New technologies and approaches to measure ecosystem services locally and engage local stakeholders

2020 #GGSD Forum

Securing natural capital

24 - 26 November

Issue Paper Conference version

New technologies and approaches to measure ecosystem services locally and to engage local stakeholders From concepts to real-life applications of ecosystem services modelling and decision support tools

Peter van Bodegom and Roy Remme, Leiden University

OECD Green Growth and Sustainable Development Forum The GGSD Forum is an OECD initiative aimed at providing a dedicated space for multi-disciplinary dialogue on green growth and sustainable development.

It brings together experts from different policy fields and disciplines and provides them with an interactive platform to encourage discussion, facilitate the exchange of knowledge and ease the exploitation of potential synergies. By specifically addressing the horizontal, multi-disciplinary aspects of green growth and sustainable development, the GGSD Forum constitutes a valuable supplement to the work undertaken in individual government ministries. The GGSD Forum also enables knowledge gaps to be identified and facilitates the design of new works streams to address them.

Authorship & Acknowledgements This issue note was prepared for the 2020 GGSD Forum to inform the discussion. The authors are Peter van Bodegom and Roy Remme from Leiden University. The note benefitted from comments and suggestions by Kumi Kitamori, Mario Cervantes, Katia Karousakis, Claire Jolly, Alexander Mackie and Enrico Botta from the OECD. The note was produced under the supervision of Kumi Kitamori, Head, Green Growth and Global Relations Division, OECD. Financial contribution by the Permanent Representation of the Kingdom of the Netherlands to the OECD is gratefully acknowledged.

The opinions expressed herein do not necessarily reflect the official views of the OECD member countries. This document, as well as any data and map included herein, are without prejudice to the status of or sovereignty over any territory, to the delimitation of international frontiers and boundaries and to the name of any territory, city or area.

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1 Introduction Following the entry into force of the Convention on Biological Diversity, there is an international fundament to protect and conserve biodiversity. At the same time, the natural environment, including biodiversity, is under increasing pressure (Newbold et al., 2016[1]). With a decline in biodiversity, ecosystem functioning is also deteriorating. Nature itself and the goods and services that nature provides are vital to human well-being (Brondizio et al., 2019[2]). Thus, it is of critical importance to consider the sustained quality of the fundaments of nature, including an appropriate functioning of ecosystems.

Fortunately, environmental quality is increasingly acknowledged in national and international decisionmaking as a foundation for (sustainable) economic development.

Two key concepts have played an important role to establish the connection between nature and human society. The first concept is ecosystem services, which was introduced to highlight the connection between nature and human well-being. In the past decades, scientific research has embraced the concept of ecosystem services. While the concept of ecosystem services has been around since the 1980s (e.g.

(Ehrlich and Mooney, 1983[3]), the field has taken off following two key publications by (Daily, 1997[4]) and (Costanza et al., 1997[5]). Several conceptual frameworks have been proposed to visualize the beneficial relations between ecosystems and society (e.g. (Daily et al., 2009[6]); (de Groot et al., 2010[7]); (HainesYoung and Potschin, 2010[8]); Figure 1). Each of these frameworks have their pros and cons, but most emphasize how nature provides material and immaterial contributions to human well-being. Through those contributions by nature, products from nature are transferred to the human society. Some of these products can be replaced easily, others can be replaced less easily or are irreplaceable.

This brings us to the second key concept: natural capital. Natural capital is basically the stock from which the services can be derived (Figure 1; (Costanza and Daly, 1992[9])) and includes e.g., the number of individuals of plants and animals, including biodiversity as well as abiotic natural sources such as clean air, good quality fresh water and carbon. In a sustainable society, the provision of ecosystem services – to maintain human well-being- is arranged in such a way that the natural capital is replenished or actually restored to capitals associated with undisturbed conditions. Restoration is essential, given that only one out of five of the strategic objectives of six global agreements on nature and the protection of the environment are on track to be met (Brondizio et al., 2019[2]). There is thus an urgent need for better decision-making to improve the management of our natural resources. Natural capital accounting is the most well-known tool to measure the changes in the stocks of natural capital at a variety of scales (La Notte et al., 2017[10]). Given that multiple countries employ major efforts into protecting, conserving and enhancing their natural capital (as exemplified by the Green Deal in the EU (European Commission, 2019[11])), the importance of developing, evaluating and improving tools for natural capital accounting is evident. This issue paper focuses on ecosystem services and its quantitative assessment since this is a central aspect of capital accounting systems. Various data challenges faced by natural capital accounting (Hein et al., 2020[12]) are similar to those of ecosystem services. The insights provided by this issue paper can thus also be applied to natural capital accounting.

The concept of ecosystem services directly links human well-being to nature. Through this link, ecosystem services considerations help generating public understanding about the dependence of society on ecosystems, and aid cost-benefit analyses for environmental decision-making, landscape management, nature conservation or a mix of the above ( (Balmford et al., 2002[13]) (Fisher, Turner and

4| Morling, 2009[14])). Indeed, better understanding of ecosystem services are often recognised to help inform policy and decisions e.g. for biodiversity conservation, natural resource management, and spatial planning (Daily et al., 2009[6]) (Laurans and Mermet, 2014[15]) (Martinez-Harms et al., 2015[16]).

Ecosystem Natural capital

Figure 1:The cascade of ecosystem services from natural capital as defined by ecosystem properties and capacity via the actual delivery and flow of ecosystem services to contributions to human well-being.

Adapted from (Haines-Young and Potschin, 2010[8]).

Despite claims on the potential of ecosystem services to support decision making and nature protection, until recently it was mostly an academic concept, with limited evidence on actual impacts (GGKP, 2020[17]). Recently, ecosystem services have been embedded in national and supranational policy agendas, such as that of the United States (Arkema et al., 2015[18]), the European Union (Maes et al., 2012[19]) and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (www.ipbes.net). Concrete national initiatives that are being initiated to assess ecosystem service benefits are, amongst others, the ‘Atlas Natuurlijk Kapitaal’ in the Netherlands (‘atlas natural capital’; atlasnatuurlijkkapitaal.nl/en), and ‘National Ecosystem Assessments’ in the United Kingdom, Germany, Spain and Japan (Wilson et al., 2014[20]). However, the uptake of the concept in general and of specific ecosystem services in policy decision making seems to have occurred slowly and perhaps not as comprehensively as initially expected (Bouwma et al., 2018[21]) (Schleyer et al., 2015[22]).

At the same time, the concept is only beneficial if it is leading to local and national implementation in decision making.

A number of challenges needs to be tackled to ensure that ecosystem services are understood by decision makers and reflected in their decision-making. First, decision makers must be made aware of the concept. The various national and international policy-related initiatives listed above have certainly helped and illustrate the uptake of the concept in policy decision-making. Indeed, decision makers are increasingly interested in ecosystem services assessments (Maes et al., 2016[23]) (Pascual et al., 2017[24]).

A recent study (van Oudenhoven et al., 2018[25]) showed that particularly broad, unspecified ecosystem services are recognised and adopted by policy actors.

