarhynchos (Fig. 2b). These species are characteristic of ‘‘bocage’’
landscape where the hedgerows network is well developed. The variables which best explained the presence of these species in these sites were the landscape variables Hedges, CON and NP in 1963 (Fig. 2a). The second assemblage grouped together two types of species i.e. (i) Perdix perdix, Corvus corone and Alauda arvenis which are characteristic of the homogeneous crop landscapes and thus explained by the ‘‘Crops’’ and SEI landscape variables and (ii) Anas platyrhyncos or Rallus aquaticus which are species of wet habitats included in the landscape variables ‘‘Others’’. The third group is composed of species such as Anthus pratensis, Saxicola rubetra, Fulica atra and Cisticola juncidis. These species are characteristic of reed bed habitats and mowed wet grassland habitats. They are characteristic of open habitats with an extensive management. The variables explaining the presence of this group in these sites were the variables MSI and grasslands.
3.1.2.
‘‘Bocage’’ bird assemblages
The variations of the three diversity indices (Species richness, inter-site heterogeneity index and intra-site heterogeneity
Table 5 – The RV coefficient of each co-inertia analysis for the four studied groups and for the three dates Global birds community RV coefficient (%) 2000 1985 1963
54.68 56.71 59.18
p <0.001 <0.001 <0.0005
Bocage birds community RV coefficient (%) 47.03 46.6 44.64
Grasslands assemblage
Hedges assemblage
p
RV coefficient
p
RV coefficient
<0.01 <0.05 0.065
60.14 61.37 54.73
<0.005 <0.01 <0.05
54.18 60.67 59.09
p <0.005 <0.005 <0.01
The higher the RV coefficient is, the better the composition variations of a group are explained by the landscape variables of the analysis. The bold values indicate the strongest correlations.
8
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B I O L O G I C A L C O N S E RVAT I O N
Axis 2:13.23% -1
1 -1
1
MSI
Axis 1: 58.19%
S.eur
Group 3
L.nae
S.rub V.van P.vir G.chl S.vul A.pra A.sch C.bra C.cig S.tor D.min H.pol A.pal P.col P.pic H.rus P.pal M.fla S.bor S.comA.pla E.schC.cet A.arv C.olo A.cau T.mer C.pal C.can E.rub F.tin S.tur M.alb C.can C.car P.per G.gla A.noc
Grasslands
Others SEI
F.atr C.jun
Group 1
L.meg A.tri P.mon M.str P.pho
NP Orchards Copses CON IJII Hedges Buildings
P.och
Crops
a
Group 2 b
2.4 -3.2 2.9 -2.5
D.maj R.aqu
Fig. 2 – Results of the global birds co-inertia analysis describing the co-structure between the birds species AFC and the 1963 landscape variables ACP: (a) correlation circle of 1963 landscape variables in the plan 1–2 of the co-inertia and (b) birds species ordination: groups 1–3, are the three groups resulting from the clusters analysis performed on species scores. The names of some species are deleted for more legibility. Full name is given in Appendix A.
index) were better correlated with the same date of landscape pattern as those determined for the whole bird species (Table 2). The landscape pattern in 1985 best explained the variations of species richness. Only the variable SEI was included in the model. The variations of inter-site heterogeneity index were better explained by the present-day landscape. The two landscape variables which participated in the model were the mean shape index and the connectivity of the hedgerow net-
work. The variations of intra-site heterogeneity index were better explained by two variables of the landscape pattern in 1963: crops and the SEI. On the other hand, the variations of the species composition of the ‘‘bocage’’ bird assemblages were better explained by the present day landscape pattern, whereas it was the landscape pattern in 1963 which explained better the bird composition for the overall bird assemblages (Fig. 3). Four groups of species were determined by the co-
Axis 2: 13.12% 1 -1 1 -1
a M SI
SEI
Grasslands
Others Copses NP Buildings Hedges IJI CON Orchards
Axis 1: 65.55%
1.3 -1.5 4.1 -1.8
P.vir
b
P.pal
H.pol
P.dom E.rub
S.vul P.cae
A.noc
A.cau
P.maj S.dec D.min
Crops
Group 4
C.bra P.pho
Group 3
A.tri P.mon M.str
F.tin P.och
Group 1
S.eur
Group 2 C.chl
Fig. 3 – Results of the bocage birds co-inertia analysis describing the co-structure between the birds species AFC and the present-day landscape variables ACP: (a) correlation circle of present-day landscape variables in the plan 1–2 of the co-inertia and (b) birds species ordination: groups 1–4 are the four groups resulting from the clusters analysis performed on species scores. The names of some species are deleted for more legibility. Full name is given in Appendix A.
B I O L O G I C A L C O N S E RVAT I O N
inertia. The first group grouped together species such as Carduelis chloris and Falco tinnunculus (Fig. 3b). It was principally the variable crops which explained the presence of these species in the bocage landscape (Fig. 3a). The second group included two species: Sitta europaea and Phoenicurus ochruros. The presence of this group in the landscape was principally linked to the presence of orchards in landscapes and to the connectivity of the hedgerow network. The third group included species like Sturnus vulgaris, Anthus trivialis and Muscicapa striata. This group was present in bocage landscape where the hedgerow network was particularly well developed, where copses were present and where there was a large number of patches. Finally the last group grouping together species like Parus major, Dendrocopus minor are an intermediate group and was found in ‘‘typical bocage’’ landscape.
3.2.
