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Master’s Thesis, 60 ECTS

Social-ecological Resilience for Sustainable Development Master’s programme 2020/06, 120 ECTS

Biodiversity-Ecosystem Services Relationships within the Biosphere

Integrity Planetary Boundary

Satnarain Anil Singh

Stockholm Resilience Centre

Sustainability Science for Biosphere Stewardship

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Master’s Thesis- Satnarain Anil Singh

Title: Biodiversity-Ecosystem Services Relationships within the Biosphere Integrity Planetary Boundary

Contents

Abstract ... 2

Introduction ... 3

Theoretical Framework ... 4

Methods ... 6

Part I: Identifying biodiversity-ecosystem services relationships . ... 6

Part II: Identifying global trend data for ecosystem services ... 7

Part III: Quanitfying ecosystem services interactions ... 12

Results ... 15

Part I: Identifying biodiversity-ecosystem services relationships . ... 15

Part II: Identifying global trend data for ecosystem services ... 18

Part III: Quanitfying ecosystem services interactions ... 19

Discussion ... 21

Conclusions ... 26

References ... 27

Appendices ... 32

Appendix A: Biodiversity-ecosystem services effect size data and analysis ... 32

Appendix B: Proportion change in ecosystems services data and analysis ... 40

Appendix C: Supplementary data ... 47

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Abstract

The biosphere integrity boundary of the Planetary Boundaries Framework seeks to highlight biodiversity loss and its effect on humanity's 'safe operating space'.

Biodiversity plays a critical role in sustaining ecosystem function and by extension, the ecosystem services on which human wellbeing depends. As currently

conceptualized, biodiversity and the provisioning and regulating ecosystem services with which it is associated, is not adequately captured in the boundary. Literature searches for data-synthesis were carried out to identify and assess the balance of evidence for the relationship between biodiversity and ecosystem services. The change in global ecosystem service trends over time were assessed along with the interactions between ecosystem services. Twelve provisioning and 9 regulating ecosystem services associated with biodiversity were identified in the literature.

Biocontrol and carbon sequestration were the most studied services. The Fischer exact test showed that there was a significant difference between the extent to which

provisioning versus regulating ecosystem services are studied. Mann-Whitney U tests showed non-significant relationships between provisioning services and regulating services for trend and effect size data. All provisioning services showed increasing trends over time. The results for regulating services were mixed. Of the 115 ecosystem service interactions assessed, 66 were trade-offs and 49 were synergies.

Crop yield and climate-related ESS (carbon sequestration and carbon storage) represented almost one-third of these interactions (n = 22) while crop yield and erosion control represented over a quarter (n = 19). These interactions alone accounted for 36% of the total interactions. This paper provides an initial database which could be refined and expanded. It also demonstrates a comprehensive approach to assessing biodiversity ecosystem service relationships, providing a tangible

approach to assessing a safe operating space for humanity. Further, it provides a platform for future research on biodiversity-ecosystem services human well-being links, which will provide better insights to policymakers, managers and practitioners.

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Introduction

The planetary boundaries (PB) approach seeks to provide a framework in which the state of the biogeochemical processes which sustain human life on planet earth can inform a safe operating space for humanity (Rockström et al. 2009). Biodiversity loss due to human activity has resulted in what has been termed a sixth mass extinction (Chapin et al. 2000). The biosphere integrity wedge of the PB framework seeks to draw attention to this challenge by highlighting species loss through extinction rates and declines in phylogenetic diversity (Steffen et al. 2015).

As currently conceptualized the biosphere integrity boundary seems to represent a universal operating space for all species rather than a safe operating space for

humanity, as is its stated purpose. In other words, the current perspective is limited to considering the drivers of biogeochemical processes. The global roles of biodiversity in sustainability, specifically the critical role in sustaining ecosystem function and by extension the ecosystem services on which humans depend, are not adequately captured in the current framework.

There is a growing realization of the need for transdisciplinary research to meaningfully address the sustainability challenges we face in the Anthropocene.

Research on the links and interactions between biodiversity and ecosystem services lie at the nexus of social, i.e. how do humans value and rely on ecosystem services and the ecological i.e. how ecosystem function is regulated and underpinned by biodiversity. The rapid loss of biodiversity and the widespread degradation of

ecosystems and the services they provide, services on which humanity relies for food and materials, have highlighted the urgency for which research into social-ecological systems is needed.

Therefore, there is a need to develop an approach to capturing the global role of biodiversity to better inform the safe operating space. This study proposes a biodiversity-ecosystem services (B-ES) framework through focusing on B-ES specifically related to provisioning and regulating ecosystem services. The main research question is: How can biodiversity- ecosystem services relationships be integrated into the PB framework? It is proposed that this is explored through three separate but interrelated sub-questions: Which ecosystem services depend directly and indirectly on biodiversity? What are the current global trends in relation to those

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ecosystem services, i.e. are they declining, neutral or increasing? How do these ecosystem services interact, specifically in terms of trade-offs and synergies?

Theoretical Framework

A brief review is necessary to contextualize the aims of this study and to summarize how the biosphere integrity PB has evolved since the original paper was published by Rockström et. al. (2009). The original paper indicated that the primary motivation for including biodiversity in the PBs was to highlight its role in ecological function and regulation of biophysical earth system processes. In this paper extinction rates were identified as the control variable. Subsequently, Mace et. al. (2014) identified extinction rates and species richness as weak metrics for the biodiversity boundary and proposed three “facets” on which the boundary could be based:the genetic library of life; functional type diversity; and biome condition and extent. In an update to the original PB study, Steffen et. al. (2015) integrated phylogenetic species variability (PSV) and functional diversity as control variables. However, due to a lack of global data for PSV, species extinction rates were retained as an interim variable and due to issues with scaling up data based on functional diversity, the Biodiversity Intactness Index (BII) was proposed as an interim variable. The focus of this study is to explore an alternative way of conceptualizing the biosphere integrity PB by exploring how biodiversity underlies ecosystem processes and therefore facilitates the provision and regulation of ecosystem services. For example, with the BII as a control variable, changes in ecosystem services could theoretically act as a response variable.

