nelson valdivia

Nelson Valdivia

Effects of biodiversity on ecosystem stability

Distinguishing between number and composition of species

PhD thesis

University of Bremen

Germany, December 2008

Biologische Anstalt Helgoland

Alfred Wegener Institute for Polar and Marine Research

Marine Station

Ph.D. thesis

Effects of biodiversity on ecosystem stability: distinguishing between number

and composition of species

Dissertation zur Erlangung des Doktorgrades der Naturwissenschaften

Vorgelegt dem Fachbereich Biologie/Chemie der Universität Bremen von

Nelson Valdivia

Gutachter: 1. Prof. Dr. Kai Bischof

2. Prof. Dr. Christian Wiencke

December 2008

i

Abstract

Declines in biodiversity have caused concern because of ethical and aesthetic reasons, but

also because of the consequences for the goods and services provided by natural ecosys-

tems. Consequently, ecologists have focused for decades on testing the idea that systems

with more species are more stable. The results, however, have been complex and inconsis-

tent. In particular, it is still unclear whether high stability in species-rich communities is due

to the number of species per se (species richness) or to the increased likelihood of including

particular species or functional types (species composition). In this thesis, I evaluated the

contribution of species richness and species identity to the stability of marine hard-bottom

communities. Combining observational and manipulative experimental methods, I con-

ducted three field studies in intertidal and shallow subtidal habitats of Helgoland Island, NE

Atlantic. First, I conducted an observational study to test whether intertidal communities

containing many species are more stable (i.e. do vary less over time) than communities con-

taining fewer species. Species covers were estimated every 6 months for 24 months and an

index of stability was calculated for total community cover across time (S = mean SD-1).

Second, I conducted a synthetic-assemblage experiment––in which I increased the diversity

of field-grown sessile suspension-feeding invertebrates––to determinate whether assem-

blages containing several functional groups consume a greater fraction of resources than is

caught by any of the functional types grown alone. (A functional group is a group of species

with the same effect on an ecosystem property.) Finally, I conducted a removal experiment

to test whether the loss of the canopy-forming alga Fucus serratus and mechanical distur-

bances that provide free substratum affect the temporal variability in cover of intertidal

communities. In the removal experiment, species covers were estimated every 3 months for

18 months and the temporal variance was analysed.

In general, the effects of the number of species and functional groups on ecosystem sta-

bility were weaker than those of species composition. In the observational study, stability

was a negative and curvilinear function of species richness, which probably resulted from

the dominance of few species. In accordance, the synthetic-assemblage experiment showed

that there was no relationship between resource consumption and functional group diversity

per se, but that different functional groups had idiosyncratic effects. On the other hand, the

removal of Fucus changed the physical environment by increasing temperature, irradiance,

and amount of sediment, which depressed the abundance of sensitive species like encrusting

algae and small sessile invertebrates, but raised the abundance of more tolerant species like

ii

ephemeral green algae. This resulted in a significant increase in the variability of species

abundances, but not in that of communities. The negative covariances resulting from the

compensation between sensitive and tolerant species buffered the community stability

against the environmental disturbances. These patterns were consistent across two sites,

suggesting a consistent effect of canopies across the spatial variability of this system.

Species composition appears to be more important for ecosystem stability than taxo-

nomic and functional richness. Yet, the occurrence of compensatory dynamics in the face of

environmental changes (i.e. the removal of Fucus) suggests that a variety of species with

differing environmental tolerances is needed to maintain the functioning of this ecosystem.

Therefore, predicting the consequences of species loss requires a detailed knowledge about

the effects of species on ecosystem functioning and their responses to the environment. Con-

servational managers should strive (i) in identifying species with disproportional effects on

ecosystem functioning, and (ii) in maintaining a redundancy of species with similar effects

on ecosystem functioning and a diversity of species with different sensitivities to a suite of

environmental conditions.

Keywords:

Biodiversity, ecosystem stability, species compensation, conservation

iii

Contents

Preface .................................................................................................................................... iv

Acknowledgments................................................................................................................... v

List of papers..........................................................................................................................vi

1 Introduction...................................................................................................................... 1

The context: the value of biodiversity ................................................................................... 1

Definitions ............................................................................................................................. 1

Theory ................................................................................................................................... 3 Size of ecosystem properties.............................................................................................. 4 Variance in species properties............................................................................................ 6

Observations and experiments .............................................................................................. 8

The model system: hard-bottom ecosystems ......................................................................... 9

Aims....................................................................................................................................... 9

2 Methods........................................................................................................................... 11

Study sites ............................................................................................................................ 11

Sampling and experimental designs.................................................................................... 11

3 Results and Discussion................................................................................................... 14

Species richness vs. species composition ............................................................................ 14

Species’ response traits influence community stability ....................................................... 17

The role of replication in biodiversity experiments............................................................. 17

Conclusion........................................................................................................................... 18

References.............................................................................................................................. 20

Glossary ................................................................................................................................. 25

Appendix ............................................................................................................................... 28

iv

Preface

This thesis reports the outcome of field-based experiments carried out during the last three

years and designed to explore the role of biological diversity in maintaining the stability of

coastal ecosystems. The experiments were designed to test theoretical predictions and

mechanisms that explain the effects of biodiversity. So, at the first glance, the scope of this

thesis might seem confined to the academic realm. However, the ultimate aim is to predict

the ecological consequences of anthropogenic impacts on biological species, and also to

predict the likely consequences for human welfare. This work bites a small piece of an im-

mense puzzle.

The core of this thesis comprises four peer-review papers (I-IV) that can be found in the

Appendix section. The thesis summarises the major outcomes of the papers, and it is organ-

ised according to the IMRAD format (Introduction, Methods, Results, and Discussion). The

Introduction contains a review of the current knowledge about biodiversity and ecosystem

functioning. I was interested in illustrating mechanisms instead of describing patterns al-

ready described by others. The Methods section summarises briefly the characteristics of the

study sites, as well as the design, the set up, and the analysis of experiments. The results and

their interpretation are in the Result and Discussion section. Additionally, I provide a glos-

sary of terms at the end of the thesis in order to help the reader to understand the mecha-

nisms and processes mentioned in the text.

Paper I shows the results of an observational study where I compare the stability of in-

tertidal communities with naturally differing number of species. I test the hypothesis that

stability is a positive function of species richness. In paper II, I evaluate the role of resource

complementarity as a mechanism explaining the effects of functional group richness on the

rate of resource consumption of subtidal organisms. In paper III, I test the interactive effects

of disturbances on the stability of intertidal communities. Finally, paper IV assesses the

level of replication needed to represent the number of species occurring in intertidal hard-

bottom communities, which may be important when analysing the relationship between di-

versity and ecosystem stability.

v

Acknowledgments

Over the last three years, many friends and colleagues have contributed directly or indirectly

to this thesis. Those I have worked with in designing, setting up, and analysing experiments

have shared information and ideas. Summer interns have been enthusiast during long hours

of field work; editors and anonymous reviewers have brought part of this work to publica-

tion. Karin Boos helped me during the copy edition and printing process. Prof. Dr. Christian

Wiencke found always the way to fund materials, trips, and personnel (me). Andreas Wag-

ner gave valuable technical assistance, and organised coffee breaks just in the best moment.

This thesis would not have been written without the constant support, encouragement, and

counsel of Dr. Markus Molis, who provided invaluable guidance and friendship. I thank all

these people.

I also thank my family for joining me on the adventure of moving to Helgoland. While

this work was being prepared, I was saddened by the loss of a member of my family:

mother-in-law Ingrid Wallberg. It is to her memory that I dedicate this contribution.

vi

List of papers

This thesis is based on the following papers, which will be referred to in the text by their

roman numerals.

I Valdivia N, Molis M (In press) Observational evidence of a negative biodiversity-

stability relationship in intertidal epibenthic communities. Aquatic Biology

II Valdivia N, de la Haye K, Jenkins SR, Kimmance SA, Thompson R, Molis M (In

press) Functional composition, but not richness, affected the performance of sessile suspen-

sion-feeding assemblages. Journal of Sea Research

III Valdivia N, Molis M (Under review) Species compensation buffers community stabil-

ity against the loss of an intertidal habitat-forming rockweed. Marine Ecology Progress Se-

ries

IV Canning-Clode J, Valdivia N, Molis M, Thomason JC, and Wahl M (2008) Estimation

of regional richness in marine benthic communities: quantifying the error. Limnology and

Oceanography: Methods. 6: 580-590

Introduction

1

1 Introduction

The context: the value of biodiversity

Human activities are altering the global climate (IPCC 2007). In addition, destruction of

habitats, over harvesting, and introduction of exotic species are changing the local biodiver-

sity of terrestrial and aquatic ecosystems (Dirzo & Raven 2003, Sax & Gaines 2003, Byrnes

et al. 2007). As a consequence, today’s species extinction rate is probably the highest in

Earth’s history (Dirzo & Raven 2003). The question therefore is not whether we are losing

species, but what the likely consequences of such biodiversity loss are. Seminal research

suggests that biodiversity influences the magnitude of and variability in ecosystems proc-

esses (reviewed by Cottingham et al. 2001, Stachowicz et al. 2007). In particular, the work

of MacArthur (1955) and Elton (1958) inspired the assertion that communities with many

interacting species are more stable than communities with fewer species. Ecologists there-

fore have raised the concern that changing biodiversity can impair ecosystem properties and

the goods and services provided by ecosystems, which in turn might have high societal costs

(Costanza et al. 1997, Armsworth & Roughgarden 2003).

It is not surprising therefore that the biodiversity-stability relationship had drawn the at-

tention of ecologists for decades (Hooper et al. 2005), and that ecosystem stability had be-

come an issue for policymakers (Christensen et al. 1996). However, the actual contribution

of biodiversity research to conservation is still under debate, because of the contrasting re-

sults of studies testing the idea that biodiversity begets ecosystem stability (Thompson &

Starzomski 2007). Specifically, there remains controversy over what constitutes a ‘richness

effect’ and how to untangle the effects on ecosystem functioning based on species richness

per se from the usually stronger effects of species identity and composition (Bruno et al.

2006).

Definitions

The exploration of biodiversity-stability relationships requires us to clarify the meaning of

biodiversity, stability, and other terms. Biodiversity is “the sum of all biotic variation in the

biosphere from the level of gene to ecosystem” (Purvis & Hector 2000). This includes, but

Biodiversity and stability

2

is not limited to, the number of species (species richness), the distribution of their abun-

dances, and the presence or absence of key species.

The influence of biodiversity on ecosystem functioning depends on the suite of func-

tional characteristics of the interacting species (Chapin et al. 2000). Functional traits are

those characteristics of species that influence ecosystem properties (functional effect traits)

or species’ responses to the environment (functional response traits). Functional groups are

therefore defined by either the effect of species on ecosystem functioning or their response

to the environment.

The term ecosystem functioning (or ecosystem performance) is a simple contraction for

‘how ecosystems work’, but encompasses complex mechanisms that regulate the transfor-

mation and transport of energy across the ecosystem. Ecosystem properties consist of sizes

of pools of materials like nutrients and carbon, and rates of processes like energy fluxes

across trophic levels (Christensen et al. 1996). Ecosystem goods and services are ecosystem

properties that contribute to human welfare both directly and indirectly. Food and materials

for construction are examples of ecosystem goods; nutrient cycling and buffering of coastal

erosion are examples of ecosystem services (Costanza et al. 1997). In this thesis, I use the

percent cover of benthic species as a surrogate for biomass, and filtration rates of sessile

suspension-feeding invertebrates as a surrogate for resource consumption and energy flux.

Stability has several meanings in ecology; indeed, a galaxy of definitions can be found in

the literature, and each of them can lead to a different conclusion about the biodiversity-

stability relationship (Grimm et al. 1992, Johnson et al. 1996). The six commonest defini-

tions of stability are: the magnitude of disturbances a system can tolerate (domain of attrac-

tion, Holling 1973); how long a measure stays without change (persistence, Pimm 1991);

how much a measure changes by a disturbance (resistance, Pimm 1991); how long a meas-

ure needs to return to a specified fraction of its initial value (resilience, Pimm 1991); how

likely is that a system will continue functioning (reliability, Naeem 1998); and how much a

measure varies over time (variability, Pimm 1991). Pioneer biodiversity-stability researchers

explicitly considered stability to be related to temporal variability in ecosystem properties

(MacArthur 1955, Elton 1958). In accordance, I focus on the effects of biodiversity on the

temporal variance of ecosystem properties. For example, when comparing two temporal

Introduction

3

series of abundances, the “more stable” one will be that with the smallest fluctuations rela-

tive to its mean.

Theory

Temporal variability can be calculated using the variance in time series of species abun-

dances. Because average abundances can differ, variance must be scaled relative to the mean

(Gaston & McArdle 1994). Usually, this is done using the coefficient of variation (CV = 100

� ��being � the standard deviation and � the mean), which decreases as stability in-

creases. In this thesis, stability (S) is defined as S = ��(Tilman 1999). In contrast to CV,

the magnitude of S increases as stability increases; in addition, it approaches 0 when the

variation is large in relation to the mean.

On the other hand, the variance in and aggregate ecosystem property (e.g. total commu-

nity abundance––the sum of the abundances of all of the species in the community) can be

expressed using a statistical rule (Schluter 1984, Doak et al. 1998):

),(2)()()( 2222ji

njii

nii

nie xxxxx �� (1)

being xi the abundance of an individual species i, xe the aggregate community abundance

made by summing the abundance of all species, �2 the variance, �2 (xi, xj) the covariance

between species i and j, and n the number of species. Therefore, the variance of an aggre-

gate ecosystem property depends on the sum of all species variances and the sum of all pair-

wise species covariances. If species vary independently, their covariance is zero and the

variance of the ecosystem property equals the summed species variances. However, when

species do not vary independently, their nonzero summed covariances cause the overall

variability to increase or decrease. Stability therefore will be defined as:

Covariance2Variance

��

��S (2)

In accordance with equation (2), stability will increase with increasing species richness

if the mean value of the property increases (�), or the summed variances decrease, or the

summed covariances decrease, or a combination of these occurs (Lehman & Tilman 2000).

Consequently, the factors influencing the size of ecosystem properties, the summed vari-

ances, and the summed covariances across gradients of species richness have been the focus


4

of theoretical work on biodiversity-stability relationships (Cottingham et al. 2001, Hooper et

al. 2005).

0

1000

2000

Resource AResource BResource C

Species a Species b Species c Mixtureof all species

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ourc

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ll sp

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0

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Transgressiveoveryielding

Non-transgressiveoveryielding

Figure 1Graphical depiction of resource complementarity (a), transgressive overyielding (b), and positive sampling effects (c) in communities containing 1 (species a, b, or c), and 3 (mixture) species. Total consumption rate is the sum of the consumption rates of all resources. If species consume different resources (a), positive species interactions increase the performance of the species-rich community relative to any of the constituent species grown alone (b). On the other hand, if the species-rich community includes a species with extreme functional value, the performance of the community will reflect the performance of that species instead of an average response of all of the species in the community; in these cases, the performance of the species-rich community is larger than that of the average monoculture, but not larger than that of the best-performing species grown alone (c).

Size of ecosystem properties

Changes in the size of an aggregate ecosystem property can affect ecosystem stability (equa-

tion [2]). Theory predicts that ‘overyielding’, an increase in the size of an ecosystem prop-

erty with increasing species richness, can result from resource partitioning or facilitation

(complementarity effect; Tilman et al. 1997a, Loreau 2000), or by the increased probability

that more diverse communities include species with extreme functional impacts (sampling

effect or positive selection effect; Huston 1997, Loreau et al. 2001). Complementarity ef-

fects (Fig. 1a) lead to the phenomenon called transgressive overyielding, in which produc-

Introduction

5

tivity or resource use of species-rich mixtures exceeds (transgress) that of the best-

performing species grown alone (Fig. 1b; Fridley 2001). This is because interspecific com-

petition is reduced when species use different resources or use the same resource at different

moments or points in space. For example, sessile suspension-feeding invertebrates that con-

sume small-sized plankton can coexist with others that consume larger particles (Gili &

Coma 1998). Species

CV = 0.3

CV = 0.19

CV = 0.09

Time

Abu

ndan

ce o

f spe

cies

i

1 species

3 species

5 species

Tota

l abu

ndan

ceFigure 2Simulation showing the effect of statistical averaging on the variability in an aggregate community property (total abundance, heavy lines, right y-axis) made up by summing the abundance of single species (abundance of species i, left y-axis). Fluctuations in species and community abundances were simulated using Tilman’s model (1999) where species abundances are assumed to be independent, equally abundant, and with the same coefficient of variation (cv = 0.3). Average total community abundance was held constant at 100 percent cover. Statistical averaging of individual fluctuations dampens the variability of the aggregate community property as the number of species increases (note decreasing CV for total abundance).

0

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of benthic algae can share the resource ‘space’ and minimise competition: seaweeds need a

relatively small area in the substratum in order to stay attached, but they are still able to de-

velop a large canopy over an area where the substratum was monopolised by encrusting

forms or turf-forming algae (e.g. Connell 2003). In addition, canopies provide settlement

substratum for other smaller species, such as filamentous algae and sessile invertebrates.


6

Positive selection effects occur when the performance (e.g. resource use or productivity)

of the most efficient species explains that of the entire community (Fig. 1c; Ives et al. 2005).

For example, plant communities are usually dominated by individuals of the largest species

(e.g. Polley et al. 2007). Therefore, most of the biomass in a species-rich community may be

contributed by one or few dominant species and reflects the biomass of those species instead

of an average value of all species present in the community (Huston 1997).

CV = 0.28

CV = 0.21

CV = 0.05

Time

Abun

danc

e of

spe

cies

i

cor = 0.95

cor = 0

cor = -0.95

0 5 10 15 200

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0 5 10 15 200

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Figure 3Graphical depiction of the effect of the covariance in species fluctuations on the variability an aggregate community property (total abundance, heavy lines, right y-axis)made up by summing the abundance of single species (abundance of species i, left y-axis) in communities of 2 species (either solid or dashed lines). Fluctuations in species abundances were simulated by generating 20 pairs of normal random values with mean 50, coefficient of variation 0.3, and specified coefficient of correlation (cor = 0.95, almost perfect positive correlation; 0, no correlation, -0.95 almost perfect negative correlation). Average total community abundance was held constant at 100 percent cover. Negative pair-wise species covariance dampens the variability of the aggregate community property (note decreasing CV for total abundance).

Tota

l abu

ndan

ce

Variance in species properties

As expressed in equations (1) and (2), stability in an aggregate ecosystem property like total

community abundance will be affected by the variances and covariances in species abun-

Introduction

7

dances. Statistical averaging, also called the portfolio effect, is a pivotal mechanisms leading

to a negative relationship between species richness and the variability of aggregate ecosys-

tem properties (Doak et al. 1998, Tilman et al. 1998). When total community abundance is

the sum of the abundances of many species, each varying over time, then adding more spe-

cies together will increase the probability that the fluctuations in these individual abun-

dances will average out statistically (Fig. 2; Doak et al. 1998). This reduces the variability in

the aggregate property in relation to that of the average individual abundances. Accordingly,

whenever species fluctuations are not perfectly correlated, increasing species richness will

reduce the variability of the community mainly on statistical grounds.

However, because asynchrony among species results from differential environmental

tolerances, statistical averaging is due in part to ecological difference among species

(Cottingham et al. 2001). Moreover, the strength of statistical averaging seems to be

strongly affected by the relative abundance of species. For example, high dominance of few

species can dampen the richness-stability relationship (Doak et al. 1998), and lead to nega-

tive and curvilinear functions (Lhomme & Winkel 2002). Therefore, ecological processes

leading to heterogeneity and temporal asynchrony in species abundances influence the effect

of biodiversity on ecosystem stability.

These ecological processes affect also the covariances in species abundances, which in

turn influence the stability of the community (equations [1] and [2]). Pairs of species com-

peting for the same resource or with differing abilities to respond to the environment should

show compensatory responses, such that when the abundance of one species increases and

that of the other decreases; the resulting negative covariance decreases the variability in the

total community abundance (Fig. 3; Doak et al. 1998, Yachi & Loreau 1999). If the variety

of environmental tolerances increases as species richness increases, then adding more spe-

cies will increase the probability that some species compensate the function of other that

failed due to changes in the environment (Yachi & Loreau 1999, Ives et al. 2000). There-

fore, maintenance of species with different functional response traits can be crucial for eco-

system stability. Surprising, conservation managers usually concentrate on rare species that

may be unable to compensate the loss of a dominant species (Thompson & Starzomski

2007).


8

Observations and experiments

Observational and manipulative experiments support the idea that increasing species rich-

ness leads to increasing ecosystem stability (e.g. McNaughton 1985, Tilman & Downing

1994, Naeem & Li 1997, Ptacnik et al. 2008), but most of these studies are confounded by

other variables (Hooper et al. 2005). For example, to demonstrate that biodiversity increases

drought resistance in grasslands, Tilman and Downing (1994) altered plant species richness

by using nutrient additions. However, increased resistance could have resulted either from

species compensation (Tilman 1996) or from differences in species composition caused by

the fertilisations (Huston 1997). In the observational study of McNaughton (1985) on Ser-

engeti’s grasslands, the negative correlation between proportional diversity (H’) and com-

munity variability was probably influenced by differences in species composition and

abiotic conditions between sites.

In order to detect confounding effects of species richness and composition, investigating

the relationship between biodiversity and ecosystem functioning and stability should be

complemented by different experimental methods, including assembling communities in

controlled environment, manipulating diversity in the field, and observing patterns in nature

(Díaz et al. 2003). There is no single best method, as not all questions can be addressed

equally well by these three approaches. For example, the effects of species richness per se

are better addressed by synthetic-assemblages experiments, because of the greater control of

species composition across replicates and levels of species richness (e.g. Loreau & Hector

2001, Benedetti-Cecchi 2004). In these experiments, random selections of species or func-

tional groups are assembled to generate different diversity treatments. However, because

such experiments do not represent how natural communities are assembled or dissembled––

species extinction are rarely random, for example––, the interpretation of studies using syn-

thetic communities is difficult (Wardle et al. 2000, Loreau et al. 2001).

On the other hand, the effects of non-random species extinctions are better addressed by

removal experiments, in which target species or functional groups are removed from the

natural community (e.g. Wardle et al. 1999, O'Connor et al. 2008). As they are based on

naturally assembled communities, removal experiments allow incorporating the effects of

large-scale processes like variations in climate, disturbance regime, and biotic interactions

Introduction

9

on the regional species pool (Belyea & Lancaster 1999). However, the cost of manipulating

diversity in the field restricts the size and duration of removal experiments, which in turn

limits the interpretation of results to the local characteristics or context. Alternatively, obser-

vational studies allow using larger spatial and time scales, and broader ranges of species

usually used in manipulative experiments (e.g. Troumbis & Memtsas 2000). Observational

studies require to be carefully designed to account for the diversity of the sites under inves-

tigation. The number of species is related to sampling effort (Ugland et al. 2003), so proper

replication may be especially important in observational studies linking ecosystem stability

and species richness.

The model system: hard-bottom ecosystems

Intertidal and shallow subtidal rocky habitats offer potential to test the diversity-stability

relationship. In these habitats, abiotic stressors change and biological processes occur at

small temporal and spatial scales (Underwood & Chapman 1996). As a consequence, inter-

tidal and shallow subtidal assemblages represent tractable experimental systems at the land-

scape scale and small-scale experiments are usually appropriate (Giller et al. 2004).

An example of the potential of rocky shores for biodiversity research is given by the re-

lationship between community structure and canopy-forming species. These species modify

the environment so that it becomes more suitable for some species, but less suitable for oth-

ers (e.g. Irving & Connell 2006, Lilley & Schiel 2006, Morrow & Carpenter 2008). In sub-

tidal habitats, for example, canopy loss reduces the abundance of species adapted to shaded

conditions (e.g. encrusting coralline algae), yet it allows the increase of species adapted to

more exposed conditions (Irving & Connell 2006). In intertidal habitats, however, the re-

sponse of understorey communities to canopy disturbances is still unclear (Lilley & Schiel

2006).

Aims

The aim of this study was to determinate the effects of species richness and species compo-

sition on the stability of intertidal and shallow subtidal hard-bottom communities. A combi-


10

nation of manipulative and observational approaches was used to address different but com-

plementary hypotheses.