Secondly, the proposed ecosystem services must be informative and engaging in order to be accepted as a tool for (environmental) decision-making (Laurans and Mermet, 2014[15]). So-called indicators play a crucial role in this process. Indicators express the characteristics and trends in an ecosystem service to track and communicate (trends in) the quantity and quality of ecosystem services (Layke et al., 2012[26]) (Maes et al., 2016[23]). For ecosystem services to be adopted, the selected indicators

|5 should be of immediate use for decision making. However, discussion on the suitability of indicators has remained mainly academic and the main criteria discussed have been their scientific credibility or precision (van Oudenhoven et al., 2018[27]). Only once indicators of ecosystem services that connect to decisionmaking processes are identified, ecosystem services can go beyond conceptual discussions and be applied to instrumental decisions. The measurement and implementation of indicators of ecosystem services is therefore central to this paper.

Indicators to be instrumental for decision making needs to meet for key characteristics. van Oudenhoven et al. (2018[27]) provided guidance on how to develop appropriate indicators to be of instrumental use for decision making and linked indicators to credibility, salience and legitimacy (Wright, Eppink and Greenhalgh, 2017[28]), but also to feasibility (Figure 2). The latter aspect is frequently neglected in literature, but -without feasibility- indicators of ecosystem services are often unachievable in practice. A similar framework may also be applied to evaluate natural capital indicators in relation to specific impacts on decision-making, which is currently a major knowledge gap (GGKP, 2020[17]).

Figure 2: Synthesis of criteria mentioned in literature that are necessary for the adoption of (indicators of) ecosystem services in decision-making along the four axes of credibility, salience, legitimacy and feasibility. From: (van Oudenhoven et al., 2018[27]) This paper discusses the data analysis tools available for assessing the indicators of ecosystem services at the local level. The second section reviews the efforts made so far to derive and quantify indicators of ecosystem services, with emphasis on the use of spatial databases for quantifying ecosystem services. In the third section, new developments and approaches thanks to emerging technologies are discussed. This section emphasizes how new technologies allow providing high resolution assessments, which is an essential prerequisite for going to the local scales at which many decision making processes take place. In addition, the use of digitalisation and open science technologies to support decision making are discussed. In the fourth section, the connection of emerging technologies to the process of decision making is evaluated. The importance of appropriate indicators is revisited in the context of co-design and open science technologies and examples are provided to show how these new technologies aid decision making in real life applications.

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2 Tools for assessing ecosystem services

A wide variety of ecosystem services modelling and assessment tools exists, including look-up tables, value transfer functions and comprehensive ecosystem models (Bagstad et al., 2013[29]) (Crossman et al., 2013[30]). These tools vary in complexity and application and fall at different positions of our credibility - salience - legitimacy - feasibility framework. Most tools have in common that they summarize the information in spatial maps, e.g. through the use of geographic information systems (GIS).

GIS provides a powerful tool for visualising ecosystem services within a landscape. Spatial information is important, because ecosystems are heterogeneous and the provision of ecosystem services varies across space and time. Also demands for ecosystem services are spatially explicit: stakeholders (e.g. farmers or citizens) having interests in specific areas and landscape settings (Nemec and Raudsepp-Hearne, 2013[31]). We distinguish different categories of spatial tools that provide estimates of indicators of ecosystem services. It should be noted though that in practice there is overlap and gradual differences among these categories.

Look-up tables Look-up tables provide information on ecosystem services based on typologies of ecosystem types and land use types (for the more anthropogenic ecosystems). These typologies relate to the primary landscape units that provide many of the ecosystem services and express the fact that ultimately ecosystem services are derived from natural capital and the associated ecosystem processes and structure. There are many classifications of ecosystems, including ecoregions (Olson et al., 2001[32]), and the IUCN Global ecosystem typology as advocated for the United Nations System of Environmental Economic Accounting (SEEA) (Bogaart et al., 2019[33]), to name a few. Each of these classifications aims at clustering ecosystem functions, which credibly link to ecosystem services. The wide availability of (open source) information and maps of land use types from regional to global scales makes this type of approach also highly feasible. Look-up tables of ecosystem services capacity -ordered from high to low- in relation to land cover have been developed (Burkhard et al., 2009[34]). Also the first version of EU Mapping and Assessment of Ecosystems and their Services (MAES) was to major extents based on this concept.

River catchments, i.e. the area of land from which water flows into the river, are also used for the classification of ecosystem services. Catchment delineations are particularly helpful for mapping indicator values of water-related ecosystem services. The study of (Maes et al., 2012[19]) is a representative example of this approach. In their study, water purification services delivered by freshwater ecosystems were modelled for South-Western France. For each catchment, the average residence time 1 of water and thus the retention of nitrogen within a catchment was given an aggregate value depending on the hydrological and geomorphological conditions. The proportion of nitrogen inputs removed, as a measure of the water purification service, was determined based on this residence time (Maes et al., 2012[19]).

1

Residence time is the average number of days that a water particle stays within a catchment

|7 The Achilles’ heel of using look-up tables is to derive representative and unbiased values of ecosystem services for each land use type. While ecosystem types conveniently aggregate information on ecosystem functioning, coming up with representative indicator values that are generically applicable is often complicated, with possibly negative impacts on the salience of the indicator. Moreover, these mapped indicators provide a static view of the situation, while in reality the capacity to provide ecosystem services varies in both space and time. A final disadvantage of using look-up tables alone is that it only allows mapping the potential supply, i.e. the capacity to deliver ecosystem services and not the actual supply or societal demands for those services. In combination, these disadvantages constrain the suitability the usefulness of this approach to decision making (Grêt-Regamey et al., 2017[35]).

Value transfer functions Value transfer functions allow estimating and mapping indicator values based on their dependencies on local conditions (Troy and Wilson, 2006[36]). The value transfer function approach entails designing the “transfer functions” that allow to estimate the ecosystem services provided by a specific location. Factors that are known to affect the ecosystem service under consideration and that are available in data sources feed into the transfer function. Once the function is designed, the actual estimation of the resulting ecosystem service value is performed through specific software (e.g. ArcGIS).

Transfer functions can be used in any situation for which it is not possible to collect data on the actual supply and demand of ecosystem services. Value transfer functions commonly use continuous variables in contrast to look-up tables- to identify differences in values due to local differences in drivers of ecosystem services. Many of the well-known studies on ecosystem services (e.g. (Costanza et al., 1997[5])) are based on value transfer functions.

Different types of valuation have been developed over time, to reflect that ecosystem service values may span over highly different value dimensions (see e.g. (Martín-López et al., 2014[37]) (Pascual et al., 2010[38])). Biophysical and monetary valuation are the most commonly applied types of valuation, expressing the effect of underlying physical parameters and its conversion into monetary units, respectively. Socio-cultural valuation, expressing importance or preferences expressed by people, has been gaining attention more recently (Scholte, van Teeffelen and Verburg, 2015[39]). Using biophysical valuation (or: ecological valuation or biophysical quantification), ecosystem services are measured in terms of the contribution of ecological functions and processes to the provision of services (in biophysical units (de Groot et al., 2010[7])). For example, carbon sequestration can be measured as tonnes of carbon dioxide (CO2) stored and water purification as the amount of dissolved nitrogen in the water. The pros and cons of using biophysical vs. monetary valuation are beyond the scope of this issue paper and are extensively discussed elsewhere (e.g. (Gómez-Baggethun and Ruiz-Pérez, 2011[40])).