Several studies have explored the relationship between biodiversity, ecosystem function, and ecosystem services (Worm et. al., 2006, Mace et. al., 2012, Bastian, 2013, Harrison et. al., 2014, Cardinale et al., 2012; Duncan et. al., 2015, Truchy et.

al., 2015, Isbell et al., 2017). However, when considering biodiversity’s relationship to ecosystem services and human well-being, it is not always a priority but is rather addressed as “another issue to solve rather than as a part of the solution to existing problems” (Pires et. al., 2018). There is a need to take the B-ES research further e.g.

by exploring how ecosystem services or groups of ecosystem services interact, how these interactions change over time, and identifying the drivers of these changes (see e.g. Renard et. al., 2015). Trade-offs (increase in one ESS related to a decrease in another) or synergies (increase in one ESS is related to an increase in another).

Assessing these interactions allows for a more nuanced assessment of how changes in

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biodiversity and related changes in ESS contribute directly to changes in human health, wealth, food & material provision etc. (Naeem et al., 2016).

The increase in human wellbeing and the counterintuitive degradation of the

ecosystems which enable this increase in wellbeing, has been described by Raudsepp- Herne et al. (2010) as the “environmentalist’s paradox”. The authors explore four hypotheses for this phenomenon, two of which are relevant to this research— first, the primacy of provisioning ecosystem services for human wellbeing, specifically food production and agricultural growth and second, that there is potentially a time lag between the degradation of ecosystems and the effect on human wellbeing. The present study explores the strength and direction of the relationship between

provisioning and regulating services, related to the first hypothesis, and examines the relationship between the change in provisioning and regulating ecosystem services over time, which may have implications for the second.

Definitions of biodiversity abound and there is a plethora of ways of measuring it (Mace et. al. 2012). For this study, we use the Convention on Biological Diversity’s (CBD’s) definition: ‘the variability among living organisms from all sources

including, inter alia, terrestrial, marine and other aquatic ecosystems and the ecological complexes of which they are part; this includes diversity within species, between species and of ecosystems’. This definition is, “in common usage, has policy status and is inclusive” (Mace et. al., 2012). ESS are defined as the benefits humans receive from ecosystems (MEA, 2005) and can be conceptualized as a good, a final service or a process (Mace et. al. 2012). In this study, we focus on the first two classifications. The reason for not including the third, biodiversity as a good, is that this study focuses on provisioning and regulating services and does not seek the capture the cultural, spiritual, educational, and recreational aspects of biodiversity or ecosystem services. This is not to ignore the importance of biodiversity or ecosystems as viewed from these perspectives, but the subjectivity and wide variety of approaches to assessing them does not allow for comparisons and assessments of relationships as conceptualized in this study.

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Methods

Part I: Identifying B-ES Relationships

Two assessments (IPBES, 2019, GDB, 2016) and four published journal articles (Cardinale et al., 2012; Diaz et al., 2019; Jørgensen et al., 2018; Butchart et al., 2010) were used as the initial reference points for identifying a list of 12 regulating and 21 provisioning ecosystem services that showed direct and indirect links to biodiversity.

For each B-ES, data syntheses were identified and where these did not exist, primary searches were carried out. Two measures were identified and recorded from the data syntheses and primary searches: “vote-tallies”, which represent the number of B-ES relationships identified that showed a positive, non-significant, or negative association and effect sizes. This approach is based on a previous meta-analysis by Cardinale et al., (2012).

For vote tallies, the number of positive, negative, and insignificant links which

showed negative links were calculated (no. of studies with negative links/total number of papers). For vote-tally data, due to having some cell counts below 5, the Fischer exact test was carried out in R studio (version 3.63) to investigate if the differences between positive and negative relationships were significant.

For the effect sizes, log-response ratios were recorded, a commonly used tool for measuring effect sizes (Hedges et al., 1999). It should be noted that while this approach is useful, as it can be applied to nearly every study, it only compares

extreme values of diversity i.e. it does not inform the diversity-function relationship in between extremes (Cardinale et al., 2011). The Mann-Whitney U test was used to compare data for effect sizes between provisioning and regulating ecosystem services.

Provisioning and regulating effect sizes were independent, with non-normal

distributions of similar variance, meeting the assumptions for the test (see Appendix B). This was conducted using SPSS version 25.

Additional information was recorded for each study in the final review, including ecosystem service stability, service providing units (for example, whether the service is provided by an animal or plant), diversity level (genetic, species, or trait), and the type of study (observational or experimental). The full database can be found here:

https://app.box.com/s/8hnlvm5r6h3vzuz5ils1ij8je89jx1ha

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Part II: Identifying Global Trend Data for Ecosystem Services

Literature searches were carried out in the Web of Science database for 12

provisioning and 21 regulating ecosystem services identified in Part 1, with the aim of collecting information on the change in ESS over time (see table 1 for list of search terms used). “State” data is a snap-shot of a given ESS captured at a specific point in time whereas “trend” data is information collected continuously over a period of time.

Given that ESS can be quantified using different units across studies, the proportion change per year was the metric used to represent change over time. Therefore, state data must have been reported at least two time points to be included in this analysis.

The limitations of state data notwithstanding, proportion change per year calculated from both state and trend data are referred to as “trend data” throughout this thesis.

The PRISMA flow chart method (Moher et al., 2009) was used with the following criteria: identification, screening, eligibility, and inclusion (see figure 1). Each search was first filtered by the period 2010-2020 to find the most recent data, then filtered again by relevance. The first 100 search results were then selected. Duplicates were removed by importing into Mendeley reference software using the remove duplicates tool. Titles and abstracts were scanned for global data relevant to the specific

ecosystem service. et al. The data was then compiled in an MS Excel table. The Mann-Whitney U test assumptions of independence, non-normality and of similar variance between provisioning and regulating ESS were met (see Appendix B).

