One Ecosystem :
Research Article
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Corresponding author: Petteri Vihervaara (petteri.vihervaara@ymparisto.fi)
Academic editor: Benjamin Burkhard
Received: 02 May 2018 | Accepted: 04 Apr 2019 | Published: 18 Apr 2019
© 2019 Petteri Vihervaara, Arto Viinikka, Luke Brander, Fernando Santos-Martín, Laura Poikolainen, Stoyan Nedkov
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Vihervaara P, Viinikka A, Brander L, Santos-Martín F, Poikolainen L, Nedkov S (2019) Methodological interlinkages for mapping ecosystem services – from data to analysis and decision-support. One Ecosystem 4: e26368. https://doi.org/10.3897/oneeco.4.e26368
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A broad array of methods have been developed and applied to map ecosystem services and their values at various geographic scales. For example, the ESMERALDA project developed methods for ecosystem service mapping across Europe. This paper describes how different methodological interlinkages can be used in ecosystem service mapping and assessment and how the integration of information can be facilitated to assist in decision-making processes related to sustainable use and protection of ecosystem services. This paper is based on a literature review and expert consultations throughout the project. The accumulation of knowledge in ecosystem assessment processes will be described through multiple steps: 1) data compilation, 2) analyses run via independent or linked methods applications and tools, 3) integration of information from multiple analyses and 4) finally, feeding into the decision-support frameworks. The challenges and possibilities of using combinations of various datasets and methods will be discussed. This workflow is demonstrated with real-world applications. In addition, technical pitfalls and challenges, as well as linkages to overall ecosystem assessments and policy questions, are analysed and discussed.
biodiversity, ecosystem services, mapping, ESMERALDA, MAES, Europe
Harmonising the broad array of methods for mapping and assessing ecosystem services (ES) has been recognised as an important step in delivering quantitative and comprehensive information on the status and trends of ecosystems and their services. This is particularly important in regional scale assessments such as the MAES*
An initial and non-trivial challenge in linking methods is the diverse terminology that is used to describe and define methods. The terminology that has been used in previous ES classifications, literature and ecosystem assessment processes is far from consistent and multiple terms are often conflated. For example, in the literature review conducted by the ESMERALDA project, there is mixed use of terms such as datasets, indicators, indexes, methods, models, tools for quantification mapping, assessment and decision-support (
For the ESMERALDA project, a comprehensive glossary of terms was produced by
In this article, we emphasise the technical classification (i.e. focusing on methods, opposite to thematic classification related to, for instance, particular ecosystem type or ecosystem service) of individual biophysical, social and economic methods that can be applied to one or more ecosystem services. We have reviewed the definitions of ES quantification models and methods used in previous projects, such as OPERAs and OpenNESS and the literature on mapping and assessing ecosystem services. In addition, experts attending the ESMERALDA workshops (approximately 50 persons per workshop) have been consulted to obtain a comprehensive overview of available methods and their views on the classification of methods described in this paper.
The aims of this paper are:
The materials for this paper are based on findings of the synthesis reports on social (
The various terms used to describe the process of mapping and assessing ecosystems and their services were refined towards more of a structured scheme (Fig.
Framework for mapping and assessment of ES that links multiple methods and integrates multiple types of information. Direct and indirect measurement can produce data that is used to calculate indicators and/or indices (see below). Direct and indirect measurements can either feed in to methods, including single models and integrated modelling frameworks or to decision-support frameworks (directly or via methods). Subsequently, the integrated information can be used for wider ecosystem assessment processes, which can have different aims, for instance, reporting for policy targets or detecting status and trends of ecosystems.
