Introduction

The initial ES matrix approach

A systematic review of the matrix approach

Applications of the ES matrix approach

Over the last 10 years, the number of published studies increased progressively, especially during the last five years (Fig. 1).

Figure 1.  

Number of analysed studies using the ES matrix approach to assess ES supply, demand or flows/use (at the end of 2019).

The flow of ES from nature to society is not always as straightforward as one could perhaps expect. Instead, it includes several components, including the ecosystem-based supply of ES and the societal demand for ES. In literature, many different terms are used, depending on the different ES frameworks, the perception of the authors and the individual applications. In the reviewed studies, we also found a diversity of ES components that were assessed through the ES matrix approach: ES capacity (e.g. Vihervaara et al. 2010), ES potential (e.g. Depellegrin et al. 2016), potential ES supply (e.g. Kamlun and Arndt 2019), potential and actual ES supply (e.g. Hainz-Renetzeder et al. 2015), ES supply (e.g. Nedkov et al. 2014; Sohel et al. 2015), ES flow (e.g. Burkhard et al. 2014), ES use (e.g. Karstens et al. 2019) , current ES use (e.g. Nahuelhual et al. 2013), demand for ES (e.g. Burkhard et al. 2012). As the concepts behind the terms were not very clear in a majority of cases and, for simplification reasons, ES “capacity”, “potential”, “potential supply” and “supply” were grouped into “ES supply”. With this regroupment, the ES matrix approach was mainly used for assessing and mapping the “supply” of ES (Fig. 1). Matrices of ES flow or use were regrouped and matrices of ES demand are shown here without regrouping. The number of assessments dealing with demand for ES has increased during the last years of the reviewed period (e.g. Tao et al. 2018; Nurokhmah et al. 2019, Bicking et al. 2019), as well as the number of studies of ES flow or use (e.g. Li et al. 2016, Egarter Vigl et al. 2017) (Fig. 1).

The ES matrix approach has been applied mainly in Europe in 73 analysed studies with a concentration of studies in Germany and neighbouring countries (Fig. 2). There is also a notable increasing number of studies outside Europe, especially with 18 applications in East Asia and the Pacific (e.g. Li et al. 2016; Cai et al. 2017; Sun et al. 2018; Tao et al. 2018; Liu et al. 2019). The ES matrix approach was used at different spatial scales, for example, continental scale (Stoll et al. 2015), national scale (Depellegrin et al. 2016) and local scale (Nedkov and Burkhard 2012). The ES matrix approach was, however, mainly applied at the local (54 studies) and the regional scale (33 studies). The continental scale was used in 12 studies (Fig. 2). The extent of the individual case study sites varies between less than 1 km² to the area of the whole of Europe.

Figure 2.  

Distribution of the analysed studies amongst world regions, publication dates and scale of studies.

The ES matrix approach has been applied for a large diversity of purposes. While each study presents its own context and objectives, we can observe a broad pattern of application types (using illustrative examples):

• ES assessments in data-scarce areas: for instance in Nepal by Tamang (2011), in Burkina Faso by Sinare et al. (2016) or in Kenya by Wangai et al. (2018);
• Assessment of a specific ES: flood regulating services by Nedkov and Burkhard (2012); nutrient regulating services by Bicking et al. (2019) or global climate regulation by Ma et al. (2019);
• ES assessments provided by a specific ecosystem type: namely grasslands in Villoslada et al. (2018) or seabed biotopes in Salomidi et al. (2012);
• Past and future ecosystem/geospatial units changes' impacts on ES: the ES matrix approach was very often related to change of specific types of geospatial units such as LULC data (e.g. Polce et al. 2016; Huq et al. 2019). We found 20 studies that conducted spatio-temporal analyses (e.g. Kokkoris et al. 2019; Karstens et al. 2019 and Mukul et al. 2017). Temporal ES evolution assessments were done based on LULC change in García-Llamas et al. (2018) and Sanchez-Porras et al. (2018). Spatial ES supply-demand dynamics were assessed in Tao et al. (2018).
• Impacts ES assessment-orientated studies: Kaiser et al. (2013) present an example of a natural hazard impact assessment related to the 2004 Pacific tsunami, based on land cover and ES changes. The effects of EU Life+ project management actions on ES was analysed by Campagne and Roche (2018). Mangi (2016) did an impact assessment on ecological integrity and ES supply before and after a shallow water area creation in the river Elbe's tide management.