Therefore, the test was used to compare data for proportion change per year between provisioning and regulating ecosystem services. This was conducted using SPSS version 25.

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Figure 1: PRISMA 2009 Flow Diagram- BES Trend Search Records identified through

database searching (n = 5,592)

ScreeningIncludedEligibilityIdentification Additional records identified

through other sources (n = 10)

Records after duplicates removed (n = 5,213)

Records screened (n = 5,213)

Records excluded (n = 3,394)

Full-text articles assessed for eligibility

(n = 387)

Full-text articles excluded, with reasons

(n = 366)

Studies included in qualitative synthesis

(n = )

Studies included in quantitative synthesis

(meta-analysis) (n = 21)

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Table 1: Ecosystem services search terms.

Ecosystem Services Search Terms Reference

Provisioning

Food Crops crop AND (yield OR production

OR productivity)

Cardinale et al., (2012)

Food Crop Stability crop AND (yield OR production OR productivity) AND (stability OR variability OR resistance OR resilience)

Cardinale et al., (2012)

Biofuels (fuel OR biofuel) AND (yield OR

output OR production)

Cardinale et al., (2012)

Biofuel Stability (fuel OR biofuel) AND (yield OR output OR production) AND (stability OR variability OR resistance OR resilience)

Cardinale et al., (2012)

Wood or Fibre (wood OR fibre) AND (yield OR

production OR productivity)

Cardinale et al., (2012)

Wood or Fibre Stability (wood OR fibre) AND (yield OR production OR productivity) AND (stability OR variability OR resistance OR resilience)

Cardinale et al., (2012)

Fodder fodder AND (yield OR production

OR productivity)

Cardinale et al., (2012)

Fodder Stability fodder AND (yield OR production OR productivity) AND (stability OR variability OR resistance OR resilience)

Cardinale et al., (2012)

Utilized Vertebrate Species utilized vertebrate species Butchart, (2010) Food and medicine “species used for food AND

medicine”

“species used for food OR

Butchart, (2010)

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medicine”

Fisheries fish* AND (yield OR production

OR productivity)

Cardinale et al., (2012)

Fisheries Stability fish* AND (yield OR production OR productivity) AND (stability OR variability OR resistance OR resilience)

Cardinale et al., (2012)

Regulating

Biocontrol (Human infectious disease prevalence)

“infectious disease*” GBD Causes of Death

Collaborators, 2017; Jørgensen, 2018

Biocontrol and disease prevalence (biocontrol OR "biological control") AND (disease OR pathogen* OR infect* OR illness OR epidemic)

Cardinale et al., (2012)

Biocontrol (insecticide resistance- treatment potential)

(insecticide) AND (resistance*) Jogensen, (2018)

Agricultural Pests (biocontrol OR "biological control") AND (agriculture OR agricultural OR crop) AND (pest$

OR prey OR insects OR herbivore$)

Cardinale et al., (2012)

Invasion Resistance (biocontrol OR "biological control") AND (exotic OR invasive) AND (plant OR algae OR producer)

Cardinale et al., (2012)

Biocontrol (herbicide resistance- treatment potential)

(biocontrol) AND (herbicide resistance*)

Jorgensen, (2018)

Pollination (wild and domesticated?)

(pollinator diversity) OR (pollen deposition) OR (abundance wild pollinator*) OR (domesticated pollinator*) OR (pollinat*)

Cardinale et al., (2012); IPBES, (2019)

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Dispersal of seeds “seed dispersal”

Erosion control “erosion control” OR “erosion” Cardinale et al., (2012)

Flood regulation Flood* AND (control OR

regulation)

Cardinale et al., (2012)

Freshwater (quantity, quality and purification?)

(freshwater quantity) OR (freshwater quality) OR (freshwater purification)

freshwater (decontamination OR nutrient OR purification OR quality)

Cardinale et al., (2012); IPBES, (2019)

Soil regulation (soil organic matter) OR (soil quality)

IPBES, (2019)

Soil nutrient remineralization soil AND (fertility AND nutrient AND moisture) AND

(remineralization OR cycling)

Cardinale et al., (2012)

Soil moisture soil AND (moisture OR humidity

OR water retention OR water consumption OR drought)

Cardinale et al., (2012)

Soil organic matter soil AND organic matter Cardinale et al., (2012) Air quality regulation “air quality”

(Ecosystem retention) OR (prevention emission*air pollutant*)

IPBES, (2019)

Ocean acidification “ocean acidification”, “marine calcification”

IPBES, (2019)

Atmospheric/Climate regulation “atmospheric concentration greenhouse gases”

IPBES, (2019)

Carbon sequestration (carbon sequestration OR C- sequestration)

Cardinale et al., (2012)

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Carbon storage (carbon storage OR C-storage) Cardinale et al., (2012) Primary production or

photosynthesis

(photosynthesis OR oxygen production OR O2 production)

Cardinale et al., (2012)

*Searches in this table were combined with search string: ALL=(trend OR chang* OR

"global" OR "global estimate" OR "global change" OR "global dataset" OR "historical change")

Part III: Quantifying Ecosystem Service Interactions

A review of the literature was carried out to find which interactions between the 12 provisioning and 9 regulating services have been previously quantified. Table 2 shows the list of search terms used on the Web of Science database. The PRISMA flow chart method (Moher et al. 2009) was used with the following criteria: identification, screening, eligibility and inclusion. The procedure was conducted in two stages: first a general search and then a specific search with each combination of provisioning and regulating service. After compiling a list of search results, duplicates were removed by importing into Mendeley Reference software using the remove duplicates tool.

While the search yielded a few different methods for assessing interactions between ESS, the Pearson’s correlation coefficient was the only measure that looked at relationships between individual services, rather than a cluster/group of services.

Positive correlations were interpreted as “synergies” and negative as “trade-offs”. A network diagram of the interactions between ESS interactions was made using Cytoscape software (Shannon et. al., 2003).