The process of mapping ES and their values falls within the broader process of ecosystem service assessment. The term “assessment” is defined as “the analysis and review of information derived from research for the purpose of helping someone in a position of responsibility to evaluate possible actions or think about a problem” (
Ecosystem assessments have been compiled for various policy processes, such as measuring and reporting indicators for the UN Sustainable Development Goals (SDG), Aichi Targets of the Convention on Biological Diversity (CBD) or, recently, for regional ecosystem assessments of IPBES. Earlier assessment processes related to ES have been, for instance, the
ES assessment is arguably the most useful form of assessment to guide development towards sustainable social-ecological systems. This recognition is at the core of the ESMERALDA project and MAES work of the EU. The EU Biodiversity Strategy Action 5 sets the requirement for an EU-wide knowledge base designed to be: a primary data source for developing Europe’s green infrastructure; a resource to identify areas for ecosystem restoration; and, a baseline against which the goal of ‘no net loss of biodiversity and ecosystem services’ can be evaluated. In addition to these aims, there are also other valid assessment purposes, for instance, related to environmental impact assessments or integrated natural capital accounting (
For ecosystem service assessments, the distinction between capacities (potential supply) and flow (determined by demand) is particularly important (
Quantification of ES in biophysical terms is a prerequisite for their social and economic evaluation and subsequent integration of this information into decision-making processes (
In the ESMERALDA project, biophysical methods are classified in three major groups: direct methods, indirect methods and modeling methods. Direct measurement methods are the measurements of a state, a quantity or a process from ecosystem observations, monitoring, surveys, questionnaires or data from remote sensing and earth observations, which cover the entire study area in a representative manner. Direct measurements deliver a biophysical value of ES in physical units which correspond to the units of the indicator and quantify or measure a stock or a flow value. Direct measurements can be used as primary data inputs to other methods or used directly as ES indicators. The use of direct measurements, however, are often impractical and prohibitively expensive beyond the site level and, therefore, are usually used as an input into a biophysical mapping method or to validate certain mapping and assessment elements. In some cases, direct measurements are simply not available for all ES.
Indirect measurement methods rely on the use of different data sources that provide biophysical values in physical units but process this information through further interpretation or classification. They can be based on remote sensing and Earth observation derivatives such as land cover, normalised difference vegetation index (NDVI), surface temperature, soil moisture etc. which are extracted from the original sources using specific procedures. For example, land cover can be derived from remote sensing images by visual interpretation or automated classification; and NDVI is derived by measuring the difference in solar radiance absorption and re-emittance of vegetation using particular spectral bands.
Use of indicators is common in ecosystem assessments and there is extensive literature which discusses them (e.g.
Reviewing, acquiring and compiling the required spatial data is one of the most important and, at the same time, challenging and laborious task as data are usually dispersed across various sources and/or may need to be pre-processed to be suitable for analyses, which can be very time consuming. There are many factors affecting the availability of data in different countries, such as level of economic development, funding or technological capabilities. As a result, harmonised datasets covering a large area can be difficult to find. Still, the development of technology applicable at the global scale has allowed for more opportunities to produce consistent, detailed and accurate data. Sometimes, the data must be purchased from the data producer but many existing datasets are freely available. A preliminary study on spatial data and analytical methods for assessing the ecosystem services and connectivity of the protected areas network of the Green Belt of Fennoscandia, i.e. a chain of protected areas on the borders of Russia, Finland and Norway, resulted in a list of 108 potential datasets across the study area varying from regional to global scales. Of the datasets reviewed, only eight were commercially available, while others were freely available or through co-operation (
Biophysical methods include direct and indirect measurement methods and modelling. The modelling includes several groups of approaches that come from ecology or other earth sciences fields such as hydrology, climatology, soil science etc. The biophysical model groups described in ESMERALDA are: phenomenological models, macro-ecological models, trait-based models, process-based models, statistical models, ecological connectivity models and state and transition models (see Table
Modelling methods - data and software needs and examples of detailed methods
Class |
Data and software needs |
Examples of methods |
Phenomenological models describe relationships amongst biodiversity, ecosystems and ES by highlighting the biological mechanisms underpinning ES supply |
Data: Information from other studies/ meta-analysis Land use or land cover (GIS data), soil conditions, climatic conditions, accessibility Software: Statistical software, GIS software, Independent modelling tool |
Snow slide susceptibility model Schröter et al. 2014 http://dx.doi.org/10.1016/j.ecolind.2013.09.018 Preliminary assessment method (PAM) Zepp, H. et al. 2016 |
Macro-ecological models assess ES supply, based on the specific biodiversity components, such as species and habitat distribution, presence (or abundance). |
Data: Species distribution data (e.g. Atlases, in-situ data) inventories Habitat / land cover data (GIS data), additional parameters: soil, climate, land use etc. Remote sensing to derive environmental variables and processes to be coupled with models. Software: Statistical software, GIS software, Independent modelling tool |