ES matrix elements

The ES matrix approach has been used to assess an average of 15.6 different ES per matrix, whereof 7.0 (on average) were regulating ES, 6.7 provisioning ES and 3.8 cultural ES (Fig. 3). Next to ES, other elements were also included in the ES matrix: we found supporting ES (e.g. Lundy and Wade 2011 ; Hermann et al. 2014; La Bianca et al. 2018 and Liu et al. 2019) and components of ecosystem functions (e.g. Weyland et al. 2017). Due to the similarity and, sometimes, confusion between components (and related indicators) of ecological condition, ecosystem integrity and supporting ES and in order to avoid double-counting, we grouped these elements under the term “ecological condition indicators” (Fig. 3). They were assessed in 35 studies with a mean of 4.8 components of ecological condition (e.g. Burkhard et al. 2012, Burkhard et al. 2014, Cai et al. 2017, Kamlun and Arndt 2019).

Figure 3.  

Mean number of assessed ES and Ecosystem Disservices (EDS) and mean number of ecological condition and biodiversity components used in the published matrix studies (bars) with 95% error bars. Number of analysed studies assessing ES, ecological condition, ecosystem disservices and biodiversity (orange diamond). The error interval was not computed for the ecosystem disservices since only 2 studies evaluated them.

Ecosystem disservices (ES with a negative impact on human well-being; for further definition, see Lyytimäki 2014; von Döhren and Haase 2015 and Shackleton et al. 2016) have been added to the matrix by Campagne and Roche (2018) and Campagne et al. (2018) with 1 and 6 ecosystem disservices, respectively.

Biodiversity was added in addition to ES in 16 studies mainly through one indicator - called "Biodiversity" (e.g. Tamang 2011; Bhandari et al. 2016; Depellegrin et al. 2016 and Kamlun and Arndt 2019).

The geospatial units, that were mainly used in the different ES matrices, were related to LULC types and many studies used the European CORINE Land Cover typology or a related typology (EEA 1995). Besides CORINE Land Cover, the EUNIS habitat classification (European Nature Information System - EEA 2017) was used, notably for marine and benthic habitats (e.g. Salomidi et al. 2012; Galparsoro et al. 2014; Depellegrin et al. 2017). The ES matrix was generally used across different ecosystem types, but in some cases, it had also been used for specific ecosystem types, such as agroecosystems in Augstburger et al. (2018), wetland ecosystems in Ricaurte et al. (2017) or forest ecosystems in Zarandian et al. (2016).

Furthermore, the ES matrix approach has been adapted in a “species matrix”, which linked different species types to ES supply, as in Potts et al. (2014) and Burdon et al. (2017) with seabird species.

Methodologies used in the studies

Precise elaborations of the stepwise ES matrix application can been found in literature (Burkhard 2017, Campagne and Roche 2018, Elliott et al. 2019, Gorn et al. 2018). Burkhard (2017) proposed a 10-step ES matrix methodology for ES mapping, based on ES indicators and a collection of suitable spatial data. Campagne and Roche (2018) and Elliott et al. (2019) proposed methodologies on how to collect and integrate expert knowledge to address some of the biases and limitations of the expert-based elicitation method. Gorn et al. (2018) combine the ES matrix assessment with the Delphi approach, confidence ratings, standardised confidence levels and scenario assessment.

Several methodological steps are common in all applications of the ES matrix and we propose to look closer at the data and approaches used in and with the matrix, the scoring systems and the scoring process used, as well as the confidence and realiability analyses done in the analysed papers.

The ES matrix approach involves a scoring process to assess ES (supply, flow/use, demand) in ecosystem types or other explicit geospatial units. These scores can be based on or can integrate data from diverse sources of varying quantity and quality (Burkhard et al. 2012). Following the ”tiered approach” (Grêt-Regamey et al. 2015), the data used in the ES matrix can be seen as a gradient of increasing complexity (Fig. 4). Spatial GIS data, such as LULC data, are the main data used in the reviewed studies to define the geospatial units in the matrix. At the same time, LULC types can be used as proxies for the supply and use of many ES (e.g. rather obvious LULC - ES relations such as timber provisioning ES in forest ET, water supply ES in water bodies). Thus, LULC can be a suitable base for ES mapping, which has been used intensively.