Several of the currently most widely used tools quantify and map the biophysical values of ecosystem services, and its subsequent conversion to monetary values. This fits in the more pluralistic view on ecosystem services valuation that has been emerging. This pluralistic view considers that ecosystem services may contain different value dimensions and advocates to integrate the different types of valuation (i.e. so-called ‘integrated valuation’; (Jacobs et al., 2016[41])). Moreover, decision makers often have preferences for different valuation methods, or for different ways of expressing indicators in general. Providing multiple expressions of indicators of ecosystems services may thus aid decision making, particularly when the indicators are developed in co-design with decision makers (see section 4.).

A wide range of tools exists (see reviews by (Crossman et al., 2013[30]) (Bagstad et al., 2013[29]) (GrêtRegamey et al., 2017[35])). Examples include the Land Utilisation and Capability Indicator (LUCI; (Sharps et al., 2017[42])), and Multiscale Integrated Model of Ecosystem Services (MIMES; (Boumans et al., 2015[43])). i-TREE (www.itreetools.org) is widely used to estimate the ecosystem services provided by (individual) trees and has e.g. been employed to determine the benefits of urban green infrastructure (e.g.

8| (Parsa et al., 2019[44])). Two of the most widely used tools for mapping and valuating of both terrestrial, freshwater and marine ecosystem services are InVEST 2 and ARIES 3. InVEST is a GIS-tool based on land use data (and other environmental variables) from which different indicators of ecosystem services can be calculated (see Box 1 for an extensive example). ARIES, on the other hand, emphasizes the model infrastructure and the ease to connect and integrate different models and input data. Both tools are opensource software models that use spatially explicit information to derive values of indicators of ecosystem services. Both tools have the flexibility to incorporate different valuation methods and indicators for which ARIES is more flexible than InVEST. In all cases, maps are used as information sources and through value transfer functions translated into maps of indicator values. Depending on the indicator, the transfer function and the information sources available, outcomes may be presented at different spatial resolutions.

Both indicators on the capacity to supply ecosystem services and on the actual delivery of ecosystem services have been incorporated in the various tools available. While the capacity and actual delivery of ecosystem services have sometimes been mixed up in past valuation studies, its distinction is crucial in the context of decision making. The capacity of delivering ecosystem services is an important property of the ecosystem or land use type involved as it refers to the potential of providing multiple ecosystem services. As such it is directly related to ecosystem quality and natural capital. The actual delivery expresses the balance between (ecosystems) supply and (stakeholders’) demand and provides direct insights in the current use and valuation of an ecosystem service, but may be less related to the sustainability of that use.

When biophysical values are computed, key sources of uncertainties are the value transfer function applied and the input data used (Figures 3 and 4). In figure 4, the outcome on annual water yield, as a provisioning service, for a catchment in the United Kingdom is provided for three different approaches determining this indicator (Sharps et al., 2017[42]). While there are commonalities in the patterns, due to the common topography, local indicator value can vary substantially. Without local validation it is not always possible to judge the validity of the outcomes. Variation in the outcome of the indicator may also be induced by the use of different definitions for an indicator and the scale at which the indicator is defined.

The method used for monetary valuation is an additional source of uncertainty. Different monetary valuation approaches have been developed for ecosystem services, accommodating for the different settings in which the various ecosystem services prevail (Bateman et al., 2011[45]) (Farber et al., 2006[46]).

A recent study (Schild et al., 2018[47]) showed that the monetary value of a service strongly depended on the valuation approach applied. This is not always apparent from the information in the value transfer function and thus may lead to over- or underestimates in the calculated value.

The number of approaches that modelled and mapped the demand for ecosystem services by stakeholders is more limited than for supply (see review by (Wolff, Schulp and Verburg, 2015[48])).

Some approaches for mapping ecosystem services demands have focused on consumption and use of services as a measurement of demand, using the assumption from economics that consumption reflects the equilibrium between supply and demand (Burkhard et al., 2012[49]). Others have focused on desires and preferences to indicate demand, allowing demands to exceed the current supply of ecosystem services (Villamagna, Angermeier and Bennett, 2013[50]). Using desires and preferences is particularly useful to non-commodity services, such as the use of a natural site for religious rituals. So far, the demands for ecosystem services have not been explicitly modelled in any of the tools treated above. Instead, it is commonly assumed that the value of an indicator expresses the demand for the service. Whether that is indeed the case depends on the (monetary) valuation approach applied.

2

https://naturalcapitalproject.stanford.edu/software/invest

3

http://aries.integratedmodelling.org/

|9 Box 1: Impacts of input data on model results – an example for the InVEST urban cooling model The increasing availability of open access high-resolution spatial data enables the ecosystem service analyses of previously understudied areas, such as urban areas. Natural capital in cities is highly fragmented and requires high-resolution data to adequately model the ecosystem services related to it. Here we provide an example of urban heat mitigation by urban green spaces, using the InVEST urban cooling model for Amsterdam, the Netherlands (Figure 3).

Coarse resolution data, such as the Copernicus Corine land cover only detects large vegetated spaces, such as large city parks and peri-urban green spaces, which is insufficient to capture the local importance of urban natural capital. Higher resolution land use data, more tailored to urban spaces, such as the Copernicus Urban Atlas, enables capturing the impact of natural capital in small green spaces such as pocket parks. These maps, however, do not capture all urban greenery, such as street trees. Satellite-based open access data is increasingly becoming available to capture all greenspaces. For the Netherlands, the Atlas Natuurlijk Kapitaal provides a national 10m resolution tree cover map. By combining this data with the Urban Atlas data, the InVEST urban cooling model results provide insight into highly localized impacts of the city’s vegetation on heat mitigation, thus informing planning and facilitating decision making at local level.

Figure 3: Impact of different land use inputs on the results of the InVEST urban cooling model for Amsterdam, the Netherlands for a) CORINE 2018 land cover at 100m resolution (https://land.copernicus.eu/pan-european/corine-land-cover/clc2018), b) the Urban Atlas 2018 at 10m resolution (https://land.copernicus.eu/local/urban-atlas/urban-atlas-2018), and c) a combination of the Urban Atlas 2018 and the tree cover map of the Atlas of Natural Capital at 10m resolution (www.atlasnatuurlijkkapitaal.nl). The Heat Mitigation Index indicates the importance of areas for mitigating the urban heat island.

10 | The credibility and salience of the indicators highly depend on the transfer function applied. For the credibility it matters whether the transfer function is accepted by the scientific community and whether the indicator is actually a valid representation of the ecosystem service. The latter condition is often difficult to validate as the ecosystem service itself is commonly unknown (van Oudenhoven et al., 2018[27]). For the salience, the scalability and transferability of the transfer functions matter. This is more likely to be true when the function is more directly related to the ecological and societal processes. The most crucial property in relation to salience is likely whether the indicator is understandable and relevant from a decision maker’s point of view. The chosen indicator has to be relevant within the context of decision making. This is not always guaranteed which can negatively affect the legitimacy of the indicator (see section 4). In some cases, relevance is reduced due to limitations in data availability, which reduces the capacity to make a link to human well-being (e.g. remote sensing data allows quantifying net primary production in grasslands, but is not able to account for how much is actually consumed or used by humans). Finally, with respect to feasibility, tools like InVEST and ARIES (and others) aim to be as user-friendly as possible.