In an observational study, I tested the hypothesis that (1) ecosystem stability is positively

related to species richness; in a synthetic assemblage experiment, I tested the hypotheses

that (2) different functional groups use different resources and that (3) increasing number of

functional groups increases the efficiency of resource consumption of the assemblage (i.e.

functional richness leads to transgressive overyielding in filtration rate); in a removal ex-

periment I tested the hypotheses that (4) the loss of a key canopy-forming species affects the

stability of the community and that (5) the effects of canopy removal depend on the pres-

ence of mechanical disturbances that provide free space. Finally, analysing species abun-

dance data (here after referred to as the richness-estimation study), I tested whether the

sampling effort of the observational study was enough to represent the number of species of

the shores here studied. The results, reported as peer-review papers and referred to by their

roman numerals (I-IV, see Appendix), suggest that species composition and identity had far

stronger effects on ecosystem stability than species richness per se.

Methods

11

2 Methods

Study sites

Observational and manipulative studies were conducted at Helgoland Island, NE Atlantic,

between March 2006 and March 2008. The observational study, removal experiment, and

richness-estimation study (papers I, III, and IV) were conducted in the mid-low intertidal

zone. This zone is characterised by canopy-forming algae (e.g. Fucus spp. and Laminaria

digitata), turf-forming algae (e.g. Ceramium virgatum, Chondrus crispus, Cladophora

rupestris, Corallina officinalis, Mastocarpus stellatus), and encrusting algae (e.g. Phymato-

lithon spp.). Frequent sessile invertebrates are Dynamena pumila, Spirorbis spirorbis, and

Electra pilosa, and conspicuous mobile consumers include Carcinus maenas and several

species of periwinkles. Temporal patterns in community structure have an important sea-

sonal component, as ephemeral algae like Ulva spp. and seasonal Cladophorales (e.g.

Cladophora sericea) become abundant during summer (Janke 1990). In the observational

study, stability was compared across five sites with naturally different number of species.

Each site was of ca. 200 m2 and adjacent sites were � 100 m apart from each other. The re-

moval experiment was replicated at two intertidal sites with differing degree of wave expo-

sure in order to test for generality of findings. Finally, the richness-estimation study was

based on data from the northern intertidal area of Helgoland.

The synthetic-assemblage experiment (paper II) was conducted in the shallow subtidal

habitat, and sessile invertebrates growing on vertical surfaces were used as experimental

organisms. This assemblage is characterised by mussels (e.g. Mytilus edulis), ascidians (e.g.

Ciona intestinalis, Ascidiella aspersa, and Diplosoma listerianum), barnacles (e.g. Elminius

modestus and Balanus crenatus), and bryozoans like Cryptosula pallasiana, and Membrani-

pora membranacea (Anger 1978, Wollgast et al. 2008).

Sampling and experimental designs

In the observational study, I compared community stability across the five sample sites.

Temporal variances in species abundances were calculated from repeated estimations of

species percent covers on plots of 0.25 m2 that were marked with stainless screws and sam-

pled every 6 months for 24 months. The temporal variance and temporal mean of total


12

community cover (cover summed across all species in a sampling unit) were used to calcu-

late the S index of stability (S = ��-1). On the other hand, species-accumulation curves were

used to calculate the number of species occurring at each sample site. I used regression

analyses to test the hypothesis that community stability is positively related to species rich-

ness. Additionally, I partitioned the temporal variance into the sum of all species variance

and the sum of all pair-wise species covariances as in equation (1). The summed covariances

were used as a measure of compensatory dynamics (see Theory section) and also to test

whether increases in the variance of species abundances are counterbalanced by increasingly

negative species covariances.

In the removal experiment (paper III), I tested the separate and interactive effects of the

removal of the canopy-forming alga Fucus serratus and mechanical disturbances on com-

munity stability. As in the observational study, temporal variances were obtained from re-

peated measures of species covers, but plots were of 0.09 m2 and sampled every 3 months

for 18 months. Fucus plants were removed used a knife and mechanical disturbance treat-

ments consisted of a biomass removal with 50 % of the effort required to remove all organ-

isms of the plot. I analysed two aspects of community stability: the temporal variability at

the community level (i.e. temporal variance of community total cover), and the temporal

variability at the species level (i.e. summed covariances and Bray-Curtis1 index).The effects

of canopy removal, disturbance, and site on all of the measures of temporal variability were

analysed using 3-way mixed analyses of variance (ANOVAs) with the factors Fucus canopy

(2 levels: present or removed) and disturbance (2 levels: undisturbed or disturbed) consid-

ered fixed and the factor site (2 levels, Nordostwatt or Westwatt) considered random.

The synthetic-assemblage experiment (paper II) was designed to test the effect of the

number of functional groups on filtration rates of suspension-feeding invertebrates, and to

separate this effect from that of functional identity. The design consisted of 3 functional

groups growing alone (i.e. mono-specific assemblages of mussels, colonial ascidians and

bryozoans, and barnacles) and one complete mixture containing all groups. Organisms used

1 The Bray-Curtis index (BC) measures the variability between samples in terms of species abundances. The

advantage of this method is that ignores the ‘double zeros’; i.e., it downplays the similarity between samples in

which the same species is absent. BC works well on ecological data, which are usually plagued by zeros.

Methods

13

to construct the assemblages were obtained by exposing artificial substrata (3 × 25 × 25 mm

PVC tiles) to colonisation in the water column for 7 months, starting on May 2006 to match

with the main recruitment period of epibenthic species. After that, tiles containing one func-

tional group each were used to construct the experimental assemblages (Stachowicz et al.

2002).

The synthetic assemblages were maintained in the field (1 m depth), but filtration rate

assays were conducted in the laboratory. Filtration rate was measured as the volume of wa-

ter cleared per unit of time from a mixed-culture microalgal suspension. Four microalgae of

different size were used as food in order to allow for resource complementarity in terms of

particle size. A cytometric technique allowed the identification of each species of microal-

gae, and so for testing whether functional types consumed different resources. ANOVA and

planned contrasts were used to tease apart the effects of functional richness (richness effect)

from those of each functional group (identity effect). The hypothesis that functional richness

leads to overyielding was tested using a planned contrast between the filtration rate of the

mixture and the average filtration rate of the monocultures (see Fig. 1b-c). The hypothesis

that functional richness leads to transgressive overyielding was tested by comparing the

performance of the mixture and the best-performing functional group in monocultures (see

Fig. 1b-c). Permutational multivariate analysis of variance (PERMANOVA) was used to test

for resource partitioning among functional groups on the basis of consumer-specific changes

in the multivariate structure of prey (Fig. 1a).

For the richness-estimation study (paper IV), I quantified the abundance of species oc-

curring on fifty-two 0.04 m2 replicate plots in spring 2006. Species-accumulation curves

were used to calculate the number of species in the maximum number of quadrats. Then, the

probable regional richness was estimated by fitting a curvilinear growth model that provides

the asymptotic number of species as the number of replicates approaches infinity (Morgan et

al. 1975).


14

3 Results and Discussion

Biodiversity, broadly defined, significantly influenced the magnitude and variability of eco-

system properties such as community biomass (measured as percent cover) and resource

consumption (measured as filtration rate). Nevertheless, the effects of species composition

seemed to be more important than those of species richness. Contrarily to our predictions,

the observational study showed a negative and curvilinear diversity-stability relationship. In

the synthetic-assemblage experiment (paper II), filtration rates differed significantly among

functional groups grown alone, but their average filtration rate did not differ from that of the

high-diversity mixtures––i.e. the presence of more functional groups did not increase filtra-

tion rate. Finally, the removal of the canopy-forming alga Fucus serratus increased the vari-

ability of species without affecting the variability of communities (paper III). Compensatory

dynamics, such that the abundance of some species increases while that of other decreases,

buffered the community-level stability against the environmental changes caused by the

canopy removal––such patters were consistent across two sites.

Collectively, these results agree with biodiversity studies on marine macroalgal (Bruno

et al. 2006), terrestrial plant (Hooper et al. 2005), and freshwater communities (Downing

2005, Weis et al. 2008). These previous studies have shown that richness effects are actually

subtle and that compositional effects are strong. The loss or gain of particular species there-

fore may have a stronger effect on ecosystem stability than species richness per se. There-

fore, predicting the consequences of biodiversity loss remains complicated, because it re-

quires an accurate knowledge of the system and natural life history and should be drawn

from sound experimental evidence, not from generalised models.

Species richness vs. species composition

Overyielding, or an increase in an ecosystem property with increasing species richness, was

detected in the observational study (paper I). Theory predicts that overyielding is due to

resource complementarity (Tilman et al. 1997b), which may occur in benthic communities.

Epibenthic species can partition the available space by forming multilayered spatial struc-

tures––the experiments conducted on the intertidal areas (papers I, III, and IV) showed that

Results and discussion

15

encrusting forms, turf-forming, and canopy-forming algae formed three biotic layers, and

the observational study showed that this layering tended to increase across the richness gra-

dient. Multilayered structure is a characteristic of benthic communities made up of numer-

ous species (Bruno et al. 2003), so resource complementarity in terms of differential use of

the space might be widespread among these communities.

However, overyielding might well have resulted from the increased probability that spe-

cies-rich communities included particular (“key”) species with strong effects on community

abundance––i.e. positive sampling effects. Indeed, few species dominated the communities,

so the apparent pattern of the community abundance could well have reflected those of the

dominant species instead of an average response of all species in the community. Moreover,

one of the dominant species, the canopy former Fucus serratus, had significant effects on

the species composition and stability (paper III); so the relationships between site species

richness, community abundance, and stability (paper I) would have been strongly influenced

by changes in the abundance of this species. On the other hand, the negative and curvilinear

richness-stability relationship (paper I) may have resulted from the dominance of species––

experiments have shown that the stability of communities dominated by few species is

driven by these particular taxa (Steiner et al. 2005, Polley et al. 2007), and simulations sug-

gest that strong heterogeneity among species abundances may lead to negative and nonlin-

ear relationships between species richness and stability (Lhomme & Winkel 2002).

In consequence, even when complementarity in the use of space may be common in

natural benthic communities, there is a fair chance that selection effects also operate within

these assemblages. Both, selection effects and positive species interactions (including com-

plementarity and facilitation) can act simultaneously or sequentially (Hooper et al. 2005,

Bruno et al. 2006). The challenge is therefore to develop analytical tools that allow quantify-

ing the relative contribution of each of these mechanisms to ecosystem function (e.g. Loreau

& Hector 2001, Fox 2005).

The synthetic-assemblage experiment (paper II), provided the opportunity to test

whether resource complementarity occurs within subtidal suspension feeders. The experi-

ment was replicated at two locations in NE Atlantic coasts, and the results from both loca-

tions suggest that complementarity was actually absent: the high efficiency of mussels in

filtrating most of the phytoplankton species suggests that filtration rate of the mixtures was

mostly due to the activity of this functional group, which may have prevented resources


16

complementarity and led to no richness effects. These results, complemented with the cor-

relative evidence of the observational study, suggest that species identity and composition

may have strong effects on the functioning and stability of natural community and that the

consequences of biodiversity changes can not be predicted from the number of species that

are loss or gained. Identity effects may be common within benthic communities, as sug-

gested by recent experiments conducted on intertidal (O'Connor et al. 2008) and shallow

subtidal communities (Bruno et al. 2006, O'Connor & Bruno 2007), as well as reviews and

meta analyses of published datasets (Cardinale et al. 2006, Stachowicz et al. 2007).

Because the synthetic-assemblage experiment did not represent how natural communi-

ties are assembled, its interpretation in a real scenario of biodiversity change may become

difficult. For example, whether species are assembled as larvae and juveniles or adults can

influence the outcome of synthetic experiments (Garnier et al. 1997). The removal experi-

ment (paper III) tested in natural conditions what happened with the community and species

stability when a particular species went locally extinct. Interesting, the results of this ‘natu-

ral’ experiment also suggest that the functional characteristics of species affect stability, al-

beit it was not designed to tease richness effects apart from identity effects.

In the removal experiment, a single species had strong effects on composition and stabil-

ity of species. The removal of Fucus serratus significantly influenced the physical surround-

ings of the remaining species, as shown in other communities where canopies ameliorate

stressors like temperature and water evaporation (Bertness et al. 1999, Moore et al. 2007),

and also where canopies reduce sedimentation (Kennelly & Underwood 1993). In my ex-

periment, these changes had negative effects on species sensitive to sedimentation and os-

motic stress, such as encrusting algae and small sessile invertebrates, yet they had positive

effects on species more tolerant, such as ephemeral green algae. Therefore, disturbance can

have differing effects on species, which may play an important role in maintaining the sta-

bility of the community (Micheli et al. 1999).


17

Species’ response traits influence community stability

The compositional changes caused by the canopy removal reduced the stability of species,

but, these severe disturbances did not affect the stability of communities. Probably, the

negative covariance resulting from the compensation between sensitive and tolerant species

maintained the stability of the community. This also may explain why the additional me-

chanical disturbances significantly decreased species stability only in removal plots, without

affecting the community stability. Mechanical disturbances can have significant effects on

species richness and composition (Valdivia et al. 2008), species coexistence (Shea et al.

2004), and stability (Bertocci et al. 2007). So, species compensation can maintain the stabil-

ity of communities in the face of strong environmental disturbances. The role of species

compensation in buffering community stability against stochastic change has been shown in

mathematical simulations (Fig. 3; Doak et al. 1998, Yachi & Loreau 1999) and field obser-

vations (Ernest & Brown 2001).

The importance of negative covariances and compensatory dynamics was also noted in

the observational study (paper I), where the lack of correlation between the summed covari-

ances and species richness contributed to the negative richness-stability relationship found–

–theory predicts that increasingly negative covariances should offset the increases in species

variances as more species are present (equation [2]; Tilman et al. 1998). Therefore, even

when the number of species seemed to have little effects on ecosystem functioning, the re-

sults from the intertidal experiments agree with the assertion that the presence of a variety of

responses to the environment is fundamental in maintaining community stability (Walker

1992, Yachi & Loreau 1999).

The role of replication in biodiversity experiments

In the richness-estimation study (paper IV), by extrapolating species-accumulation curves

we predicted a probable regional richness similar to the maximum number of species quanti-

fied in the observational study (65 vs. 72). This suggests that the sampling effort in the latter

was enough to represent the number of species occurring on mid-low intertidal areas of Hel-

goland. On the other hand, comprehensive inventories of species suggest that 53 (Janke

1986, Reichert & Buchholz 2006) sessile and 39 macroalgal (I. Bartsch, unpubl. data) spe-


18

cies can occur in these shores. According to these values, our extrapolations of the species-

accumulation curves are clearly below the actual number of species. However, these exten-

sive inventories included probably species occurring in different year and habitats, so these

species do not necessarily coexist at the local scale.

In observational studies linking species richness and the variability in ecosystem proper-

ties, the level of replication may be particularly important: first, assuring a proper replica-

tion can be critical for reducing unwanted variability derived from spatial and temporal

patchiness in species distributions (Cottingham et al. 2001). Second, account of rare species

might be important when rare species have disproportional effects on ecosystem properties

(e.g. keystone species; Lyons et al. 2005). Therefore, the ability of an experimental design to

detect compositional effects on ecosystem function can depend on sample size (Allison

1999).

Conclusion

Species composition seemed to be more important for the stability of this ecosystem than

the number of species and functional groups. Consequently, predicting the consequences of

the widespread human-driven changes in biodiversity needs an accurate knowledge on life

history and biology of species. So, descriptive work on basic life history traits is fundamen-

tal in this context. On the other hand, we should not assume that mechanisms predicted by

theory to lead to positive richness-stability (and functioning) relationships are unimportant

in the systems here studies. Resource complementarity influences species coexistence

(Ricklefs 1990), and the impact of species richness on ecosystem properties can grow

stronger through succession (Cardinale et al. 2004, Cardinale et al. 2007).

Further research should address the influence of the relative abundance of species (i.e.

evenness) and different types of disturbances on the relationship between biodiversity and

ecosystems stability, in addition to the occurrence of species compensation under different

levels of environmental stress. We still need to unravel the relationship between taxonomic

and functional diversity. Identifying those traits of species that influence ecosystem proper-

ties and species’ responses to the environment requires us to assess the impacts of environ-


19

mental disturbances at the levels of communities, populations, and organisms, and to inves-

tigate the variations in physiological traits across geographical scales (e.g. Dahlhoff &

Menge 1996, Chown & Gaston 2008).

Management of natural communities is generally based on the conservational status of

species; that is, species are usually managed if they are endangered or introduced. However,

conservation managers only rarely consider the functional effects of species (Thompson &

Starzomski 2007). According to the results of this thesis, conservational efforts should be

directed to identify the functional traits that make species important for the functioning and

stability of ecosystems. Key functional traits should be conservation priorities. Finally, man-

agers should assure that natural communities contain many species with different functional

responses and also many species with similar functional effects. This will allow species

compensation in the face of rapid environmental changes.


20

References

Allison GW (1999) The implications of experimental design for biodiversity manipulations. Am Nat 153:26-45

Anger K (1978) Development of a subtidal epifaunal community at the island of Helgoland. Helg Mar Res 31:457-470

Armsworth PR, Roughgarden JE (2003) The economic value of ecological stability. P Natl Acad Sci USA 100:7147-7151

Belyea LR, Lancaster J (1999) Assembly rules within a contingent ecology. Oikos 86:402-416

Benedetti-Cecchi L (2004) Increasing accuracy of causal inference in experimental analyses of biodiversity. Funct Ecol 18:761-768

Bertness D, Leonard G, Levine J, Schmidt P, Ingraham A (1999) Testing the relative contribution of positive and negative interactions in rocky intertidal communities. Ecology 80:2711-2726

Bertocci I, Vaselli S, Maggi E, Benedetti-Cecchi L (2007) Changes in temporal variance of rocky shore organ-ism abundances in response to manipulation of mean intensity and temporal variability of aerial expo-sure. Mar Ecol Prog Ser 338:11-20

Bruno JF, Lee SC, Kertesz JS, Carpenter RC, Long ZT, Duffy JE (2006) Partitioning the effects of algal spe-cies identity and richness on benthic marine primary production. Oikos 115:170-178

Bruno JF, Stachowicz JJ, Bertness D (2003) Inclusion of facilitation into ecological theory. Trends Ecol Evol 18:119-125

Byrnes JE, Reynolds PL, Stachowicz JJ (2007) Invasions and extinctions reshape coastal marine food webs. PLoS ONE 2:e295

Cardinale BJ, Ives AR, Inchausti P (2004) Effects of species diversity on the primary productivity of ecosys-tems: extending our spatial and temporal scales of inference. Oikos 104:437-450

Cardinale BJ, Srivastava DS, Duffy JE, Wright JP, Downing AL, Sankaran M, Jouseau C (2006) Effects of biodiversity on the functioning of trophic groups and ecosystems. Nature 443:989-992

Cardinale BJ, Wrigh JP, Cadotte MW, Carroll IT, Hector A, Srivastava DS, Loreau M, Weis JJ (2007) Impacts of plant diversity on biomass production increase through time because of species complementarity. P Natl Acad Sci USA 104:18123-18128

Chapin FS, Zavaleta ES, Eviner VT, Naylor RL, Vitousek PM, Reynolds HL, Hooper DU, Lavorel S, Sala OE, Hobbie SE, Mack MC, Diaz S (2000) Consequences of changing biodiversity. Nature 405:234-242

Chown SL, Gaston KJ (2008) Macrophysiology for a changing world. P R Soc B 275:1469-1478

Christensen NL, Bartuska AM, Brown JH, Carpenter S, D'Antonio C, Francis R, Franklin JF, MacMahon JA, Noss RF, Parsons DJ, Peterson CH, Turner MG, Woodmansee RG (1996) The report of the Ecological Society of America committee on the scientific basis for ecosystem management. Ecol Appl 6:665-691

Connell SD (2003) The monopolization of understorey habitat by subtidal encrusting coralline algae: a test of the combined effects of canopy-mediated light and sedimentation. Mar Biol 142:1065-1071

Costanza R, dArge R, deGroot R, Farber S, Grasso M, Hannon B, Limburg K, Naeem S, ONeill RV, Paruelo J, Raskin RG, Sutton P, van den Belt M (1997) The value of the world's ecosystem services and natural capital. Nature 387:253-260

Cottingham KL, Brown BL, Lennon JT (2001) Biodiversity may regulate the temporal variability of ecological systems. Ecol Lett 4:72-85

References

21

Dahlhoff EP, Menge BA (1996) Influence of phytoplankton concentration and wave exposure on the ecophysi-ology of Mytilus californianus. Mar Ecol Prog Ser 144:97-107

Díaz S, Symstad AJ, Chapin FS, Wardle DA, Huenneke LF (2003) Functional diversity revealed by removal experiments. Trends Ecol Evol 18:140-146

Dirzo R, Raven PH (2003) Global state of biodiversity and loss. Annu Rev Env Resour 28:137-167

Doak DF, Bigger D, Harding EK, Marvier MA, O'Malley RE, Thomson D (1998) The statistical inevitability of stability-diversity relationships in community ecology. Am Nat 151:264-276

Downing AL (2005) Relative effects of species composition and richness on ecosystem properties in ponds. Ecology 86:701-715

Elton SC (1958) The ecology of invasions by animals and plants. The University of Chicago Press, Chicago, USA

Ernest SKM, Brown JH (2001) Homeostasis and compensation: the role of species and resources in ecosystem stability. Ecology 82:2118-2132

Fox JW (2005) Interpreting the 'selection effect' of biodiversity on ecosystem function. Ecol Lett 8:846-856

Fridley JD (2001) The influence of species diversity on ecosystem productivity: how, where, and why? Oikos 93:514-526

Garnier E, Navas M-L, Austin MP, Lilley JM, Gifford RM (1997) A problem for biodiversity-productivity studies: how to compare the productivity of multispecific plant mixtures to that of monocultures? Acta Oecologica 18:657-670

Gaston KJ, McArdle BH (1994) The temporal variability of animal abundances - measures, methods and pat-terns. Philos T Roy Soc B 345:335-358

Gili JM, Coma R (1998) Benthic suspension feeders: their paramount role in littoral marine food webs. Trends Ecol Evol 13:316-321

Giller PS, Hillebrand H, Berninger U-G, Gessner MO, Hawkins S, Inchausti P, Inglis C, Leslie H, Malmqvist B, Monaghan MT, Morin PJ, O'Mullan G (2004) Biodiversity effects on ecosystem functioning: emerging issues and their experimental test in aquatic environments. Oikos 104:423-436

Grimm V, Schmidt E, Wissel C (1992) On the application of stability concepts in ecology. Ecol Model 63:143-161

Holling CS (1973) Resilience and stability of ecological systems. Annu Rev Ecol Syst 4:1-23

Hooper DU, Chapin I, F. S., Ewel JJ, Hector A, Inchausti P, Lavorel S, Lawton JH, Lodge DM, Loreau M, Naeem S, Schmid B, Setälä H, Symstad AJ, Vandermeer J, Wardle DA (2005) Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecol Monogr 75:3-35

Huston MA (1997) Hidden treatments in ecological experiments: re-evaluating the ecosystem function of bio-diversity. Oecologia 110:449-460

IPCC (2007) Climate Change 2007: Synthesis Report. Cambridge University Press, Cambridge

Irving A, Connell S (2006) Predicting understorey structure from the presence and composition of canopies: an assembly rule for marine algae. Oecologia 148:491-502

Ives AR, Cardinale BJ, Snyder WE (2005) A synthesis of subdisciplines: predator-prey interactions, and biodi-versity and ecosystem functioning. Ecol Lett 8:102-116

Ives AR, Klug JL, Gross K (2000) Stability and species richness in complex communities. Ecol Lett 3:399-411

Janke K (1986) Die Makrofauna und ihre Verteilung im Nordost-Felswatt von Helgoland. Helg Mar Res 40:1-55

Janke K (1990) Biological interactions and their role in community structure in the rocky intertidal of Helgo-land (German Bight, North Sea). Helg Mar Res 44:219-263


22

Johnson KH, Vogt KA, Clark H, Schmitz O, Vogt D (1996) Biodiversity and the productivity and stability of ecosystems. Trends Ecol Evol 11:372-377

Kennelly SJ, Underwood AJ (1993) Geographic consistencies of effects of experimental physical disturbance on understorey species in sublittoral kelp forests in central New South Wales. J Exp Mar Biol Ecol 168:35-58

Lehman CL, Tilman D (2000) Biodiversity, stability, and productivity in competitive communities. Am Nat 156:534-552

Lhomme JP, Winkel T (2002) Diversity-stability relationships in community ecology: Re-examination of the portfolio effect. Theor Popul Biol 62:271-279

Lilley S, Schiel D (2006) Community effects following the deletion of a habitat-forming alga from rocky ma-rine shores. Oecologia 148:672-681