Maximum entropy modelling (MAXENT) Vallecillo et al. 2016 https://doi.org/10.1016/j.ecolind.2016.05.008 Extensive Niche Modelling Rolf et al. 2012 |
Trait-based models analyses ecosystem functions and, thus, ES by describing the relationship and interactions between species and environment. |
Data: Observational or empirical data on functional traits, plant traits, traits of soil microorganisms. Explanatory variables: land use/ land cover, soil variables, climate variables. Software: Statistical software, GIS software, Independent modelling tool. |
Utilisation of plant functional diversity Balzan et al. 2015 |
Process-based models rely on the explicit representation of ecological and physical processes, such as carbon sequestration or nutrient cycling, that determine the functioning of ecosystems. |
Data: High-quality data on climate, atmospheric CO2 concentrations, land use conservation, sequestration Software: Note: Process-based models require very good expertise to use the models properly. |
KINEROS Nedkov & Burkhard 2012 http://dx.doi.org/10.1016/j.ecolind.2011.06.022 MedREM model Guerra, A. C. et al. 2014 http://dx.doi.org/10.1007/s10021-014-9766-4 MOSES Aitkenhead et al. 2011 |
Statistical models are mathematical measures of the attributes of certain populations that are usually based on the estimation of the relationship between the response variable (i.e. ES) and explanatory variables (e.g. biophysical functions) |
Data: Environmental variables Software: Statistical software (e.g. R, SPSS, MatLab) Visualisation could be done separately in GIS software. |
K-mean cluster analysis Queiroz et al. 2015 https://doi.org/10.1007/s13280-014-0601-0 Principal Component Analysis (PCA) García-Nieto et al. 2015 https://doi.org/10.1016/j.ecoser.2014.11.00 Moran's Index Palomo, I. et al. 2014 |
Ecological connectivity models evaluates the degree of the landscape to facilitate or impede the movement of different ecological processes. |
Structural connectivity Data: Land cover or land use data, habitat data, features restricting movements, e.g. road and rail networks Functional connectivity Data: Species/ habitats distribution data, species suitability data, land cover or land use data, habitat data, features restricting movements, e.g. road and rail networks Software: Conefor (also plugin for Qgis or ArcGis available), Guidos, Fragstats, MatrixGreen, FunCon, GrapHab. Many calculations could be done separately in GIS software. |
Conefor Vogt et al. 2007 https://doi.org/10.1016/j.ecolind.2006.11.001 Morphological spatial pattern analysis Esterguil et al. 2012 MSPA: European forest connectivity Conefor Vogt et al. 2009 https://doi.org/10.1016/j.ecolind.2008.01.011 Zonation Moilanen et al. 2005 |
State and transition models evaluates the specific conditions of systems by focusing on threshold points that can separate one system state from another by showing the transition between them. |
Data: Temporal land use data, remote sensing data, Software: GIS-software, RS software |
Land use scenario modelling Larondelle, N. & Haase, D. 2012 https://doi.org/10.1016/j.ecolind.2012.01.008 Carbon emission models Vleeshouwers & Verhagen 2002 |
Conceptual models are descriptions of a process which help to understand the subject behind the model. |
Data: Information from other studies Software: Visualisation tools |
Cascade model Haines-Young, R. and Potschin, M. 2010 DPSIR Santos-Martin et al. 2013 |
Integrated modelling frameworks are tools designed specifically for ES modelling and mapping. They can integrate various biophysical, social and economic methods to model various services the ecosystems provide. |
Data: Land cover data (GIS layers): terrain, vegetation, soil, bathymetry, habitat distribution etc., environmental statistics Software: GIS-software, stand-alone tools e.g. InVEST. |
InVEST Lupa, P. 2016 MCDA Comino, E. et al. 2014 |
Social methods for mapping and assessing ES measure individual and collective preferences in order to support the implementation and further development of the ecosystem service concept. By definition, social methods involve people in the assessment process. In ESMERALDA, social methods were divided into three main categories in relation to how stakeholder are engaged. Observation methods require multiple data as they are quantitative methods and are usually developed in collaboration with researchers (i.e. preference assessment, time-use and photo-elicitation). Some of the social methods could also be used in selecting assessment targets, for instance, by analysing social preferences and associated values of ES. Consultation methods are based on qualitative data that are usually applied in collaboration with non-academic stakeholders (i.e. narratives, Q-methodology). These methods are usually articulated through in-depth and semi-structured interviews that allow research participants to express their motivations and the diverse values of ES through their own stories and direct actions (both verbally and visually). These types of methods are usually applied in order to understand and describe the variety of motivations behind the social value that different stakeholders attribute to nature. Engagement methods are able to gather qualitative and quantitative data by collaborating with researchers and non-academic stakeholders (i.e. Public Participatory GIS, participatory scenario planning and deliberative assessment). These methods are usually articulated through participatory and deliberative tools (focus groups, citizens juries, participatory or rapid rural appraisal (PRA/RRA), Delphi panels etc.). This third group of methods can contribute to solve social conflicts by learning and knowledge co-production, as it fosters discussion between different stakeholder groups regarding trade-offs amongst different ES (deliberative valuation), their spatial distribution (PGIS) and the future trends of ES and their implications for human well-being (participatory scenario planning) (
A variety of methods have been developed for estimating the economic value of ES that are designed to span the range of valuation challenges raised by the application of economic analyses to the complexity of the natural environment. Fig.
