Dataset for: Navigating Ecosystem Services Trade-offs: A Global Comprehensive Review

Methods

The dataset is the output of a comprehensive literature-based search that aims to collate all the evidence on where ES relationships have been mentioned and addressed. We applied systematic mapping which is based on the “Guidelines for Systematic Review in Environmental Management” developed by the Centre for Evidence-Based Conservation at Bangor University (Pullin and Stewart 2006).

The methodological framework followed the standard stages outlined for systematic mapping in environmental sciences (James et al. 2016). Briefly, we defined the scope and objectives:

·       We comprehensively review and further explore the global evidence of ES trade-offs and synergies focusing on all systems including terrestrial, freshwater, and marine.

·       We compiled the evidence on trade-offs and synergies among multiple ES interacting across various ecosystems.

·       We performed a geographical and temporal trend analysis exploring the distribution of studies across the world examining how the focus on various ecosystem types and ES categories has evolved to highlight gaps and biases.

Then we set the criteria for study inclusion (Table 1), searched the evidence, coded, and produced the database. Extracted article information including the specific criteria is detailed in Table 1.

The first step was to search the ISI Web of Knowledge core collection (http://apps.webofknowledge.com) database, targeting the search on the ecosystem services literature and studies dealing with trade-offs/synergies, win-win outcomes or bundles when managing different ecosystem services in the landscape/seascape. All peer-reviewed journal articles written in English and Spanish have been considered for review.

The peer-reviewed literature from 2005 to 2021 was reviewed identifying relevant studies according to specific search terms. The relevant search terms and descriptive words derived from (Howe et al. 2014) adding “bundles” and “co-benefits”. Boolean nomenclatures ‘*’ = all letters were allowed after the *, were used on the root of words where several different endings applied (Figure 1). Search terms used were:

(“*ecosystem service*” OR “environment* service*” OR “ecosystem* approach*” OR “ecosystem good*” OR “environment* good*”)

AND

(“*trade-off*” OR “tradeoff*” OR “synerg*” OR “win-win*” OR “bundle*” OR “cost*and benefit*” OR “co-benefit*”) n=5194

Papers were preliminarily coded with a semantic analysis using the R package Bibliometrix (http://www.bibliometrix.org).

In the second step (Figure 1) papers were preliminarily coded with a semantic analysis using the R package Bibliometrix (http://www.bibliometrix.org). Papers were classified according to three systems: terrestrial, marine, and freshwater (Table 1). Papers with multiple systems, transitional habitats or those that could not be classified were classified as “other” (Mazor et al. 2018). Articles were classified based on the occurrence of the most frequent system words in their title, keywords, and abstract (Mazor et al. 2018). The set of system-specific words was determined by extracting the 250 most frequently used keywords from all considered articles and assigning each word to either system (articles could fall into just one of the four categories). Using this technique, we managed to classify 100% of the papers. To further enrich the dataset and make it a useful repository for science and policy, an additional sub-classification was performed, categorizing papers into the following categories: Coastal, Urban, Wetlands, Forest, Mountain, Freshwater, Agroecosystems, and Others that mainly represented multiple ecosystems (Table S1). This comprehensive classification approach enhances the dataset’s utility for various scientific and policy-making applications.

In the third step (Figure 1), applying the same technique, we classified the papers into four ES categories: habitat (supporting biodiversity related), provisioning, regulating, and cultural services (De Groot et al. 2010; MEA 2005; Sukhdev 2010; Wallace 2007). For the classification into ES categories, articles could fall into one or more of the four categories (see Table 1 for example the keywords used to classify ecosystems, ES categories, and countries). Applying this technique, we excluded 2149 papers that weren’t classified in any of the ecosystem services types categories resulting in 3629 papers (see Figure 1).

In the fourth step (Figure 1), an initial screening was conducted to identify papers that did not align with the review objectives of assessing ecosystem services trade-offs and synergies to inform policy and management decisions. We manually reviewed the titles of each paper in the dataset, excluding those that were from other fields or did not align with the review objectives. In this initial assessment, we excluded 347 papers, leaving a total of 3,286 papers for further review. A descriptive analysis of this 3286 article dataset was performed to examine the distribution of ES categories within each ecosystem type over the specified period. This analysis allowed us to conclude the prevalence of each ecosystem service category in different ecosystem types, identifying temporal trends and patterns. The number of occurrences was calculated for each ES category within each ecosystem type, expressed as counts. This allowed for the comparison of ecosystem service distributions across the selected ecosystem types.