Loreau M (2000) Biodiversity and ecosystem functioning: recent theoretical advances. Oikos 91:3-17

Loreau M, Hector A (2001) Partitioning selection and complementarity in biodiversity experiments. Nature 412:72-76

Loreau M, Naeem S, Inchausti P, Bengtsson J, Grime JP, Hector A, Hooper DU, Huston MA, Raffaelli D, Schmid B, Tilman D, Wardle DA (2001) Biodiversity and ecosystem functioning: current knowledge and future challenges. Science 294:804-808

Lyons KG, Brigham CA, Traut BH, Schwartz MW (2005) Rare species and ecosystem functioning. Conserv Biol 19:1019-1024

MacArthur RH (1955) Fluctuations of animal populations and a measure of community stability. Ecology 36:533-536

McNaughton SJ (1985) Ecology of a grazing ecosystem: the Serengeti. Ecol Monogr 55:259-294

Micheli F, Cottingham KL, Bascompte J, Bjornstad ON, Eckert GL, Fischer JM, Keitt TH, Kendall BE, Klug JL, Rusak JA (1999) The dual nature of community variability. Oikos 85:161-169

Moore P, Hawkins SJ, Thompson RC (2007) Role of biological habitat amelioration in altering the relative responses of congeneric species to climate change. Mar Ecol Prog Ser 334:11-19

Morgan PH, Mercer LP, Flodin NW (1975) General model for nutritional responses of higher organisms. P Natl Acad Sci USA 72:4327-4331

Morrow K, Carpenter R (2008) Shallow kelp canopies mediate macroalgal composition: effects on the distri-bution and abundance of Corynactis californica (Corallimorpharia). Mar Ecol Prog Ser 361:119-127

Naeem S (1998) Species redundancy and ecosystem reliability. Conserv Biol 12:39-45

Naeem S, Li SB (1997) Biodiversity enhances ecosystem reliability. Nature 390:507-509

O'Connor NE, Bruno JF (2007) Predatory fish loss affects the structure and functioning of a model marine food web. Oikos 116:2027-2038

O'Connor NE, Grabowski JH, Ladwig LM, Bruno JF (2008) Simulated predator extinctions: Predator identity affects survival and recruitment of oysters. Ecology 89:428-438

Pimm SL (1991) The balance of nature?: ecological issues in the conservation of species and communities. The University of Chicago Press, Chicago

Polley HW, Wilsey BJ, Derner JD (2007) Dominant species constrain effects of species diversity on temporal variability in biomass production of tallgrass prairie. Oikos 116:2044-2052

References

23

Ptacnik R, Solimini AG, Andersen T, Tamminen T, Brettum P, Lepisto L, Willen E, Rekolainen S (2008) Diver-sity predicts stability and resource use efficiency in natural phytoplankton communities. P Natl Acad Sci USA 105:5134-5138

Purvis A, Hector A (2000) Getting the measure of biodiversity. Nature 405:212-219

Reichert K, Buchholz F (2006) Changes in the macrozoobenthos of the intertidal zone at Helgoland (German Bight, North Sea): a survey of 1984 repeated in 2002. Helg Mar Res 60:213-223

Ricklefs RE (1990) Ecology. W. H. Freeman and Company, New York

Sax DF, Gaines SD (2003) Species diversity: from global decreases to local increases. Trends Ecol Evol 18:561-566

Schluter D (1984) A variance test for detecting species associations, with some example applications. Ecology 65:998-1005

Shea K, Roxburgh SH, Rauschert SJ (2004) Moving from pattern to process: coexistence mechanisms under intermediate disturbance regimes. Ecol Lett 7:491-508

Stachowicz JJ, Bruno JF, Duffy JE (2007) Understanding the effects of marine biodiversity on communities and ecosystems. Annu Rev Ecol Evol S 38:739-766

Stachowicz JJ, Fried H, Osman RW, Whitlatch RB (2002) Biodiversity, invasion resistance, and marine eco-system function: Reconciling pattern and process. Ecology 83:2575-2590

Steiner CF, Long ZT, Krumins JA, Morin PJ (2005) Temporal stability of aquatic food webs: partitioning the effects of species diversity, species composition and enrichment. Ecol Lett 8:819-828

Thompson R, Starzomski BM (2007) What does biodiversity actually do? A review for managers and policy makers. Biodivers Conserv 16:1359-1378

Tilman D (1996) Biodiversity: population versus ecosystem stability. Ecology 77:350–363

Tilman D (1999) The ecological consequences of changes in biodiversity: a search for general principles. Ecol-ogy 80:1455–1474

Tilman D, Downing JA (1994) Biodiversity and stability in grasslands. Nature 367:363-365

Tilman D, Knops J, Wedin D, Reich P, Ritchie M, Siemann E (1997a) The influence of functional diversity and composition on ecosystem processes. Science 277:1300-1302

Tilman D, Lehman CL, Bristow CE (1998) Diversity-stability relationships: Statistical inevitability or ecologi-cal consequence? Am Nat 151:277-282

Tilman D, Lehman CL, Thomson KT (1997b) Plant diversity and ecosystem productivity: Theoretical consid-erations. P Natl Acad Sci USA 94:1857-1861

Troumbis AY, Memtsas D (2000) Observational evidence that diversity may increase productivity in Mediter-ranean shrublands. Oecologia 125:101-108

Ugland KI, Gray JS, Ellingsen KE (2003) The species-accumulation curve and estimation of species richness. J Anim Ecol 72:888-897

Underwood AJ, Chapman MG (1996) Scales of spatial patterns of distribution of intertidal invertebrates. Oecologia 107:212-224

Valdivia N, Stehbens JD, Hermelink B, Connell SD, Molis M, Wahl M (2008) Disturbance mediates the effects of nutrients on developing assemblages of epibiota. Austral Ecol 33:951-962

Walker BH (1992) Biodiversity and ecological redundancy. Conserv Biol 6:18-23

Wardle DA, Bonner KI, Barker GM, Yeates GW, Nicholson KS, Bardgett RD, Watson RN, Ghani A (1999) Plant removals in perennial grassland: vegetation dynamics, decomposers, soil biodiversity, and eco-system properties. Ecol Monogr 69:535-568


24

Wardle DA, Huston MA, Grime JP, Berendse F, Garnier E, Laurenroth WK, Setälä H, Wilson SD (2000) Bio-diversity and ecosystem function: an issue in ecology. Bull Ecol Soc Am 81:235-239

Weis JJ, Madrigal DS, Cardinale BJ (2008) Effects of algal diversity on the production of biomass in homoge-neous and heterogeneous nutrient environments: a microcosm experiment. PLoS ONE 3:e2825

Wollgast S, Lenz M, Wahl M, Molis M (2008) Effects of regular and irregular temporal patterns of disturbance on biomass accrual and species composition of a subtidal hard-bottom assemblage. Helg Mar Res 62:309-319

Yachi S, Loreau M (1999) Biodiversity and ecosystem productivity in a fluctuating environment: The insur-ance hypothesis. P Natl Acad Sci USA 96:1463-1468

Glossary

25

Glossary

Aggregate ecosystem property: a property that is calculated by summing that property

across the species living in the ecosystem.

Biodiversity: the sum of all biotic variation in the biosphere from the level of gene to eco-

system.

Compensation, species compensation, compensatory dynamics: a decrease in the abun-

dance of one species that is accompanied by a compensatory increase in the abundance of

other.

Complementarity effect: an increase in an ecosystem property due to resource complemen-

tarity or facilitation among species in a species-rich community.

Ecosystem: the level of biological organisation that includes animals and plants in associa-

tion, together with the physical variables of their surroundings. Ecological interactions be-

tween species regulate the transformation and transport of energy across the ecosystem.

Such transformations include the assimilation of carbon dioxide into organic carbonic com-

pounds by plants and the consumption of plants by grazers and animals by carnivorous.

Ecosystem functioning, performance: a simple contraction for ‘how ecosystems work’

and encompasses ecosystem properties, goods, and services.

Ecosystem goods and services: the ecosystem properties that contribute to human welfare

both directly and indirectly.

Ecosystem properties: 1 the sizes of pools of materials like nutrients and carbon. 2 the

rates of processes like energy fluxes across trophic levels.

Functional traits: the characteristics of species that influence ecosystem properties.

Functional effect traits: the characteristics of species that influence ecosystem properties;

e.g., biomass, size, rate of nutrient uptake.

Functional group: a classification of species according to either their effect to ecosystem

properties or their response to the environment.


26

Functional response traits: the characteristics of species that define how species respond

to the environment; e.g., ranges of tolerance to salinity, temperature, solar radiation, or other

environmental variables.

Observational study: a study in which a variable is measured across individuals, popula-

tions, or higher levels of organisation. No attempt is made to affect the response of the ob-

servational units––no treatment is applied, for example.

Overyielding: an increase in the magnitude of an ecosystem property (e.g. community bio-

mass) as species richness increases.

Positive selection effects: the increased probability that more diverse communities include

species with extreme functional values. The performance of the community represents then

the performance of these particular species instead of the average response of all of the spe-

cies in the community.

Removal experiment: an experiment in which the individuals of a species or functional

group is removed from a community.

Resource complementarity, partitioning, niche partitioning: the capacity of species to

use different resources or use them in different points of time or space.

Richness effect: an increase in an ecosystem property in a species-rich community relative

to a species-poor one. The property in the species-rich community is larger than the average

property calculated across the constituent species grown alone (monocultures).

Species accumulation curve: a graphical method used to estimate the species richness in

areas where the observer is unable to sample all of the species. The procedure consists of

calculating and plotting the average number of species (and its standard deviation) of the

smallest sample size (1). Then all combinations of the next sample size are randomised and

the mean cumulative species richness is plotted. This procedure is repeated for all sample

sizes.

Species richness: the number of species living in a given area.

Stability: the state of being not likely to change or fail.

Glossary

27

Statistical averaging, portfolio effect: a reduction in the variability in an ecosystem prop-

erty as species richness increases. This occurs because the ecosystem property is the sum of

that property across (temporally) fluctuating species; adding more species increases the

probability that these fluctuations will ‘average out’.

Summed covariances: the sum of all of the pair-wise species covariances (calculated

throughout time) within a biological community

Summed variances: the sum of all of the species variances (calculated throughout time)

within a community.

Synthetic-assemblage experiment: an experiment in which the researcher constructs com-

munities by placing together individuals of several species into an experimental unit. The

selection of species is usually done at random from a subset of the local species pool.

Transgressive overyielding: the phenomenon in which the productivity or resource use of

species-rich mixtures exceeds (transgress) that of the best-performing species grown alone.

The presence of transgressive overyielding suggests that positive species interactions may

be responsible for richness effects.


28

Appendix

Papers I – IV

Paper I

Paper I

Nelson Valdivia*, Markus Molis (In press) Observational evidence of a negative

biodiversity-stability relationship in intertidal epibenthic communities. Aquatic Biology

Biologische Anstalt Helgoland, Alfred Wegener Institute for Polar and Marine Research,

Section Seaweed Biology, Kurpromenade 201, 27498 Helgoland, Germany

* Corresponding author

Email [email protected]

Tel. ++49(0)47258193294

Fax ++49(0)47258193283

1

Appendix

2

Paper I

ABSTRACT: The idea that diversity begets the functioning and stability of ecosystems has

been intensely examined in terrestrial habitats, yet these relationships remain poorly studied

in the marine realm. Theoretical and empirical work suggests that diversity enhances the

stability of communities, but decreases the stability of populations. This is because

compensatory dynamics, such that when one species decreases while other increases,

stabilise the community as long as species richness increases the variety of responses to the

environment. In an observational field study, the temporal variability in species abundance

was used as a measure of stability that was compared among five intertidal sites of naturally

different species richness. Percent covers of macrobenthic species were estimated every 6

months for 2 years. Stability in total community cover was a negative but curvilinear

function of species richness. In addition, the stability of single populations (averaged over

all species) fluctuated across the species richness gradient, without showing the predicted

negative pattern. We found no evidence for increasing compensatory dynamics with

increasing species richness, suggesting that the variety of responses to environmental

changes was unrelated to diversity. Diversity-stability relationships in natural communities

may be more complex than those predicted by theory and manipulative experiments.

KEY WORDS: Diversity-stability hypothesis · Hard-bottom · Intertidal · Marine · Portfolio

effect · Species compensation · Species richness · Statistical averaging · Temporal variability

3

Appendix

INTRODUCTION

The effects of biodiversity on ecosystem processes have received considerable attention

because of the concern that loss of biodiversity can impair the functioning of ecosystems

(reviewed by Hooper et al. 2005, Stachowicz et al. 2007). Greater species diversity

represents more adaptive responses to environmental fluctuations (MacArthur 1955, Elton

1958). By this, the probability that some species maintain functioning when other species

fail ensures the persistence of ecosystem properties under variable environmental conditions

(Walker 1992, Yachi & Loreau 1999). Indeed, influential research in terrestrial habitats has

shown that diversity is beneficial for the functioning and stability of ecosystems (e.g.

Tilman 1996, Hector et al. 1999, Loreau & Hector 2001, Tilman et al. 2006). However,

these ideas remain poorly examined in aquatic ecosystems, for which there is also a need for

understanding the ecological consequences of species loss (Gessner et al. 2004, Hooper et

al. 2005). Considering the differences between terrestrial and aquatic ecosystems (Giller et

al. 2004), generalisations obtained from terrestrial habitats may not apply for marine

habitats.

Stability has several meanings in ecology, including the resistance to and the resilience

from disturbances, the resistance to invasions, and the temporal variability in an community

property (Johnson et al. 1996, Shea & Chesson 2002). In this study we focus on temporal

variability, expressed as the temporal variance in total species cover of intertidal epibenthic

communities, and on the role of statistical averaging (also called the portfolio effect) and

overyielding as two mechanisms by which community variability decreases with increasing

diversity. Statistical averaging occurs when an aggregate community property (e.g. total

species abundance) is calculated by adding that property across species. If the temporal

variations of species are asynchronous, adding more species will increase the probability

4

Paper I

that those fluctuations are averaged out and the variability in total abundance will decrease

merely on statistical grounds (Doak et al. 1998). Nevertheless, because asynchrony among

species can result from the different abilities of species to tolerate environmental changes,

statistical averaging is due in part to ecological differences among species (Cottingham et al.

2001).

Asynchrony in species fluctuations lead to compensatory dynamics, such that the

abundance of one species decreases while that of other increases; the resulting negative

covariance buffers the community stability (e.g. Vasseur & Gaedke 2007). If species

richness increases the variety of responses to the environment, then the presence of more

species increases the probability that some species compensate the loss of others (Yachi &

Loreau 1999, Ives et al. 2000). Increasing compensatory dynamics with increasing diversity

will tend to stabilise the community but will cause individual populations to be more

unstable (Lehman & Tilman 2000).

The strength of statistical averaging effects depends on the relative abundance of species

(Steiner et al. 2005). When species contribute unequally to the community abundance, the

negative effect of diversity on community variability is dampened (Doak et al. 1998), as

shown in terrestrial plant communities (Polley et al. 2007). Moreover, high species

dominance can inverse the diversity-stability relationship, and may lead to a non-linear

diversity-stability relationship (Lhomme & Winkel 2002).

Overyielding, increases in the mean of an aggregate property with species richness, is the

second mechanisms that influences the diversity-stability relationship. Overyielding comes

from differences among species–if many species compete for several resources, then

coexistence results in a greater proportion of space covered by the community (Tilman et al.

1997). A more diverse assemblage stands for greater variety of species traits, which can

cause the average community property to increase in comparison to the property of the

5

Appendix

average population. This overyielding effect will temporally stabilise the community as

species richness increases (Lehman & Tilman 2000).

Experimental manipulation of marine epibenthic diversity shows that diversity enhances

community stability (reviewed by Stachowicz et al. 2007). Spatial models based on

observational data, however, predict the contrary (Dunstan & Johnson 2004, 2006). This is

possible when species produce aggregate structures (e.g. aggregations of conspecifics or

colonies), as result from differential use of the space among species. These structures raise

spatial refuges, leading to enhanced probabilities of survival and to more stable

communities at low diversity sites (Dunstan & Johnson 2004, 2006). Contrarily, theory

predicting a positive diversity-stability relationship is based on the assumption of well-

mixed communities, where aggregations of conspecifics are almost absent (Dunstan &

Johnson 2006).

In an observational study, we tested the relationships between species richness and

community stability. Observational studies permit the inspection of broader ranges of

species richness and more realistic environmental conditions than those usually present in

manipulative experiments (Stachowicz et al. 2007). We tested whether species richness is

positively related to community stability (temporal variability in cover summed across all

species in a sampling unit), but negatively related to population stability (temporal

variability in the cover of individual epibenthic species). In addition, we investigated

whether species richness is positively related to average community cover (i.e. overyielding

effect), and whether species richness is positively related to the occurrence of species

compensation (i.e. whether species covariances become more negative as species richness

increases).

6

Paper I

MATERIAL AND METHODS

Study sites. The study was conducted at 5 intertidal sites of naturally differing species

richness on the rocky shore of Helgoland Island, NE Atlantic. Each site was ca. 200 m2 in

area and adjacent sites were � 100 m apart from each other. Two sites, ‘Barren Ground’

(BG) and ‘Semi-sheltered Fucus Bed’ (SFB), were located on the moderately exposed

north-eastern shore, which is partly sheltered from wave action by a 250 m long concrete

jetty running from north to south. The mid-intertidal at BG was formerly dominated by the

blue mussel Mytilus edulis and fucoid seaweeds (Bartsch & Tittley 2004). Today, the

community at BG is dominated by encrusting coralline algae (Phymatolithon spp.) and high

densities of the periwinkle Littorina littorea, while mussels and fucoid seaweeds have

almost disappeared. During September and November 2007, the average densities of L.

littorea were 227 and 281 ind. m-2 at BG, but 16 and 90 ind. m-2 at SFB (M. Molis, unpubl.

data). At SFB, the canopy-forming brown seaweed Fucus serratus extensively covers the

substrate from the lower intertidal to the upper subtidal, where the understorey is dominated

by Phymatolithon spp. and the turf-forming algae Cladophora rupestris, Chondrus crispus,

and Corallina officinalis (Bartsch & Tittley 2004). The third site, ‘Exposed Fucus Bed’

(EFB) was located at the western wave-exposed rocky shore of Helgoland. Here, the dense

F. serratus canopy had been gradually replaced by the red algae C. crispus and

Mastocarpus stellatus (Bartsch & Tittley 2004). The fourth and fifth sites were located on

concrete harbour walls at south-eastern Helgoland. ‘Exposed Mole’ (EM) is a wave-exposed

site that is dominated by dense turfs of C. rupestris, patches of the barnacle Verruca

stroemia, and Phymatolithon spp. The fifth site, ‘Sheltered Harbour’ (SH) is a wave-

sheltered site that is dominated by a number of red algae such as Phyllophora spp.,

7

Appendix

Ceramium virgatum, and Bonnemaisonia hamifera (Trailliella-phase). In addition F.

serratus and the encrusting bryozoan Electra pilosa exist here in high abundance.

Community sampling. During March 2006, fifteen 0.5 × 0.5 m plots were randomly

positioned and permanently marked with stainless screws at each site. All sites were

sampled every six months between March 2006 and March 2008, except that the final

sampling of SH was delayed by one month. Due to time constrains, a random sub-sample of

nine fixed plots was followed throughout time. In species accumulation curves, seven or

eight plots were enough to represent the number of species at each site (Appendix 1,

available as Supplementary Material). Over the two-year study period, two plots were lost at

SH and EFB and one plot at EM.

For each plot, percent cover of each macrobenthic species was estimated to the nearest 1

%. Species with <1 % cover in a plot were uniformly recorded with 0.5 % abundance. Due

to the multilayered structure of the assemblages, total community cover could well exceed

100 %. Taxa were identified to the lowest possible taxonomic level in the field. For

ambiguous taxa, sub-samples collected from adjacent areas were identified in the laboratory.

Some taxa were identified to genus level, such as Phymatolithon spp., Porphyra sp., and

Ulva spp. Small burrowing spionids were grouped as family Spionidae and Ectocarpales

were identified to order level (Appendix 2).

Data analysis. Because species richness did vary over time, the gradient of species

richness was defined by using species accumulation curves that were generated separately

for each site, using the data of all sample dates. The maximum number of species obtained

from each curve corresponded to the site-specific richness used in the analyses. Species

occurring in less than three out of the five sample dates or contributing <1 % to total

community cover were omitted from all analyses, except for rare species with a consistent

8

Paper I

seasonal pattern (defined as the occurrence of a species during the same season across

years).

The PRIMER Similarity-Percentages routine, SIMPER, was used to identify the species

with larger contribution to the multivariate structure of each site. Bray-Curtis (BC)

similarities (1 – BC) were calculated between all pairs of samples in the entire data set. The

average similarities between all pairs of within-site samples were then broken down into

separate contributions from each species to the structure of each site (Clarke & Warwick

2001).

The � �-1 ratio (temporal stability, S) was used as a measure of community stability,

where � is the temporal mean community total cover for a time period and � is its temporal

standard deviation over the same interval (Tilman 1999). In comparison to the frequently

used coefficient of variability (100 � �-1), which approaches zero as stability decreases, S is

advantageous because its magnitude increases with stability. The stability of the ith species,

Si, was calculated by dividing its mean cover by its standard deviation. Population stability

was then calculated for each plot by averaging Si across all species (Tilman et al. 2006).

The temporal variance in total community cover was partitioned into the sum of all (N) of

the species variances and that of species covariances. This was done by calculating an N × N

covariance matrix across time for each plot; the sum of the diagonal corresponds to the

summed species variances and the sum of the off diagonals to the summed species

covariances. The sum of the full covariance matrix corresponds to the net variance (i.e.

summed variances plus summed covariances). The summed covariances were used as a

measure of compensatory dynamics–if species compensation increases, then the summed

covariances become more negative.

Regression analyses of the relationship between diversity and stability were conducted

using R environment version 2.7.2 (R Development Core Team 2008). We conducted

9

Appendix

orthogonal polynomial regressions to assess curvilinear patterns of diversity-stability

relationships. We tested up to the fourth-order fit (one minus the number of richness levels)

and we used the procedure described by Sokal and Rohlf (1995), in which the significance

of each polynomial regression is tested as part of the ANOVA table. All curves were fitted

using least squares regression and analyses of variance used the general linear models

routines. All measures of stability were Loge transformed due to their patchy statistical

distribution. The transformation assured normality and allowed the use of general linear

models.

Regression analyses were also used to investigate the relationship between richness and

(1) the average total community cover (averaged over the five sample dates), (2) the sum of

all species variances, (3) the sum of all pair-wise species covariances, and (4) the net

variance in total community cover. Analysis 1 was done to test whether increasing species

richness leads to overyielding and analyses 2, 3, and 4 to test whether increases in the

variance of species abundances are offset by increasingly negative species covariances.

Statistical averaging effects depend on the way in which the temporal variance in the

abundance of a species changes with the temporal mean (Tilman et al. 1998). The general

tendency of the variance �2 to increase with the mean � is described with Taylor’s power

function, �2 = c �z, where c is a constant and z is the scaling coefficient (Taylor 1961). The

value of z affects the strength of the statistical averaging, with 1 < z < 2 meaning that

diversity dampens the community variability but increases the population variability

(Tilman et al. 1998, Tilman 1999). The logarithmic transformation of �2 = c �z results in a

linear equation in the form of log (�2) = c + z log (�). We fitted this regression to the most

important species identified by SIMPER routines and to the entire data set, combining all

species.

10

Paper I

RESULTS

Seventy-three taxa were identified during the study; 52 were included in the analyses

(Appendix 2). Site species richness was of 30 at BG, 34 at EFB, 36 at EM, 40 at SFB, and

43 at SH. The total community cover averaged over the five sample dates (± SEM) ranged

from 119 ± 7 % (BG) to 211 ± 6 % (SFB). The taxa contributing most to the community

structure at each site were identified using SIMPER routines (Table 1). At BG, EM, and

SFB, 3 to 4 species contributed the 90 % of the communities; at EFB and SH, 6 and 8

species respectively. The taxa with the highest and most consistent contribution to within-

site similarities were Phymatolithon spp., Fucus serratus and Cladophora rupestris (Table

1). These three species represented 61 % of the sum of all of the species abundances from

the five sample sites.

Contrary to our predictions, community stability was a negative and curvilinear function

of species richness (Fig. 1). Accordingly, both the linear and cubic models significantly fit

these data (Fig. 1, Table 2). Highest community stability values were found at BG, while

lowest values were found at EFB and SH. Population stability showed large fluctuations

over the species richness gradient, and no clear trend to decrease was observed.