Plants in grassland habitat
Whatever the diversity measures studied, all diversity components of the grasslands assemblage were always better explained by the present-day landscape pattern (Table 3). For the variations of the species composition, the RV coefficient value (Table 5) was very close between 2000 (60%) and 1985 (61%) but the very strong robustness of the 2000 correlation (p < 0.005 compared to p < 0.01 in 1985) implies keeping the most robust co-inertia i.e. the co-inertia in 2000. The present-day landscape pattern explained 89% of the total variance of richness at the floodplain scale compared to 81% for 1985 and 59% for 1963 (Table 3). The three landscape variables selected in this model were the Connectivity, the orchards area and the length of hedgerows in the landscape. The inter-site heterogeneity variations were only explained by the present-day landscape patterns which accounted for 54% of the variance. No correlation was found between the variations of this index and the past landscape pattern. The Shannon diversity evenness (SDE) best explained the variation of this index.
Axis 2: 29.45%
The intra-site heterogeneity index variation was also best explained by 2000 landscape features. This model included two variables (Others and Buildings) together accounting for 95% of the variance. Only the past landscape pattern in 1985 explained the intra-site heterogeneity variations, no correlation was found for the past landscape pattern in 1963. Finally, the present composition of the grassland assemblage at the floodplain scale was well correlated and with more robustness with the 2000-landscape variables. Three grassland species groups were identified in the Seine floodplain (Fig. 4b). The group 1 grouped together species such as Centaurea nigra, Filipendula ulmaria and Anthriscus sylvestris. This species assemblage is linked to the variables NP, orchards area and the hedgerow connectivity (Fig. 4a). The second group (i.e. species such as Geranium dissectum, Cynosurus cristatus and Bellis perenis) was associated with the landscape variables ‘‘Crops’’ and SEI. The last group was associated with the variables MSI, ‘‘Grassland’’ and ‘‘Copses’’. This group was constituted of species such as Hydrocotyle vulgaris, Oenanthe fistulosa and Juncus articulatus.
3.3.
Plants in the hedge habitat
The results underlined the important role of the past landscape pattern in 1985 in the explanation of the variations of the diversity measurements for hedge assemblages (Table 4). Nevertheless, we noticed that the variations in the intrasite heterogeneity index at the scale floodplain were not correlated with the landscape patterns. The model for total species richness accounting for 73% of the variance (compared to 70% for 2000 and for 53% for 1963) was explained by two landscape variables: the orchards area and the Shannon diversity evenness. The variations of inter-site heterogeneity index were explained at 90% by two 1985 landscape variables: the grasslands area and the inter-juxtaposition index.
b
a
-1
1 -1
9
1 2 7 ( 2 0 0 6 ) 1 –1 7
A.pro P.hyd H.hel
Group 1
1
3.9 -2.8 3.7 -3.3
A.syl
Group 2
SEI Crops Axis 1: 45.54%
Buildings CON Orchards NP Others Hedges IJI
Copses Grasslands MSI
C.are
G.mol
F.exc
E.aug A.cap C.nig L.mul A.ela P.vul P.aru T.aqu C.arv G.ver G.fra F.ulm E.tet E.hir V.sp D.glo J.ger C.vul P.aus D.ces C.bie P.tri C.bin A.bul P.rep A.mil A.sto B.ste S.off L.per L.num C.dia C.otr C.fla C.sp H.lan A.rep B.rac L.flo C.cri H.lup M.lup S.asp C.pra C.dit L.ped M.disc B.per G.dis C.arv G.pal J.sp T.pra V.ver A.odo G.flu C.con E.par Group 3 J.art O.fis P.min A.vul S.vul J.acuH.vul
Fig. 4 – Results of the grasslands co-inertia analysis describing the co-structure between the grasslands species AFC and the present-day landscape variables ACP: (a) correlation circle of current landscape variables in the plan 1–2 of the co-inertia and (b) grasslands species ordination: groups 1–3, are the three groups resulting from the clusters analysis performed on species scores. The names of some species are deleted for more legibility. Full name is given in Appendix B.
10
B I O L O G I C A L C O N S E RVAT I O N
1 2 7 ( 2 0 0 6 ) 1 –1 7
Axis 2: 23.69% 1 -1 1 -1 MSI Grasslands
Axis 1: 46.23%
SEI
Copses Others
IJI
NP Buildings Hedges CON Orchards
Crops
a
C.dia T.dub 2.5 Group 3 L.his J.art S.gra -3.3 4 A.eup Group 4 J.eff -2.6 C.mar C.pal L.ped E.paluM.off M.lup L.vul R.acu P.vul L.flo R.fla A.mil C.bet E.tet A.glu C.fon B.cre C.pse B.erc G.ang F.syl C.cri S.gal V.scu E.eur I.aqu M.arv T.fla I.pse S.cin G.mol V.hed S.aur C.bie S.asp H.per T.com L.vul G.pal O.fis C.san A.arv S.vul P. dys E.hel S.nig C.pra E.cruA.bel G.par C.rip A.sp C.arv C.syl S.pal J.inf A.sto A.rep F.ulm S.ver S.off S.off F.aru E.praL.com B.ste P.rho B.mol C.fla C.alb J.reg C.nig S.cap A.pet H.lupA.pla A.vul V.per B.syl S.syl C.acu P.nig D.car A.cam B.nig C.spi A.vinA.pse L.pra B.tri V.rei Group 1 V.off C.palu S.nod M.aqu G.tet C.dit A.can A.can Group 2 b A.lan
Fig. 5 – Results of the hedges co-inertia analysis describing the co-structure between the hedges species AFC and the 1985 landscape variables ACP: (a) correlation circle of 1985 landscape variables in the plan 1–2 of the co-inertia and (b) hedges species ordination: groups 1–4 are the four groups resulting from the clusters analysis performed on species scores. The names of some species are deleted for more legibility. Full name is given in Appendix B.