Figure 4.  

Data or approaches commonly used in or with the ES matrix approach.

For the ES scoring process, expert scoring was the dominant data source, as it was used in 82 of the reviewed studies. When the scoring was expert-based, the number of experts involved in the scoring exercises varied between 2 and 170 with a mean of 31 experts. Nevertheless, the number of involved experts was not specified in 32 studies out of 82. Expert consultation was undertaken through workshops in 34 studies, interviews were conducted in 15 studies and specific surveys were carried out in 9 studies.

The second dominant data source was literature data transfer, which is when ES scores are based on an already-published ES matrix or deduced by information found in related scientific publications. This was used in 57 studies, more than half of the studies. Other data or approaches, such as statistical data (13 studies, e.g. national statistics of yield production), models (12 studies), remote sensing data (6 studies) and field data (6 studies) were used less in the analyses studies (Fig. 4).

Several types of data or approaches were used in 57 studies, of which 29 only combined two types of data or approaches: literature data transfer and expert scoring.

Scoring systems

One main characteristic of the ES matrix approach is to express ES provision with an ordinal scale and so allows the comparison of different ES. Several ranking scales were used to fill in the matrix. However, a numeric score ranging from 0 (no [relevant] supply) to 5 (very high [maximum] supply), as originally proposed by Burkhard et al. (2009), was mainly used (64 studies out of the 109 studies).

Several other scoring scales were used with 2, 3, 4, 5 or 7 levels in 33 studies (e.g. “0 to 1” in Baró et al. 2017; “0 to 2” Vihervaara et al. 2010; “1 to 3” in Távora and Turetta 2016; Nowak and Grunewald 2018 and Karrasch et al. 2019; “0 to 3” in Cotillon 2013 and La Bianca et al. 2018; “1 to 4” in Saunders et al. 2015; “0 to 4” in Van Looy et al. 2017; “1 to 5” in Clius and Pătroescu 2014; “1 to 7” in Weyland et al. 2017 and Maebe et al. 2019). A somewhat stretched scale from 0 to 100 can be found in Koschke et al. (2012) and Müller et al. (2020). The choice of the applied type of scoring system in the reviewed studies generally referred to a published study which, in the case of the 0 to 5 scale, was usually Burkhard et al. (2009). A different scoring system had been used in a few studies with specific aims: positive and negative rankings such as “-1 to 1” (Dechazal et al. 2008); “-2 to 2” (Hornung et al. 2018) and “-3 to 3” (Elliott et al. 2019; Karstens et al. 2019; Kopperoinen et al. 2014). Kopperoinen et al. (2014) used a scoring system with positive and negative values to assess favourable and harmful effects of the land cover on ES provision potential. This type of scoring can be related to an ecosystem services or disservices assessment, but disservices assessment can also be separated for services in the matrix (e.g. Campagne et al. 2018).

In two studies, the scoring system used non-numeric values (+) to (+++) (Maltby et al. 2017) or colour codes with 4 levels (Geange et al. 2019).

ES scoring process

A first step for implementing a matrix-based ES evaluation is to define the initial matrix that is to be used (Campagne and Roche 2018). It can be either based on an existing matrix from an already-published study or an empty ES matrix that is to be filled. Seventy of the analysed studies used an empty ES matrix, whereas in 32 studies, it was specified that the initial ES matrix (with or without modification afterwards) came from an already-existing ES matrix or several existing matrices that were found in literature (Fig. 5 and Table 1). Amongst those studies, 23 studies specified that the initial ES scores came from one of the Burkhard et al. publications (Burkhard et al. 2009, Burkhard et al. 2012, Burkhard et al. 2014). For 7 studies, unclear information on the initial ES matrix was provided. For example, the number of experts involved are presented in Bhandari et al. (2016), but no information on the initial matrix, the fill-in process or whether there has been a compilation of scores can be found in the paper.

Table 1.

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XLSX

Number of times the published matrix was used as an initial matrix.