In addition to the above-mentioned specified method classes, it was observed that there are a number of tools that, although often referred to in literature as “methods”,are actually better described as software tools or platforms that combine multiple individual models (Fig.
The literature survey clearly shows that, despite the numerous papers published in the separate fields of ES research, i.e. biophysical, social or economic studies alone, studies using multiple methods are still rare (
The challenges, related to data availability and quality and selection of the right combination of methods for specific cases, need to be taken into consideration in the early stages of an ecosystem assessment. The availability and accuracy of data may vary between areas, for instance between terrestrial and marine areas, between member states in EU and between provisioning and regulating or cultural ES (cf.
Evaluation of the quality and accuracy of the data, methods and models is challenging since mapping and assessment of different ES use different approaches with different data and methods. Complexity in using more than one type of method to quantify and map certain ES might end up with significantly different outcomes. This variation and uncertainty from the different methods should be considered in designing ecosystem assessments (
The integration of results from mapping and assessment applications is essential if we are to make informed decisions regarding ecosystem use and management. We use the term "decision-support frameworks" to describe the set of methods that are designed to structure and integrate information from multiple sources with the purpose of providing information for decision-making. Examples of such methods include Bayesian Belief Networks (BBN), multi-criteria analysis (MCA) and cost-benefit analysis (CBA) (
In this paper, we synthesised and organised the available methods for ecosystem mapping and assessment in a workflow graph that describes the production of information from direct observations and measurements through to various methods to support decision-making. The first step in any ecosystem assessment is to orientate the process to the overal objectives, which can either be the direct aims of assessing the status and trends of ecosystems or be defined by the specific needs of policy processes such as IPBES, CBD or EU Biodiversity Strategy. We identified separate biophysical, social and economic models and methods, integrated modelling frameworks and decision-support frameworks. The results of mapping and modelling methods can provide quantified spatial data, which can be used directly or through decision-support tools in ecosystem assessments, such as MAES and IPBES. Ecosystem assessments aim tomeasure the status and trends of ecosystems and their services, which implies that such processes need to be repeated at regular intervals of time. This enables us to critically analyse the advantages and challenges of the currently applied methods and how these should be improved for the future assessments. There has been a significant development in the understanding and knowledge of ecosystem services in Europe during the last decade. The ESMERALDA project has provided a flexible methodology for EU member states that helps to set new goals for sustainable management and protection of ES towards the 2030 agenda. Mapping and assessment of ecosystems and their services necessarily require the linking of biophysical, social and economic methods to achieve a holistic understanding of the values and benefits provided by nature. We observed that many existing applications used mis-matched combinations of highly sophisticated biophysical models with over-simplified economic or social methods and vice-versa. Understanding the applicability and restrictions of the output data from the biophysical mapping and assessment is needed if the data are to be used as input for social or economic methods.
Improving guidance on how to optimally link assessment methods is seen as one of the aspects that requires further study and development in the future. In addition, there is a need to better integrate separate information outputs from biophysical, economic and socio-cultural mapping and assessment applications. This is where the combination of complementary pieces of information are used to measure different aspects of an ecosystem service (e.g. sustainability, value and distribution) to support decision-making. The workflow, developed in this paper, can be used to plan for better integration across information sources. We believe that the workflow will help future communication and collaboration between disciplines and contribute to a better understanding of the assessment process by the wide variety of stakeholders involved in ecosystem assessments.
No conflicts of interest.