In the same way, the present composition of the hedge assemblage at the scale floodplain was better explained by the landscape pattern in 1985. The co-inertia analysis revealed four groups which correspond to the various sets of species occurring in hedge habitats encountered in the Seine floodplain (Fig. 5b). The first group made up of species such as Papaver rhoeas, Senecio vulgaris and Veronica persica was related to the 1985 landscape variable Crops (Fig. 5a). These species are weeds and are generally found in the crops landscape. The second species assemblage (Populus nigra, Galeopsis tetrahit, Lathyrus pratensis,. . .) was characterised by the hedgerow connectivity, the length of hedgerows and the number of patches characteristic of 1985 landscapes. The third group (Caltha palustris, Cladium mariscus, Carex diandra,. . .) was explained by Grasslands, Copses, and MSI of 1985 landscapes. Finally, the last group of hedge species (Veronica hederifolia, Cerastium fontanum, Fagus sylvatica. . .) was representative of the intermediate 1985 landscape.
4.
Discussion
Whatever the taxonomic group or the habitat, there is an overall variability of the structure and the composition of biodiversity in the floodplain, from one site on the floodplain to another. Because of the design of data collection, landscape patterns are likely to explain a large portion of this variability. This usually assumes a causal relationship between the current landscape pattern and the species diversity pattern (Mazerolle and Villard, 1999). Our results confirm that there was always a significant relationship between present-day landscape metrics and almost all the
components of biodiversity that we measured (i.e. species richness, distribution heterogeneity and composition). For all the groups studied, species richness is correlated with the present landscape variables although the variables involved in this relationship differ from one taxonomic group to another. For example, the hedges connectivity, the orchards area and hedges length vs. copses, grasslands areas and hedges connectivity are important for explaining plant species richness in grasslands and bird species richness respectively. This relationship between species richness and landscape variables has been confirmed by many studies (Pearson, 1993; Miller et al., 1997; Pino et al., 2000; Burel et al., 2003; Millan de la Pena et al., 2003). However, our results suggest that despite the significance of the relationships between present-day landscape features and diversity metrics, a better relationship may be sometimes obtained when considering the past landscape pattern. The dynamics of landscape pattern can explain, to a certain extent, such trends: the present landscape is the result of recent changes which have affected landscape patterns since the 1960s (Ernoult et al., submitted) and may therefore lead to different species dynamics in response to these changes. This suggests that plants and birds are not likely to be in equilibrium with landscape patterns. Moreover, these landscape changes took place in the 1960s, after a period of relative stability (Meeus, 1993), suggesting that species were probably at equilibrium with landscape features at this period. In our study, 1963 may therefore be considered as an initial reference stage, ultimately ‘‘disturbed’’ by the recent changes, which were driven by agricultural trends and have affected landscapes (Poudevigne et al., 1997). As a consequence, the relationship between diversity metrics and present landscape
B I O L O G I C A L C O N S E RVAT I O N
features is likely to be more significant when the time lag of species response is short. Our study suggests that the species response time varies among the two taxonomic groups (birds and vascular plants) and among the different habitats within the same taxon (grasslands plants and hedge plants). When considering the plants in grassland and hedge habitats, all the indices calculated for each habitat were correlated with the same date of the landscape pattern but the date differed according to the habitat (2000 for grasslands and 1985 for hedges). A major difference between grassland and hedge habitats is the management intensity and especially in the disturbance rate experienced. Grasslands are generally managed in a semi-intensive way, involving a rotation cycle lasting several years with mowing, grazing and even ploughing, while hedges are occasionally cut and seldom weeded. The types of changes of habitats are important determinants of the reaction speed of species. Species from stable habitats such as hedges will react slower to changes than species from frequently disturbed habitats, such as intensively managed grasslands. This could explain why diversity in grasslands mostly responds to present environmental conditions, due to the influence of management, which overrides the spatial effects of surroundings. Conversely, species removal in hedge habitat is likely to result more from a spatial effect i.e. landscape fragmentation and hedgerow network connectivity (de Blois et al., 2002; Deckers et al., 2004) rather than from direct management pressure. This could explain why species composition and distribution in hedges is correlated with past landscape features and confirms that species turnover is likely to be slower in hedgerow networks than in grassland patches. For birds, the results are more complex. Whatever the level studied (i.e. the whole set of birds species vs. the ‘‘bocage’’ ecological