stockholm resilience centre1446078/fulltext01.pdf · represented almost one-third of these...

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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)

Scre

en

ing

Incl

ud

ed

El

igib

ility

Id

enti

fica

tio

n 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)


Biofuels (fuel OR biofuel) AND (yield OR

output OR production)


Biofuel Stability (fuel OR biofuel) AND (yield OR

output OR production) AND

(stability OR variability OR

resistance OR resilience)


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

production OR productivity)


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

production OR productivity)

AND (stability OR variability OR

resistance OR resilience)


Fodder fodder AND (yield OR production

OR productivity)


Fodder Stability fodder AND (yield OR production



resilience)


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)


Fisheries Stability fish* AND (yield OR production



resilience)


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)


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$)


Invasion Resistance (biocontrol OR "biological

control") AND (exotic OR

invasive) AND (plant OR algae

OR producer)


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)


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)


Soil moisture soil AND (moisture OR humidity

OR water retention OR water

consumption OR drought)


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)


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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)


*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:



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Figure 2: PRISMA 2009 Flow Diagram- ESS Interactions Search

Records identified through database searching

(n =193)

Scre

en

ing

Incl

ud

ed

El

igib

ility

Id

enti

fica

tio

n

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

FISH

Y

SFIS

HY

BFY

SBFY WP

SWP FY SFY

HD

R

AH

PB

AH

PT

RP

I

DP

P

DP

A

PP

P CS

CST FR

SNM SM SOC

FWP EC

P

DS

AQ

R

OA

R

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

Per

cen

t

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

Rel

atio

nsh

ips

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

Per

cen

t

20

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.

21

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

22

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

23

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

https://onlinelibrary.wiley.com/doi/full/10.1111/ele.13456#ele13456-bib-0086

24

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

25

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.

26

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

27

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.

28

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32

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




of natural enemy diversity) 0,736


of natural enemy diversity) 0,00



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

https://www.statisticshowto.com/independent-variable-definition/


33

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

https://www.statisticshowto.com/assumption-of-independence/



34

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


Median -0.0877

Variance 0.712


Minimum -1.50

Maximum 0.94

Range 2.44


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.

35

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)

37

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

38

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

39

b. Not corrected for

ties.

The MWU test shows that differences in the mean effect sizes is not significant

between provisioning and regulating services.

40

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

41

II: Analysis

Assumptions for the Mann Whitney U Test

Differences in

proportion change

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

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:

Descriptives, Effect size

ESS2 Statistic Std.

Error

EffectSize

Provisioning

Mean -0.119 0.5417

95%

Confidence

Interval for

Mean

Lower

Bound -1.6229

Upper

Bound 1.3849


Median 0.31

Variance 1.467


Minimum -2.21

Maximum 0.91

Range 3.12


Skewness -1.843 0.913

Kurtosis 3.804 2

Regulating

Mean -0.0323 0.2813

95%

Confidence

Lower

Bound -0.681






42

2) Test for normality

Tests of Normality

ESS Kolmogorov-Smirnova Shapiro-Wilk

Statistic Df Sig. Statistic Df Sig.

Proportion

change

per year

Provision

ing

0.385 12 0.000 0.667 12 0.000

Regulati

ng

0.374 9 0.001 0.580 9 0.000

a. Lilliefors Significance Correction

Reject the null hypothesis that both groups are normally distributed, so non-

parametric test is needed

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

Standard deviations of both distributions (highlighted in descriptives above) indicate

that distibutions may not be the same shape. Histogram shows though that both are

skewed left (so do mimic same shape) but box plot shows different heights between

Interval for

Mean Upper

Bound 0.6164


Median -0.0877

Variance 0.712


Minimum -1.5

Maximum 0.94

Range 2.44


Skewness -0.402 0.717

Kurtosis -0.978 1.4

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

Statistics cannot be computed for this level.

43

the quartiles, indicating different shape. The picture so far is unclear. So we need to

do the homogeneity of variances test.

44

Test of Homogeneity of Variance

Levene

Statistic

df1 df2 Sig.

Proportion Based on

Mean

1.571 1 19 0.225

Based on

Median

0.587 1 19 0.453

Based on

Median

and with

adjusted

df

0.587 1 12.020 0.458

Based on

trimmed

mean

0.915 1 19 0.351

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

groups, ie. these two groups have the same shape. Therefore can move forward with

the Mann Whitney U test

45

MANN WHITNEY U TEST

Ranks

ESSnr N Mean

Rank

Sum of

Ranks

Proportion

change

per year

1 12 11.17 134.00

2 9 10.78 97.00

Total 21

Test Statisticsa

Proportion

Mann-

Whitney

U

52.000

Wilcoxon

W

97.000

Z -0.142

Asymp.

Sig. (2-

tailed)

0.887

Exact Sig.

[2*(1-

tailed

Sig.)]

.917b

a. Grouping Variable:

ESSnr

46

b. Not corrected for

ties.

P value for Mann Whitney U test indicates no significant difference in median

proportion change between provisioning and regulating services

47

APPENDIX C: SUPPLEMENTARY DATA

Supplementary data for this study can be found at the following link:



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