In the fifth step (Figure 1), we employed an approach to visually represent the geographical distribution and focus of ES studies across the world. With the classification of studies in ES categories and the types of ecosystems, the papers were coded according to the country where the study was performed. It was possible to assign a specific country to 2636 studies, removing 650 studies that did not specify the country of study. From these 2636 papers classified, a proportion were global studies that consider several countries under study (499 global studies).

We developed global maps (Figure 1), each offering a unique perspective on the ES research landscape. The first map presents the total number of ES trade-off studies conducted worldwide, illustrating the geographical spread and concentration of research efforts to provide a clear overview of regions that have been extensively studied and those that may require more attention in future research. Additionally, we calculated two key metrics to assess research productivity more comprehensively: the number of research papers per capita and the number of research papers relative to Gross Domestic Product (GDP). For population and GDP, we used the most recent available data from the World Bank (https://data.worldbank.org). These alternative metrics normalize the data based on economic output and population size, providing a more balanced view of research activity across different countries (Figures S3).

Detailed maps were created featuring pie charts that highlight the different categories of ES and ecosystem types addressed for each country. These charts offer an understanding of how various ES categories and ecosystems are represented in different parts of the world. Finally, we assessed ES trade-off studies to world regions (Africa, Antarctica, Asia, Australasia, Europe, Latin America, and North America) looking at the relationships between the categories of ES. We considered papers that evaluated more than one category of ES and the papers that considered only one category of ES. This country-level analysis offers insights into regional research trends and priorities, contributing to a more localized understanding of ES studies.

In the sixth step (Figure 1), each publication in this review was critically appraised to evaluate the quality of the papers included in the review. The foundation for our critical appraisal stems from the comprehensive and multidimensional approach of Belcher et al. (2016) that is framed to evaluate research quality, which aligns well with the interdisciplinary nature of our study. Belcher et al. (2016) developed a robust framework that incorporates essential principles and criteria for assessing the quality of transdisciplinary research. This is particularly relevant for ecosystem services science and our review that contributes to advancing current knowledge by systematically synthesizing evidence on relationships among various ES across these diverse systems.

The Belcher et al. (2016) framework emphasizes four main principles: relevance, credibility (which we have adapted as methodological transparency), legitimacy (generalizability in our context), and effectiveness (significance). A continuous scoring system (ranging from 0 to 1) was applied for the four main criteria to maintain simplicity and consistency across the large number of studies. In this system, a value closer to 0 indicates that the criteria are not met, while a value closer to 1 indicates that the criteria are more closely met. This scoring method was a useful indicator of the overall quality of the paper and how well the article met the review's goals overall.

Methodological Transparency was assessed based on the clarity and completeness of methodological descriptions, including data availability, the rigor of statistical analyses, methodological detail, and reproducibility of the findings. This criterion assesses the transparency and rigor of the study's methodology, including data collection, analysis, and reporting (Belcher et al. 2016). Relevance was evaluated by the study's alignment with the review's objectives, its importance to the field, and its practical applicability. This includes the extent to which the study addresses pertinent research questions of the study (Belcher et al. 2016). Significance was determined by the novelty of the study, its theoretical contributions, and practical implications. This includes evaluating whether the study presents new ideas, concepts, or frameworks that advance the field of ES relationships (Belcher et al. 2016). Generalizability was judged based on the contextual applicability and transferability of the study's findings to other settings. This includes assessing whether the study's results can be applied broadly and whether the context is sufficiently described to understand its applicability (Belcher et al. 2016).

In the final seventh step (Figure 1) a detailed assessment was performed incorporating sub-categories within each main critical appraisal criteria to allow for a more comprehensive analysis (Table S2). To this end, a random sample of 20% of the studies was selected (574 papers) and a thorough revision by subcategories for each main criteria was performed (see Table S2). For the random selection of 20% of the studies from the dataset, we loaded the dataset into R and set a random seed to ensure reproducibility. We then calculated the sample size as 20% of the total dataset and used the sample function to select this subset of studies randomly. This method ensured an unbiased and representative sample. The selected subset was assessed for a further detailed critical appraisal adding sub-categories to each criterion (Table S2). A qualitative assessment sub-category was also inclded to explain the rationale behind each assigned score in the dataset.