group), each index was linked to a specific date. But, because birds are mobile organisms, they should respond rapidly to landscape changes and therefore should not be correlated with past landscape features (Lindborg and Eriksson, 2004). The correlations between the past landscape metrics and various metrics of diversity suggest that other traits than mobility could be involved in explaining such a trend. There are probably ‘‘site fidelity effects’’ that may counterbalance the potential impacts of landscape changes for some bird species, although opportunistic species may react rapidly. However, differences that were observed between the two levels considered (the whole set of species vs. the bocage ecological group) suggest that the taxonomic resolution is likely to influence the patterns revealed. For example, the fact that the overall species composition is linked to the 1963 landscape pattern, while it is linked to the present landscape pattern for the bocage birds, may be explained by the change in the fine-grain perception of a more homogeneous set of species, the ‘‘bocage birds’’ being likely to respond more finely to changes of the bocage structure. The underlying hypothesis is that the ‘‘under-dispersion’’ of some traits in the species assemblages is the result of the filter effect of the environment. More generally, the consideration of vital attributes of species (plants or animals) is a further step that should be developed in order to improve our results, especially the
1 2 7 ( 2 0 0 6 ) 1 –1 7
11
consideration of the null hypothesis on trait dispersion linked to filter effects or assembly rules (Weiher and Keddy, 1995). Our results confirm the major problem of surrogate data and surrogate species for the assessment and monitoring of biodiversity at the landscape level. First, biodiversity response to landscape change cannot be summarised by one single indicator because every taxon (and even inside a taxon for an ecological or functional group) may react differently to the landscape pattern. This suggests that in the Seine floodplain different taxa should be studied at the same time. This result has been confirmed (Jeanneret et al., 2003) or not (Sauberer et al., 2004) in other studies. Second, the role of the landscape histories in the explanation of the biodiversity highlights several important points for conservation. Indeed similar present landscapes that may result from different histories (Ernoult et al., submitted), may have contrasting biodiversity levels. This gap between the cause (the landscape) and the effect (the biodiversity) may greatly alter the value of surrogate data. Landscape history could play a more active part in explaining biodiversity than is currently recognised. For example, even if many studies (Dauber et al., 2003; Steiner and Kohler, 2003; Weibull et al., 2003) show a high correlation between some present landscape metrics and species richness, more complex processes may act on this relation (species richness can be better correlated with past landscape pattern). It is the same observation for the species composition, which is also well correlated with present landscape features, but it is even better related to past landscape features. The search for surrogates for biodiversity that has been classically developed (Burel et al., 2003; Virkkala et al., 2004) should thus be completed with a more ‘‘functional’’ approach taking into account the time lag between the cause and the effect. For example, we should search for surrogates of ecological coherence i.e. the distance to equilibrium between the present stage of biodiversity and the present stage of landscape which are both changing with time. Another way should be the search for the best ‘‘taxonomic resolution’’, based on some species traits that could avoid such a time lag. From a management point of view, knowledge of the time lag can be used to estimate the priorities for the management. When time lags are short, conservation is more urgent because the loss of species is rapid. On the other hand, when time lags are long, it may be difficult, or at least it will take a long time, to observe the effects of environmental changes or of conservation measures taken to improve survival. Biodiversity management cannot be effective without setting up long-term research sites and programs.
Acknowledgements This work was made possible by financial support from the ‘‘Conseil regional de Haute–Normandie’’ and a research grant to A. Ernoult. The authors would like to acknowledge Ludovic Jardin, Emmanuel Bazin and Ve´ronique Chalandon for their help in data recording. The authors also wish to thank R. Britton and L. Galmiche for correcting the English.
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Appendix A. Correspondence between species abbreviations and species names
birds
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Appendix A (continued)
Species abbreviation Species abbreviation A.pal A.sch A.sci A.cau A.arv A.pla A.pra A.tri A.noc C.can C.car C.chl C.bra C.cet C.dub C.cig C.jun C.pal C.cor C.can C.olo D.maj D.min E.cit E.sch E.rub F.tin F.coe F.atr G.chl G.gla H.pol H.rus L.nae L.meg M.alb M.fla M.str P.cae P.maj P.mon P.pal P.dom P.mon P.per P.col P.och P.pho P.col P.tro P.pic P.vir P.mod