At the same time, it turns out that the technical knowledge of many decision makers for using spatial databases is quite limited, constraining the actual feasibility of such tools. This topic will be touched upon again in section 4.

Transfer function based on

Land use and land Cover look-up table

Hydrological flow model for a standard crop

Statistical model based on actual measurements

Figure 4: Example of differences in impacts of different modelling approaches on the determination of provided ecosystem services. In this example, three approaches were applied to the same ecosystem services for the Conwy catchment in the United Kingdom. While overall patterns are similar, induced by the topography of the landscape, local differences are substantial. Adapted from (Sharps et al., 2017[42]).

Comprehensive (ecosystem) models The final category consists of (process-based) ecosystem models. A wide variety of local to global ecosystem models exists, modelling ecosystem processes from primary productivity till litter decomposition and balances of inputs and outputs of water, carbon and nutrients (see recent comprehensive overview by (Geary et al., 2020[51])). These ecosystem models aim at comprehensively describing ecosystem processes and thus provide the most process-based descriptions of the capacity of ecosystems to supply an ecosystem service. Various ecosystem models have been applied to estimate the supply of ecosystem services. Dynamic Global Vegetation Models (DGVMs), which are the comprehensive models underlying the land component in the models used by the IPCC to determine climate change, have been used for

| 11 mapping and predicting the supply of ecosystem services. Ecosystem models, including DGVMs, are particularly useful for modelling regulation services, such as carbon sequestration. For example, (Bachelet et al., 2018[52]) used a DGVM to model carbon sequestration and the amount of biomass consumed by wildfires as contributions to climate regulation. Also the Soil and Water Assessment Tool (SWAT), a hydrological model, has been used in multiple occasions to calculate water-related ecosystem services.

(Francesconi et al., 2016[53]) reviewed the various applications of SWAT in the context of modelling ecosystem services and concluded that SWAT has been most successfully applied to determine water regulating services and sediment regulation. In principal, ARIES (and to some extents InVEST too) allow the inclusion of ecosystem models in their framework to calculate ecosystem services. This has the advantage that links to input data can be made more easily.

The capacity and -in the case of regulating services- also the actual provisioning of ecosystem services can be modelled with ecosystem models. However, as was the case for value transfer functions, modelling the demand for ecosystem services is much harder. On the other hand, dedicated models have been developed to determine the flow of ecosystem services, thus linking supply to demand.

These modeling approaches (e.g. (Bagstad et al., 2013[54]) (Serna-Chavez et al., 2014[55]) (Schrรถter et al., 2018[56])) determine the flow of a services from provision to beneficiaries as a function of landscape properties and the dispersal properties of the service involved.

Because comprehensive (ecosystem) models enable estimating the supply (and demand) of ecosystem services over space and time using process-based descriptions, indicators are potentially highly credible. Moreover, the process-based descriptions allow indicator values to be scalable and transferable, two characteristics of salience. However, whether they provide information that is directly relevant and understandable to decision makers is not directly guaranteed. The outcome of such models is easily considered a black box which may negatively affect the legitimacy of the indicators. Also the feasibility of the applying ecosystem models in a decision-making context are limited. Most decision makers will not be able to run the models themselves and thus depend on consultancies or academics to supply them with results. This does not only hamper the legitimacy, but it also affects the decision-making process itself as it reduces the flexibility of updating and revising the indicators during the process.

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3 Emerging technologies to assess ecosystem services locally

Emerging technologies can increase the performance of assessment tools in terms of these four criteria for applicability and adoption in decision-making: credibility, salience, legitimacy and feasibility. Here, we focus on digital technologies (OECD, 2019[57]). With digital technologies, new data layers are added that can be used as input data and ultimately support the provision of evidence-based indicators of ecosystem services. Many of these data layers are increasingly openly accessible. This allows access to data by other researchers and fits efforts of the OECD to promote open science (OECD, 2015[58]).

It also opens up possibilities for involving stakeholders and citizens and enhances the accountability and inclusiveness of the assessments and subsequent decision-making. Digital technologies enriches ecosystem services assessments in three crucial facets.

1. Higher resolution local input data Among the most important contributions of emerging technologies to ecosystem services assessments is the provision of high-resolution input data. High spatial (and temporal) resolution data allow providing estimates of ecosystem services at a local scale, i.e. at the scale at which many local and regional decision makers take and implement their decisions. This provides decision makers with the local visualization and understanding needed to link ecosystem services to the locality for which they are responsible. Apart from providing more insight in the local distribution of ecosystem services supply and the mechanisms that influence this supply, the use of high-resolution data may provide less biased estimates (Kandziora, Burkhard and MĂźller, 2013[59]). Ecosystem service assessments based on representative values per land use type (see section 2.) are unable to provide such detailed information, although they are still useful at national and international scales.

Maps of land cover and land use are increasingly available at a high spatial resolution. The global dataset of the CORINE Land Cover inventory, as part of the European Copernicus services, has mapping units of 25ha. European Space Agency’s (ESA) global land cover Climate Change Initiative (CCI) product already has a better spatial resolution of 300 meters. Still, for many local decision making processes, these resolutions are too coarse. Recent local datasets of the Copernicus services on e.g. urban and riparian systems have a much higher resolution (with a minimum mapping unit of 0.25 ha), which in many cases resembles the scale at which decision makers would like to have the information (see text box 1 below).

These development are made possible by the quickly increasing spatial resolution of the satellite missions. MODIS, available on the Terra (launched 1999) and Aqua (launched 2002) satellites, was for a long period of time the most widely used instrument for land observations, and has a spatial resolution of minimally 250 meters. Landsat has a spatial resolution of 30 meters for most bands, while Sentinel-2 of ESA (launched in 2015 and 2017) has a spatial resolution of minimally 10 meters. The new prototypes of the land cover CCI product therefore also have a resolution of 10 meters, thanks to the use of Sentinel-2 data. For specific applications, information from so-called very high resolution satellites with a spatial resolution of less than 1 meter (such as SPOT, GeoEye-1 or Worldview-3) is used to derive local land use

| 13 in Europe, e.g. to monitor agricultural land use to evaluate EU’s Common Agricultural Policy, as part of the Controls with Remote Sensing (CwRS) programme.

The use of satellite data for data products has major advantages over data products based on surveying information. First of all, satellite algorithms have global applications, leading to internally consistent products. Most satellite-derived products are openly accessible. Another very important advantage compared to surveying approaches is that satellite data come with a high temporal resolution of e.g. 1-2 days (MODIS), 5 days (Sentinel-2) to 16 days (for Landsat), allowing to capture the temporal dynamics much more profoundly than with any surveying method. These opportunities apply equally well to data products for freshwater and marine ecosystem services (see review by (de Araujo Barbosa, Atkinson and Dearing, 2015[60])).