Additional information about the included studies was recorded, including the scale (global, regional or local), specific study area (location), type of study (observational or experimental), and whether the study was spatial and/or temporal. Sample size data was recorded where available and when data was not available, authors were emailed to retrieve this information. This database is available at:

https://app.box.com/s/8hnlvm5r6h3vzuz5ils1ij8je89jx1ha

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Figure 2: PRISMA 2009 Flow Diagram- ESS Interactions Search Records identified through

database searching (n =193)

ScreeningIncludedEligibilityIdentification

Additional records identified through other sources

(n = 0)

Records after duplicates removed (n = 193)

Records screened (n = 193)

Records excluded (n =0)

Full-text articles assessed for eligibility

(n = 193)

Full-text articles excluded, with reasons

(n =162)

Studies included in qualitative synthesis

(n =0)

Studies included in quantitative synthesis

(meta-analysis) (n = 31)

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Table 2: Search terms for ecosystem service interactions

“ecosystem service interaction*” OR “ecosystem service trade-off*” OR “ecosystem service synergy*” OR “ecosystem service bundle*” OR “ecosystem services

interaction*” OR “ecosystem services trade-off*” OR “ecosystem services synergy*”

OR “ecosystem services’ bundle*”

(interact* OR (trade-off*) OR (synerg*) OR bundle*) AND ProESx AND RegESy) ProESx and RegESy refer to specific combinations of provisioning and regulating service interactions e.g. (“crop yield” AND “pollination”)

Database for BES Links and BES Global Trends

When all B-ES relationships data and ESS global trend data were consolidated and cleaned, these two databases were then linked by assigning common IDs. These were then joined in a junction table and imported to Microsoft Access through which queries could be run.

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Results

Part I: Identifying B-ES Relationships

Table 3 shows the 12 provisioning and 21 regulating ecosystems services with

relationships to biodiversity identified in the initial reference material. The number of positive, negative, and insignificant associations, or vote-tallies, found for these relationships were collected as were their effect sizes.

Table 3: Provisioning and regulating ecosystem services with relationships to biodiversity.

Category Ecosystem Service Source

Provisioning services

Food Crop yield Cardinale et al., 2012

Food Stability of crop yield Cardinale et al., 2012

Fisheries Fishery yield Cardinale et al., 2012

Fisheries Stability of fishery yield Cardinale et al., 2012

Biofuel Biofuel yield Cardinale et al., 2012

Biofuel Stability of biofuel yield Cardinale et al., 2012

Wood Wood production Cardinale et al., 2012

Wood Stability of wood production Cardinale et al., 2012

Fodder Fodder yield Cardinale et al., 2012

Fodder Stability of fodder yield Cardinale et al., 2012

Multiple Utilized Vertebrate Species Butchart et al., 2010

Food &

medicine

Food and Medicine Butchart et al., 2010

Regulating services

Biocontrol Human infectious disease prevalence (Human Disease Regulation)

Jørgensen et al., 2018 Biocontrol Insecticide Resistance (treatment potential) Cardinale et al., 2012 Biocontrol Herbicide Resistance (treatment potential) Cardinale et al., 2012 Biocontrol Abundance of herbivorous pests

(bottom-up effect of plant diversity)

Cardinale et al., 2012 Biocontrol Abundance of herbivorous pests (top-down effect of

natural enemy diversity)

Cardinale et al., 2012 Biocontrol Resistance to plant invasion Cardinale et al., 2012 Biocontrol Disease prevalence (for plants) Cardinale et al., 2012 Biocontrol Disease prevalence (for animals) Cardinale et al., 2012

Climate Primary production Cardinale et al., 2012

Climate Carbon sequestration Cardinale et al., 2012

Climate Carbon storage Cardinale et al., 2012

Flood Flood regulation Cardinale et al., 2012

Soil Soil nutrient remineralization Cardinale et al., 2012

Soil Soil moisture Cardinale et al., 2012

Soil Soil organic matter Cardinale et al., 2012

Water Freshwater purification Cardinale et al., 2012

Erosion Erosion control Cardinale et al., 2012

Pollination Pollination Cardinale et al., 2012

Seed dispersal Dispersal of Seeds IPBES, 2019; Diaz et al., 2019

Air Air Quality Regulation IPBES, 2019; Diaz et al., 2019

Water Ocean Acidification Regulation IPBES, 2019; Diaz et al., 2019

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Vote-tallies for B-ES Relationships

Figure 3 shows the distribution of vote-tally counts of positive, non-significant, and negative relationships for each ESS (see appendix II for data in tabular form). The abundance of herbivorous pest’s bottom-up effect of plant diversity (AHPB), abundance of herbivorous pest’s top-down effect of plant diversity (AHPT), and carbon sequestration (CS) were the most studied B-ES relationships. CS showed the highest number of positive relationships to biodiversity whereas AHPB had the highest number of negative relationships. In sum, 44% showed positive, 15% non- significant, and 36% negative relationships (figure 4).

Figure 3: Stacked column chart for B-ES vote-tallies. Y-axis: number of votes for each B-ES relationship with each column showing proportion of positive, non-significant and negative votes. X-axis: ESS: CY-Crop Yield, SCY-Stability of Crop Yield, FISHY-Fish Yield, SFISHY-Stability of Fisheries Yield, BFY-Biofuel Yield, SBFY-Stability of Biofuel Yield, WP-Wood Production, SWP-Stability of Wood Production, FY-Fodder Yield, SFY-Stability of Fodder Yield, HDR-Human Disease Regulation, AHPB-Abundance of Herbivorous Pests Bottom-up, Abundance of Herbivorous Pest Top-down, RPI-Resistance to Plant Invasion, DPP-Disease Prevalence on Plants, DPA-Disease Prevalence on Animals, PPP-Primary Production of Photosynthesis, CS-Carbon Sequestration, CST-Carbon Storage, FR-Flood Regulation, SNM-Soil Nutrient Remineralization, SM-Soil Moisture, SOC-Soil Organic Carbon, FWP-Freshwater Purification, EC-Erosion Control, P-Pollination, DS-Dispersal of Seeds, AQR-Air Quality Regulation and Ocean Acidification Regulation.