21	Burkhard et al. publications: Burkhard et al. (2009), Burkhard et al. (2012), Burkhard et al. (2014)
3	Salomidi et al. (2012)
2	Stoll et al. (2015), Hermann et al. (2014), Galparsoro et al. (2014)
1	Xie et al. (2008); Nedkov and Burkhard (2012); Scolozzi et al. (2012); Potts et al. (2014); Depellegrin et al. (2016); Li et al. (2016); Goldenberg et al. (2017); Wu et al. (2019);

Figure 5.  

Number of analysed studies with explicit information on the initial matrix (in the centre) and on the scoring data and approaches used in the initial matrix (arrows).

After the definition of the initial matrix, the scoring process can be carried out with diverse sources of data. In the 32 studies that used an existing ES matrix as an initial matrix, 18 studies used no additional data to define the matrix scores and, therefore, made no adaptation of the values provided in the existing matrix (Fig. 5), whereas 14 studies modified the existing initial matrix, based on literature or expert opinions or models for one study. For example, Cai et al. (2017) used Burkhard et al. (2012) supply matrix and local expert adjustments to determine the final scores. Tao et al. (2018) applied the original matrices presented by Burkhard et al. (2014) and Li et al. (2016) and updated their scores through interviews of eight experts and “several rounds of panel discussions amongst all the experts to obtain consistent final scores for the matrix” (Li et al. 2016: P. 252). For the studies with an empty initial matrix, the scoring was mainly done through expert scoring or by a combination of expert scoring and data extracted from literature (Fig. 5). In addition, Maebe et al. (2019) and Ma et al. (2019) used a literature review and a mix of data from published matrices and published quantitative data. They are noted here with empty initial matrix and scoring data with literature.

Finally, the methodology to define the final scores, using all kind of data and approaches presented in Fig. 5, should be detailed in the studies (e.g. for experts' scoring, how the experts’ scores are collected and merged; when several data types and sources are used, how are they combined in a final score). From the 109 studies that were reviewed, only 61 studies explained clearly how the scores were obtained, for 19 studies the scores came from an existing ES matrix without modification or review of literature. Altogether, 29 studies did not provide any information about the method used to determine the scores, i.e. how exactly the scores were determined with the different data used in the paper.

Confidence and reliability

Hou et al. (2013) and Burkhard (2017) elaborated in detail the potential uncertainties in the overall ES matrix approach. In the scoring process, an uncertainty analysis should also be included (Campagne et al. 2017). Depending on the data source used, we can find diverse methods for confidence analysis in literature. With a score based on literature data and expert opinions, Potts et al. (2014), Saunders et al. (2015), Burdon et al. (2017) and Geange et al. (2019) used a confidence analysis, depending on the data source and the difference between “expert opinion”; “grey literature from overseas” and “grey and peer reviewed literature from the country of the study”. Maebe et al. (2019)P.13) put confidence scores “determined by agreement and evidence quality of the literature review” with “high agreement (i.e. data corroboration, no or minor disagreements) and robustness evidence (i.e. data are supported by scientific argumentation or analysis)” to “medium/yellow = medium evidence and medium agreement”. La Bianca et al. (2018) used a confidence score, based on the geographic similarity between the location of the case study site of the literature used and their study area. Whenever the ES scores result from the combination of several values, the variability of the scores needs to be considered and a confidence score or indices of reliability or agreement can be used to evaluate the degree of agreement or consistency amongst the individual experts’ answers (Jacobs et al. 2014, Campagne et al. 2017). Variability of the expert scores was, for instance, analysed in Tao et al. (2018). Confidence scores, expressed by experts together with their scores for each ES, were used in Campagne et al. (2017) and Elliott et al. (2019). Gorn et al. (2018) used a confidence level that was “developed for the means and standard deviations of the assessments of the ES provision potential and assessments for the confidence rating according to IPCC”. In case the ES matrix scores were based on methods from different tiers (see above), the values can be cross-checked in order to find the most suitable, reliable and useful (for the specific purpose) ES quantification method (Burkhard 2017, Roche and Campagne 2019). Data from the different tiers should, of course, be valid for the same area, time and spatial scale and in comparable resolution. In the reviewed studies, only 13 studies did a confidence analysis, including eight newer studies from 2018 and 2019.

Discussion

Acknowledgements

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