Secondly, the number of spectral bands measured by satellites is increasing to better capture the properties of light reflected by the earth’s surface. Given that different materials reflect at different parts of the light spectrum, this spectral information can be used to derive which materials are responsible for this reflectance. Sentinel-2 has 10 bands in the optical to near-infrared wavelengths, while Landsat has only 5 bands in this part of the spectrum and MODIS only four. Bands in the optical and near-infrared range of wavelengths are directly related to land properties. Various vegetation properties can be derived from this spectral information. The most well-known is Leaf Area Index (LAI), the number of layers of vegetation per surface area of soil, and the most well-known product is likely the MODIS product of LAI. The LAI determines the amount of radiation that can be captured by vegetation and therefore links to the productivity of vegetation and associated ecosystem services. However, many other land properties can be derived from this spectral information as well (as reviewed by (Homolova et al., 2013[61])). This includes e.g., chlorophyll contents –related to productivity and nitrogen regulation services- and canopy water contents – related to water regulating services. With more bands available, these properties can be derived more precisely and support the assessment of ecosystem services. Similarly, for marine ecosystem services, the chlorophyll contents and ocean colour, can be derived more precisely with the higher number of bands of the Sentinel-3 instrumentation.

Thirdly, satellites also have one or multiple bands in the short-wave infrared (SWIR) range of wavelengths, which facilitate the estimation of water regulation services. These bands are very useful for water and water vapour characteristics, and atmospheric properties (incl. atmospheric corrections). This information is highly useful for deriving water regulation services, e.g. for deriving evapotranspiration at high resolution (Peng et al., 2020[62]) and to monitor crop drought. In addition, SWIR data is frequently used in the context of climate regulation services. The Visible Infrared Imaging Radiometer Suite (VIIRS) on NOAA satellites is used in an ecosystem services context to monitor aerosols (as affecting urban health), fire occurrences and associated aerosol production (e.g. (Huff et al., 2015[63])), and light pollution. Other examples include monitoring oil pollution, ice movements and marine wave and wind observations, as made possible by e.g. Sentinel-1. Altimetry instruments (such as available at e.g.

Sentinel-3) allow estimating wave and wind properties. For instance, altimetry approaches have been employed to track changes in coastal currents, as affecting swimmer’s safety in the context of evaluating ecosystem services provided by nature-based solutions. Likewise, the number of tourists has been monitored through radar applications to evaluate whether nature-based solutions increase recreation opportunities (Luijendijk and van Oudenhoven, 2019[64]).

Finally, the thermal infrared information from satellites allow estimating sea and land surface temperatures. Figure 5 illustrates an application for ecosystem services, using 30-m resolution temperatures from Landsat8. By combining high-resolution information on temperatures with those on the location of green infrastructure, the extent to which green infrastructure contributes to the reduction of the urban heat island effect could be evaluated ( (van Oorschot et al., 2020[65]); Figure 5). Moreover, the reduction of the urban heat island effect could be coupled to specific properties of the green infrastructure, providing local decision makers of the municipality with important information on the type of green infrastructure that regulate temperatures most effectively. This way, the results of the study, commissioned

14 | by the municipality of the Hague, contributed to developing a municipality strategy to support urban planning of green infrastructure.

Figure 5: Using high resolution land cover information and local temperature data within neighbourhoods of the municipality of the Hague, the Netherlands, to quantify the contribution of green infrastructure in reducing the urban heat island effect. The top left panel shows different land cover types within the municipality. The top right panel shows the local air temperature on a summer day in 2018, highlighting the neighbourhoods with the lowest and highest temperatures. The bottom panel shows the relationship between land cover type and its average air temperature in comparison to the temperature in outside the city. Local urban forests and ‘other’ green infrastructure reduced the urban heat island effect. Compared to sealed surfaces, a temperature reduction of 2 °C was obtained. Adapted from (van Oorschot et al., 2020[65]) In the near future, particularly the spectral information from satellites is likely to improve with the foreseen launch of several hyperspectral satellites. NASA is planning HyspIRI, ESA is planning the FLEX mission and also the German EnMAP mission will be a hyperspectral mission. Each of these missions have an open science and open data policy. Finally, thanks to data assimilation tools (e.g. (Lewis et al., 2012[66])) and artificial intelligence methods (see (Verrelst et al., 2019[67]) for an overview), the methods to retrieve information from satellite data is continuously improving.

In addition to satellite data, the spatial resolution of soil maps has also strongly improved.

Resolution increased from of 25 kilometres in the early 1990s to global soil maps with a resolution of about 1 kilometre (Harmonized Soil World Database; (FAO et al., 2012[68])). SoilGrids is a system of global digital soil mapping, based on machine learning methods to map the spatial distribution of soil properties across the globe at a spatial resolution of even 250 meters. SoilGrids does not only provide mean estimates per grid cell but also an estimate of uncertainty (Hengl et al., 2017[69]).

| 15 Similarly, hydrological information has become available at higher spatial resolution. The Worldwide Hydrogeological Mapping and Assessment Programme (WHYMAP) brings together data on groundwater resources and catchments. Thanks to digital elevation data at high spatial resolution (e.g. at 90 m), maps that show the spatial network of rivers are continuously improving and available as datasets, e.g. HydroSHEDS (hydrosheds.org; (Lehner, Verdin and Jarvis, 2008[70])), to delineate river catchments and the location of rivers and lakes. Modelling approaches such as PCR-GLOBWB (globalhydrology.nl; (Sutanudjaja et al., 2018[71])) provide 10-km resolution estimates of moisture storage and water exchange (through evaporation, runoff and groundwater recharge). While these models are indispensable for global applications, for local applications in decision making processes, these approaches are too coarse. Water managers across the globe are therefore developing hydrological models for individual catchments or regions at higher resolutions, usually consisting of process-based descriptions supported by high resolution soil and digital elevation maps. For instance, the national hydrological instrumentation (NHI) of the Netherlands provides information at a 250-m resolution across the Netherlands on groundwater levels and soil moisture concentrations (nhi.nu).

For full use in decision making, it is of critical importance that the datasets are made openly available. Indeed, each of the datasets referred to above is openly available. Open data access facilitates transparency in the decision-making process. Given that the data science needed to apply and understand some of the models can be substantial, various down-stream services have been developed. Examples of such services include the local to global data products of Copernicus introduced above. Also at national level, various initiatives make data available for users, including decision makers. For instance, the Atlas Natuurlijk Kapitaal compiles and releases local to national maps that can be used for deriving ecosystem services as well as estimates of ecosystem services themselves. However, even with an open access of input data, the actual use of those data may demand skills that decision makers may not have. Attempts to bridge those gaps with data science will be treated in the next subsections. Still, for many real-life applications, close interactions among data experts and decision makers remain essential (see section 4.).

2. Data science tools for locally fit transfer functions The release of new datasets creates the need to develop new transfer functions. First of all, not all transfer functions are full scalable, i.e. a function may change when using a different resolution of the input data, even when seemingly the same input parameter is used. This may be due to other processes affecting the input parameter or because the input parameter gets a different meaning at a different resolution. More importantly, with the availability of local (high resolution) information, there is a demand for incorporating that information into estimates of ecosystem services.

Data science tools can be very helpful in facilitating the process of defining value transfer functions by allowing deriving the optimal function given the data, i.e. through machine learning tools. Moreover, existing tools like InVEST and ARIES - both having links to various open data sources - allow for multiple ways to calculate the ecosystem service of interest. Depending on the scale and availability of datasets, different transfer functions may be applied. For instance, InVEST ensures that global datasets exist to run all models in any given location, and additionally encourages the use of higher resolution and/or more localized data. The model design allows for consistent mapping of ecosystem services in data poor regions as well as for an urban planning case with high quality data.