Consequently, the linear model was insignificant, whereas the quadratic and cuartic models

explained significant portions of the population stability data (Fig. 1, Table 2). Population

stability was highest at EM, and lowest at EFB and SH.

The average total community cover significantly increased with site species richness (y =

–54.10 + 5.95×, R2 = 0.5, F1, 38 = 38, p < 0.0001). In addition, total community cover

increased with site diversity at each of the sample dates (separate regressions performed at

each sample date, p � 0.004).

11

Appendix

The summed variances showed an oscillating pattern across the species richness gradient,

and a significant trend to increase (Fig. 2, Table 3). On the other hand, the summed

covariances were independent of species richness (Fig. 2, Table 3). Summed covariances

were on average less than zero (–766.2 ± 188.5, one sample t-test, p � 0.001). When

analysing each site separately, however, we found that summed covariances were less than

zero at BG, EM, and SH (one sample t-tests, p � 0.03), but not at EFB and SFB (one sample

t-test, p � 0.09). As a consequence of the insignificant relationship between the summed

covariances and diversity, the net variance (i.e. summed variances plus summed

covariances) followed a similar pattern to that of the summed variances, showing an

irregular increase over the species richness gradient (Fig 2, Table 3).

The fitted z-value (± SEM) for the three taxa with the highest contribution to the within-

site similarities were 1.26 ± 0.14 for Phymatolithon spp., 1.38 ± 0.06 for Fucus serratus,

and 1.12 ± 0.1 for Cladophora rupestris; the fitted z-value for the entire data set was 1.34 ±

0.01. According to their z-values, the stability of these taxa should have decreased with

species richness; but, the individual regressions showed differing patterns. The stability of

Phymatolithon spp. tended to decrease with increasing species richness, while that of Fucus

serratus and Cladophora rupestris showed large departures from linearity that resulted in a

significant cuartic fit for both species (Fig. 3, Table 4).

12

Paper I

DISCUSSION

Community stability

These observations suggest that community stability decreased as the number of species

increased, in contrast to what most theoretical and empirical work predicts (reviewed by

Hooper et al. 2005, Stachowicz et al. 2007). In addition, the patterns of community and

population stability were highly complex. In this study, the average total community cover

significantly increased with species richness (i.e. overyielding) and the variance scaled with

the mean cover with 1 < z < 2–overyielding and such variance-mean rescaling should have

led to a positive diversity-stability relationship (Tilman et al. 2006, van Ruijven & Berendse

2007). Yet, increasing stability with increasing diversity also requires increasingly negative

species covariances and an even distribution of species abundances.

On average, summed covariances were significantly less than zero. At both sites

dominated by the canopy forming Fucus serratus, however, covariances were equal or

larger than zero. Positive covariances in these sites may have resulted from the positive

effect of F. serratus on obligate understorey species (N. Valdivia, unpublished data).

Moreover, persistent removals of the F. serratus canopy caused compensatory dynamics of

species with different environmental tolerances; the resulting negative covariances buffered

the community stability but reduced the population stability (N. Valdivia unpublished data).

Therefore, the covariance in the species responses to environmental disturbances can

strongly influence the stability of the here studied shores. In the present study, the

insignificant relationship between the species covariances and species richness probably

prevented a positive effect of diversity on stability.

13

Appendix

The relationship between species richness and stability was also influenced by the relative

abundance of species. In this experiment, three taxa explained ca. the 60 % of the sum of all

of the species covers. When few taxa numerically dominate the system, community stability

can be driven by fluctuations of these components (Steiner et al. 2005, Polley et al. 2007).

In addition, large differences among species abundances can result in negative and

curvilinear richness-stability relationships when z = 1.2 (Lhomme & Winkel 2002). In our

case, the z-values were close to 1.2 (e.g. 1.26 ± 0.14 for Phymatolithon spp., but 1.35 ± 0.01

for all species), suggesting that large heterogeneity among species abundances may also

explain the negative and complex pattern of community stability.

Overyielding probably resulted from the multilayered structure of macrobenthic

assemblages, which allows single species to expand by differential use of the available

space. Erect life forms use little space of primary substratum but can expand above the

substratum and thus increase in abundance. This causes the total percent cover to exceed

100 %. For instance, seaweeds can develop and expand a canopy in an area where the

primary substratum was monopolised by encrusting forms (Connell 2003). Such a spatial

structure was apparent in this study, as encrusting, turfing, and canopy-forming algae

formed 3 layers of biota. This suggest that if we would have focused on one layer of species

(i.e. do not allow total percent covers > 100 %), probably we would have found no

overyielding. On the other hand, large spatial structures may also have caused community

stability to decrease with species richness, because such structures raise refugee and

increase the probability of survival in communities dominated by few species (Dunstan &

Johnson 2004, 2006).

Population stability

14

Paper I

We detected significant fluctuations in the pattern of population stability across the

species richness gradient, but we did not find a clear trend to decrease. Population stability

should decrease with increasing diversity if the latter is positively related to the number of

potential competitive interactions or to the variety of adaptive responses to the environment

(Ives et al. 2000). In this study, the absence of a negative diversity-covariance relationship

suggests that both the variety of environmental tolerances and the number of competitive

interactions were independent of diversity. Differential use of the space could have

alleviated competition at high diversity sites, reducing the probability of compensatory

changes that cause individual populations to be more variable.

According to their z-values, the stability of single species should have decreased with

species richness (Tilman 1999). However, individual species tended to be more stable at

sites where they were more abundant. Similarly, a recent experiment in which species

abundances varied across the diversity gradient showed that the stability of single species

performed differently than expected based on the variance-mean rescaling (van Ruijven &

Berendse 2007). Because constancy in species abundance is an assumption of statistical

averaging (Doak et al. 1998), this mechanisms may be well supported by manipulative

experiments, but probably not by observational studies.

Our observations agree with studies conducted in multitrophic systems showing no clear

diversity effect on population stability (McGrady-Steed & Morin 2000, Steiner et al. 2005),

but contradict studies conducted on single trophic levels that show negative relationships

(Tilman et al. 2006, van Ruijven & Berendse 2007). In our case, primary producers

dominated the assemblages in terms of abundance, but 57 % of taxa were invertebrates. On

the other hand, keystone consumers can strongly control the community structure (Paine

1966). Therefore, the high stability of Phymatolithon spp. at the species-poor site Barren

Ground might be related to the large density of the periwinkle Littorina littorea observed at

15

Appendix

the study site. Epibenthic grazers like L. littorea control the recruitment of algae, thereby

affecting the structure of macrobenthic assemblages (McQuaid 1996). The grazing activity

of L. littorea at Barren Grounds may be an important factor in depressing species richness

and simultaneously promoting the persistence of encrusting algae like Phymatolithon spp. at

high abundances. Manipulative experiments are necessary to address the role of trophic

interactions on relationship between diversity and stability.

In conclusion, we observed a negative and curvilinear pattern in community stability and

a complex pattern in population stability. Probably, putative positive effects of overyielding

and variance-mean rescaling on community stability were offset by strong heterogeneity

among species abundances and invariant species covariances across the species richness

gradient. The observational evidence presented here is not unequivocal, since we did not

control for factors that might have covaried with species richness, such as wave exposure or

nutrient levels. In addition, ecosystem properties such as fluxes of nutrients and carbon were

not assessed. Because different ecosystem properties can have different responses to

changes in diversity (Jiang et al. 2008), experiments that explore multiple ecosystem

properties can provide a more comprehensive view of the functional role of diversity. Even

though, we suggest that the relative abundance of species and ecological interactions

influencing the covariances among species may play a pivotal role in the relationship

between diversity and ecosystem stability.

Acknowledgements. We are grateful to a number of friends and colleagues who

enthusiastically helped during long hours of field work, including J. Ellrich, A. Engel, M.

Honens, M. Marklewitz, A. Wagner, and H.Y. Yun. A. Knox polished the English of an

early version of the manuscript. This work forms part of the MarBEF responsive model

16

Paper I

project BIOFUSE. Financial support by the Alfred-Wegener-Institute for Marine and Polar

Research to N.V. is acknowledged.

17

Appendix

References Bartsch I, Tittley I (2004) The rocky intertidal biotopes of Helgoland: present and past. Helg

Mar Res 58:289-302 Clarke KR, Warwick RM (2001) Change in marine communities: an approach to statistical

analysis and interpretation PRIMER-E Ltd, Plymouth Connell SD (2003) The monopolization of understorey habitat by subtidal encrusting

coralline algae: a test of the combined effects of canopy-mediated light and sedimentation. Mar Biol 142:1065-1071

Cottingham KL, Brown BL, Lennon JT (2001) Biodiversity may regulate the temporal variability of ecological systems. Ecol Lett 4:72-85

Doak DF, Bigger D, Harding EK, Marvier MA, O'Malley RE, Thomson D (1998) The statistical inevitability of stability-diversity relationships in community ecology. Am Nat 151:264-276

Dunstan PK, Johnson CR (2004) Invasion rates increase with species richness in a marine epibenthic community by two mechanisms. Oecologia 138:285-292

Dunstan PK, Johnson CR (2006) Linking richness, community variability, and invasion resistance with patch size. Ecology 87:2842-2850

Elton SC (1958) The ecology of invasions by animals and plants. The University of Chicago Press, Chicago, USA

Gessner MO, Inchausti P, Persson L, Raffaelli DG, Giller PS (2004) Biodiversity effects on ecosystem functioning: insights from aquatic systems. Oikos 104:419-422

Giller PS, Hillebrand H, Berninger U-G, Gessner MO, Hawkins S, Inchausti P, Inglis C, Leslie H, Malmqvist B, Monaghan MT, Morin PJ, O'Mullan G (2004) Biodiversity effects on ecosystem functioning: emerging issues and their experimental test in aquatic environments. Oikos 104:423-436

Hector A, Schmid B, Beierkuhnlein C, Caldeira MC, Diemer M, Dimitrakopoulos PG, Finn JA, Freitas H, Giller PS, Good J, Harris R, Högberg P, Huss-Danell K, Joshi J, Jumpponen A, Körner C, Leadley PW, Loreau M, Minns A, Mulder CPH, O'Donovan G, Otway SJ, Pereira JS, Prinz A, Read DJ, Scherer-Lorenzen M, Schulze ED, Siamantziouras ASD, Spehn EM, Terry AC, Troumbis AY, Woodward FI, Yachi S, Lawton JH (1999) Plant diversity and productivity experiments in european grasslands. Science 286:1123-1127


Ives AR, Klug JL, Gross K (2000) Stability and species richness in complex communities. Ecol Lett 3:399-411

Jiang L, Pu Z, Nemergut DR (2008) On the importance of the negative selection effect for the relationship between biodiversity and ecosystem functioning. Oikos 117:488-493

Johnson KH, Vogt KA, Clark H, Schmitz O, Vogt D (1996) Biodiversity and the productivity and stability of ecosystems. Trends Ecol Evol 11:372-377

Lehman CL, Tilman D (2000) Biodiversity, stability, and productivity in competitive communities. Am Nat 156:534-552

Lhomme JP, Winkel T (2002) Diversity-stability relationships in community ecology: Re-examination of the portfolio effect. Theor Popul Biol 62:271-279

Loreau M, Hector A (2001) Partitioning selection and complementarity in biodiversity experiments. Nature 412:72-76

18

Paper I

MacArthur RH (1955) Fluctuations of animal populations and a measure of community stability. Ecology 36:533-536

McGrady-Steed J, Morin PJ (2000) Biodiversity, density compensation, and the dynamics of populations and functional groups. Ecology 81:361-373

McQuaid CD (1996) Biology of the gastropod family Littorinidae .2. Role in the ecology of intertidal and shallow marine ecosystems. Oceanography and Marine Biology, Vol 34 34:263-302

Paine R (1966) Food web complexity and species diversity. Am Nat 100:65-75 Polley HW, Wilsey BJ, Derner JD (2007) Dominant species constrain effects of species

diversity on temporal variability in biomass production of tallgrass prairie. Oikos 116:2044-2052

R Development Core Team (2008) R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna

Shea K, Chesson P (2002) Community ecology theory as a framework for biological invasions. Trends Ecol Evol 17:170-176

Sokal RR, Rohlf RJ (1995) Biometry. Freeman and Company, New York Stachowicz JJ, Bruno JF, Duffy JE (2007) Understanding the effects of marine biodiversity

on communities and ecosystems. Annu Rev Ecol Evol S 38:739-766 Steiner CF, Long ZT, Krumins JA, Morin PJ (2005) Temporal stability of aquatic food

webs: partitioning the effects of species diversity, species composition and enrichment. Ecol Lett 8:819-828

Taylor LR (1961) Aggregation, variance and the mean. Nature 189:732-735 Tilman D (1996) Biodiversity: population versus ecosystem stability. Ecology 77:350–363 Tilman D (1999) The ecological consequences of changes in biodiversity: a search for

general principles. Ecology 80:1455–1474 Tilman D, Lehman CL, Bristow CE (1998) Diversity-stability relationships: Statistical

inevitability or ecological consequence? Am Nat 151:277-282 Tilman D, Lehman CL, Thomson KT (1997) Plant diversity and ecosystem productivity:

Theoretical considerations. P Natl Acad Sci USA 94:1857-1861 Tilman D, Reich PB, Knops JMH (2006) Biodiversity and ecosystem stability in a decade-

long grassland experiment. Nature 441:629-632 van Ruijven J, Berendse F (2007) Contrasting effects of diversity on the temporal stability

of plant populations. Oikos 116:1323-1330 Vasseur DA, Gaedke U (2007) Spectral analysis unmasks synchronous and compensatory

dynamics in plankton communities. Ecology 88:2058-2071 Walker BH (1992) Biodiversity and ecological redundancy. Conserv Biol 6:18-23 Yachi S, Loreau M (1999) Biodiversity and ecosystem productivity in a fluctuating

environment: The insurance hypothesis. P Natl Acad Sci USA 96:1463-1468

19

Appendix

Table 1. Dominant taxa at sites with a naturally differing number of species. Decomposition

of within-site Bray-Curtis similarities into contribution of taxa to the structure of each site

(Contributioni). Taxa contributions are also expressed as percent (%i). A value of the ratio

Contributioni/SD � 1 indicates that the contribution of taxon i to the within-site similarity is

consistent across all pairs of samples. Percent cover (averaged over plots and all sample

dates) of each taxon is given (%-coveri). Site-specific species richness is given in brackets.

Taxa cumulating up to 90 % of the contribution to the with-site similarities are shown.

Taxon %-coveri Contributioni (%i) Contributioni/SD

BG, Barren Ground (30)

Phymatolithon spp. 70.78 49.31 78.90 2.95

Littorina littorea 6.67 3.96 6.33 1.92

Hildenbrandia rubra 7.92 2.62 4.19 0.56

Haemescharia hennedyi 12.32 2.56 4.09 0.31

EFB, Exposed Fucus Bed (34)


Fucus serratus 32.37 9.08 22.71 0.75

Chondrus crispus 17.89 5.27 13.19 0.81

Corallina officinalis 8.63 2.09 5.23 0.61

Mastocarpus stellatus 9.24 1.96 4.91 0.50

Ulva spp. 11.66 1.70 4.26 0.38

20

Paper I

EM, Exposed Mole (36)

Cladophora rupestris 86.68 49.02 73.15 3.10


Verruca stroemia 22.75 5.88 8.78 0.94

SFB, Semi-sheltered Fucus Bed (40)

Fucus serratus 76.27 29.30 40.33 2.43


Cladophora rupestris 38.40 12.52 17.24 1.96

SH, Sheltered Harbour (43)

Ceramium virgatum 31.79 7.63 23.60 0.67

Fucus serratus 30.69 6.24 19.28 0.59

Electra pilosa 22.29 4.85 15.00 0.73

Bonnemaisonia hamifera 23.57 4.03 12.47 0.39

Phyllophora spp. 12.14 2.34 7.24 0.62

Chondrus crispus 9.97 2.31 7.13 0.73

Ulva spp. 8.87 1.30 4.02 0.56

Ectocarpales 10.81 1.14 3.53 0.31

21

Appendix

Table 2. Results of orthogonal polynomial regressions of species richness on community

and population stability.

Source df MS F p

Community stability

Species richness, N 4 1.81 9.40 <0.0001

Nlinear 1 3.44 17.82 0.0002

Nquadratic 1 0.79 4.08 0.0511

Ncubic 1 2.63 13.61 0.0008

Ncuartic 1 0.40 2.09 0.1569

Residual 35 0.19

Population stability

Species richness, N 4 0.59 7.56 0.0002

Nlinear 1 0.15 1.94 0.1725

Nquadratic 1 0.48 6.17 0.0179

Ncubic 1 0.30 3.89 0.0566

Ncuartic 1 1.42 18.25 0.0001

Residual 35 0.08

22

Paper I

Table 3. Results of orthogonal polynomial regressions of species richness on summed

variances, summed covariances, and net variance (summed variances + summed

covariances).

Source df MS F p

Summed variances

Species richness, N 4 8490828 9.98 <0.0001

Nlinear 1 7589923 8.92 0.0051

Nquadratic 1 142765 0.17 0.6846

Ncubic 1 19102565 22.45 <0.0001

Ncuartic 1 7128060 8.38 0.0065

Residual 35 850863

Summed covariances

Species richness, N 4 1564017 1.11 0.3662

Nlinear 1 426974 0.30 0.5851

Nquadratic 1 555457 0.40 0.5337

Ncubic 1 4858397 3.46 0.0715

Ncuartic 1 415240 0.30 0.5902

Residual 35 1405830

Net variance (summed variances + summed covariances)

Species richness, N 4 5137088 7.31 0.0002

23

Appendix

Nlinear 1 11617286 16.54 0.0003

Nquadratic 1 135017 0.19 0.6638

Ncubic 1 4693597 6.68 0.0141

Ncuartic 1 4102454 5.84 0.0210

Residual 35 702542

24

Paper I

Table 4. Results of orthogonal polynomial regressions of species richness on the stability of

each of the three dominant taxa

Source df MS F p

Phymatolithon spp.


Nlinear 1 3.75 10.00 0.0032

Nquadratic 1 1.14 3.03 0.0904

Ncubic 1 12.09 32.25 <0.0001

Ncuartic 1 1.82 4.86 0.0341

Residual 35 0.37

Fucus serratus

Species richness, N 4 3.05 7.97 0.0002

Nlinear 1 4.88 12.72 0.0013

Nquadratic 1 1.83 4.79 0.0372

Ncubic 1 3.53 9.22 0.0051

Ncuartic 1 1.97 5.15 0.0312

Residual 28 0.38

Cladophora rupestris


Nlinear 1 0.28 0.54 0.4659

25

Appendix

Nquadratic 1 20.80 40.19 <0.0001

Ncubic 1 1.30 2.51 0.1225

Ncuartic 1 21.14 40.84 <0.0001

Residual 34 0.52

26

Paper I

Figure captions

Fig. 1. Relationship between species richness and stability in percent cover of epibenthic

species. Stability, S, was calculated as the quotient between the temporal mean in cover, �,

and its standard deviation, �, over the same time period. (a) Stability of total community

cover. (b) Stability of cover of single species averaged across 52 species. Each circle

represents the stability of a 0.25 m2 plot that was followed over time. Codes for sites are as

follows. BG: Barren Ground, EFB: Exposed Fucus Bed, EM: Exposed Mole, SFB: Semi-

sheltered Fucus Bed, and SH: Sheltered Harbour.

Note: regression parameters of site species richness (N) are as follows. Community stability

= 227.25 – 18.53N + 0.50N2 – 0.004N3. Population stability = 31450 – 346N + 14N2 –

0.25N3 + 0.0017N4

Fig. 2. Relationship between site species richness and (a) summed variances, (b) summed

covariances, and (c) net variance (summed variances plus summed covariances). Codes for

sites as in Fig. 1

Note: regression parameters of site species richness (N) are as follows. Summed variances =

– 7465000 + 810900N – 32790N2 + 585N3 – 4N4. Net variance = – 5522000 + 602900N –

24530N2 + 441N3 – 3N4

Fig. 3. Patterns of stability in percent cover of the 3 taxa with the highest and most

consistent contributions to the within-site similarities. Codes for sites as in Fig. 1

Note: regression parameters of site species richness (N) on stability (S) are as follows.

SPhymatolithon spp. = – 3014 + 346.5N – 14.83N2 + 0.27N3 – 0.002N4. SFucus serratus = – 7531 +

841N – 0.35N2 + 0.6N3 – 0.004N4. SCladophora rupestris = 1980 – 1324N + 54.51N2 – 0.9N3 +

0.007N4

27

Appendix

0

1

2

3

4

30 35 40

-1

0

1

2

3

Com

mun

ity s

tabi

lity

[log(��-

1 )]

Pop

ulat

ion

stab

ility

[log(��

� )](a)

(b)

Site species richness

SHSFBEMEFBBGSites:

R2 = 0.4636

R2 = 0.4891

Fig. 1. Valdivia and Molis

28

Paper I

Varia

nce


SHSFBEMEFBBGSites:

(c) Net variance (variances + covariances)R2 = 0.4552

(a) Summed variances, R2 = 0.5328

(b) Summed covariances, NS

0

2000

4000

6000

-4000

-2000

0

2000

30 35 40

0

2000

4000

6000


29

Appendix

0

2

4

6

0

2

4

6

30 35 40

0

5


SHSFBEMEFBBGSites:

Spec

ies

stab

ility

[log(��

� )]

(a) Phymatolithon spp., R2 = 0.5889

(b) Fucus serratus, R2 = 0.5323

(c) Cladophora rupestris, R2 = 0.7120


30

Paper I

The following appendix accompanies the article

Observational evidence of a negative biodiversity-stability relationship in intertidal

epibenthic communities

Nelson Valdivia*, Markus Molis



*Email [email protected]

Appendix 1. Species accumulation curves of data from the first sample date on March 2006

at each site. We used a random method that finds the mean species accumulation curve and

its standard deviation (vertical bars) from random permutations of the data

31

Appendix

2 4 6 8 10 12 140

10

20

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20

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2 4 6 8 10 12 140

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20

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2 4 6 8 10 12 140

10

20

30

Number of plots

Num

ber o

fspe

cies

Sheltered harbour

Semi-sheltered Fucus bed

Exposed mole

Exposed Fucus bed

Barren ground

32

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36

Paper II

Nelson Valdivia a, *, Kate L. de la Haye b, c, Stuart R. Jenkins b, d, Susan A. Kimmance e,

Richard Thompson c, Markus Molis a (In press) Functional composition, but not richness,

affected the performance of sessile suspension-feeding assemblages. Journal of Sea Research

a Biologische Anstalt Helgoland, Alfred Wegener Institute for Polar and Marine Research,


b Marine Biological Association of the United Kingdom, The Laboratory, Citadel Hill,

Plymouth PL1 2PB United Kingdom

c Marine Biology and Ecology Research Centre, School of Biological Sciences, University of

Plymouth, Plymouth PL4 8AA United Kingdom

d School of Ocean Sciences, Bangor University, Menai Bridge, Anglesey LL59 5AB United

Kingdom

e Plymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth PL1 3DH, UK



Tel. ++49(0)47258193294

Fax ++49(0)47258193283

Appendix

2

Paper II

Abstract

The efficiency by which communities capture limiting resources may be related to the

number of species or functional types competing therein. This is because species use different

resources (i.e. complementarity effect) or because species-rich communities include species

with extreme functional traits (positive selection effect). We conducted two manipulative

studies to separate the effects of functional richness and functional identity on the feeding

efficiency (i.e. filtration rate) of suspension-feeding invertebrates growing on vertical surfaces.

In addition, one experiment tested whether the density of organisms influences the effect of

functional diversity. Monocultures and complete mixtures of functional types were fed with a

solution of microalgae of different sizes (6 �m – 40 �m). Experiments conducted at two

locations, Helgoland and Plymouth, showed that functional identity had far larger effects on

filtration rate than richness. Mixtures did not outperform the average monoculture or the best-

performing monoculture and this pattern was independent on density. The high efficiency of

one of the functional types in consuming most microalgae could have minimised the resource

complementarity. The loss or gain of particular species may therefore have a stronger impact on

the functioning of epibenthic communities than richness per se.