Species name Acrocephalus palustris Acrocephalus schoenobaenus Acrocephalus scirpaceus Aegithalos caudatus Alauda arvensis Anas platyrhynchos Anthus pratensis Anthus trivialis Athene noctua Carduelis cannabina Carduelis carduelis Carduelis chloris Certhia brachydactyla Cettia cetti Charadrius dubius Ciconia ciconia Cisticola juncidis Columba palumbus Corvus corone Cuculus canorus Cygnus olor Dendrocopos major Dendrocopos minor Emberiza citrinella Emberiza schoeniclus Erithacus rubecula Falco tinnunculus Fringilla coelebs Fulica atra Gallinula chloropus Garrulus glandarius Hippolais polyglotta Hirundo rustica Locustella naevia Luscinia megarhynchos Motacilla alba Motacilla flavissima Muscicapa striata Parus caeruleus Parus major Parus montanus Parus palustris Passer domesticus Passer montanus Perdix perdix Phasianus colchicus Phoenicurus ochruros Phoenicurus phoenicurus Phylloscopus collybita Phylloscopus trochilus Pica pica Picus viridis Prunella modularis
P.pyr R.aqu S.rub S.tor S.ser S.eur S.dec S.tur S.vul S.atr S.bor S.com T.tro T.mer T.phi T.vis V.van
Species name Pyrrhula pyrrhula Rallus aquaticus Saxicola rubetra Saxicola torquata Serinus serinus Sitta europaea Streptopelia decaocto Streptopelia tutur Sturnus vulgaris Sylvia atricapilla Sylvia borin Sylvia communis Troglodytes troglodytes Turdus merula Turdus philomelos Turdus viscivorus Vanellus vanellus
Appendix B. Correspondence between species abbreviations and species names
Species abbreviation A.cam A.pse A.mil A.pod A.cyn A.eup A.can A.rep A.can A.cap A.sto A.pla A.pet A.sp A.tri A.vin A.glu A.bul A.gen A.arv A.syl A.odo A.syl A.vul A.nod A.hir A.lap A.ela A.vul
plants
Species name Acer campestre Acer pseudoplatanus Achillea millefolium Aegopodium podagraria Aethusa cynapium Agrimonia eupatoria Agropyrum caninum Agropyrum repens Agrostis canina Agrostis capillaris Agrostis stolonifera Alisma plantago-aquatica Alliaria petiolata Allium sp Allium triquetrum Allium vineale Alnus glutinosa Alopecurus bulbosus Alopecurus geniculatus Anagallis arvensis Angelica sylvestris Anthoxanthum odoratum Anthriscus sylvestris Anthyllis vulneraria Apium nodiflorum Arabis hirsuta Arctium lappa Arrhenatherum elatius Artemisia vulgaris
B I O L O G I C A L C O N S E RVAT I O N
Appendix B (continued)
Species abbreviation A.mac A.lan A.pro A.bel A.fat A.pra B.vul B.per B.erc B.tri B.syl B.nig B.ole B.ine B.mol B.rac B.sp B.ste B.cre B.dav B.umb C.syl C.pal C.sep C.rap C.bur C.pra C.cri C.acu C.bin C.dia C.dis C.dit C.fla C.hir C.mur C.otr C.ova C.pse C.rem C.rip C.sp C.spi C.bet C.jac C.nem C.nig C.pul C.arv C.con C.fon C.alb C.pol C.aca
13
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Appendix B (continued)
Species name Arum maculatum Aster lanceolatus Atriplex prostrata Atropa bella-donna Avena fatua Avenula pratensis Barbarea vulgaris Bellis perennis Berula erecta Bidens tripartita Brachypodium sylvaticum Brassica nigra Brassica oleraceae Bromus inermis Bromus mollis Bromus racemosus Bromus sp Bromus sterilis Bryonia cretica Buddleja davidii Butomus umbellatus Calamintha sylvatica Caltha palustris Calystegia sepium Campanula rapunculus Capsella bursa-pastoris Cardamine pratensis Carduus crispus Carex acutiformis Carex binervis Carex diandra Carex distans Carex disticha Carex flacca Carex hirta Carex muricata Carex otrubae Carex ovalis Carex pseudocyperus Carex remota Carex riparia Corex sp Carex spicata Carpinus betulus Centaurea jacea Centaurea nemoralis Centaurea nigra Centaurium pulchellum Cerastium arvense Cerastium conglomeratus Cerastium fontanum Chenopodium album Chenopodium polyspermum Cirsium acaule
Species abbreviation C.arv C.pal C.vul C.mar C.vit C.are C.san C.ave C.mon C.bie C.crs D.glo D.car D.ces D.ful E.cru E.pal E.aug E.hir E.pau E.par E.tet E.hel E.arv E.palu E.pra E.eur E.can E.pals F.syl F.aru F.pra F.rub Fest F.ran F.ulm F.ves F.aln F.exc G.ang G.tet G.par G.apa G.mol G.pal G.ver G.fra G.dis G.mol G.pus G.rob G.urb G.hed
Species name Cirsium arvense Cirsium palustre Cirsium vulgare Cladium mariscus Clematis vitalba Convolvulus arvensis Cornus sanguinea Corylus avellana Crataegus monogyna Crepis biennis Cynosurus cristatus Dactylis glomerata Daucus carota Deschampsia cespitosa Dipsacus fullonum Echinochloa crus-galli Eleocharis palustris Epilobium augustifolium Epilobium hirsutum Epilobium palustre Epilobium parviflorum Epilobium tetragonum Epipactis helleborine Equisetum arvense Equisetum palustris Equisetum pratense Euonymus europaeus Eupatorium cannabinum Euphorbia palustris Fagus sylvatica Festuca arundinacea Festuca pratensis Festuca rubra Festulolium Ficaria ranunculoides Filipendula ulmaria Fragaria vesca Frangula alnus Fraxinus excelsior Galeopsis angustifolia Galeopsis tetrahit Galinsoga parviflora Galium aparine Galium mollugo Galium palustre Galium verum Gaudinia fragilis Geranium dissectum Geranium molle Geranium pusillum Geranium robertianum Geum urbanum Glechoma hederacea (continued on next page)
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Appendix B (continued)
Appendix B (continued)
Species abbreviation G.flu G.max G.uli H.hel H.sph H.lan H.mur H.sec H.lup H.vul H.per H.qua H.rad I.aqu I.cap I.con I.foe I.pse J.reg J.acu J.art J.buf J.eff J.ger J.inf J.sp J.sub L.gal L.alb L.pur L.com L.pra L.his L.vul L.vulg L.mul L.per L.per L.cor L.ped L.flo L.eur L.num L.vul L.sal M.syl M.cha M.dis M.mar M.ara M.lup M.sat M.alt M.off
1 2 7 ( 2 0 0 6 ) 1 â&#x20AC;&#x201C;1 7