The estimates of individual ecosystem services depend on both input data quality and the selected transfer function (Bagstad et al., 2018[72]). This demands selecting the best approach given question and data. Some propositions to systematically deal with this selection have been proposed. For instance, (GrĂŞtRegamey et al., 2017[35]) propose a hierarchical tiered approach in which the input data are identified given the goal of the assessment- based on a meta-analysis of available literature based on which the tier that best answers the policy question is determined. Finally, the appropriate method for each tier is chosen.

16 | Obviously, this procedure demands a lot of expert knowledge and hampers inclusion of decision makers in the process of ecosystem service assessment as decision makers may drown in the information.

The tool that currently makes most explicitly use of machine learning technologies is ARIES. ARIES (Martínez-López et al., 2019[73]) allows for automatically switching between local high resolution data and other data sources where appropriate. ARIES uses the so-called k.LAB development environment to contain all models and in which users can adapt or add customized models. ARIES is a ‘context aware’ modelling system, which implies that data, models and model parameterization are selected based on an automated computational strategy. This allows using the best available data and directly linking knowledge and data sources to diverse modelling techniques including data-driven models using artificial intelligence.

For instance, machine learning techniques have been used to provide a model for spatial biodiversity patterns (Willcock et al., 2018[74]).

3. Insights in trade-offs and synergies among ecosystem services Digitalisation of (calculations of) ecosystem services facilitates overlaying the spatial occurrences of ecosystem services. Such overlays allow identifying hotspots of ecosystem services (i.e. locations where values of multiple ecosystem services are high) and locations where hardly any ecosystem service is being provisioned. This is critical information to decision makers: hotspots of ecosystem services may be cherished or protected to maintain their long-term contribution to human well-being. On the other hand, locations with hardly any contribution to ecosystem services (and natural capital accounting), can either be accepted as is - putting more pressure on surrounding locations for provisioning services - or could be priority locations for management. In the latter case, targeted management measures can be taken.

Information on spatial (co-)occurrences help to identify trade-offs and synergies among ecosystem services. Understanding trade-offs in a local situation is of particular importance for decision makers as it shows that not all ecosystem services may be provided simultaneously at the same location. Some tradeoffs are the result of fundamental constraints in ecosystem capacities (i.e. in natural capital properties).

For instance, for a given vegetation productivity, it will not be possible to increase soil carbon sequestration without negatively affecting carbon available for other functions. In other occasions, trade-offs may be eliminated by measures and synergies may be enhanced, allowing the provision of multiple services (Bennett, Peterson and Gordon, 2009[75]). If trade-offs cannot be eliminated by specific measures, not all stakeholders may be served simultaneously, as different stakeholders tend to prefer different ecosystem services (e.g. (Cáceres et al., 2015[76])). Therefore appropriate communication on trade-offs is essential.

The evaluation of trade-offs and synergies among ecosystem services is closely linked to the concept of multifunctionality, which refers to the variety of functions produced by an ecosystem.

This is a generalisation of the definition of multifunctionality in relation to agriculture (i.e. agro-ecosystems) to identify and stimulate the production of non-commodity outputs in addition to crop yields (OECD, 2001[77]). This generalisation extends the definition to all land use and all landscapes and stimulates the use of landscapes for multiple purposes simultaneously. Given that ‘function’ means different things in ecological vs more economically oriented literature, (Manning et al., 2018[78]) made a distinction between the multifunctionality of ecosystem functions vs. multifunctionality of ecosystem services. Ecosystem function multifunctionality refers to the array of biological, geochemical and physical processes in an ecosystem. In the context of ecosystem services, multifunctionality is about the co-supply of multiple services. In the latter context, which complies to the context of most decision making, it is key to evaluate how land use can satisfy the multiple demands that society places on the services of ecosystems (Wiggering et al., 2006[79]). Through scenario analyses, a solution that expresses the optimal balance among the various services in a landscape may be found. Given that such optimal solution will be highly context-dependent, the intimate engagement of decision makers in this process is essential. This engagement is the topic of the next section.

| 17

4 Engaging local decision makers in ecosystem services assessments

The objective of several decision-making processes is to identify a solution that optimally provides multiple (demanded) services simultaneously, without negatively affecting others. Examples of decision-making processes include improving environmental sustainability (as linked to natural capital accounting) or creating the transition towards a circular economy and related transitions (in climate, energy etc.). Finding an optimal solution may conceptually be considered as evaluating the extent to which a design (e.g. a landscape design or a suite of measures) leads to the desired improvement of human wellbeing through the enhancement of multiple ecosystem services (Figure 6). Through an understanding of the drivers of these services, measures can be designed to achieve the desired changes in human wellbeing. Although, in reality, decision making processes are far from linear, the elements depicted in Figure 6 are common to most propositions for ecosystem services assessment in decision-making (e.g.

(Rosenthal et al., 2015[80]) (Staes et al., 2017[81])).

Figure 6: Conceptual diagram illustrating the process of optimising multiple ecosystem services simultaneously by landscape management. The boxes represent concepts or decision-making steps, the arrows represent flows of information (dashed) and/or causal links (regular arrows).

Co-designing and co-evaluating solutions for provisioning ecosystem services allow to better manage this objective. Co-designing helps balancing the opportunities and challenges provided by emerging technologies. Opportunities include their credibility (through the production of richer and transparent datasets), salience (allowing monitoring over time, and in a transferable way), legitimacy (by providing high resolution data at the local scale at which decisions are taken) and feasibility (by having open data policies, accessible datasets and publicly available tools with user interfaces). At the same time, the extensive amount of data that is needed to quantify ecosystem services may be daunting and has been identified as a major obstacle towards implementation (Cook and Spray, 2012[82]). Moreover, many decision makers do not have the skills to derive fit-for-purpose data from emerging technologies. In this section, a number of examples of co-design practices in the context of local decision-making are provided to illustrate how emerging technologies may be applied. Here, local is defined as the landscape level at which governance by municipalities and districts/provinces is active. At these local scales, the demand for

18 | high resolution data and for locally-adapted (fit-for-purpose) assessments of ecosystem services is biggest.

Moreover, the challenges with finding optimal solutions for multiple local stakeholders may be most explicit at such scales. Local stakeholders include land owners (including farmers, and nature organisations), citizens, (semi-)governmental bodies and various NGOs.

Co-designing the ecosystem services assessments tool Our first example concerns developing a tool on ecosystem services assessments for the Dutch Water boards (for a full description of the applied concepts, a description of the various transfer functions and a user manual -in Dutch- see (van Bodegom et al., 2018[83])). In the Netherlands, water boards are responsible for maintaining and improving water quality and water quantity management, e.g. in the context of the Water Framework Directive. At the same time, most decisions in relation to landscape management and design that affect water quality are taken by municipalities and provinces. The Dutch Water boards aimed to start a dialogue with the other stakeholders and managers of a ‘catchment’ to optimally manage the landscape while also accounting for water-related services. They considered the use of ecosystem services appropriate for this goal. Hence, their aim was to create a tool that quantifies ecosystem services and how these are affected by local landscape management with which the discussion on optimal landscape and water management could be started with all stakeholders involved. The formulation of this aim is in itself already very interesting, because in most decision-making, ecosystem services remain implicit (as has been the case in the implementation of various nature-based solutions, e.g. (van Oudenhoven et al., 2018[25])).