3

Appendix

Keywords

Biodiversity-Ecosystem Functioning; Complementarity Effect; Density; Ecosystem

functioning; Filtration rates, Selection Effects; Resource Consumption; Suspension feeders

4

Paper II

1. Introduction

Present rates of species invasion and extinction (Thomas et al., 2004; Byrnes et al., 2007)

have stimulated research linking biodiversity and ecosystem functioning (hereafter BEF),

because of the potential loss of ecosystem ‘goods and services’ (Chapin et al., 2000). A

substantial number of theoretical and empirical studies suggest that taxonomic and functional

diversity are linked to the performance of ecosystems (Hooper et al., 2005; Stachowicz et al.,

2007). However, to gain a mechanistic understanding of BEF relationships requires us to

accurately distinguish changes in ecosystem processes due to the number of species (richness

effect) from changes attributable to the usually stronger effect of species identity or

composition (Schwartz et al., 2000).

Energy transfer in an ecosystem underlies services such as food production and nutrient

cycling (Christensen et al., 1996). In coastal ecosystems, assemblages of sessile suspension-

feeding invertebrates mediate the coupling and energy transfer between two major habitats––

the water column and the benthos (Gili and Coma, 1998). Suspension feeders can directly

control pelagic primary production by capturing large amounts of plankton, and indirectly

regulate secondary production in coastal trophic webs by supporting populations of mobile

predators (Navarrete et al., 2005; Nielsen and Maar, 2007). Assemblages of sessile suspension

feeders provide therefore an opportunity to examine the functional consequences of biodiversity

change.

Positive richness effects on ecosystem properties can result from resource complementarity

or facilitation (complementarity effect; Tilman et al., 1997; Loreau, 2000). This theory was

developed from observations on terrestrial plant communities, but it may also apply to aquatic

5

Appendix

ecosystems (Bruno et al., 2005; Griffin et al., 2008). For example, suspension feeders able to

partition a resource on the basis of particle size can form complex assemblages made up of

numerous species (Lesser et al., 1994): species feeding on smaller particles can coexist in the

same community with other species feeding on larger prey. At the same time, higher diversity

results in higher spatial complexity, which alters patterns in near-bottom currents and increases

particle capture of individuals; this allows a species-rich assemblage to capture more food than

any of its constituent species grown alone (Cardinale et al., 2002). These observations suggest

that resource complementarity and facilitation may occur within suspension-feeding

assemblages; however, few experiments have been designed to test the role of these processes

in linking suspension feeder diversity and energy transfer (e.g. Cardinale and Palmer, 2002;

Cardinale et al., 2002).

On the other hand, richness effects can result from the increased probability that species-rich

communities include species with extreme functional values (sampling effect or positive

selection effect; Huston, 1997; Loreau et al., 2001). This mechanism is based on the assumption

that competitive ability and functional impact of species are positively correlated; thereby the

performance of a mixture of species is explained by that of the best-performing species in

monoculture (Tilman et al., 1997). However, when competitive abilities and functional impact

are weakly or negatively correlated, negative selection effects can offset the resource

partitioning and dampen, or even reverse, a positive BEF relationship (Bruno et al., 2006; Jiang

et al., 2008).

Most BEF experiments suggest that sampling effects are the major mechanisms explaining

richness effects (Cardinale et al., 2006; Stachowicz et al., 2007). However, recent evidence

suggests that complementarity operates in natural communities, and also that its expression is

6

Paper II

influenced by other factors (Cardinale et al., 2007; Griffin et al., 2008). The density of

organisms within assemblages influences the strength of intraspecific and interspecific

competition, which in turn can affect the relationship between species richness and ecosystem

properties (Cardinale et al., 2004; Weis et al., 2007). Despite recognition of its importance,

empirical tests of the potential role of density in mediating the richness effects are still rare (but

see Griffin et al., 2008).

While BEF research has largely focused at the species level (Hendriks and Duarte, 2008),

grouping species into functional types allows for a more tractable study of complex systems:

patterns are more consistent because the variability among functionally similar species is

averaged out (Hooper et al., 2005). Here we present the results of field manipulative studies in

which we tested the hypotheses that (1) the number of functional types increases the feeding

rate of assemblages of suspension-feeding invertebrates, (2) these effects are influenced by the

density of the assemblage, and (3) suspension feeders show different preferences for food

particles of different size. In order to accurately test these hypotheses, we separated the effects

of functional richness from the effects of functional identity. We used field-grown epibiota,

grouped together according to functional traits related to their abilities to deplete limiting

resources. A mixture of differently sized microalgae was used as food in order to allow for

resource complementarity on the basis of size-specific filtration rates. Selective feeding in

terms of particle size has been shown in barnacles (Crisp, 1964), bryozoans (Pascoe et al.,

2007), ascidians (Petersen, 2007), and mussels (Rouillon and Navarro, 2003). In addition, we

sought to maximise the ecological relevance of this work by replicating the experiment at two

locations in north-eastern Atlantic coasts.

7

Appendix

2. Methods

2.1. Experimental design

The experiments were conducted at Helgoland Island, south-eastern North Sea and at

Plymouth, in the western English Channel. The original design encompassed the replication of

identical experiments at both locations. Settlement patterns, however, were different between

locations, as the abundance and diversity of settlers were larger at Helgoland than at Plymouth.

Therefore, we did not conduct identically replicated experiments but we used the data from

both locations to address different but complementary hypotheses. The data from Helgoland

were used to test hypothesis 1 (the number of functional types increases the feeding rate of

assemblages of suspension-feeding invertebrates) and hypothesis 2 (density influences the

effect of functional richness). The data from Plymouth were used to test hypotheses 1 (see

above) and 3 (suspension feeders show different preferences for food particles of different

size).

Experiments included three functional types in monoculture and a complete mixture

containing all functional types. Each treatment was replicated five times at Helgoland and three

times at Plymouth. At Helgoland, all treatments were crossed with the factor density (either

high or low) to test for density-dependent diversity effects on filtration rates. Our experimental

design, in which overall density was kept constant across the different diversity treatments (i.e.

a replacement design), allowed a clear partitioning of the effects of functional richness and

identity and detection of non-transgressive and transgressive overyielding (Loreau, 1998; Bruno

et al., 2006). Non-transgressive overyielding occurs when the mixture performance exceeds that

8

Paper II

of the average of its component species in monocultures, while transgressive overyielding

occurs when the mixture performance exceeds that of the best-performing monoculture

(Fridley, 2001).

Species were selected in accordance with natural patterns in the distribution and abundance

of epibiota on vertical surfaces at both locations. Barnacles, bryozoans, ascidians, and mussels

characterise these assemblages (Anger, 1978). We defined the functional types on the basis of

morphological differences that may lead to resource partitioning in terms of particle size (Table

1). For example, barnacles use thoracic appendages (the cirri) to catch and handle food; a

variety of setae present in the cirri allow barnacles to catch plankton across a large size range

from flagellates to small crustaceans (Chan et al. 2008). Ascidians use ciliary pumps to drive

water through a mucus-net that retains the suspended food particles. Bryozoans use lateral cilia

to drive the water toward a filter formed by a band of stiff cilia. Mussels use gill filaments with

lateral cirri that beat against the current (reviewed by Riisgård and Larsen, 2000).

We also considered the individual size as a functional trait, because the morphology of the

organisms influences the physical habitat they occupy and may influence flow patterns and

delivery of food. For example, in assemblages dominated by small and opportunistic species

like bryozoans and colonial ascidians, increasing abundance of massive forms (e.g. mussels)

increases spatial complexity, flow retention, and thus the efficiency of resource capture of the

community (Gili and Coma, 1998). In this study, barnacles, bryozoans, colonial ascidians, and

mussels were used as functional types, but bryozoans and colonial ascidians were classified as

the same type (‘colonials’) because of similar size spectra and modularity (Table 1).

Additionally, at Helgoland we included tiles without suspension feeders but covered with

encrusting algae, which are common in these epibenthic assemblages (Wollgast et al., 2008).

9

Appendix

This was done to provide open settlement space and thus allow for potential effects of early

colonisers on filtration rates. ‘Open space’ was included as monoculture and also within the

mixture assemblages.

The organisms were obtained by exposing settlement tiles (25 × 25 mm Polyvinyl chloride)

to natural colonisation in the water column for 7-8 months. One side of each tile was roughened

to constant texture, on which larvae and propagules were allowed to settle. The other side of the

tile had a Velcro strip used to attach the tile onto a 100 × 100 mm PVC plate. Sixteen

(Helgoland) or nine (Plymouth) tiles were attached onto each plate so that each experimental

unit consisted of a flat grid with 16 or nine interchangeable subunits. The plates were vertically

submerged in the water during May 2006, and recovered on 14 December 2006 (Helgoland)

and 31 January 2007 (Plymouth) to assemble the experimental assemblages.

The tiles were detached from the plates and those tiles covered by a single functional type

were placed into the respective categories. Each monoculture was assembled by reattaching a

group of tiles covered by the same functional type on a plate. For both monocultures and

mixtures, tiles were randomly allocated within each plate. In order to facilitate attachment via

byssal threads, mussels were transplanted onto the tiles by either laying mussels for few days

on the tiles or gluing a short piece of fishing thread onto one of the valves of each mussel and

knotting it to the tile. By keeping the number of tiles constant across functional types, we

assured that replicates for the mixture treatment had the same functional composition.

We manipulated the factor density at Helgoland by varying the number of tiles on each plate:

the high density treatment consisted of plates with 16 tiles covered with organisms, while the

low density treatment consisted of plates with eight tiles covered with organisms plus eight

uncovered tiles. Tiles were suspended in the water column from 20-litre buoys or pontoons so

10

Paper II

that they were constantly immersed at a depth of 1 m below the sea surface and at least 4 m

above the seabed on low spring tides.

2.2. Laboratory determination of clearance rates

Grazing experiments were conducted in laboratory conditions to estimate the filtration rate of

the assemblages growing on the settlement plates. At Helgoland, two filtration rate assays were

conducted on 28 December 2006 and 10 January 2007. At Plymouth we conducted one assay

on 2 April 2007 because it took longer for the assemblages to develop. A second assay at

Plymouth was not possible because of deterioration of the organisms. Filtration rate was

measured as the volume of water cleared per unit of time from a mixed-culture microalgal

suspension. We used four algal strains from the Plymouth Algal Culture Collection:

Cryptomonas rostrella PLY405, Isochrysis galbana PLY680, Prorocentrum micans PLY97A,

and Tetraselmis suecica PLY305. These species will be hereafter referred to by genus.

Microalgae differed in size: Isochrysis (6 �m max length) < Tetraselmis (15 �m max length) <

Cryptomonas (25 �m max length) < Prorocentrum (40 �m max length). Isochrysis and

Tetraselmis, however, were considered as a single small-sized group because it was impossible

to accurately distinguish them in the suspension. Microalgae were cultured on Provasoli

medium in a climate-controlled room (15° C) in 12:12 h light: dark cycle of 50 �mol quanta s-1

m-2 (PAR).

For each assay, frames were taken out from the water and the plates were cut loose for

examination of filtration rates in the laboratory. Each plate was submerged for 1 h in an opaque

plastic container (1-litre volume) filled with 800 ml of microalgal suspension. Four containers

11

Appendix

that received 800 ml of the microalgal suspension but no plate served as consumer-free

controls. To measure the filtration rate of each epibenthic assemblage, 25 ml of macroalgal

suspension was taken before and after the assay.

In Helgoland the chlorophyll-a concentration of each suspension sample was measured with

a non-destructive fluorometric technique (BBE Cuvette Fluorometer, BBE Moldaenke GmbH

Germany). This instrument is equipped with light-emitting diodes to excite chlorophyll-a

fluorescence, which in turn is linearly related to the concentration of the pigment. Chlorophyll-

a concentrations measured with fluorometric methods are highly correlated to those measured

with HPLC (Beutler et al., 2002) and counts of cells (Lürling and Verschoor, 2003). In

Plymouth, we used a FACSort flow cytometer (Becton Dickinson, Oxford, UK) equipped with

a 15 mW laser exciting at 488 nm and with a standard filter set up. Specific phytoplankton

groups were discriminated by differences in side scatter and red/orange fluorescence. Flow

rates of the flow cytometer were calibrated daily using quality control beads (0.5 µm,

Polysciences) of a known concentration. Samples were analysed at high flow rate (~ 142 �l

min-1) to quantify algal size distribution and thus assess size-specific filtration rates. Average

initial Chlorophyll-a concentration at Helgoland was 1.41 �g l-1 and average initial number of

cells (pooling all phytoplankton species) at Plymouth was ca. 14000 cells ml-1 (Table 1). In

preliminary assays at Helgoland, this Chlorophyll-a concentration enabled us to detect

differences in filtration rate among functional types––lower concentrations were totally

consumed in one hour.

Filtration rates (m, ml h-1 g-1) based either on Chlorophyll-a concentration or number of cells

were calculated using the equation of Fox et al. (1937): m = M (b – a) (n t)-1, where M is the

volume of suspension (ml), a is the logarithmic decrement in the controls (i.e. LN(Ac, t=0) –

12

Paper II

LN(Ac, t=1), Ac, t=0 and Ac, t=1 being the concentration of suspension initially and after time t), b in

the logarithmic decrement in the test suspension (LN(AT, t=0) – LN(AT, t=1)), n is the wet biomass

(g) of each plate (wet weight of the entire experimental unit minus that of the PVC plate and

tiles), and t is the duration of the experiment (1 hour).

2.3. Statistical analysis

Analyses of variance (ANOVAs) were used to test the hypotheses that functional richness

increases feeding rate and that density influences the effect of richness. Planned contrasts were

used to separate the effects of functional richness from identity. Each of the two assays from

Helgoland was analysed using a 2-way ANOVA with the factors assemblage (5 levels: open

space, barnacles, colonials, mussels, or mixture) and density (2 levels: high or low) considered

fixed. Data from Plymouth were analysed using a 1-way ANOVA with the factor assemblage (4

levels: barnacles, colonials, mussels, or mixture) considered fixed. After the ANOVAs, the sum

of squares (SS) of the factor assemblage was partitioned into a planned contrast between the

mixture and the average response of the functional types in monoculture (richness effect). The

residual SS corresponded to the differences among the assemblages made up of a single

functional type and tested the effects of the functional type identity (Bruno et al., 2005).

Transgressive overyielding was then tested using planned contrasts between the mixture and the

best-performing functional type in monoculture. We tested the hypothesis that suspension

feeders have different patterns of resource consumption––i.e. there are differences in the

relative abundance of microalgal species consumed by the three functional types analysed at

Plymouth––using permutational multivariate analysis of variance (PERMANOVA; Anderson,

13

Appendix

2001). In this way, we tested for resource partitioning among functional types on the basis of

changes in the multivariate structure of prey (Griffin et al., 2008). At Plymouth, one replicate

was lost for the ‘colonials’ treatment because of severe weather and so we replaced this missing

value with the mean for the remaining plates of the treatment and one degree of freedom was

subtracted from the residual (Underwood, 1997). Data were square-root transformed to achieve

homogeneity of variances (tested with Levene’s test).

14

Paper II

3. Results

On average, final phytoplankton concentrations were 0.67 �g l-1 (Chlorophyll-a) at

Helgoland, and ca. 5500 cells ml-1 at Plymouth (Table 1). At both locations, mussels consumed

most of the phytoplankton in one hour; at Helgoland this group totally depleted the resource,

but 20 % of the decrease in phytoplankton concentration was probably due to cell precipitation

or cell damage (Table 1). At Helgoland, ca. 15 % of the microalgae was consumed by

suspension feeders that colonised the open tiles (Table 1).

The results from both assays conducted at Helgoland showed that the effects of functional

richness were negligible in comparison to the significant effects of identity (Table 2). In

addition, there was no support for transgressive overyielding, because consumption rates of the

mixtures were never higher than those of the best-performer monoculture (mussels). In the first

assay conducted at Helgoland, the clearance rate of the mixture was statistically equivalent to

that of mussels (F1, 46 = 0.08, p = 0.78; from planned contrast), while in the second assay the

performance of the mixture was significantly lower than the performance of mussels (F1, 46 =

18.12, p < 0.01; from planned contrast).

Density effects were significant only in the first Helgoland assay (Helgoland 1 in Table 2).

The filtration rates of the high density assemblages were higher than those of the low density

assemblages (Fig. 1). A non-significant interaction term indicated that the effects of functional

richness and identity were independent from those of density (Table 2).

The results from the work conducted at Plymouth confirmed the trends from the experiment

at Helgoland and showed that the effect of functional richness was weak compared to that for

identity (Fig. 2, Table 2). Planned contrasts between the mixture and the best-performing

15

Appendix

monoculture showed no significant difference in filtration rates (i.e. we found no evidence for

transgressive overyielding; F1, 9 = 0.3, p = 0.59). Multivariate analyses revealed distinct patterns

in resource use among functional types (Fig 2, unbalanced PERMANOVA, pseudo-F2, 5 =

23.66, p < 0.01). Barnacles tended to prefer the larger microalgal species, while mussels

showed maximum filtration rates on microalgae from all three size classes.

16

Paper II

4. Discussion

Results from Helgoland and Plymouth suggest that efficiency in resource use was strongly

affected by the identity but not by the richness of functional types. Our findings are similar to

those from studies in terrestrial and marine habitats showing that identity and compositional

effects are common, but that richness effects and transgressive overyielding are more evasive

phenomena (Bruno et al., 2005; Hooper et al., 2005; O'Connor and Crowe, 2005; Bruno et al.,

2006). Our results might have important ecological implications, because they suggest that the

loss or gain of particular species has the strongest impact on the functioning of epibenthic

ecosystems instead of richness per se.

The high efficiency of the blue mussel in removing the majority of algae may have

minimised complementarity in the mixed assemblage and prevented a richness effect. Also, the

filtration rate of mussels probably explained that of the mixture assemblages, because the

performance of both treatments tended to be similar––i.e. a positive selection effect. However,

the assessment of the relative contribution of complementarity and selection effects (either

positive or negative) requires measuring the performance of each species in the mixture

(Stachowicz et al., 2007). Experiments using such techniques have shown that putative positive

selection effects (because mixtures yielded the same than the best-performing species) were

actually the outcome of resource complementarity being offset by strong negative selection of

the best-performing species (Bruno et al., 2005; Reusch et al., 2005; Bruno et al., 2006).

Negative selection effects occur when the competitive ability and functional impact of species

are weakly or negatively correlated (Loreau and Hector, 2001; Jiang et al., 2008). Such trade-

offs between competitive ability and functional impact are widespread in nature (e.g. keystone

17

Appendix

species; Lyons et al., 2005) and negative selection effects may be particularly frequent for other

than biomass functions (Jiang et al., 2008). Selection effects and positive species interactions

(including resource complementarity and facilitation) can operate simultaneously or

sequentially (Hooper et al., 2005).

On the other hand, we found that the effects of functional identity were independent of

density. In addition, in one assay density increased the consumption rate of monocultures and

mixtures, suggesting that there were enough resources to prevent strong consumptive

competition even at a high density of suspension feeders––if phytoplankton concentration

would have been a limiting factor, then increased density would have decreased the per capita

(per gram of tissue) consumption rates in monocultures because of consumptive competition

among conspecifics. The absence of strong competition, in terms of resource use, probably

contributed to the insignificant effects of functional richness on filtration rates. For example,

Griffin and collaborators (2008) showed that the effects of predator richness were detectable

only at high predator density where competitive interactions were intensified. Similarly, early

studies showing significant richness effects have been conducted usually in nutrient-poor

systems (e.g. Tilman et al., 2001), where consumptive competition was probably high.

Collectively, the results from these experiments stress the importance of competition in linking

diversity and ecosystem functioning.

Our experiments should be considered in context with natural assemblages. Even when we

sought to maximise the ecological relevance of the study by replicating the experiment at two

locations of the NE Atlantic, the actual impact of functional types on filtration rates may differ

from our results. For instance, predators were excluded by suspending the set up in the water

column. If predators are present, richness effects may be stronger due to selective predation on

18

Paper II

the dominant species (Duffy, 2002). Also, we used a maximum of six species, while sessile

epibenthic assemblages can contain more that 30 species (Wollgast et al., 2008). The richness

effect may be stronger at higher levels of species richness, as shown by several studies on

mobile grazers (Stachowicz et al., 2007); yet, richness effects at low number of species have

been shown by experiments on primary producers (Loreau et al., 2001; Tilman et al., 2001),

invertebrate predators (Griffin et al., 2008), and invertebrate suspension feeders (Cardinale et

al., 2002). Whether the effect of richness is greater at higher richness levels is still an open

question.

Despite these limitations, we have presented a novel approach to assess the role of resource

partitioning in the relationship between diversity and ecosystem functioning. The ultimate goal

is to predict the ecological consequences of the widespread human-driven alterations of biota;

our work therefore represents a small piece of a much larger puzzle.

Acknowledgements

Thanks are due to J. Ellrich and M. Stillfried for their support during the clearance rate

assays, to A. Kraberg and A. Wagner for helping in culturing the algal, and to K. Boos for

valuable discussion. Two anonymous reviewers improved an early version of this manuscript.

This work benefited from discussions with members of the MarBEF responsive mode project

BIOFUSE. Financial support by the Alfred-Wegener-Institute for Marine and Polar Research to

N.V. is acknowledged. Thanks to the University of Plymouth Diving and Marine Centre for

facilitating in-water trails in Plymouth.

References

19

Appendix

Anderson, M.J., 2001. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 26, 32-46.

Anger, K., 1978. Development of a subtidal epifaunal community at the island of Helgoland. Helg. Mar. Res. 31, 457-470.

Beutler, M., Wiltshire, K.H., Meyer, B., Moldaenke, C., Luring, C., Meyerhofer, M., Hansen, U.P., Dau, H., 2002. A fluorometric method for the differentiation of algal populations in vivo and in situ. Photosynth. Res. 72, 39-53.

Bruno, J.F., Boyer, K.E., Duffy, J.E., Lee, S.C., Kertesz, J.S., 2005. Effects of macroalgalspecies identity and richness on primary production in benthic marine communities. Ecol. Lett. 8, 1165-1174.

Bruno, J.F., Lee, S.C., Kertesz, J.S., Carpenter, R.C., Long, Z.T., Duffy, J.E., 2006. Partitioning the effects of algal species identity and richness on benthic marine primary production. Oikos 115, 170-178.

Byrnes, J.E., Reynolds, P.L., Stachowicz, J.J., 2007. Invasions and extinctions reshape coastal marine food webs. PLoS ONE 2, e295.

Cardinale, B.J., Palmer, M.A., 2002. Disturbance moderates biodiversity-ecosystem function relationships: Experimental evidence from caddisflies in stream mesocosms. Ecology 83, 1915-1927.

Cardinale, B.J., Palmer, M.A., Collins, S.L., 2002. Species diversity enhances ecosystemfunctioning through interspecific facilitation. Nature 415, 426-429.

Cardinale, B.J., Ives, A.R., Inchausti, P., 2004. Effects of species diversity on the primaryproductivity of ecosystems: extending our spatial and temporal scales of inference. Oikos 104, 437-450.

Cardinale, B.J., Srivastava, D.S., Duffy, J.E., Wright, J.P., Downing, A.L., Sankaran, M., Jouseau, C., 2006. Effects of biodiversity on the functioning of trophic groups and ecosystems. Nature 443, 989-992.

Cardinale, B.J., Wrigh, J.P., Cadotte, M.W., Carroll, I.T., Hector, A., Srivastava, D.S., Loreau, M., Weis, J.J., 2007. Impacts of plant diversity on biomass production increase through time because of species complementarity. P. Natl. Acad. Sci. USA 104, 18123-18128.

Chapin, F.S., Zavaleta, E.S., Eviner, V.T., Naylor, R.L., Vitousek, P.M., Reynolds, H.L., Hooper, D.U., Lavorel, S., Sala, O.E., Hobbie, S.E., Mack, M.C., Diaz, S., 2000. Consequences of changing biodiversity. Nature 405, 234-242.

Christensen, N.L., Bartuska, A.M., Brown, J.H., Carpenter, S., D'Antonio, C., Francis, R., Franklin, J.F., MacMahon, J.A., Noss, R.F., Parsons, D.J., Peterson, C.H., Turner, M.G.,Woodmansee, R.G., 1996. The report of the Ecological Society of America committeeon the scientific basis for ecosystem management. Ecol. Appl. 6, 665-691.

Crisp, D.J., 1964. An assessment of plankton grazing by barnacles. In: Crisp, D.J. (Ed.), Grazing in terrestrial and marine environments. Blackwell Scientific Publications,Oxford.

Duffy, J.E., 2002. Biodiversity and ecosystem function: the consumer connection. Oikos 99, 201-219.

Fox, D.L., Sverdrup, H.U., Cunningham, J.P., 1937. The rate of water propulsion by the California mussel. Biol. Bull. 72, 417-438.