0 100 200 300 400 500 600

CY SCY FISHY SFISHY BFY SBFY WP SWP FY SFY HDR AHPB AHPT RPI DPP DPA PPP CS CST FR SNM SM SOC FWP EC P DS AQR OAR

Positive Non-significant Negative

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Figure 4: Total vote-tally percentages for positive, negative, and non-significant B-ES relationships.

Investigating the differences between positive and negative vote-tally relationships, the Fischer exact test showed that there is a significant relationship with ESS type (p- value = 0.0004998). Furthermore, there is a large variation in vote-tally data among different ESS and between ESS in provisioning and regulating groups i.e. each ESS is not studied to the same extent (figure 5).

0 10 20 30 40 50 60 70 80 90 100

Votes

Percent

Positive Negative Non-significant

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Figure 5: Number of B-ES relationships for provisioning and regulating services. ESS type is dependent on vote-tally relationship (p-value = 0.0004998).

Effect Sizes (e) for Biodiversity Ecosystem Services Relationships

The review of the initial reference material identified 5 effect sizes for provisioning services and 9 for regulating services (see Appendix A). Table 4 shows summary statistics and figures 7 & 8 shows the strength of effect sizes. The Mann-Whitney U test shows that differences in the effect sizes is not significant between provisioning and regulating services (U=22.000, p=1.000).

Table 4: Descriptive statistics for effect sizes of provisioning and regulating services.

Ecosystem

services N Mean Std.

Error Median* Variance Std.

Deviation Minimum Maximum Provisioning 5 -0.119 0.542 0.310 1.467 1.211 -2.210 0.910

Regulating 9 0.172 0.121 0.068 0.131 0.362 -0.030 1.111

*Mann-Whitney U test statistic = 22.000, p-value = 1.000

Part II: Global Trend Data for Ecosystem Services

The proportion change per year was identified and calculated for 12 provisioning services and 9 regulating services. Table 5 shows summary statistics and figures 7 &

8 show the direction of proportion change. The Mann-Whitney U test shows that differences in proportion change per year are not significant between provisioning and regulating services (U=52.000, p=0.917).

0 100 200 300 400 500 600 700 800 900

Provisioning Regulating

No. of Relationships

ESS Type

Positive Negative Non-significant

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Table 5: Descriptive statistics of the proportion change per year for ecosystem services.

Ecosystem

services n Mean Std.

Error Median* Variance Std.

Deviation Minimum Maximum

Provisioning 12 0.102 0.044 0.026 0.023 0.152 -0.004 0.392

Regulating 9 0.172 0.121 0.068 0.131 0.362 -0.03 1.111

*Mann-Whitney U statistic = 52.000, p-value = 0.917

Part III: Ecosystem Services Interactions

The literature search resulted in a total of 193 records of which 31 records were included as part of this synthesis after full screening of all records (see Appendix B).

A total of 115 ESS interactions (figures 7 & 8), 28 of which were unique, were identified and added to a database. Sixty-six interactions represented trade-offs while 49 represented synergies between provisioning and regulating ecosystem services (see figure 6).

Figure 6: Percentage of trade-offs and synergies. The literature search for ESS interactions resulted in 115 interactions of which 66 (57%) were trade-offs and 49 (43%) were synergies.

One provisioning service stood out amongst the others regarding the number of interactions identified. The interaction between crop yield, a provisioning service, and its regulating services represented 61% (n = 70) of all ESS interactions. Crop yield and climate-related ESS (carbon sequestration and carbon storage) represented almost

0 10 20 30 40 50 60 70 80 90 100

Trade-off Synergy

Percent

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one-third of these interactions (n = 22) while crop yield and erosion control

represented over a quarter (n = 19). These interactions alone accounted for 36% of the total interactions in the database.

Figure 7: Network diagram showing edges between B-ES relationships and ESS interactions found in literature searches. The same figure is in figure 8 but in a circular layout for better visualization of ESS interactions. Red nodes and green nodes represent decreasing and increasing proportion changes/yr, respectively. Red edges and green edges represent negative and positive effect sizes, respectively. Edge widths from the biodiversity node reflect effect sizes. Orange nodes represent ESS for which no proportion change/yr data was found.

Purple nodes reflect ESS with no data. Circles represent provisioning services: CY-Crop Yield, WP-Wood Production, FY-Fodder Yield, BFY-Biofuel Yield, FISHY-Fish Yield.

Rectangles represent regulating services: EC-Erosion Control, PPP-Primary Production of Photosynthesis, FP-Freshwater Purification, CST-Carbon Storage, AQ-Air Quality Regulation, FP-Flood Regulation, SOC-Soil Organic Carbon, SNM-Soil Nutrient Remineralization, AHPB-Abundance of Herbivorous Pests Bottom-up, Abundance of Herbivorous Pest Top-down, RPI-Resistance to Plant Invasion, CS-Carbon Sequestration, DS-Dispersal of Seeds, DHB-Domesticated Honey-bees (Pollination), DPP-Disease Prevalence on Plants, DPA-Disease Prevalence of Animals, OAR-Ocean Acidification Regulation, RPI-Resistance to Plant Invasion and SM- Soil Moisture.

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Figure 8: Circular layout network diagram showing edges between B-ES relationships and ESS interactions found in literature searches. Red nodes and green nodes represent decreasing and increasing proportion changes/yr, respectively. Red edges and green edges represent negative and positive effect sizes, respectively. Edge widths from the biodiversity node reflect effect sizes. Orange nodes represent ESS for which no proportion change/yr data was found.

Purple nodes reflect ESS with no data. Circles represent provisioning services: CY-Crop Yield, WP-Wood Production, FY-Fodder Yield, BFY-Biofuel Yield, FISHY-Fish Yield.