The first important step in co-designing (a tool for) an optimal management of ecosystem services was the joint definition of the indicators. Based on the criteria outlined in Figure 2, the indicators were designed with the stakeholders to ‘speak the language of the stakeholders’ (salient), while at the same time, care was taken that the selected indicators could be quantified and change with the appropriate management. A long-list of more than 90 indicators of water-related ecosystem services was composed and in discussions with water managers reduced to a short-list of 15 indicators for which quantifications would be attempted. Attempts were made to cover as many aspects of ecosystem services as possible to relate to as many stakeholders as possible (to avoid decision-making based on biased selections (Seppelt et al., 2012[84])). Each of the selected indicators were explicitly related to so-called usage values, which is a central element in Dutch legislation and (national) policy of water. This ensured that the indicators could bridge the gap between ecosystem services concepts and legislation.

The second step was designing transfer functions to relate data sources that are easily accessible to stakeholders. Openly accessible high resolution data as e.g. available in the Atlas Natuurlijk Kapitaal (see section 3.) were chosen in combination with outputs from hydrological models as maintained and used by the water boards (related to the NHI, see section 3.). These data sources were selected by the water managers to ensure that users of the tool would have minimal efforts to access the data. Interestingly though, during implementation of the tool in actual decision making processes, it turned out that particularly hydrological data were less easy to obtain because of water board-internal communication issues (e.g. data compiled and managed by different departments). The transfer functions themselves were derived from functions available in scientific literature (and partly coincide with functions implemented in InVEST and ARIES) to fulfil the criterion of legitimacy (Figure 2). The transfer functions were discussed with the water managers to ensure credibility and adapted where necessary. For instance, the indicator for fish migration was adapted by replacing the indicator fish species by one that was considered more relevant for management. Also the indicator of eutrophication was discussed to include impacts of both nitrogen and phosphorus given that nitrogen pollution is relevant in some parts of the Netherlands while phosphorus pollution is relevant in other.

| 19 Third, the transfer functions were implemented in software that was well-known to stakeholders.

ArcGIS was selected, because (almost) all water boards in the Netherlands use ArcGIS (and therefore it was preferred over an open-source alternative such as QGIS). A user-friendly interface that directly allowed linking to open-access data sources and to the appropriate transfer functions was made. The stakeholders preferred such a platform over a web-based interface for which they expected that most users would have more problems in using it (although the differences are likely to be marginal). Scenarios were supposed to be run outside the tool itself. It was expected that experts (e.g. from the water board) would evaluate scenarios with e.g. their hydrology model to provide input data to the tool. The main advantage of such set-up is that it provides the full flexibility to apply the best available models for the scenario analyses. It also leaves the scenario analyses in the hands of the decision makers to support the iterative process of refining and designing alternative scenarios, for which examples remain scarce (Bigard, Pioch and Thompson, 2017[85]) (Partidรกrio and Gomes, 2013[86]). There is also a major disadvantage to this setup: Once an alternative design has been decided upon among stakeholders, it needs water board experts to evaluate these designs. While this ensures high data quality and detail, it turns out to be a hurdle in practice given the organisational structure of most water boards. Moreover, this procedure introduces lag times in the evaluation of alternative designs which is not always desired in a decision-making process.

Figure 7: Example of an application of the decision-support tool developed for the Dutch water boards to evaluate the various services provided by waterways, beyond the traditional services of sufficient and clean water. In a case study for one of the Dutch water boards, a polder - called Nieuwe Driemanspolder - that used to have a mostly agricultural destination (top left panel) was meant be turned into a water retention basin as a climate mitigation measure (bottom left panel). While the necessity of constructing the water retention basin was a result of legislative actions, the implementation itself was open to discussion.

Through determining the multiple ecosystem services that are provided in the new situation, the impacts of the measures were illustrated for the various stakeholders in the region. Here, two more traditional waterrelated services are shown; the amount of fresh water provided by surface and groundwater systems in the current (central panels) and prospective situations (right panels).

The tool has already been used in a number of local decision-making processes. For instance, the tool has been used to evaluate whether payment for ecosystem services may be a fruitful approach to mitigate summer drought issues in one of the water boards and to investigate which design of remeandering rivers may provide the best combination of ecosystem services. Figure 7 illustrates an

20 | application in which the tool was employed to increase acceptance of establishing a water retention basin.

In that local application, the hydrology and soil properties was re-evaluated for current and future land use.

Based on these scenario outcomes, values of selected indicators were calculated within the tool. In another case study, natural capital and associated ecosystem services were used to enrich discussions on future scenarios of landscape management in a region dominated by peatland meadows prone to soil subsidence due to intensive agriculture (demanding drainage of the peatlands and causing its oxidation). Alternative business models are being developed, e.g. a co-operative cheese factory based on flower-rich meadows (https://www.degraafstroom.com/en/), and combinations of less-intensive agriculture with landscape tourism and paludiculture (i.e. wet agriculture). A cranberry farm has started in the area. Upon request of the cranberry farmer, the assessment tool was used to evaluate the changes in various ecosystem services if more parcels would switch to cranberry farming (Figure 8).

Figure 8: Application of a decision-support tool developed for the Dutch water boards in a region with highintensive dairy farming on peat meadows, the Krimpenerwaard in the Netherlands. Despite its current high agricultural productivity, its long-term productivity is not sustainable due to ongoing soil subsidence. To avoid further soil subsidence, alternative land use are initiated and stimulated, such as a cranberry farm (indicated with an arrow in the left panel). Further stimulation of paludiculture will aid avoiding further soil subsidence. To achieve large-scale implementation, alternative business models based on more than commodity products alone are needed. In a scenario analysis, the consequences of paludiculture in half the area on carbon sequestration (right panel) and other ecosystem services were evaluated. In a payment for ecosystem services scheme, this could be one of the foundations of an alternative business model.

Presenting trade-offs and synergies among ecosystem services To fully engage local stakeholders in the decision-making process, a summary of the changes in ecosystem services needs to be presented. Such summary should provide direct insight in the tradeoffs and synergies in ecosystem services caused by the envisioned management. This aids discussions among stakeholders as recognition of the synergies helps to think about opportunities, while trade-offs highlight (potential) frictions. This enforces mutual awareness to those issues and helps thinking about further optimization of the provisioning of ecosystem services. In the case presented in Figure 9, it is quite clear that there is a strong trade-off between agricultural productivity on the one hand and all water-related ecosystem services on the other hand. While these patterns were generally known prior to the ecosystem services assessment, the quantification of a multitude of additional services and possible associated business opportunities helped to increase the support for the measures by the stakeholders. At the same time it also made very clear that farmers needed to be compensated and the quantifications also helped them to re-inforce their claims.

| 21 The presentation of such summaries is a critical factor for the success of the decision-making process. If the information is presented in complex schematics, information may be misinterpreted (Wright, Eppink and Greenhalgh, 2017[28]), while if it is too simplified, it will not be used. In the example presented in Figure 9, it was decided to show the relative change in each ecosystem service based on consultation with the water boards. This allowed direct comparison among the ecosystem services despite their different quantities and units. A drawback is that it did not allow comparison and assessment of importance in absolute terms. However, such importance also partly depends on the valuation method chosen. Given that valuation methods are open to debate, and negatively affect acceptance by the stakeholders, it was decided not to include a comparison in absolute terms (but please note that other assessment tools, discussed in sections 2. and 3, do allow for this). It was also decided not to provide an optimal management option, even though you might derive such by e.g. a multi-criteria analysis or similar tools (and emerging technologies allow the direct implementation and execution of such analyses). In this case, there were two interrelated reasons for not including such option. First, presenting an optimal solution was considered potentially offensive to the stakeholders in the region as it takes away the discussions on alternative solutions. Second, it also reduces the wiggle room of decision makers to differently weigh ecosystem services and possible measures. These considerations clearly illustrate the need to understand better how different presentation formats from ecosystem services assessment tools are perceived, processed, and used by stakeholders and decision makers alike (Klein, Celio and GrĂŞt-Regamey, 2015[87]) (Wright, Eppink and Greenhalgh, 2017[28]).