Fridley, J.D., 2001. The influence of species diversity on ecosystem productivity: how, where, and why? Oikos 93, 514-526.

20

Paper II

Gili, J.M., Coma, R., 1998. Benthic suspension feeders: their paramount role in littoral marinefood webs. Trends Ecol. Evol. 13, 316-321.

Griffin, J.N., De la Haye, K.L., Hawkins, S.J., Thompson, R.C., Jenkins, S.R., 2008. Predator diversity and ecosystem functioning: Density modifies the effect of resource partitioning. Ecology 89, 298-305.

Hendriks, I.E., Duarte, C.M., 2008. Allocation of effort and imbalances in biodiversity research. J. Exp. Mar. Biol. Ecol. 360, 15-20.

Hooper, D.U., Chapin, I., F. S., Ewel, J.J., Hector, A., Inchausti, P., Lavorel, S., Lawton, J.H., Lodge, D.M., Loreau, M., Naeem, S., Schmid, B., Setälä, H., Symstad, A.J., Vandermeer, J., Wardle, D.A., 2005. Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecol. Monogr. 75, 3-35.

Huston, M.A., 1997. Hidden treatments in ecological experiments: re-evaluating the ecosystemfunction of biodiversity. Oecologia 110, 449-460.

Jiang, L., Pu, Z., Nemergut, D.R., 2008. On the importance of the negative selection effect for the relationship between biodiversity and ecosystem functioning. Oikos 117, 488-493.

Lesser, M.P., Witman, J.D., Sebens, K.P., 1994. Effects of flow and seston availability on scope for growth of benthic suspension-feeding invertebrates from the gulf of maine. Biol. Bull. 187, 319-335.

Loreau, M., 1998. Separating sampling and other effects in biodiversity experiments. Oikos 82, 600-602.

Loreau, M., 2000. Biodiversity and ecosystem functioning: recent theoretical advances. Oikos 91, 3-17.

Loreau, M., Hector, A., 2001. Partitioning selection and complementarity in biodiversity experiments. Nature 412, 72-76.

Loreau, M., Naeem, S., Inchausti, P., Bengtsson, J., Grime, J.P., Hector, A., Hooper, D.U., Huston, M.A., Raffaelli, D., Schmid, B., Tilman, D., Wardle, D.A., 2001. Ecology - Biodiversity and ecosystem functioning: Current knowledge and future challenges. Science 294, 804-808.

Lürling, M., Verschoor, A.M., 2003. F0-spectra of chlorophyll fluorescence for the determination of zooplankton grazing. Hydrobiologia 491, 145-157.

Lyons, K.G., Brigham, C.A., Traut, B.H., Schwartz, M.W., 2005. Rare species and ecosystemfunctioning. Conserv. Biol. 19, 1019-1024.

Navarrete, S.A., Wieters, E.A., Broitman, B.R., Castilla, J.C., 2005. Scales of benthic-pelagic coupling and the intensity of species interactions: From recruitment limitation to top-down control. P. Natl. Acad. Sci. USA 102, 18046-18051.

Nielsen, T.G., Maar, M., 2007. Effects of a blue mussel Mytilus edulis bed on vertical distribution and composition of the pelagic food web. Mar. Ecol. Prog. Ser. 339, 185-198.

O'Connor, N.E., Crowe, T.P., 2005. Biodiversity loss and ecosystem functioning: distinguishing between number and identity of species. Ecology 86, 1783-1796.

Pascoe, P.L., Parry, H.E., Hawkins, A.J.S., 2007. Dynamic filter-feeding responses in fouling organisms. Aquat. Biol. 1, 177-185.

Petersen, J.K., 2007. Ascidian suspension feeding. J. Exp. Mar. Biol. Ecol. 342, 127-137. Reusch, T.B.H., Ehlers, A., Hammerli, A., Worm, B., 2005. Ecosystem recovery after climatic

extremes enhanced by genotypic diversity. P. Natl. Acad. Sci. USA 102, 2826-2831.

21

Appendix

Riisgård, H.U., Larsen, P.S., 2000. Comparative ecophysiology of active zoobenthic filter feeding, essence of current knowledge. J. Sea. Res. 44, 169-193.

Rouillon, G., Navarro, E., 2003. Differential utilization of species of phytoplankton by the mussel Mytilus edulis. Acta. Oecol. 24, S299-S305.

Schwartz, M.W., Brigham, C.A., Hoeksema, J.D., Lyons, K.G., Mills, M.H., van Mantgem,P.J., 2000. Linking biodiversity to ecosystem function: implications for conservation ecology. Oecologia 122, 297-305.

Stachowicz, J.J., Bruno, J.F., Duffy, J.E., 2007. Understanding the effects of marinebiodiversity on communities and ecosystems. Annu. Rev. Ecol. Evol. S. 38, 739-766.

Thomas, J.A., Telfer, M.G., Roy, D.B., Preston, C.D., Greenwood, J.J.D., Asher, J., Fox, R., Clarke, R.T., Lawton, J.H., 2004. Comparative losses of British butterflies, birds, and plants and the global extinction crisis. Science 303, 1879-1881.

Tilman, D., Lehman, C.L., Thomson, K.T., 1997. Plant diversity and ecosystem productivity: Theoretical considerations. P. Natl. Acad. Sci. USA 94, 1857-1861.

Tilman, D., Reich, P.B., Knops, J., Wedin, D., Mielke, T., Lehman, C., 2001. Diversity and productivity in a long-term grassland experiment. Science 294, 843-845.

Underwood, A.J., 1997. Experiments in ecology. Their logical design and interpretation using analysis of variance. Cambridge University Press, Cambridge, 504 pp.

Weis, J.J., Cardinale, B.J., Forshay, K.J., Ives, A.R., 2007. Effects of species diversity on community biomass production change over the course of succession. Ecology 88, 929-939.

Wollgast, S., Lenz, M., Wahl, M., Molis, M., 2008. Effects of regular and irregular temporalpatterns of disturbance on biomass accrual and species composition of a subtidal hard-bottom assemblage. Helg. Mar. Res. 62, 309-319.

22

Paper II

Figure legends

Fig. 1. Results of two assays conducted at Helgoland to test the effects of functional richness,

identity, and density on the feeding efficiency of epibiota. Outcomes of statistical tests are

given in Table 2. Values are given as mean ± SEM (n = 5).

Fig. 2. Results of the experiment at Plymouth to investigate the rate of filtration of each

microalga for individual suspension feeders and a mixed assemblage. Each replicate containing

either a monoculture or a mixture was supplied with a mixed suspension containing all

microalgae in equal proportions. Outcomes of statistical tests are given in Table 2. Values are

given as mean ± SEM (n = 3).

23

App

endi

x

Tab

le 1

Spec

ies o

f sus

pens

ion-

feed

ing

inve

rtebr

ates

gro

uped

as f

unct

iona

l typ

es a

ccor

ding

to m

odul

arity

, siz

e of

indi

vidu

als,

and

feed

ing

mod

e (A

).

Ave

rage

con

cent

ratio

n of

phy

topl

ankt

on in

itial

ly (t

0) a

nd o

ne h

our a

fter (

t 1) th

e fil

tratio

n ra

te a

ssay

s (B

). Th

e pe

rcen

t fro

m in

itial

phy

topl

ankt

on

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entra

tion

cons

umed

in o

ne h

our b

y ea

ch fu

nctio

nal t

ype

and

mix

ture

sis g

iven

(%-r

emov

ed).

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n sp

ace

corr

espo

nds t

o til

es c

over

ed b

y

encr

ustin

g al

gae.

Con

trols

wer

e us

ed to

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ntify

the

chan

ges i

n m

icro

alga

l con

cent

ratio

n th

at c

an n

ot b

e ex

plai

ned

by fi

ltrat

ion

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ity (e

.g. c

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tion

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. 1So

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: Riis

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(200

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ill fi

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ents

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(Chl

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[cel

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t 0t 1

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-rem

oved

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5 14

189

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526

Col

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ls1.

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54 6

1 13

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315

0 76

Mus

sels

1.43

0.00

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1491

4

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96

Mix

ture

1.43

0.49

65

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3

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94

24

Pape

r II

Ope

n sp

ace

1.49

0.98

34

N

I

NI

NI

Con

trols

1.37

1.09

20

1430

912

591

12

25

Appendix

Table 2

ANOVAs of three experiments testing the effect of invertebrate functional richness and

identity on community-level filtration rates. The factor assemblage included three or four

functional types of suspension feeders in monocultures and one mixed assemblage. At

Helgoland, density (either low or high) was included as an orthogonal factor. Richness and

identity effects were tested with orthogonal planned contrasts (shown indented). Data were

square-root transformed before the analysis to ensure homogeneity of variances (Levene’s

test, P > 0.05)

Source of variation SS df MS F P

Helgoland 1 Assemblage 15.663 4 3.916 8.642 <0.0001 Richness 1.559 1 1.559 2.092 0.1546 Identity 14.104 3 4.701 10.376 <0.0001Density 1.970 1 1.970 4.347 0.0435Density × Assemblage 1.575 4 0.394 0.869 0.4910 Density × Richness 0.475 1 0.475 1.049 0.3118 Density × Identity 1.099 3 0.366 0.809 0.4965Residual 18.124 40 0.453

Helgoland 2 Assemblage 22.504 4 5.626 27.945 <0.0001 Richness 0.070 1 0.070 0.107 0.7450 Identity 22.434 3 7.478 37.149 <0.0001Density 0.230 1 0.230 1.140 0.2920Density × Assemblage 0.663 4 0.166 0.824 0.5179 Density × Richness 0.347 1 0.347 0.518 0.4755 Density × Identity 0.317 3 0.106 0.525 0.6679Residual 8.053 40 0.201

PlymouthAssemblage 149.913 3 49.971 36.398 <0.0001 Richness 19.544 1 19.544 1.383 0.2669 Identity 130.369 2 65.185 41.545 0.0001Residual 10.983 7a 1.569

a Degrees of freedom were corrected for missing data

26

Paper II

0

2

4

6

8

10

0

2

4

6

8

10

Assay 1

Assay 2

Bar

nacl

es

Col

onia

ls

Ope

n sp

ace

Mus

sels

Mix

ture

Low densityHigh density

Cle

aran

ce ra

te [m

l h-1

g-1 ]

Ave

rage

mon

ocul

ture

Fig. 1. Valdivia et al

27

Appendix

0

20

40

60

80

100C

lear

ance

rate

[ml h

-1g-

1 ]

Bar

nacl

es

Col

onia

ls

Mus

sels

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ture

Isochrysis-Tetraselmis complexCryptomonasProrocentrum

Ave

rage

mon

ocul

ture


28

Paper III

Nelson Valdivia*, and Markus Molis (Under review) Species compensation buffers community

stability against the loss of an intertidal habitat-forming rockweed. Marine Ecology Progress

Series





Tel. ++49(0)47258193294

Fax ++49(0)47258193283

Appendix

2

Paper III

ABSTRACT: Most of the research on ecosystem stability has focused on the relationship

between biodiversity and community temporal variability, yet the role of biotic interactions in

maintaining stability has received less attention. Compensatory changes in species populations,

such that the abundances of some species increase while those of others decrease when the

environment changes, can maintain a steady state between resource supply and resource

consumption. In a fluctuating environment, therefore, the variability of species abundances may

be larger than the variability of the community abundance. Here, we show that removal of a key

structural component of hard-bottom communities, the canopy-forming alga Fucus serratus,

significantly increased the temporal variability of populations but not of communities. Results

from factorial experiments replicated at two shores of Helgoland Island, NE Atlantic,

consistently suggest that environmental changes resulting from the canopy removal triggered

compensatory dynamics of species with differing functional traits. The provision of additional

open substratum (i.e. additional mechanical disturbances) did not influence these patterns.

These results agree with previous studies involving the removal of canopy-forming species, but

contradict recent analyses suggesting that species compensation is rare in several ecosystems.

We suggest that compensatory dynamics will have a critical role in maintaining the stability of

systems where biological habitat amelioration has both positive and negative effects on other

species.

Key words: bioengineering, canopy, disturbance, Fucus serratus, intertidal ecology, temporal

variability, species compensation, stability

3

Appendix

INTRODUCTION

Stability is a fundamental aspect of ecological systems (Worm & Duffy 2003). In particular,

ecosystem stability can be highly valuable because of the societal dependence on the good and

service that ecosystems provide to mankind (Costanza et al. 1997, Armsworth & Roughgarden

2003). Accordingly, it is not surprising that the stability of ecological processes–such as the

temporal persistence of communities and their resistance to environmental change–had drawn

the attention of ecologists and policymakers for decades (e.g. MacArthur 1955, Christensen et

al. 1996). Studies on ecosystem stability have focused on the effects of decreasing diversity on

the temporal variability of community and population properties, such as biomass and resource

use (e.g. Tilman & Downing 1994, McCann 2000, Ptacnik et al. 2008). However, the effects of

biotic interactions on ecosystem stability have received less attention (reviewed by Hooper et

al. 2005).

Models on the effect of biotic interactions on ecosystem processes predict that stability is

maintained via compensatory population dynamics, such that the contribution of some species

to ecosystem properties decreases while that of others increases (e.g. Austin & Cook 1974, Ives

et al. 1999). This occurs because species respond differently to environmental changes, thus the

role of stressed or disturbed species may be assumed by unharmed species if the latter provide

lost functional traits (Yachi & Loreau 1999). Species compensation can be revealed by a high

temporal variability of species abundances relative to the variability of the community

abundance, and also by prevalent negative covariances within the community (Schluter 1984,

Micheli et al. 1999). These ideas however have been tested by relatively few empirical studies

4

Paper III

(e.g. Ernest & Brown 2001, Bai et al. 2004, Vasseur & Gaedke 2007), and recent analyses

suggest that species compensation is actually rare in real communities (Houlahan et al. 2007).

Habitat-forming species provide an opportunity to test whether species compensation occurs

within natural communities. Canopy-forming algae are key structural elements on temperate

rocky shores, where they modify the environment and facilitate or suppress other species (e.g.

Irving & Connell 2006, Lilley & Schiel 2006, Morrow & Carpenter 2008). Under the stressful

conditions that characterise the intertidal zone, canopy-forming algae can ameliorate the habitat

by shading, reducing desiccation, and buffering temperatures (Bertness et al. 1999, Lilley &

Schiel 2006). In addition, canopies can also reduce the accumulation of sediments (Kennelly &

Underwood 1993). If the canopy-mediated habitat amelioration facilitates some species but

suppress others, then species compensation may be recurrent within these assemblages.

However, these effects of canopies on community stability may interact with those of other

factors. Space available for settlement is often a limiting resource for sessile epibenthic

organisms (Connell 1961, Connolly & Muko 2003), but it can be provided by mechanical

disturbances that remove biomass from the community (Shea et al. 2004). Since canopies may

limit the subset of species that are able to colonise the substratum (Jenkins et al. 2004), the

effects of disturbances on the understorey may be stronger after the removal of the canopy-

forming species. On the other hand, the putative effects of canopy removal can be exacerbated

by additional provisions of settlement space. Therefore, the effect of canopy removal on

community stability may depend on disturbance and vice versa.

Here, we present the results of a manipulative field study on the effect of the canopy-forming

algae Fucus serratus (here after Fucus) on the stability of intertidal hard-bottom communities.

We explored (1) whether the removal of Fucus affects the temporal variability of community

5

Appendix

abundance and species populations, and (2) whether these effects depend on mechanical

disturbances that provide additional space. Factorial experiments were replicated at two

intertidal sites of different wave exposure to test for the generality of patterns. We found that

the removal of Fucus consistently increased the variability of species populations without

affecting the variability of communities, because of compensatory dynamics of species with

different functional traits.

MATERIALS AND METHODS

Study sites. The experiment was replicated on two differently wave-exposed rocky shores in

the natural reserve of Helgoland Island (North Sea, NE Atlantic). ‘Westwatt’ is exposed to

strong prevailing south-westerly winds, while ‘Nordostwatt’ is protected by a 250 m long

concrete jetty. Losses in dry mass of domes (Ø at base = 62 cm) made of Plaster of Paris that

were deployed during two 3-day periods (20 – 23 April 2007 and 8 – 11 June 2007) were

significantly higher at Westwatt than at Nordostwatt (repeated measures ANOVA: F1, 8 =

13.29, P < 0.01), irrespectively of the period (F1, 8 = 1.92, p = 0.2).

At both sites, dense Fucus stands extend from the lower intertidal to the shallow sublittoral.

The understorey of the Fucus beds is dominated by encrusting coralline algae (mostly

Phymatolithon spp.), and the turf-forming algae Cladophora rupestris, Chondrus crispus, and

Corallina officinalis (Bartsch & Tittley 2004). The most frequent sessile invertebrates are

Dynamena pumila, Spirorbis spirorbis, and Electra pilosa, while conspicuous mobile

consumers include Carcinus maenas and several species of periwinkles (Reichert & Buchholz

2006). During spring and summer, foliose and filamentous ephemeral algae like Ulva spp.,

6

Paper III

Dumontia contorta, Ectocarpales, and seasonal Cladophorales are abundant in gaps between

Fucus patches (Janke 1990).

Experimental design and set-up. During March 2006, twenty 0.3 × 0.3 m² plots with Fucus

cover of ca. 90 % were permanently marked with stainless screws at each site. This small plot

size was used to minimise the impact of our manipulations on the natural Fucus population.

Nevertheless, plots of the same size were also used in other experiments involving the removal

of fucoid canopies (Moore et al. 2007). In each site, holdfasts and erect fronds of Fucus were

removed from 10 randomly selected plots using a knife although avoiding damage the

understorey. Edge effects in the removal plots were avoided by trimming all Fucus plants

within a margin of ca. 40 cm wide along each plot. Fucus recruits were regularly removed

throughout the 18-month study. Mechanical disturbance treatments, consisting of a biomass

removal with 50 % of the effort required to remove all organisms of the plot, were randomly

applied to half of the canopy-present plots and half of the canopy removal plots. Thirty-six

passages of a 2 cm wide chisel were needed to remove all organisms (excluding encrusting

algae and organisms occurring in small crevices) from a 0.09 cm2 area. Eighteen passages of

the chisel were applied on each disturbed plot therefore. The cover of bare rock, measured 1-3

days after applying the disturbances, significantly increased as result of the treatment (F1, 32 =

5.04, p = 0.03). All manipulations were conducted during low tide.

Sampling. The percent cover of each macrobenthic species was estimated per plot to the

nearest 1 % by the same observer before and 1-3 days after the manipulations in March 2006.

Species covers were subsequently estimated every 3 months following the initial sampling. Due

to the multilayered structure of the assemblages, total percent cover could well exceed 100 %.

Species with <1 % cover were uniformly recorded with 0.5 % abundance. Using the same

7

Appendix

method, we quantified the percent cover of particulate material (sediment) on each plot. Visual

estimation of percent surface cover of sediment is one of the principal methods used to quantify

sedimentation in rocky intertidal habitats (Airoldi 2003). Taxa were identified to the lowest

possible taxonomic level in the field, but ambiguous taxa were identified in the laboratory using

samples collected from areas adjacent to the plots. Four taxa were identified to genus level:

Porphyra sp., Phymatolithon spp., Sagartia spp., and Ulva spp. Small burrowing spionids were

grouped as family Spionidae and Ectocarpales were identified to order level. For some

analyses, species were grouped into five functional types: (1) ‘encrusting algae’, comprising

Haemescharia hennedyi, Hildenbrandia rubra, and Phymatolithon spp.; (2) ‘ephemeral algae’,

dominated by Ulva spp., Cladophora sericea, Dumontia contorta, and Ectocarpales; (3) ‘turf-

forming algae’, dominated by Chondrus crispus, Cladophora rupestris, Corallina officinalis,

and Mastocarpus stellatus; (4) ‘sessile invertebrates’, dominated by Dynamena pumila,

Sagartiogeton laceratus, Semibalanus balanoides, Spionidae spp., and Spirorbis spirorbis; and

(5) ‘mobile consumers’, dominated by Littorina obtusata and Littorina littorea. The congeneric

species C. sericea and C. rupestris were classified as different functional types because of

different life histories. Cladophora sericea is a seasonal species, while C. rupestris is perennial

(Bartsch & Tittley 2004).

Statistical analyses. We tested the separate and interactive effects of canopy removal and

mechanical disturbance on two facets of community variability: the variability of community

abundance and the variability of species populations. Both types of variance were calculated

from the 7 repeated measures of cover of each taxon over the course of the experiment (18

months). A single value per type of variance was calculated for each plot and these values were

then considered independent in the analyses. We calculated the temporal variance in total

8

Paper III

community cover, which in turn can be expressed as the sum of all of the species variances plus

the sum of all of the pair-wise species covariances (Schluter 1984). This partition provides a

useful tool for detecting compensatory dynamics within communities (Tilman 1999, Houlahan

et al. 2007). We used the variance and summed variance as measures of community-level

variability, and the summed covariance as a measure of population-level variability and

compensation. If species undergo compensatory dynamics, then the summed covariance of all

species-pairs will be negative. In addition, we explored the temporal variability in the identity

and relative abundance of the component species (i.e. species composition). For each plot, we

calculated a Bray-Curtis dissimilarity matrix across all sample dates. The average of the Bray-

Curtis matrix provided a single value of compositional variability per plot. The effects of

canopy removal and disturbance on each of the measures of temporal variability were analysed

using 3-way mixed ANOVA with the factor site (2 levels: Nordostwatt or Westwatt) considered

random, and the factors canopy (2 levels: present or removed) and disturbance (2 levels:

undisturbed or disturbed) both considered fixed. Homogeneity of variances was graphically

explored and tested using Cochran’s test; when necessary data were ln-transformed to meet the

assumptions. Tukey HSD post-hoc test was used for unplanned comparisons.

We showed the temporal changes in species composition using non-metric multivariate

(nMDS) ordination plots. For each site, we separately plotted centroids of the 28 time (7 levels)

× canopy (2 levels) × disturbance (2 levels) cells, because there were too many observation

points to view in a single ordination. Centroids were computed as the averages of principal

coordinates calculated from Bray-Curtis matrices. nMDS ordinations were then plotted on the

basis of the Euclidean distance between each pair of centroids (see Terlizzi et al. 2005 for

details).

9

Appendix

Finally, we conducted a redundancy analysis (RDA) to show the correlation between pairs of

functional types in function of the experimental factors. In addition, we included the percent

cover of sediment as response variable. RDA focuses on the relationship between two sets of

variables (i.e. the matrix of functional types and the matrix of factors) as summarised in a

matrix of regression coefficients. The results of the RDA were visualised in a correlation bi-

plot, in which each response variable (i.e. each functional type and sediment) was standardised

to zero mean and unit variance (Ter Braak & Looman 1994). All statistical analyses were

conducted using the R environment version 2.7.2 (R Development Core Team 2008). nMDS

ordinations were plotted using PRIMER version 5.

RESULTS

The variability at the community level was neither affected by canopy removal nor by

disturbance (Fig. 1a, b; Table 1). The variance of total community cover (ln-transformed) was

independent of the factor site, while the summed variance was significantly higher at

Nordostwatt than at Westwatt (Fig. 1a, b; Table 1).

Canopy removal strongly affected the variability of species populations. On the one hand, the

summed covariance became significantly more negative as result of the canopy removal,

irrespectively of site and disturbance (Fig. 1c, Table 2). On the other hand, the variability of

species composition (Bray-Curtis dissimilarities) was influenced by canopy removal and site

(Fig. 1d, Table 2). Separate 2-way ANOVAs conducted for each site showed that canopy

removal significantly increased the Bray-Curtis dissimilarities at Nordostwatt (Fig. 1d; F1, 16 =

10.69, p < 0.01) irrespectively of disturbance (F1, 16 < 0.01, p = 0.94). At Westwatt, however, a

10

Paper III

significant canopy by disturbance interaction (Fig. 1d, F1, 16 = 6.13, p = 0.02) indicated that the

destabilising effect of disturbance was significant only on the canopy removal plots (Fig 1d, p =

0.03; from Tukey HSD multiple comparisons).

The nMDS plots show the strong influence of canopy removal on the multivariate structure

of these assemblages (Fig. 2). At both sites, temporal changes of species composition were

larger on canopy-removed plots than on canopy-present plots. These patterns showed a seasonal

component, as communities tended to diverge during summer months (labels 2 and 6 in Fig. 2),

but to converge during the winter and early spring months (labels 1, 4 and 5 in Fig. 2).