Rectangles represent regulating services: EC-Erosion Control, PPP-Primary Production of Photosynthesis, FP-Freshwater Purification, CST-Carbon Storage, AQ-Air Quality Regulation, FP-Flood Regulation, SOC-Soil Organic Carbon, SNM-Soil Nutrient Remineralization, AHPB-Abundance of Herbivorous Pests Bottom-up, Abundance of Herbivorous Pest Top-down, RPI-Resistance to Plant Invasion, CS-Carbon Sequestration, DS-Dispersal of Seeds, DHB-Domesticated Honey-bees (Pollination), DPP-Disease Prevalence on Plants, DPA-Disease Prevalence of Animals, OAR-Ocean Acidification Regulation, RPI-Resistance to Plant Invasion and SM- Soil Moisture.

Discussion

Mace et. al. (2012) proposed that biodiversity can be “a regulator of ecosystem processes, a service in itself and a good” and called for new approaches that “reflect the many roles that biodiversity has in ecological processes, in final ecosystem

services and in the goods that humans obtain from the natural world”. This study built on previous efforts to provide empirical evidence of how biodiversity underpins the ecological processes which provide and regulate ecosystem services (Worm et. al., 2006; Mace et. al., 2012; Bastian, 2013; Harrison et. al., 2014; Cardinale et al., 2012;

Duncan et. al., 2015; Truchy et. al., 2015; Isbell et al., 2017; Pires et al., 2018). This

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study goes a step further by assessing how the ESS have changed over time and by investigating how these ESS interact in terms of trade-offs and synergies. As far as is known these approaches have not been combined before. The approach to building a database founded on these steps provides a platform for investigating the supporting role of biodiversity in service provision and regulation.

The approach to assessing B-ES relationships and changes in ecosystem services in this study can be integrated into the biosphere integrity boundary of the PB

framework. An approach to doing this could be to have the BII as a control variable and then assess changes in ecosystem services as a response variable.

The first part of this study explored the ‘balance of evidence’ linking biodiversity and ecosystem services through vote-tallies and effect sizes. This investigation built on previous B-ES meta-analyses and reviews by widening the scope of ESS assessed, identifying 12 provisioning and 21 regulating services related to biodiversity, providing a more holistic approach to analysing B-ES relationships. The majority of relationships assessed in vote-tallies showed a positive association. However, the results also showed that negative associations accounted for over a third of the total vote-tallies, while insignificant associations held a lower proportion (15%). Direction aside, these findings indicate that there is generally a significant relationship between biodiversity and ecosystem services. However, care should be taken in extrapolating these findings given that the data available varied greatly both between and within provisioning and regulating services. B-ES relationships related to climate regulation and biocontrol are clearly more studied than others in the sample. Another limitation, also identified by Cardinale et al., (2012) with regards to the specific data included in this paper, is related to scale, both spatial and temporal. Spatially, the data included encompasses an area ranging in size from 1-100 m2 and temporally, experiments lasted from 1-10 generations. Gonzalez et al., (2020) has expanded on the theoretical challenges in this regard in figure 9. While this is useful for identifying the structures which underly B-ES relationships and may reveal evidence in relation to theory over short spatial and temporal scales, it does not address the relationships at wider and longer spatial and temporal scales, respectively. Gonzalez et. al. (2020) refer to a

“new generation of studies” which strive to address these issues of scale but indicates a need for a theoretical context in which the results of such studies can be interpreted.

As this area of research is developed, evidence of the B-ES relationship may emerge

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at larger scales and allow for integration into the variables for assessing the biosphere integrity PB.

Figure 9: Figure 1 from Gonzalez et. al. (2020), “Showing the three dimensions of scale in BEF [biodiversity ecosystem functioning] research: time, space and organisation. Most empirical studies in BEF (represented by black dots) fall within a constrained volume of this scale box: days to weeks in the case of micro‐ and mesocosm experiments, and years to two decades in the case of some grassland and forest diversity experiments. The size of most experimental plots is typically less than a hectare, although the spatial extent of the largest experiment was continental (BIODEPTH, Hector et al. 1999). Empirical studies could sample larger scales of variation by combining data from remote‐sensing technologies, in situ probes and buoys, surveys using long transects and geographic networks of replicated experiments with controlled perturbations at different scales, deployed for multiple years and over broad spatial extents to capture shifting gradients of environmental heterogeneity. Images of landscape and forest plot from Encyclopedia Britannica 2013.”

The second part of this study sought the provide an update on how ESS are changing over time. A novel contribution was the introduction of new variables for assessing this change, e.g. the number of pest control agent introductions was a metric to assess biocontrol. The literature search explored >5000 published journal articles, but only 12 yielded data related to the change in ESS over time, demonstrating the difficulty of finding global data. These 12 studies included trend data for two-thirds (8 of 12) of provisioning services identified in part 1, but only less than half of the regulating services (9 of 21). The results of the analysis showed that there was no association between the proportion change per year for provisioning compared to regulating

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services. This indicates that there is not a relationship between the functioning of provisioning services and the changes in regulating services that theoretically affect them. There are two limitations that make this result rather crude and therefore unreliable. First, the mix of different and potentially unrelated ESS within the

provisioning and regulating groups may dilute any effect. Second, the sample size was small, limiting the extent it can represent true B-ES relationships. Given that this study focused on global trends, it may be assumed that global data for most of the regulating services are not currently available or have been difficult to assess with current approaches. Therefore, conclusive inferences cannot be made based on comparisons between the changes in provisioning and regulating services over time.