Species name Glyceria fluitans Glyceria maxima Gnaphalium uliginosum Hedera helix Heracleum sphondylium Holcus lanatus Hordeum murinum Hordeum secalinum Humulus lupulus Hydrocotyle vulgaris Hypericum perforatum Hypericum quadrangulum Hypochaeris radicata Ilex aquifolium Impatiens capensis Inula conyzae Iris foetidissima Iris pseudacorus Juglans regia Juncus acutiflorus Juncus articulatus Juncus bufonius Juncus effusus Juncus gerardii Juncus inflexus Juncus sp Juncus subnodulosus Lamiastrum galeobdolon Lamium album Lamium purpureum Lapsana communis Lathyrus pratensis Leontodon hispidus Leucanthemum vulgare Ligustrum vulgare Lolium multiflorum Lolium perenne Lonicera periclymenum Lotus corniculatus Lotus pedunculatus Lychnis flos-cuculi Lycopus europaeus Lysimachia nummularia Lysimachia vulgaris Lythrum salicaria Malus sylvestris Matricaria chamomilla Matricaria discoidea Matricaria maritima subsp. inodora Medicago arabica Medicago lupulina Medicago sativa Melilotus altissimus Melilotus officinalis
Species abbreviation M.aqu M.arv M.sp M.spi M.per M.tri M.mur M.arv M.disc M.sco M.aqu O.fis O.sal O.spi P.rho P.hyb P.aru P.pra P.aus P.hie P.lan P.maj P.nem P.prat P.tri P.amp P.avi P.hyd P.per P.sp P.alb P.del P.nig P.tre P.ans P.rep P.ver P.vul P.avi P.cer P.pad Pr.sp P.spi P.jap P.dys Q.pet Q.rob R.acr R.aqu R.fla R.rep R.sar R.cat R.rub
Species name Mentha aquatica Mentha arvensis Mentha sp Mentha spicata Mercurialis perennis Moehringia trinervia Mycelis muralis Myosotis arvensis Myosotis discolor Myosotis scorpioides Myosoton aquaticum Oenanthe fistulosa Oenanthe silaifolia Ononis spinosa Papaver rhoeas Petasites hybridus Phalaris arundinacea Phleum pratense Phragmites australis Picris hieracioides Plantago lanceolata Plantago major Poa nemoralis Poa pratensis Poa trivialis Polygonum amphibium Polygonum aviculare Polygonum hydropiper Polygonum persicaria Polygonum sp Populus alba Populus deloides Populus nigra Populus tremula Potentilla anserina Potentilla reptans Primula veris Prunella vulgaris Prunus avium Prunus cerasus Prunus padus Prunus sp Prunus spinosa Pseudosasa japonica Pulicaria dysenterica Quercus petraea Quercus robur Ranunculus acris Ranunculus aquatilis Ranunculus flammula Ranunculus repens Ranunculus sardous Rhamnus cathartica Ribes rubrum
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Appendix B (continued) Species abbreviation R.pse R.amp R.pal R.nas R.sp R.fru Ru.sp R.ace R.con R.cri R.hyd R.obt Rum.sp R.acu S.alb S.cap S.cin S.nig S.val S.mar S.aur S.nod S.gal S.eru S.vul S.ver S.lat S.vul S.arv S.off S.dul S.nig S.arve S.asp S.sp S.pal S.syl S.gra S.med S.off T.com T.off T.sp T.bac T.aqu T.fla T.cor T.pla T.jap T.pra Tr.sp T.cam T.dub T.pra T.rep
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Appendix B (continued) Species name Robinia pseudacacia Rorippa amphibia Rorippa palustris Rorripa nasturtium-aquaticum Rosa sp Rubus fruticosus Rubus sp Rumex acetosa Rumex conglomeratus Rumex crispus Rumex hydrolapatum Rumex obtusifolius Rumex sp Ruscus aculeatus Salix alba Salix caprea Salix cinerea Sambucus nigra Samolus valerandi Scirpus maritimus Scrophularia auriculata Scrophularia nodosa Scutellaria galericulata Senecio erucifolius Senecio vulgaris Setaria verticillata Silene latifolia Silene vulgaris Sinapsis arvensis Sisymbrium officinale Solanum dulcamaria Solanum nigrum Sonchus arvensis Sonchus asper Sonchus sp Stachys palustris Stachys sylvatica Stellaria graminea Stellaria media Symphytum officinale Tamus communis Taraxacum officinale Taraxacum sp Taxus baccata Thalictrum aquilegiifolium Thalictrum flavum Tilia cordata Tilia platyphyllos Torilis japonica Tragopogon pratensis Tragopogon sp Trifolium campestre Trifolium dubium Trifolium pratense Trifolium repens
Species abbreviation T.fla T.aes U.lae U.min U.dio V.off V.offi V.ana V.bec V.cat V.cha V.hed V.per V.scu V.ser V.sp V.ver V.opu V.cra V.sat V.sep V.can V.rei Vi.sp V.alb
Species name Trisetum flavescens Triticum aestivum Ulmus laevis Ulmus minor Urtica dioica Valeriana officinalis Verbena officinalis Veronica anagallis-aquatica Veronica beccabunga Veronica catenata Veronica chamaedrys Veronica hederifolia Veronica persica Veronica scutellata Veronica serpyllifolia Veronica sp Veronica verna Viburnum opulus Vicia cracca Vicia sativa Vicia sepium Viola canina Viola reichenbachiana Viola sp Viscum album
R E F E R E N C E S
Andersen, A.N., 1995. Measuring more of biodiversity: genus richness as a surrogate for species richness in Australian ant faunas. Biological Conservation 73, 39–43. Balmford, A., Jayasuriya, A.H.M., Green, M.J.B., 1996. Using higher-taxon richness as a surrogate for species richness: II. Local applications. Proceeding of Royal Society of London B 263, 1571–1575. Baudry, J., Bunce, R.G.H., Burel, F., 2000. Hedgerows: an international perspective on their origin, function and management. Journal of Environmental Management 60, 7–22. Belyea, L.R., Lancaster, J., 1999. Assembly rules within a contingent ecology. Oikos 86, 402–416. Bennett, A.F., Hinsley, P.E., Bellamy, P.E., Swetman, R.D., Mac Nally, R., 2004. Do regional gradients in land-use influence richness, composition and turnover of bird assemblages in small woods? Biological Conservation 119, 191–206. Bibby, C.J., Burgess, N.D., Hill, D.A., 1992. Bird Census Techniques. Academic Press, London. Bo¨hning-Gaese, K., 1997. Determinants of avian species richness at different spatial scales. Journal of biogeography 24, 49–60. Burel, F., Baudry, J., 1995. Social, aesthetic and ecological aspects of hedgerows in rural landscapes as a framwork for greenways. Landscape and Urban Planning 33, 327–340. Burel, F., Butet, A., Delettre, Y.R., Millan de la Pena, N., 2003. Differential response of selected taxa to landscape context and agricultural intensification. Landscape and Urban Planning 1018, 1–10.