The response of species composition to the canopy removal came from the differing patterns

of functional types and it was also related to changes in the physical environment (Fig. 3). The

patterns were generally similar between sites (Fig. 3). The RDA bi-plots show that the highest

abundances of encrusting algae and sessile invertebrates occurred on the canopy-present plots,

but the lowest on the removal plots. Conversely, highest abundances of ephemeral algae and

sediments occurred on the removal plots, but the lowest on the canopy-present plots.

Accordingly, there was a strong negative correlation between encrusting and ephemeral algae,

and there were strong positive correlations between encrusting algae and sessile invertebrates

and between ephemeral algae and sediments (Fig. 3). This analysis also detected a seasonal

pattern: the largest abundances of encrusting algae and sessile invertebrates occurred during

early spring, autumn, and winter (labels 1, 3, and 4 in Fig. 3), while the largest abundances of

ephemerals occurred during summer and the spring 2007 (labels 5 and 6 in Fig. 3). Turf-

forming algae and mobile consumers were not clearly related to the canopy treatments. On the

other hand, there was no consistent relationship between the disturbance treatments and the

11

Appendix

abundance of functional types and sediment. For example, mobile consumers increased during

the first summer in the disturbed plots, but only at one site (label 2 in Fig. 3a).

DISCUSSION

Here we have shown that the removal of a key dominant species, Fucus serratus, significantly

increased the variability in species populations without affecting the variability in community

abundance. Negative covariances were persistent within the communities, and they became

more negative due to the canopy removal. This indicates that compensatory dynamics, such that

the abundance of some species increases while that of others decreases, were strengthened by

the canopy removal. The removal of Fucus encouraged ephemeral algae to proliferate, but it

discouraged encrusting algae and sessile invertebrates. Additional provisions of free space

(mechanical disturbances) had limited effects on community and population variability.

Experiments replicated at two sites showed virtually the same patterns of variability, suggesting

that the effects of canopy removal can be consistent across the spatial variability of this system.

These results suggest that compensatory dynamics maintain the community stability when

bioengineering has differing (positive and negative) effects on other species.

Compensatory responses to biological habitat amelioration

Species compensation was due to the canopy-mediated changes in the physical habitat. The

removal of Fucus increased the cover of sediments, which in turn can be an important source of

stress for hard-bottom communities (Airoldi 2003). In the intertidal rocky shores of Helgoland,

12

Paper III

additionally, the removal of Fucus can increase the understorey temperature in 9° C and the

amount of irradiance in 400 % in relation to areas covered by canopies (D. Kohlmeier & K.

Bischof, unpubl. data). Moreover, the canopy-mediated amelioration of osmotic stressors like

temperature and water evaporation is well documented in the literature (e.g. Bertness et al.

1999, Lilley & Schiel 2006, Moore et al. 2007). Increased light and sedimentation led to

blooms of ephemeral algae, as shown in previous work on intertidal rocky habitats (e.g. Airoldi

2003, Lilley & Schiel 2006). At the same time, these changes in the physical conditions

probably had negative effects on the fitness of encrusting algae and sessile invertebrates. The

development of encrusting coralline algae requires shaded conditions, and probably also

sediment-free substrate (Steneck 1986, Connell 2003). Similarly, several species of sessile

invertebrates, such as sea anemones and colonial bryozoans, prefer shaded microhabitats in the

intertidal at Helgoland (Janke 1986) and perhaps do benefit from the habitat amelioration by

Fucus during periods of high thermal and irradiance stress. On the other hand, canopies can

control the abundance of grazers (Bertness et al. 1999, Jenkins et al. 2004), which in turn can

strongly influence community structure by controlling algae abundances (Aguilera & Navarrete

2007). In our study, nevertheless, the abundance of consumers was not affected by the canopy

removal. Therefore, it is likely that compensatory dynamics were related to changes in abiotic

conditions but unrelated to changes in the strength of consumption. Similar non-trophic habitat

associations have been documented by work on subtidal, intertidal, and terrestrial canopies (e.g.

Irving & Connell 2006, Lilley & Schiel 2006, Felton et al. 2008).

The differing effect of biological habitat amelioration led to compensatory dynamics that

maintained the stability of community abundance, in accordance with analyses of time series

showing high variability of populations but low variability of communities (Ernest & Brown

13

Appendix

2001, Bai et al. 2004, Vasseur & Gaedke 2007). However, the importance of species

compensation might vary with the system studied. For example Houlahan et al. (2007), who

quantified the prevalence of negative covariance in a range of ecosystems, sustain that negative

covariances are rare in comparison to positive covariances. The authors suggest that community

stability is driven by mechanisms causing species to covary positively, such as similar

responses to environmental changes. In addition, parallel effects of bioengineering and/or

asynchronous species fluctuations can lead to other than compensatory patterns (Micheli et al.

1999). Parallel effects of bioengineering can occur in highly stressed habitats, such as high

intertidal zones, where the removal of a habitat-forming species will have a negative effect on

most of the species (Bertness & Callaway 1994). Accordingly, we would anticipate that higher

on the shore species compensation might have a limited effect on stability if canopies are lost.

In systems where species compensation is unimportant, ecological and statistical mechanisms

related to the number of species can influence the stability of communities (e.g. Tilman et al.

2006). Increasing community abundance due to resource complementarity and/or facilitation

(i.e. over-yielding), and decreasing summed variances due to decreasing abundances of

individual species (i.e. portfolio effect) can increase stability as diversity increases (Lehman &

Tilman 2000).

Effects of disturbances on community variability

The limited effect of mechanical disturbances on community variability contrasts with the

results from other experiments in aquatic and terrestrial ecosystems (e.g. Tilman 1996, Bertocci

et al. 2007, Brown 2007). We used in this study punctual destructive events within a 0.09 m2

14

Paper III

area, while others have used disturbances at larger spatial scales. For instance, Tilman (1996)

and Brown (2007) tested the effects of droughts on the variability of grassland and stream

macroinvertebrate communities, respectively; Bertocci et al. (2007) directly manipulated the

aerial exposure of entire intertidal communities. Probably, we disturbed the communities at a

scale too small to provoke a significant effect on recruitment via provision of empty space.

Small patches can quickly develop stable assemblages, because they are re-colonised by both

larval dispersal and lateral expansion of adults (Underwood & Chapman 2006). An alternative

explanation to the lack of disturbance effect is the strong propagule pressure at Helgoland

(Janke 1990). Removal of canopies allowed the massive settlement of opportunistic species

during spring and summer. Intense settlement might have overrode the potential effects of

disturbance-generated patches (Berlow 1997), resulting in a weak interaction between

disturbance and canopy. Moreover, experimental work in Helgoland has shown that even

highly frequent events of destruction have limited effects on the diversity of epibenthic

assemblages (Wollgast et al. 2008). The potential effect of mechanical disturbances on the

stability of this system still needs further attention.

Conclusion

Canopy removal increased the variability of species populations, but compensatory mechanisms

buffered the variability of community abundance. Although most research on community

variability is based on long-term time series (e.g. Ernest & Brown 2001, Bai et al. 2004, Hobbs

et al. 2007, Vasseur & Gaedke 2007), our short-term experiment showed communities

responding quickly to the manipulations of Fucus (after 3 months). This fast response is similar

15

Appendix

to those in studies involving the removal of canopies in other latitudes (e.g. Benedetti-Cecchi et

al. 2001, Lilley & Schiel 2006). On the other hand, the lack of response of the community-level

variability does not mean that Fucus serratus is unimportant in this system. Populations became

more variable due to the loss of canopies, which can increase the risk of extinctions and limit

the long-term persistence of the community (Pimm 1991). We suggest that compensatory

dynamics will have a critical role in maintaining the stability of systems where biological

habitat amelioration has opposing effects on other species.

Acknowledgements. We are grateful to a number of friends and colleagues who enthusiastically

helped during long hours of field work, including S. Domisch, J. Ellrich, A. Engel, M. Honens,

M. Marklewitz, A. Wagner, and H.Y. Yun. Comments by I. Bartsch, A. Perez-Matus and M.

Wahl greatly improved an early version of this manuscript. Part of the experimental data was

generated in the frame of the MarBEF responsive mode project BIOFUSE. Financial support by

the Alfred-Wegener-Institute for Marine and Polar Research to N.V. is acknowledged.

16

Paper III

LITERATURE CITED

Aguilera MA, Navarrete SA (2007) Effects of Chiton granosus (Frembly, 1827) and other molluscan grazers on algal succession in wave exposed mid-intertidal rocky shores of central Chile. J Exp Mar Biol Ecol 349:84-98

Airoldi L (2003) The effects of sedimentation on rocky coast assemblages. Oceanogr Mar Biol41:161-236

Armsworth PR, Roughgarden JE (2003) The economic value of ecological stability. P Natl Acad Sci USA 100:7147-7151

Austin MP, Cook BG (1974) Ecosystem stability: A result from an abstract simulation. Journal of Theoretical Biology 45:435-458

Bai YF, Han XG, Wu JG, Chen ZZ, Li LH (2004) Ecosystem stability and compensatoryeffects in the Inner Mongolia grassland. Nature 431:181-184

Bartsch I, Tittley I (2004) The rocky intertidal biotopes of Helgoland: present and past. Helg Mar Res 58:289-302

Benedetti-Cecchi L, Pannacciulli F, Bulleri F, Moschella PS, Airoldi L, Relini G, Cinelli F (2001) Predicting the consequences of anthropogenic disturbance: large-scale effects of loss of canopy algae on rocky shores. Mar Ecol Prog Ser 214:137-150

Berlow EL (1997) From canalisation to contingency: historical effects in a successional rocky intertidal community. Ecol Monogr 67:435-460

Bertness D, Leonard G, Levine J, Schmidt P, Ingraham A (1999) Testing the relative contribution of positive and negative interactions in rocky intertidal communities.Ecology 80:2711-2726

Bertness MD, Callaway R (1994) Positive interactions in communities. Trends Ecol Evol 9:191-193

Bertocci I, Vaselli S, Maggi E, Benedetti-Cecchi L (2007) Changes in temporal variance of rocky shore organism abundances in response to manipulation of mean intensity and temporal variability of aerial exposure. Mar Ecol Prog Ser 338:11-20

Brown BL (2007) Habitat heterogeneity and disturbance influence patterns of community temporal variability in a small temperate stream. Hydrobiologia 586:93-106

Christensen NL, Bartuska AM, Brown JH, Carpenter S, D'Antonio C, Francis R, Franklin JF, MacMahon JA, Noss RF, Parsons DJ, Peterson CH, Turner MG, Woodmansee RG (1996) The report of the Ecological Society of America committee on the scientificbasis for ecosystem management. Ecol Appl 6:665-691

Connell JH (1961) The influence of interspecific competition and other factors on the distribution of the barnacle Chathamalus stellatus. Ecology 42:710-723

Connell SD (2003) The monopolization of understorey habitat by subtidal encrusting coralline algae: a test of the combined effects of canopy-mediated light and sedimentation. Mar Biol 142:1065-1071

Connolly SR, Muko S (2003) Space preemption, size-dependent competition, and the coexistence of clonal growth forms. Ecology 84:2979-2298

Costanza R, dArge R, deGroot R, Farber S, Grasso M, Hannon B, Limburg K, Naeem S, ONeill RV, Paruelo J, Raskin RG, Sutton P, van den Belt M (1997) The value of the world'secosystem services and natural capital. Nature 387:253-260

17

Appendix

Ernest SKM, Brown JH (2001) Homeostasis and compensation: the role of species and resources in ecosystem stability. Ecology 82:2118-2132

Felton A, Wood J, Felton AM, Hennessey B, Lindenmayer DB (2008) Bird community responses to reduced-impact logging in a certified forestry concession in lowland Bolivia. Biol Conserv 141:545-555

Hobbs RJ, Yates S, Mooney HA (2007) Long-term data reveal complex dynamics in grassland in relation to climate and disturbance. Ecol Monogr 77:545-568


Houlahan JE, Currie DJ, Cottenie K, Cumming GS, Ernest SKM, Findlay CS, Fuhlendorf SD, Gaedke U, Legendre P, Magnuson JJ, McArdle BH, Muldavin EH, Noble D, Russell R, Stevens RD, Willis TJ, Woiwod IP, Wondzell SM (2007) Compensatory dynamics are rare in natural ecological communities. P Natl Acad Sci USA 104:3273-3277

Irving A, Connell S (2006) Predicting understorey structure from the presence and compositionof canopies: an assembly rule for marine algae. Oecologia 148:491-502

Ives AR, Gross K, Klug JL (1999) Stability and variability in competitive communities. Science 286:542-544

Janke K (1986) Die Makrofauna und ihre Verteilung im Nordost-Felswatt von Helgoland. Helg Mar Res 40:1-55

Janke K (1990) Biological interactions and their role in community structure in the rocky intertidal of Helgoland (German Bight, North Sea). Helg Mar Res 44:219-263

Jenkins SR, Norton TA, Hawkins SJ (2004) Long term effects of Ascophyllum nodosum canopy removal on mid shore community structure. J Mar Biol Assoc UK 84:327-329

Kennelly SJ, Underwood AJ (1993) Geographic consistencies of effects of experimentalphysical disturbance on understorey species in sublittoral kelp forests in central New South Wales. J Exp Mar Biol Ecol 168:35-58

Lehman CL, Tilman D (2000) Biodiversity, stability, and productivity in competitivecommunities. Am Nat 156:534-552

Lilley S, Schiel D (2006) Community effects following the deletion of a habitat-forming alga from rocky marine shores. Oecologia 148:672-681

MacArthur RH (1955) Fluctuations of animal populations and a measure of communitystability. Ecology 36:533-536

McCann KS (2000) The diversity–stability debate. Nature 405:228-233 Micheli F, Cottingham KL, Bascompte J, Bjornstad ON, Eckert GL, Fischer JM, Keitt TH,

Kendall BE, Klug JL, Rusak JA (1999) The dual nature of community variability. Oikos 85:161-169

Moore P, Hawkins SJ, Thompson RC (2007) Role of biological habitat amelioration in altering the relative responses of congeneric species to climate change. Mar Ecol Prog Ser 334:11-19

Morrow K, Carpenter R (2008) Shallow kelp canopies mediate macroalgal composition: effects on the distribution and abundance of Corynactis californica (Corallimorpharia). Mar Ecol Prog Ser 361:119-127

Pimm SL (1991) The balance of nature?: ecological issues in the conservation of species and communities. The University of Chicago Press, Chicago

18

Paper III

Ptacnik R, Solimini AG, Andersen T, Tamminen T, Brettum P, Lepisto L, Willen E, Rekolainen S (2008) Diversity predicts stability and resource use efficiency in naturalphytoplankton communities. P Natl Acad Sci USA 105:5134-5138

R Development Core Team (2008) R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna

Reichert K, Buchholz F (2006) Changes in the macrozoobenthos of the intertidal zone at Helgoland (German Bight, North Sea): a survey of 1984 repeated in 2002. Helg Mar Res 60:213-223

Schluter D (1984) A variance test for detecting species associations, with some exampleapplications. Ecology 65:998-1005

Shea K, Roxburgh SH, Rauschert SJ (2004) Moving from pattern to process: coexistence mechanisms under intermediate disturbance regimes. Ecol Lett 7:491-508

Steneck R (1986) The ecology of coralline algal crusts: convergent patterns and adaptative strategies. Annu Rev Ecol Syst 17:273-303

Ter Braak CJF, Looman CWN (1994) Biplots in reduced-rank regression. Biom J 36:983-1003 Terlizzi A, Benedetti-Cecchi L, Bevilacqua S, Fraschetti S, Guidetti P, Anderson MJ (2005)

Multivariate and univariate asymmetrical analyses in environmental impact assessment:a case study of Mediterranean subtidal sessile assemblages. Mar Ecol Prog Ser 289:27-42

Tilman D (1996) Biodiversity: population versus ecosystem stability. Ecology 77:350–363 Tilman D (1999) The ecological consequences of changes in biodiversity: a search for general

principles. Ecology 80:1455–1474 Tilman D, Downing JA (1994) Biodiversity and stability in grasslands. Nature 367:363-365Tilman D, Reich PB, Knops JMH (2006) Biodiversity and ecosystem stability in a decade-long

grassland experiment. Nature 441:629-632 Underwood AJ, Chapman MG (2006) Early development of subtidal macrofaunal assemblages:

relationships to period and timing of colonization. J Exp Mar Biol Ecol 330:221-233 Vasseur DA, Gaedke U (2007) Spectral analysis unmasks synchronous and compensatory

dynamics in plankton communities. Ecology 88:2058-2071 Winer BJ, Brown DR, Michels KM (1991) Statistical principles in experimental design.

McGraw-Hill, New York Wollgast S, Lenz M, Wahl M, Molis M (2008) Effects of regular and irregular temporal

patterns of disturbance on biomass accrual and species composition of a subtidal hard-bottom assemblage. Helg Mar Res DOI: 10.1007/s10152-008-0118-7

Worm B, Duffy JE (2003) Biodiversity, productivity and stability in real food webs. Trends Ecol Evol 18:628-632

Yachi S, Loreau M (1999) Biodiversity and ecosystem productivity in a fluctuating environment: The insurance hypothesis. P Natl Acad Sci USA 96:1463-1468

19

Appendix

Table 1. ANOVAs on the effects of Fucus serratus canopy (present or removed) and

mechanical disturbance (undisturbed or disturbed) on the variance of total community cover

and the sum of all species variances. Variances were calculated across 7 sample dates on plots

established at two intertidal sites. The F-ratio of each term was calculated using as denominator

the mean square given in the column MSd. Error terms (i.e., S × C, S × D, and S × C × D) that

were non-significant at � = 0.25 were pooled with the residual to increase the power of the

hypothesis tests (Winer et al. 1991)

Source df SS MS F p MSd

Variance in total community coverSite, S 1 1.06 1.06 3.15 0.085 PooledCanopy, C 1 0.05 0.05 0.14 0.707 PooledDisturbance, D 1 0.80 0.80 2.36 0.133 PooledS × C 1 0.02 0.02 0.05 0.832 ResidualS × D 1 0.43 0.43 1.23 0.276 ResidualC × D 1 0.16 0.16 0.46 0.500 PooledS × C × D 1 0.25 0.25 0.73 0.400 ResidualResidual 32 11.13 0.35Total 39 13.89Pooled residual 35 11.82 0.34Cochran’s test C = 0.643, p = 0.21 Transformation ln(x)

Summed species variancesSite, S 1 5664695 5664695 4.60 0.039 PooledCanopy, C 1 4512241 4512241 3.66 0.064 PooledDisturbance, D 1 69036 69036 0.02 0.906 S × DS × C 1 209874 209874 0.16 0.691 ResidualS × D 1 3109735 3109735 2.52 0.121 ResidualC × D 1 441520 441520 0.36 0.553 PooledS × C × D 1 19856 19856 0.02 0.902 ResidualResidual 32 41647729 1301492Total 39 55674686Pooled residual 34 41877458 1231690Cochran’s test C = 0.634, p = 0.24 Transformation None

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Paper III

Table 2. ANOVAs on the effects of Fucus serratus canopy (present or removed) and

mechanical disturbance (undisturbed or disturbed) on the sum of all pair-wise species

covariances and the Bray-Curtis index. Variances were calculated across 7 sample dates on

plots established at two intertidal sites. The F-ratio of each term was calculated using as

denominator the mean square given in the column MSd. Error terms (i.e., S × C, S × D, and S ×

C × D) that were non-significant at � = 0.25 were pooled with the residual to increase the

power of the hypothesis tests (Winer et al. 1991)

Source df SS MS F p MSd

Summed covariance Site, S 1 49280 49280 0.15 0.704 PooledCanopy, C 1 1883167 1883167 5.60 0.024 PooledDisturbance, D 1 366274 366274 1.09 0.304 PooledS × C 1 15393 15393 0.04 0.836 ResidualS × D 1 254442 254442 0.72 0.404 ResidualC × D 1 30219 30219 0.09 0.766 PooledS × C × D 1 147437 147437 0.42 0.524 ResidualResidual 32 11361283 355040Total 39 14107495Pooled residual 35 11778557 336530Cochran’s test C = 0.640, p = 0.22 Transformation None

Bray-Curtis index Site, S 1 0.021 0.021 6.74 0.014 ResidualCanopy, C 1 0.109 0.109 21.39 0.136 S × C Disturbance, D 1 0.001 0.001 0.04 0.874 S × D S × C 1 0.005 0.005 1.64 0.210 ResidualS × D 1 0.016 0.016 5.12 0.031 ResidualC × D 1 0.010 0.010 1.12 0.482 S × C × D S × C × D 1 0.009 0.009 2.88 0.099 ResidualResidual 32 0.100 0.003Total 39 0.271Cochran’s test C = 0.218, p = 0.99 Transformation None

21

Appendix

FIGURE CAPTIONS

Fig. 1. Effects of canopy removal (Fucus serratus) and mechanical disturbance on (a) variance

of community total cover, (b) sum of all species variances, (c) sum of all pair-wise species

covariances, and (d) averaged Bray-Curtis dissimilarities. The measures of variability were

calculated across 7 sample dates on plot established at two intertidal sites (Nordostwatt or

Westwatt). Values are given as means ± SEM (n = 5).

Fig. 2. nMDS plot ordination plot on the basis of Euclidean distances among centroids of the

interaction between the factors canopy (c+ or c-, present or removed, respectively) and

disturbance (d- or d+, undisturbed or disturbed, respectively) with time sequence given as

numbers on plots (n = 5). Centroids were separately computed for each site using principal

coordinates from Bray-Curtis dissimilarities of untransformed data. Sampling months are 1:

March/06, 2: June/06, 3: September/06, 4: December/06, 5: March/07, 6: June/07, 7:

September/07.

Fig. 3. Redundancy analysis (RDA) bi-plots showing the correlation between functional types

(ECA: encrusting algae, EphA: ephemeral algae, TFA: turf-forming algae, SI: sessile

invertebrates, and MC: mobile consumers) in relation to treatments of canopy (c+ or c-, present

or removed, respectively), disturbance (d- or d+, undisturbed or disturbed, respectively), and

sampling date. The percent cover of sediment (Sed) is included as dependent variable. Time

sequence is given as numbers in the plots as follows. 1: March/06, 2: June/06, 3: September/06,

4: December/06, 5: March/07, 6: June/07, 7: September/07. Response variables are scaled to

zero mean and unit variance.

22

Paper III

Note: cumulative proportions of the variance accounted for by the two axes are as follow.

Nordostwatt: ECA = 0.82, EphA = 0.63, TFA = 0.65, SI = 0.28, MC = 0.54, and Sed = 0.79.

Westwatt: ECA = 0.82, EphA = 0.73, TFA = 0.74, SI = 0.24, MC = 0.43, and Sed = 0.76.

23

Appendix

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25

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Fig. 3. Valdivia et al.

26

Paper IV

Canning-Clode J1, 2 *, Valdivia N3, Molis M3, Thomason JC4, and Wahl M1 (2008) Estimation

of regional richness in marine benthic communities: quantifying the error. Limnology and

Oceanography: Methods. 6: 580-590

1 Leibniz Institute of Marine Sciences at the University of Kiel, Duesternbrooker Weg 20, D-

24105 Kiel, Germany

2 University of Madeira, Centre of Macaronesian Studies, Marine Biology Station of Funchal,

9000-107 Funchal, Madeira Island, Portugal

3 Biologische Anstalt Helgoland, Section Seaweed Biology, Foundation Alfred-Wegener-

Institute for Polar and Marine Research, marine station, Kurpromenade 201, D-27498

Helgoland

4 School of Biology, Newcastle University, Newcastle Upon Tyne, United Kingdom, NE1 7RU

*Corresponding author

E-mail: [email protected]

580

Introduction

Species richness is the simplest and most commonly acceptedmeasure of biodiversity (Whittaker 1972; Magurran 1988; Gaston

1996) and is crucial for testing ecological models, such as the sat-uration of local communities colonized from regional speciespools (Cornell 1999). Ecological limitation (i.e., saturation) meansthat with increasing number of available species in the regionalpool or with invasion events, local richness does not increasebeyond an intrinsically determined maximum (Srivastava 1999).Thus, if only regional richness is driving local richness, a positivelinear relationship is often predicted (Cornell and Lawton 1992;Srivastava 1999). Conversely, while concerns have been expressed(Loreau 2000; Hillebrand and Blenckner 2002; Ricklefs 2004), ithas been widely accepted that if local assemblages are saturatedwith species due to ecological interactions and niche overlap, anasymptotic relationship is expected (Cornell and Lawton 1992;Cornell and Karlson 1997; Srivastava 1999).