The results of this study partially confirm the ‘environmentalist’s paradox’ described in Raudsepp-Hearne et. al. (2010) with regards to the primacy of food production and agricultural growth, specifically the provisioning services of crop and biofuel yield and the regulating services related to biocontrol. Although trends related to biocontrol regulating services showed an increase in proportion change per year, this has

negative implications for crop yield and biofuel yield (Varah et. al., 2020). More specifically, the increase in disease prevalence among plants and the growing need for introduction of biological control agents in agriculture may make it increasingly difficult stabilize or expand crop or biofuel yields (Schutte et. al., 2017). To increase yields in the dominant agricultural model, more herbicides and pesticides need to be applied, however the increase in herbicide and pesticide resistance compromises the effectiveness of this approach (Storkey et. al., 2018). Herbicides and insecticides replace the biocontrol functions that plant and insect diversity can provide naturally and are more cost intensive. Once these have been significantly degraded, recovery is difficult and require increasing human inputs to maintain the resilience of the system.

Regarding Raudsepp-Hearne et al.’s (2010) other relevant hypothesis— that there is potentially a time lag between the degradation of ecosystem services and the effect on human wellbeing— a conclusion could not be drawn due to the lack of trend data found for regulating services.

The third part of this study investigated interactions among ESS. Most of the interactions identified were trade-offs, indicating that as provisioning services increase, regulating services decrease. The most represented of these trade-offs were between crop yield and climate regulating services (carbon sequestration and carbon

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storage) and crop yield and erosion control. Deforestation for conversion to

agricultural land was the major driver of a decrease in carbon sequestration in most of the studies in the dataset. Drivers of decreasing erosion control could not be identified but previous studies identified farming methods such as tillage farming, a lack of hedgerows and greening as sources of a decline in erosion control (see e.g. Frank et.

al., 2014). The majority of the interactions came from studies based on spatial data, a clear limitation, as the sample did not account for changes in interactions over time.

Furthermore, interactions were mostly measured at the regional (watershed) scale, though there was a wide variation with a number at the local level and one at the continental scale.

Ricketts et. al. (2016) discuss several limitations with research related to B-ES relationships, some of which are relevant to this study. The first is regarding pooled data which can mask important differences between the nature of the B-ES

relationships. The second is related to the assessment of ESS interactions in which spatial correlations are assumed to reflect functional links. Given that much of the data in this synthesis is derived from small spatial units, care should be taken when extrapolating the interactions with other ESS at larger scales. In all the studies surveyed for ESS interactions, none explicitly mentioned loss of ecosystem function based on declines of measures of biodiversity loss as drivers of trade-offs. Instead, human impacts were identified if drivers were identified at all. A third limitation is the small sample size of ESS evaluated which represents a small sample of the global ESS.

The purpose was to introduce an alternative approach to conceptualizing the

biosphere integrity PB and further elaborate on B-ES relationships, B-ES trends and B-ES interactions that can provide a more detailed picture. The data in this synthesis could be expanded upon as it provides a basis for future research. It is unlikely that the results of this study will result in the identification of specific tipping points or thresholds globally, given the heterogeneity of B-ES relationships. However, the approach taken in this study can be scaled up from local to regional and even

continental scales as better modelling techniques become available and more research is carried out.

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Broadening the base of ESS and investigating how the relationships vary by region, perhaps by combining with the Biodiversity Intactness Index (an interim control variable of the biosphere integrity boundary pioneered and elaborated by Scholes et.

al., 2005 and Newbold et al., 2016, respectively) may provide useful, targeted scientific input into policy making and management. This is especially relevant for management efforts which are seeking to strike a balance between maintenance and improvement of service provision on the one hand, and species and habitat

conservation on the other. It allows for going beyond the intrinsic value motivations for conserving biodiversity to a broader appreciation of how humans depend on biodiversity in coupled social-ecological systems. Furthermore, recent research on the PB framework is based on interactions between boundaries (Lade et. al., 2020) and ecosystems services provides a platform for bridging multiple boundaries e.g. climate regulation and climate boundary, freshwater purification and the freshwater-use boundary, as well as biocontrol regulation and novel entities. It also allows for a needed elaboration of the safe operating space of the biosphere integrity PB by

connecting the control variables of extinction rates and the interim control variables of phylogenetic and species diversity with ecosystem services which can then be linked to changes in human wellbeing, another area for future research.

Conclusions

This study sought to provide a review of current scientific knowledge on the relationship between biodiversity and ecosystem services and to integrate it with research related to ecosystem services and their interactions, thereby providing a bridge between two hitherto separate but related areas of research. Developing a comprehensive biodiversity-ecosystem services approach for assessing the role of biodiversity in supporting regulating services has many challenges. This study

provides initial steps in developing a database on which an approach could be refined and broadened in terms of scale. Combining the approaches in this study with others, such as the Biodiversity Intactness Index (BII), can provide novel ways of exploring correlations between areas of biodiversity loss or richness as well as trade-offs and synergies among ecosystem services. Other opportunities for future research include connecting biodiversity to ecosystem services within the ‘safe operating space’

imperative of the Planetary Boundaries framework. This would enable more emphasis on services which provide basic needs e.g. food, fibre, fuel etc. Future studies can

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then build upon this by exploring links to human wellbeing. Furthermore, given that the Planetary Boundaries framework is a widely used instrument in policy, integrating ecosystem services would provide new opportunities for connecting with

stakeholders, policy-makers and managers.

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APPENDIX A – DATA AND STATISTICAL ANALYSIS FOR EFFECT SIZES DATA

I. Data

Effect Sizes (e) for Biodiversity Ecosystem Services Relationships Biodiveristy Ecosystem service (BES) Effect size Provisioning services

Crop yield 0,035

Crop yield 0,91

Crop yield -2,21

Wood production 0,31

Fodder yield 0,36

Regulating services

Abundance of herbivorous pests

(bottom-up effect of plant diversity) 0,0177 Abundance of herbivorous pests

(bottom-up effect of plant diversity) -1,5 Abundance of herbivorous pests (top-down effect

of natural enemy diversity) -0,523

Abundance of herbivorous pests (top-down effect

of natural enemy diversity) -0,6

Abundance of herbivorous pests (top-down effect

of natural enemy diversity) 0,736

Abundance of herbivorous pests (top-down effect

of natural enemy diversity) 0,00

Abundance of herbivorous pests (top-down effect

of natural enemy diversity) -0,0877

Resistance to plant invasion 0,94

Carbon sequestration 0,8

Soil nutrient remineralization 0,584

Dispersal of Seeds -0,64

II: Data analysis

Assumptions for the Mann Whitney U Test

Differences in effect sizes The dependent variable should be measured

on an ordinal scale or a continuous scale. Yes The independent variable should be two

independent, categorical groups. Yes

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Observations should be independent. In other words, there should be no relationship

between the two groups or within each group. Yes Observations are not normally distributed.