16
B I O L O G I C A L C O N S E RVAT I O N
Cousins, S.A.O., Eriksson, O., 2001. Plant species occurrences in a rural hemi boreal landscape: effects of remnant habitats, site history, topography and soil. Ecography 24, 461–469. Dale, S., Mjork, K., Solvang, R., Plumptre, A.J., 2000. Edge effects on the understory bird community in a logged forest in Uganda. Conservation Biology 14, 265–276. Dauber, J., Hirsh, M., Simmering, D., Waldhart, R., Otte, A., Wolters, V., 2003. Landscape structure as an indicator of biodiversity: matrix effects on species richness. Agriculture, Ecosystems and Environment. 2083, 1–9. de Blois, S., Domon, G., Bouchard, A., 2002. Factors affecting plant species distribution in hedgerows of southern Quebec. Biological Conservation 105, 355–367. Deckers, B., Hermy, M., Muys, B., 2004. Factors affecting plant species composition of hedgerows: relative importance and hierarchy. Acta Oecologica, 1–15. Dole´dec, S., Chessel, D., 1994. Co-inertia analysis: an alternative method for studying species-environment relationships. Freshwater Biology 31, 277–294. Duelli, P., Obrist, M.K., 2003. Biodiversity indicators: the choice of values and measures. Agriculture, Ecosystems and Environment 2063, 1–12. Ernoult, A., Freire-Diaz, S., Langlois, E., Alard, D., submitted. Are similar landscapes the result of similar histories? Landscape Ecology. Faith, D.P., Walker, P.A., 1996. Environmental diversity: on the best possible use of surrogate data for assessing the relative biodiversity of set of areas. Biodiversity and Conservation 5, 399–415. Fustec, E., Lefeuvre, J.C., 2000. Fonctions et valeurs des zones humides. Dunod, Paris. Gower, J.C., Legendre, P., 1986. Metric and Euclidean properties of dissimilarity coefficients. Journal of Classification 3, 5–48. Hastie, T.J., Tibshirani, R., 1990. Generalised Additive Models, London, UK. Heikkinen, R.K., Luoto, M., Virkkala, R., Rainio, K., 2004. Effects of habitat cover, landscape structure and spatial variables on the abundance of birds in an agricultural-forest mosaic. Journal of Applied Ecology 41, 824–835. Huston, M.A., 1993. Biological diversity, soils, and economics. Science 262, 1676–1680. Jeanneret, P., Schupbach, B., Luka, H., 2003. Quantifying the impact of landscape and habitat features on biodiversity in cultivated landscapes. Agriculture, Ecosystems & Environment 98, 311–320. Keddy, P.A., 1992. Assembly and response rules: two goals for predictive community ecology. Journal of Vegetation Science 3, 157–164. Kerr, J.T., Sugar, A., Packer, L., 2000. Indicators taxa, rapid biodiversity assessment and nestedness in an endangered ecosystem. Conservation Biology 14, 1726–1734. Krauss, J., Klein, A.M., Steffan-Dewenter, I., Tscharntke, T., 2004. Effects of habitat area, isolation, and landscape diversity on plant species richness of calcareous grasslands. Biodiversity and Conservation 13, 1427–1439. Lambeck, R., 1997. Focal species: a multi-species umbrella for Nature conservation. Conservation Biology 11, 849–856. Li, H., Wu, J., 2004. Use and misuse of landscapes indices. Landscape Ecology 19, 389–399. Lindborg, R., Eriksson, O., 2004. Historical landscape connectivity affects present plant species diversity. Ecology 85, 1840–1845. Martinez-Morales, M.A., 2005. Landscape patterns influencing bird assemblages in a fragmented neotropical cloud forest. Biological Conservation 121, 117–126. Mazerolle, M.J., Villard, M.A., 1999. Patch characteristics and ladnscape context as predictors of species presence and abundance: a review. Ecoscience 6, 117–124.