Figure 9: Relative changes in the provisioning of ecosystem services induced by the proposed land use change in the Nieuwe-Driemanspolder, the Netherlands (as shown in figure 6). The analysis shows a clear trade-off in the major reduction in agricultural productivity vs. the stronger increases in all (water-) related ecosystem services.

Emerging technologies help to further increase the flexibility of ecosystem services assessment tools. While the presented tool allows for full access to open-source high-resolution and translates those - co-designed with decision makers – to indicators of ecosystem services to support policy developments, it did not fully exploit the flexibility that is possible by employing emerging technologies. For instance, in reference to the previous example, one might want to be able to change an indicator related to nutrient

22 | regulation from one based on nitrogen to one on phosphorus, or vice versa. Also, if additional data sources become available, one might want to adopt improved transfer functions. With the example above, this can only be implemented by the developers (after consultation with the water boards to ensure legitimacy).

The example provided in section 3 on ARIES illustrates how more flexibility in linking to alternative data sources can be provided. However, also in that case, the acceptance of the various transfer functions and the preferred transfer functions should be openly discussed with decision makers.

Developing scenarios to discuss trade-offs and synergies and engaging citizens in data collection Flexible ecosystem services assessment tool allow discussing different visions for a given system.

In the example above, the indicators and transfer functions were developed in co-design with the decision makers. While decision makers had various stakeholders in mind when designing, other stakeholders were not directly involved in this process. A fully flexible ecosystem services assessment tool could allow for different transfer functions to express the visions of different stakeholders on the system. This would open up further discussions on the merits of ecosystem services within the landscape and aid the development of more engaged solutions.

More flexibility is currently being implemented in the Netherlands to manage the competition for high-quality water sources. In the Westland region where greenhouse horticulture, cities and nature compete for water, an initiative has been started to fundamentally re-evaluate water-related services.

Various stakeholders, including local decision makers, have come together in multiple meetings to discuss options of water sharing and storing in space and time. The stakeholders themselves define the services needed, the indicators thereof and their demands with respect to constraints of viable options. Scientists support this process by providing maps on water quality (e.g. based on the open source pesticides atlas of the Netherlands), and water quantity (available thanks to emerging technologies) and together with stakeholders develop scenarios for further developments in demand for and supply of water-related services. Such co-designed scenarios and the analysis thereof (using tools introduced earlier) are an important component within stakeholders’ discussions as they provide crucial insights in trade-offs and synergies among ecosystem services. Assessments based on such scenarios are coarse but rapid, and their participatory nature provides a fruitful starting point for discussions among stakeholders to discuss the pros and cons of management measures and hence facilitates finding new solutions already at the planning phase as well as identifying knowledge gaps for further research.

A similar approach is being used in the so-called ‘bulb region’, where challenges include a sustainable floriculture, increasing pressures on the landscape by tourism and urban expansions.

The floricultural sector feels the urge to transform towards sustainable production systems. The municipalities seek a balance between economic growth and a liveable landscape. The water board looks for ways to improve water quality and several citizen initiatives (including initiatives with an agricultural background) aim to improve biodiversity in the region. Through multiple workshops, the combination of a viable floriculture, liveable region and enhanced natural capital was defined as common goal. In the next phase, the community of practitioners defines and executes initiatives and measures that allow natural capital (and therewith ecosystem services) to contribute to a viable floriculture and a liveable region and vice versa. The various tools introduced above are applied to substantiate these contributions.

Finally, stronger engagement of citizens in data collection can further increase the legitimacy of selected measures. An interesting example in this context is the on-line tool developed by the natural history museum of the Netherlands (Naturalis) through which citizens can upload their observations on the shape of strawberries as a measure of success of pollination. This allows mapping pollination in the region.

Likewise, citizens can upload their observations on flora and fauna that are used to enrich maps on local biodiversity. Maps of biodiversity and pollination are in turn integrated in the tool to assess ecosystem

| 23 services and combined with the various other (open access) data sources. This integration both enhances the legitimacy of ecosystem service assessments (as stakeholders provide the data for the assessment) as well as its credibility (by improved data availability). Possibly equally important is that such initiatives help to stimulate citizen engagement on the topics surrounding the management of our natural capital and fits in recent initiatives to engage citizens in the co-design of research agendas making use of digitalisation (OECD, 2017[88]).

24 |

5 Conclusions Various tools are available to provide quantitative assessments of ecosystem services. Such tools are useful as stand-alone applications, as illustrated in this issue paper, and are also an important component of natural capital accounting initiatives. Through emerging technologies, the assessment of ecosystem services is increasingly supported by high-resolution data that are often open source. This allows supporting local decision-making processes that commonly demand such high-resolution information.

Moreover, assessment tools increasingly have the flexibility to switch among data sources (and associated transfer functions) to quantify ecosystem services. Also this aids the decision-making process as not always all data sources will be available to decision makers or because decision makers prefer a different transfer function to optimally relate to the local situation. Finally, spatially overlaying maps of ecosystem services helps identifying trade-offs and synergies among ecosystem services. In turn, this aids better understanding of frictions as well as creating new solutions towards sustainable landscape management.

These developments support the credibility, legitimacy, salience and feasibility of ecosystem services assessments. Even so, each of these aspects can be further improved: •

Direct links to open source data, and the technical feasibility for direct use by stakeholders and decision makers is still often limited. This can partly be resolved by co-designing and co-executing the assessments, but demands continuous attention by developers.

•

Co-designing indicators and transfer functions applying available high-resolution data sources will enhance the credibility and legitimacy of the assessments. Such co-designs should take the different valuation methods into account to avoid biased value attributions to particular ecosystem services.

•

Continued effort is needed to create balanced co-design approaches that ensure stakeholders’ and decision-makers’ participation to reflect the societal values and its priorities on the one hand and full exploitation of available data and scientific knowledge on the other hand.

•

Further research should critically evaluate the accomplished use of ecosystem services indicators by decision makers to enhance further adoption of indicators (van Oudenhoven et al., 2018[27]) (Ruckelshaus et al., 2015[89]).

In combination, co-design approaches ensure that, instead of applying it as a posterior impact assessment, ecosystem services and natural capital will be considered at the heart of a decision-making process and thus truly contribute to a transition towards a sustainable society.

| 25

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