However, they should follow the same shape

(i.e. both are bell-shaped and skewed left). See below

1) Look at descriptive stats Descriptivesa

ESS2 Statistic Std.

Error EffectSize Provision

ing

Mean -0.1190 0.54168

95%

Confidence Interval for Mean

Lower Bound

-1.6229

Upper Bound

1.3849

5% Trimmed Mean -0.0600

Median 0.3100

Variance 1.467

Std. Deviation 1.21123

Minimum -2.21

Maximum 0.91

Range 3.12

Interquartile Range 1.72

Skewness -1.843 0.913

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Kurtosis 3.804 2.000 Regulati

ng

Mean -0.0323 0.28131

95%

Confidence Interval for Mean

Lower Bound

-0.6810

Upper Bound

0.6164

5% Trimmed Mean -0.0048

Median -0.0877

Variance 0.712

Std. Deviation 0.84393

Minimum -1.50

Maximum 0.94

Range 2.44

Interquartile Range 1.39

Skewness -0.402 0.717

Kurtosis -0.978 1.400

a. There are no valid cases for EffectSize when ESS2 = .000. Statistics cannot be computed for this level.

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2) Test for normality Tests of Normalitya

ESS2 Kolmogorov-Smirnovb Shapiro-Wilk

Statistic Df Sig. Statistic df Sig.

EffectSize Provision ing

0.351 5 0.044 0.789 5 0.066

Regulatin g

0.212 9 .200* 0.907 9 0.296

*. This is a lower bound of the true significance.

a. There are no valid cases for EffectSize when ESS2 = .000. Statistics cannot be computed for this level.

b. Lilliefors Significance Correction

Both distributions are normally distributed, but its possible that we just weren’t able to detect non-normality due to very small sample size. Therefore, continue testing the assumptions for the Mann Whitney U test.

3) Test that the two distributions are the same shape (homogeneity of variances)

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Test of Homogeneity of Variancea

Levene

Statistic

df1 df2 Sig.

EffectSize Based on Mean

0.177 1 12 0.681

Based on Median

0.001 1 12 0.972

Based on Median and with adjusted df

0.001 1 6.393 0.973

Based on trimmed mean

0.080 1 12 0.782

a. There are no valid cases for Effect Size when ESS2 = .000.

Statistics cannot be computed for this level.

The p value of the Levene test (F statistic) shows that we do not reject the null hypothesis that there is no statistical difference between the distributions of the two

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groups, ie. these two groups have the same shape. Therefore, can move forward with the Mann Whitney U test.

MANN WHITNEY U TEST Ranks

ESS2nr N Mean

Rank

Sum of Ranks

EffectSize 1 5 7.60 38.00

2 9 7.44 67.00

Total 14

Test Statisticsa

EffectSize

Mann- Whitney U

22.000

Wilcoxon W

67.000

Z -0.067

Asymp.

Sig. (2- tailed)

0.947

Exact Sig.

[2*(1- tailed Sig.)]

1.000b

a. Grouping Variable:

ESS2nr

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b. Not corrected for ties.

The MWU test shows that differences in the mean effect sizes is not significant between provisioning and regulating services.

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APPENDIX B— DATA AND STATISTICAL ANALYSIS FOR PROPORTION CHANGE PER YEAR

I. Data

Table : Global Ecosystem Services Proportion Change Per Year

Ecosystem Services Proportion

Change/year

References

Provisioning

Crop yield 1.4% Jørgensen et. al., 2018

Pollinated crop yield 4.9% Calderone et. al., 2012

Bio-ethanol production 26.6% OECD/FAO, 2017

Biodiesel production 38.4% OECD/FAO, 2017

Liquid biofuel production 39.2% IEA, 2019

Wood Production (sawn wood & wood panels) 2.4% FAOSTAT database Wood Production (paper and paperboard) 2.8% FAOSTAT database

Fodder Yield 3.5% Panuzi, 2008

Utilized Vertebrate Species 0.4% Butchart et. al., 2010

Fisheries production (total capture inland & marine) 0.3% FAO SWFA, 2018 Fisheries production (total world fisheries &

aquaculture)

2.1% FAO SWFA, 2018

Unsustainable fisheries 0.6% FAO SWFA, 2018

Regulating

Human infectious disease prevalence -1.5% GBD 2016, Jørgensen 2018

Disease prevalence on plants 25,00% Lugtenberg et. al.,

2015 No. of introductions of insect biological control agents

for the control of insect pests

1.11% Cock et. al., 2016 Insecticide resistance (treatment potential) 8.36% Jørgensen et. al., 2018 Herbicide resistance (treatment potential) 6.83% Jørgensen et. al., 2020

Domesticated honey bees 0,98% Aizen et. al., 2009;

Potts et. al., 2010

Droughts -0.04% Sheffield et. al., 2008

Soil Organic Matter -0,02% Stockmann et al., 2015

Carbon sequestration 0.07% Battle et al., 2000

References

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■ Fondens förvaltare tar hänsyn till hållbarhetsfrågor Hållbarhetsaspekter beaktas i ekonomiska bolagsanalyser och investeringsbeslut, vilket får effekt men behöver inte

”VAL Global High Yield ER Index” avser det index som beskriver nivån för den investe- ringsstrategi vars utveckling för en viss Affärsdag uppgår till Dagsutvecklingen. VAL Global