1 2 7 ( 2 0 0 6 ) 1 –1 7
McCullagh, P., Nelder, J.A., 1989. Generalized linear models. London, UK. McGarigal, K., Marks, B.J., 1995. Fragstats: spatial pattern analysis program for quantifying landscape structure. Department of Agriculture, Forest Service, Pacific Northwest Research Station, Portland, OR: US. Meeus, J.H.A., 1993. The transformation of agricultural landscapes in Western Europe. The Science of the Total Environment 129, 171–190. Meybeck, M., de Marsily, G., Fustec, E., 1998. La seine en son bassin: fonctionnement e´cologique dÕun syste`me fluvial anthropise´. Elsevier. Millan de la Pena, N., Butet, A., Delettre, Y.R., Paillat, G., Morant, P., Le Du, L., Burel, F., 2003. Response of the small mammal community to changes in western French agricultural landscapes. Landscape Ecology 18, 265–278. Miller, J.N., Brooks, R.P., Croonquist, M.J., 1997. Effects of landscape patterns on biotic communities. Landscape Ecology 12, 137–153. Moser, D., Zechmeister, H.G., Plutzar, C., N., S., Wrbka, T., Grabherr, G., 2002. Landscape patch shape complexity as an effective measure for plant species richness in rural landscapes. Landscape Ecology 17, 657–669. Noss, R.F., 1990. Indicators for monitoring biodiversity: a hierarchical approach. Conservation Biology 4, 355–364. OÕBrien, E.M., 1998. Water-energy dynamics, climate, and prediction of woody plant specie richness: An interim general model. Journal of Biogeography 25, 379–398. Pearson, S.M., 1993. The spatial extent and relative influence of landscape-level factors on wintering bird populations. Landscape Ecology 8, 3–18. Pedroli, G., Borger, G.J., 1990. Historical land use and hydrology. A case study from eastern Noord-Brabant. Landscape Ecology 4, 237–248. Pino, J., Roda`, F., Ribas, J., Pons, X., 2000. Landscape structure and bird species richness: implications for conservation in rural areas between natural parks. Landscape and Urban Planning 49, 35–48. Poudevigne, I., Baudry, J., 2003. The implication of past and present landscape patterns for biodiversity research: introduction and overview. Landscape Ecology 18, 223–225. Poudevigne, I., Van Rooij, S.A.M., Morin, P., Alard, D., 1997. Dynamics of rural landscapes and their main driving factors: a case study in the Seine valley, Normandy, France. Landscape and Urban Planning 38, 93–103. Prendergast, J.R., Quinn, R.M., Lawton, J.H., Eversham, B.C., Gibbons, D.W., 1993. Rare species, the coincidence of diversity hotspots and conservation strategies. Nature 365, 335–337. Radford, J.Q., Bennett, A.F., Cheers, G.J., 2005. Landscape-level thresholds of habitat cover for woodland-dependent birds. Biological Conservation 124, 317–337. Roux, M., 1991. Basic procedures in hierarchical cluster analysis. In: Devillers, J., Karcher, W. (Eds.), Applied multivariate analysis in SAR and environmental studies. Kluwer Academic Publishers, Dordrecht, The Netherlands, pp. 115–136. Sanchez-Zapata, J.-A., Carrete, M., Gravilov, A., Sklyarenko, S., Ceballos, O., Donazar, J.-A., Hiraldo, F., 2003. Land use changes and raptor conservation in steppe habitats of eastern Kazakhstan. Biological Conservation 111, 71–77. Sauberer, N., Zulka, K.P., Moser, D., Abensperg-Traun, M., Berg, H.-M., Bieringer, G., Milasowszky, N., Plutzar, C., Pollheimer, M., Storch, C., Tro¨stl, R., Zechmeister, H.G., Grabherr, G., 2004. Surrogate taxa for biodiversity in agricultural landscapes of eastern Austria. Biological Conservation 117, 181–190. Shriver, W.G., Hodgman, T.P., Gibbs, J.P., Vickery, P.D., 2004. Landscape context influences salt marsh birds diversity and
B I O L O G I C A L C O N S E RVAT I O N
area requirements in New England. Biological Conservation 119, 545–553. So¨derstro¨m, B., Svensson, B., Vessby, K., Glimska¨r, A., 2001. Plants, insects and birds in semi-natural pastures in relation to local habitat and landscape factors. Biodiversity and Conservation 10, 1839–1863. Steiner, N.C., Kohler, W., 2003. Effects of landscape patterns on species richness—a modelling approach. Agriculture, Ecosystems & Environment 98, 353–361. Suarez-Seoane, S., Osborne, P.E., Alonso, J.C., 2002. Large-scale habitat selection by agricultural steppe birds in Spain: identifying species-habitat responses using generalized additive models. Journal of Applied Ecology 39, 755–771. Thioulouse, J., Chessel, D., Doledec, S., Olivier, J.M., 1997. ADE-4: a multivariate analysis and graphical display software. Statistic and Computing 7, 75–83. Uezu, A., Metzger, J.P., Vielliard, J.M.E., 2005. Effects of structural and functional connectivity and patch size on the abundance of seven Atlantic Forest bird species. Biological Conservation 123, 507–519. Venables, W.N., Ripley, B.D., 2002. Modern Applied Statistics with S. Springer-Verlag, New York.
1 2 7 ( 2 0 0 6 ) 1 –1 7
17
Virkkala, R., Luoto, M., Rainio, K., 2004. Effects of landscape composition on farmland and red-listed birds in boreal agricultural-forest mosaics. Ecography 27, 273–284. Wagner, H.H., Wildi, O., Ewald, K.C., 2000. Additive partitioning of plant species diversity in an agricultural mosaic landscape. Landscape Ecology 15, 219–227. Ward, J.H., 1963. Hierarchical grouping to optimize an objective function. Journal of American Statistic 58, 238–244. Weibull, A., Ostman, O., Granqvist, A., 2003. Species richness in agroecosystems: the effect of landscape, habitat and farm management. Biodiversity and Conservation 12, 1335–1355. Weibull, A.-C., Bengtsson, J., Hohlgren, E., 2000. Diversity of butterflies in the agricultural landscape: the role of farming system and landscape heterogeneity. Ecography 23, 743–749. Weiher, E., Keddy, P.A., 1995. The assembly of experimental wetland plant communities. Oikos 73, 323–335. Williams, M., 1989. Historical geography and the concept of landscape. Journal of Historical Geography 15, 92–104. Wohlgemuth, T., 1998. Modelling floristic species richness on a regional scale : a case study in Switzerland. Biodiversity and Conservation 7, 159–177. WRB., 1998. World Reference Base for soil resources. FAO, ISRIC and ISSS.