We finally created a heatmap to visualize average scores by continent across the four criteria. Utilizing R programming (R Core Team 2023) for data aggregation with the dplyr package, the study computed mean scores for each criterion by continent. The aggregated data was visualized using a heatmap created with the ggplot2 package, where the color intensity of each cell represented the mean scores, facilitating a visual comparison across continents. This analysis offers a quantitative foundation for identifying areas of strength, evaluating performance by continent and potential for improvement, and further elucidating the different quality and focus of research within the ES trade-offs research.

References:

Belcher, B. M., Rasmussen, K. E., Kemshaw, M. R., & Zornes, D. A. (2016). Defining and assessing research quality in a transdisciplinary context. Research Evaluation, 25(1), 1-17.

De Groot, R. S., Alkemade, R., Braat, L., Hein, L., & Willemen, L. (2010). Challenges in integrating the concept of ecosystem services and values in landscape planning, management, and decision-making. Ecological Complexity, 7, 260-272.

Howe, C., Suich, H., Vira, B., Mace, G.M., 2014. Creating win-wins from trade-offs? Ecosystem services for human well-being: A meta-analysis of ecosystem service trade-offs and synergies in the real world. Global Environmental Change 28, 263-275.

Mazor, T.A.-O., Doropoulos, C.A.-O., Schwarzmueller, F.A.-O., Gladish, D.A.-O.X., Kumaran, N.A.-O., Merkel, K.A.-O.X., Di Marco, M., Gagic, V.A.-O. (2018). Global mismatch of policy and research on drivers of biodiversity loss. Nature Ecology & Evolution 2, 1071-1074.

Millennium Ecosystem Assessment (MEA).(2005). Ecosystems and Human Well-Being: Synthesis. Island Press, Washington DC.

Pullin, A. S., & Stewart, G. B. (2006). Guidelines for systematic review in conservation and environmental management. Conservation biology, 20(6), 1647-1656.

Sukhdev, P. (2010). The economics of ecosystems & biodiversity: mainstreaming the economics of nature: a synthesis of the approach, conclusions, and recommendations of TEEB. UNEP.

R Core Team. (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/

Wallace K.J. (2007). Classification of ecosystem services: Problems and solutions. Biological Conservation 139, 235-246.

Dataset

Name of the file: MMartinez-Harms-Dataset-ESTrade-offs_Zenodofinal.xlsx

Description: Complete database of the 3286  studies reviewed in this synthesis coded by:

N
Number of papers

AU
Authors

TI
Title

SO
Publication name

Included
identify papers that did alignd (yes) or did not (No) align with the review objectives.

JI
Scientific Journal

AB
Abstract

DE
Authors’ Keywords

ID
Keywords associated by  ISI database

LA
Language

DT
Document Type (article, review, editorial, book chapter, letter, meeting abstract)

TC
Times Cited

PY
Publication Year

SC
 Subject Categories

WC
Web of Science categories

System
Freshwater, terrestrial, marine, and other

Habitat
Habitat ES category

Regulating
Regulating ES category

Cultural
Cultural ES category

Provisionning
Provisionning ES category

Country_Classification
Country of study

Continent
Continent of study

Synergies_Trade-offs
The term trade-off involves losing one quality or aspect of something in return for gaining another quality or aspect. Synergy is a situation where the use of one ES directly increases the benefits supplied by another ES or a win-win situation

Stakeholder_Participation
The study interacts with stakeholders at any moment of the study or policy  decisions. 

Critical_Appraisal
Articles are appraised to ensure that they are adequate for answering the research question

Relevance
Relevance to the review question (0 no relevance, 1 relevance)

Methodological_Transparency
Assessed based on the clarity and completeness of methodological descriptions

Significance
Significance of the contribution (are new ideas offered?)

Generalizability
 Is the context specified, and do the ideas apply in other contexts?

Total_Score
The sum total score across all critical appraisal criteria 

Category
Coastal, Urban, Wetlands, Forest, Mountain, Freshwater, Agroecosystems, and Others mainly represented multiple ecosystems

Uses_INVEST
This paper uses the InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) tool from the Natural Capital Project to assess ecosystem services (Yes) or Not (No)