Several studies that seek to explain and/or test the relation-ship between local and regional diversity have assessed the

Estimation of regional richness in marine benthic communities:quantifying the errorJoão Canning-Clode1,2*, Nelson Valdivia3, Markus Molis3, Jeremy C. Thomason4, and Martin Wahl11Leibniz Institute of Marine Sciences at the University of Kiel, Duesternbrooker Weg 20, D-24105 Kiel, Germany2University of Madeira, Centre of Macaronesian Studies, Marine Biology Station of Funchal, 9000-107 Funchal, Madeira Island,Portugal3Biologische Anstalt Helgoland, Section Seaweed Biology, Foundation Alfred-Wegener-Institute for Polar and Marine Research,marine station, Kurpromenade 201, D-27498 Helgoland4School of Biology, Newcastle University, Newcastle Upon Tyne, United Kingdom, NE1 7RU

AbstractSpecies richness is the most widely used measure of biodiversity. It is considered crucial for testing numerous eco-

logical theories. While local species richness is easily determined by sampling, the quantification of regional rich-ness relies on more or less complete species inventories, expert estimation, or mathematical extrapolation from anumber of replicated local samplings. However the accuracy of such extrapolations is rarely known. In this study,we compare the common estimators MM (Michaelis-Menten), Chao1, Chao2, ACE (Abundance-based CoverageEstimator), and the first and second order Jackknifes against the asymptote of the species accumulation curve, whichwe use as an estimate of true regional richness. Subsequently, we quantified the role of sample size, i.e., number ofreplicates, for precision, accuracy, and bias of the estimation. These replicates were sub-sets of three large data setsof benthic assemblages from the NE Atlantic: (i) soft-bottom sediment communities in the Western Baltic (n = 70);(ii) hard-bottom communities from emergent rock on the Island of Helgoland, North Sea (n = 52), and (iii) hard-bottom assemblages grown on artificial substrata in Madeira Island, Portugal (n = 56). For all community types, Jack2showed a better performance in terms of bias and accuracy while MM exhibited the highest precision. However, invirtually all cases and across all sampling efforts, the estimators underestimated the regional species richness, regard-less of habitat type, or selected estimator. Generally, the amount of underestimation decreased with sampling effort.A logarithmic function was applied to quantify the bias caused by low replication using the best estimator, Jack2.The bias was more obvious in the soft-bottom environment, followed by the natural hard-bottom and the artificialhard-bottom habitats, respectively. If a weaker estimator in terms of performance is chosen for this quantification,more replicates are required to obtain a reliable estimation of regional richness.

*Corresponding author: E-mail: [email protected]

AcknowledgmentsWe appreciate the assistance of the late J. S. Gray and Robert Clarke in

the initial development of these ideas. We would like to thank HeyeRumohr for providing the soft-bottom data and Mathieu Cusson for hissuggestions and critical review in this manuscript. We further thank InkaBartsch and Manfred Kaufmann for providing additional species inventoriesfor the natural and artificial hard-bottom habitats, respectively. The manu-script was significantly improved by the suggestions of three anonymousreferees. J. Canning-Clode was supported by a Fellowship from theGerman Academic Exchange Service (DAAD) and J.C. Thomason by theRoyal Society.

Limnol. Oceanogr.: Methods 6, 2008, 580–590© 2008, by the American Society of Limnology and Oceanography, Inc.

LIMNOLOGYand

OCEANOGRAPHY: METHODS

Canning-Clode et al. Estimating regional species richness

581

regional species pool based on published species lists and byconsulting taxonomic experts (e.g., Hugueny and Paugy 1995;Lawes et al. 2000; Witman et al. 2004; Harrison et al. 2006).However, complete inventories of the fauna and flora of aregion are exceptionally hard to obtain and will probablyremain unavailable for most regions for the next few centuries(Petersen and Meier 2003; Hortal et al. 2006). This problem ismore delicate in the marine environment where there is a largephyletic diversity in certain groups and limited informationabout others, e.g., Porifera (Foggo et al. 2003). Moreover, it isdifficult to appreciate to what degree such inventories are com-plete or incomplete (Soberon and Llorente 1993), and compar-isons between published species lists are frequently unreliabledue to different sampling methods, terminology, or data han-dling systems (Dennis and Ruggiero 1996). In addition, whensaturation in certain assemblages is to be investigated, thespecies capable to recruit into this habitat type (the relevantrichness) are only a subset of the entire regional richness.

To deal with these difficulties, a number of estimation tech-niques have been developed to extrapolate from the known tothe unknown, i.e., from a reasonable number of properlyinventoried samples to the true number of relevant species ina certain area (Colwell and Coddington 1994). These tech-niques can be grouped into three classes: (i) parametric mod-els, (ii) non-parametric estimators, and (iii) extrapolations ofSAC (species accumulation curves) (Magurran 2004). Whenspecies fit a log normal distribution, i.e., a case of a paramet-ric model to estimate species richness, it is possible to estimatethe theoretical number of species in the community byextrapolating the shape of the curve. Most of the parametricmethods are, however, reported to perform improperly andhave not been used in recent years (Melo and Froehlich 2001).

In contrast, the non-parametric estimators have been sug-gested to perform better than SAC and parametric methods(Baltanas 1992; Colwell and Coddington 1994; Walther andMorand 1998; Walther and Martin 2001; Hortal et al. 2006).These non-parametric estimators were originally developed toestimate population size based on capture-recapture data andadapted to extrapolate total species richness (Williams et al.2002). With this technique, species richness is estimated fromthe prevalence of rare species in each sample but does notextrapolate beyond the last sample to an asymptote. In itsplace, these models predict richness, including species notfound in the sample, from the proportional abundances ofspecies within the total sample (Soberon and Llorente 1993).Several evaluations on the performance of different estimatorshave been carried out (see review from Walther and Moore2005). In most cases, the estimators Chao1 (Chao 1984),Chao2 (Chao 1984, 1987; Colwell 2005), first order Jackknife(Jack1 - Burnham and Overton 1979; Heltshe and Forrester1983), and second order Jackknife (Jack2 - Smith and Van Belle1984) perform better in terms of bias, precision, and accuracythan other estimators (Walther and Moore 2005). In a recentstudy, Hortal et al. (2006) compared 15 species richness estimators

using arthropod abundances data and concluded that Chao1and ACE (Abundance-based Coverage Estimator, Chao andLee 1992; Chazdon et al. 1998; Chao et al. 2000) have shownthe best performance among all estimators. For the marinesystem, Foggo et al. (2003) performed an evaluation on theperformance of six estimators using simulations. They con-cluded that the estimator’s performance was affected by sam-pling effort, and no particular estimator performed best in allcases. Nevertheless, Foggo et al. (2003) suggested Chao1 as themost appropriate choice for a limited number of samples,acknowledging that its performance may vary significantly incases of larger spatial scales and species richness. In these cir-cumstances, the frequency of rare species could deteriorate theperformance of Chao1 (Foggo et al. 2003). This was later con-firmed by Ugland and Gray (2004) in benthic assemblages ofthe Norwegian continental shelf where Chao1 provided alarge underestimation of true richness.

Finally, the third category of assessing inventory complete-ness is through the extrapolation of SAC. In such curves, thecumulative number of species is plotted against a cumulativemeasure of sampling effort, e.g., the number of individualsobserved, samples or traps (Moreno and Halffter 2000; Gotelliand Colwell 2001). The species richness can then be estimatedby fitting an equation to the curve and estimating its asymp-tote. While many functions have been proposed for this task(see Tjørve 2003 for a review in possible model candidates),the negative exponential function, the Clench equation, theWeibull function, and the Morgan-Mercer-Flodin(MMF)model have been frequently used (Soberon and Llorente 1993;Colwell and Coddington 1994; Flather 1996; Lambshead andBoucher 2003; Jimenez-Valverde et al. 2006; Mundo-Ocampoet al. 2007). In theory, the asymptote’s location represents the“true richness”, i.e., the total number of species that would beobserved with a hypothetical infinite sampling effort (Soberonand Llorente 1993; Colwell and Coddington 1994; O’hara2005; Jimenez-Valverde et al. 2006). The quality of the fittingof the equation to the curve and, thus, the reliability of theplateau should relate directly to the number of replicates.

The current study addresses the estimation of regional richnessusing a novel approach. First, we extrapolate to the asymptote of theSAC for three data sets, each with a large number of replicates andfrom three different types of marine benthic communities. Second,using the asymptote’s location as a reference for “true” regional rich-ness, we then compare precision, bias, and accuracy of the regionalrichness produced by six different estimators - Michaelis-Menten(MM), ACE, Chao1, Chao2, Jack1, and Jack2. Finally, we quantifythe estimation error as a function of sampling effort.

Materials and proceduresFor this study, we explored three sets of benthic communi-

ties: (i) soft-bottom: In Kiel Bay, Western Baltic, (54°38.3′ N,10°39.6′ E) 70 replicates of macrofaunal samples were collectedto investigate the performance of species richness estimationtechniques. The 70 samples were collected from the same site


582

in the early autumn of 1995 at the Station “Millionenviertel14” using a 1000 cm2 van Veen grab at a depth of 24 m (cov-ering a total of 7 m2 of sea bed). Samples were preserved in 4%formaldehyde and later identified to species level (Rumohr1999; Rumohr et al. 2001). (ii) In spring 2006, in HelgolandIsland, North Sea (54°11.4′ N, 07° 55.2′ E) one of us (NV) sam-pled sessile hard-bottom communities and identified them tospecies level in 52 replicate quadrates of 400 cm2 in intertidalrocky abrasion platforms. The study site “Nordostwatt” coversapproximately 450 m2 and is located in the northeast part ofthe island and was extensively studied and inventoried byJanke (1986). Janke (1986) described horizontal belts in theintertidal as the Enteromorpha, Mytilus, Fucus serratus, and Lam-inaria zones. The data we use in this report are from 7 sub-habi-tats distributed in the F. serratus habitat. (iii) In early summer2004, young hard-bottom communities were collected byimmersing 56 replicate polyvinylchloride (PVC) panels (225cm2) for 5 mo at Madeira Island, Portugal, NE Atlantic (32°38.7′N, 16° 53.2′ W). The panels were distributed in 12 PVC rings(60 cm dia, 25 cm height) hung from a buoy at approximately0.5 m depth. Minimum distance between rings was 5 m. Theoriginal study focused on the influence of disturbance andnutrient enrichment in hard-bottom assemblages (Canning-Clode et al. 2008). For the purpose of this analysis, only sessilespecies on untreated control panels were taken into considera-tion. Hereafter, these datasets are referred to as soft-bottom,natural hard-bottom, and artificial hard-bottom, respectively.

Predicting the asymptote of the SAC—Species accumulationcurves (SAC) were used (PRIMER 6, Clarke and Gorley 2006) tocalculate the total number of species observed (“Sobs Curve”) atmaximum sample size. Here, we used 52 replicates as maximumsampling size for all habitats because this was the maximumreplicate number found in all habitat samples. Replicates werepermuted randomly 999 times. The analytical form of the meanvalue of the accumulation curve over all permutations wasgiven by the UGE Index (Ugland et al. 2003). Ugland et al.(2003) developed a total species curve (T-S curve) from SACobtained from single subareas. This curve can then be extrapo-lated to estimate the probable total number of species in a givenarea (Ugland et al. 2003). They showed for the Norwegian con-tinental shelf that the conventional SAC gave a large underesti-mation compared with the T-S curve. To estimate the asymptoteof the SAC (which we treat as ‘true’ regional richness in thisanalysis) for all habitats, the nonlinear Morgan-Mercer-Flodin(MMF) growth model (Morgan et al. 1975) was chosen. TheMMF model was selected by the curve fitting software CurveEx-pert (Hyams 2005) because of its superior fit regarding coeffi-cient of correlation (r ) and standard error of the estimate (SE )in all three data sets. The MMF model takes the form:

y = (ab + cxd) / (b + xd)

where y is species richness, and x represents the number of repli-cates. The parameters a, b, c, and d have the following interpre-tation: a is the calculated ordinate intercept of the replicates-

species richness curve; c represents the maximum species rich-ness. i.e., asymptote of the curve, as the number of replicates (x)approaches infinity; b and d are model parameters that describethe shape of the curve between the two extremes (Morgan et al.1975). This model was previously used in two studies that per-formed a regional estimation of deep sea and littoral nematodes(Lambshead and Boucher 2003; Mundo-Ocampo et al. 2007). Inthose studies, estimates were obtained by extrapolation from aSAC based on number of individuals, rather than number ofsamples based on the UGE index as we do here.

Species richness estimations using variable replicate numbers—We employed the frequently used software ‘EstimateS’ (version7.5, Colwell 2005) to investigate the effect of sample size(number of sampling units representing the replicates of ‘localrichness’) in estimating regional richness. This program com-putes sample-based rarefaction curves for a variety of speciesrichness estimators, presenting the mean number of randomsample re-orderings. Rarefaction and SAC were computed tentimes (using 10 randomly drawn sub-sets of replicates fromthe entire data-set) for the replication levels 2, 4, 8, 16, 32, and52 for each habitat. Because there were 70, 52, and 56 avail-able replicates for the soft-bottom, natural hard-bottom, andartificial hard-bottom data sets, respectively, there was ahigher chance of samples overlap when selecting the 32 and52 samples sets. The rarefaction curve was based on 100 ran-domizations of the number of replicates sampled. We focusedour investigation on five non-parametric estimators as well ason the asymptotic Michaelis-Menten (MM) richness estimator(Raaijmakers 1987) (Table 1). These six estimators were previ-ously used in several evaluations and were reported to performwell (Walther and Moore 2005, see their table 3). Rosenzweiget al. (2003) theoretically differentiated these two varieties ofestimators. Non-parametric estimators intend to overcomesample-size insufficiencies and to report the number of speciespresent in sampled habitats. They operate only on the resultsobtained from a subset of the total data set and do not repre-sent an extrapolation. In contrast, MM extrapolates speciesnumber to the asymptote of the SAC (Rosenzweig et al. 2003).‘EstimateS’ calculates the MM estimator in two ways: (i) foreach of the 100 randomizations, which is then averaged(MMRuns), or (ii) the mean accumulation curve is calculatedby averaging over 100 accumulation curves derived from 100runs (MMMeans). We used the latter because of its less erraticestimation (Colwell 2005; Walther and Moore 2005).

Estimator performance evaluation—Following Walther andMoore (2005), we calculated three different quality indicatorsthat are commonly used to describe the performance of thechosen estimators: bias, precision, and accuracy. Bias quanti-fies the mean difference between an estimated richness andthe true species richness. For measuring bias, we used thescaled mean error:

Bias = (Ej – A),

where A is the asymptote of the SAC, Ej is the estimated

11An j

n

=∑


583

species richness for the jth replicate, and n is the number of

replicates. Positive and negative bias indicates overestimationand underestimation, respectively.

Precision measures the variability of estimates amongrepeated estimation runs for a given sample. For measuring pre-cision, we used the complement of the coefficient of variation,the latter being the ratio of deviation (SD) and mean ( ):

Precision = 1 – (SD / )

Accuracy measures the closeness of an estimated value tothe true richness (Brose and Martinez 2004). It is often mea-sured using the mean squared error, combining bias, and pre-cision (Hellmann and Fowler 1999). Here we applied thescaled mean square error according to the formula:

Accuracy = 1 – ( (Ej – A)2),

where A is the asymptote of the SAC, Ej is the estimated speciesrichness for the j th sample, and n is the number of replicates.

Quantifying the relation between estimation error and number ofreplicates—The relative estimation error of the six estimatorswas expressed using the following formula:

y = (E/A) 100,

where y is the estimation error (in percent), E represents theestimated species richness given by an elected estimator, andA is the asymptote of the SAC in a given habitat. The estima-tion error was then plotted against the number of replicatesusing a logarithmic model. This model takes the form:

y = a + b ln(x)

where y represents the underestimation of a given estimator

when compared with the asymptote of the SAC of a givenhabitat, x is the number of replicates, and a and b are modelparameters. Here too, the model was selected by the curve fit-ting software CurveExpert based on a high value of r and lowestimate SE.

AssessmentPredicting the location of the asymptote—In all three habitats,

species richness increased as a function of sampling effort(Fig. 1). The total number of species observed in maximumsample size, i.e., 52 replicates, was 55 species in the soft-bottomhabitat, 43 species for the natural hard-bottom assemblages,and 32 species for the artificial hard-bottom habitat (Fig. 1).

The MMF model was chosen to extrapolate and predict thelocation of the asymptote. This model described the data ofthe SAC for the three habitats very well, with r ≈ 1 for allcurves (Table 2). Nevertheless, the model performed less wellfor the natural hard-bottom assemblages as indicated by agreater standard error of the estimate. The asymptote ofspecies richness (parameter c) was located at 103 species forsoft-bottom, 65 for natural hard-bottom, and 38 for the artifi-cial hard-bottom habitat (Table 2).

Estimator’s performance—In general, Jack2 performed better(with respect to bias and accuracy) at all replicate levels (lowsampling effort: < 8 replicates; intermediate sampling effort: 8-16 replicates; high sampling effort: > 16 replicates) in the threehabitats (Fig. 2). The estimator MM also had a satisfactory per-formance at low replication for all habitats, but with increas-ing sampling effort, its performance in terms of bias and accu-racy improved less steeply as for the other estimators. In mostcases, at low and intermediate sampling effort, Chao1, Chao2,and ACE performed worse. Bias decreased with rising sampling

12 1A n j

n

=∑

E_

E_

Table 1. Summary of the species richness estimators used for this analysis

Richness estimators Type Description References

ACE NP*Abundance-based coverage estimator. It is a modification of the Chao & Lee (1992) estima-

tors based on the ratio between rare (less than 10) and common species.

Chao and Lee 1992; Chazdon

et al. 1998; Chao et al. 2000

Chao1 NP*Abundance-based estimator based on the number of rare species in a sample, i.e., represent-

ed by less than 3 individuals.Chao 1984

Chao2 NP*

Incidence-based estimator. Takes into account the distribution of species amongst samples,

i.e., the number of species that occur in only 1 sample (‘rare species’) and the number of

species that occur in exactly 2 samples.

Chao 1984, 1987

Jack1 NP* First-order Jackknife. Is based on the species occurring only in a single sample.Burnham and Overton 1979;

Heltshe and Forrester 1983

Jack2 NP*Second-order Jackknife. Is based on the species occurring in only 1 sample as well as in the

number that occur in exactly 2 samples.Smith and Van Belle 1984

MMMean P†Michaelis-Menten Mean richness estimator. Computes the mean accumulation curve. Is calcu-

lated by averaging over all accumulation curves derived from the selected runs.Raaijmakers 1987

*NP, non-parametric estimator†P, parametric estimator


584

effort and was consistently negative (i.e., underestimation) forall estimators in the soft-bottom and natural hard-bottomhabitats (Fig. 2A-B). In the artificial hard-bottom habitat, too,all six estimators underestimated the asymptote of the SACwith the single exception that at replicate level 52, Jack2 pro-duced the only overestimation ever observed (Fig. 2C). Gener-ally, the underestimation was more pronounced for the soft-bottom communities.

Accuracy improved steadily with increasing replication,with a similar slope in all community types, but generally moresmooth in the soft-bottom communities (Fig. 2D-F). Jack2 wasthe most accurate estimator in all habitats when replicationexceeded 2. At low and intermediate sampling effort, MM wasas accurate as Jack2 for the natural and artificial hard-bottomhabitats (Fig. 2E – F). In contrast, MM was the least accurateestimator for the soft-bottom community (Fig. 2D) and at highsampling effort for the other two habitats.

Precision of the estimation increased rapidly in the first 10replicates and more slowly after that (Fig. 2G-I). This patternwas similar in all communities, probably because it is a statisti-cal property (i.e., it approximates to the standard deviation).Nevertheless, in the natural hard-bottom assemblages, Jack2showed a high imprecision at intermediate sampling effort due

to a large variability of species richness within replicates (Fig.2H). In this habitat both of the Chao estimators showed weakprecision at low sampling effort. Precision was 1 at maximumsampling effort for the natural hard-bottom habitat as therewas only one possible combination of the 52 replicates (Fig.2H). MM showed high precision in almost all levels of replica-tion and all community types. While the shapes of all curvesare comparable for the 3 community types, for a similar qual-ity of estimation fewer replicates are required for the artificialhard-bottom community than for the soft-bottom community.

In summary, Jack2 seems to be the most appropriate choiceat all levels of sampling effort for all habitats. MM constitutesan alternative solution at low sampling effort for the naturaland artificial hard-bottom habitats.

For all community types and all estimators, the relativeestimation error and its error decreased with increasing repli-cation (Fig. 3). It should be noted, however, that the decreasein error, especially at replication levels 32 and 52, might be anartifact caused by the statistically increased probability of re-sampling of the same replicates.

In the soft-bottom data-set, underestimation was neverlower than 35%, even at maximum sampling size (Fig. 3A). Inthe natural hard-bottom communities, it was always largerthan 20% (Fig. 3B).The underestimation was lowest for theartificial hard-bottom habitat (Fig. 3C). At low sampling effort,which probably is the most common case in ecological stud-ies, MM and Jack2 yield a substantially better estimation ofregional richness than the other four estimators for all assem-blages. At maximum sampling size for the artificial hard-bot-tom habitat, average estimation error was below 20% for MM,ACE, Jack1, Chao1, and Chao2, while Jack2 overestimated theasymptote of the SAC (Fig. 3C).

To investigate in more detail the estimation error in all habi-tats, we have selected a logarithmic model and the Jack2 esti-mator due to its best overall performance. The logarithmicmodel properly described the data for all habitats (Fig. 4; soft-bottom: r = 0.98, SE = 2.67; natural hard-bottom: r = 0.98, SE =3.39; artificial hard-bottom: r = 0.96, SE = 5.51). The estimationerror decreases with increasing replication. Based on this model,we quantified the bias caused by low replication for all habitats(Table 3). With each doubling of replication number the esti-mation error by Jack2 decreases in average by 6.6% for the soft-bottom habitat, 8.4% for the natural hard-bottom habitat, and8.5% for the artificial hard-bottom habitat (Fig. 4, Table 3).

Fig. 1. Species accumulation curves (SAC) for the three communitytypes. These curves were plotted using the UGE index calculated inPRIMER 6.0

Table 2. Coefficients of correlation (r ), standard error of the estimate (SE ), and parameter values of the MMF model used for theextrapolation of the asymptote of the SAC for all habitats

Parameters

Habitat a b c d r SESoft-bottom –8.12 3.41 102.67 0.38 0.999 0.074

Hard-bottom –63.05 0.63 65.48 0.27 0.999 0.205

Artificial hard-bottom –3.62 1.60 37.90 0.58 0.999 0.023


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Overall, we have demonstrated that Jack2 performed bestin all habitats. Using the logarithmic model, we predict thatone would need 1865 samples to reach the asymptote of theSAC in the soft-bottom habitat (Table 3). For the natural andartificial hard-bottom habitats, a considerably less samplingeffort would be required to reach the asymptote of the SAC.

DiscussionStudies that are searching for a clear understanding of the local

versus regional diversity pattern in the marine environment haveoften defined the number of species in a region by questioningexperts or consulting available species inventories (e.g., Witman etal. 2004; Harrison et al. 2006). In many poorly studied areas, how-ever, true regional species numbers are unknown. Therefore thestatistical estimation of regional richness, based on a limitednumber of replicates, constitutes an important alternative for the

marine realm. In the present study, we have evaluated the poten-tial and limitations of such an approach. For this purpose, weselected three data sets with a large number of replicates from dif-ferent temperate shallow water habitats. We compared the per-formance of six different estimators of regional richness against theasymptote of the species accumulation curve (SAC) using ran-domly selected replicates for a range of sample sizes.

The most conspicuous outcome of this analysis is that as ageneral rule the estimation of regional species richness basedon local assemblages underestimates the asymptote of theSAC, regardless of habitat type, number of replicates, orselected estimator. The only exception was when a single esti-mator, Jack2, using 52 replicates overestimated the asymptoteof the SAC in the artificial hard-bottom habitat. For all esti-mators, the amount of underestimation gradually decreasedwith increasing sample size.

Fig. 2. Bias (panels A-C), accuracy (D-F), and precision (G-I) of the selected estimators (MM, Chao1, ACE, Chao2, Jack1, and Jack2) for the three habi-tats using variable replicate numbers. *In panel H, precision was 1 at replicate level 52 as there was only one set of 52 replicates.


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The estimation error was greatest in the soft-bottom envi-ronment, followed by the natural hard-bottom and the artifi-cial hard-bottom habitats. Nevertheless, the similarity of theestimation error patterns between the three data sets is sur-prising in view of the (intentional) differences between theselected samples regarding community type, community age,diversity and method of sampling. For instance, the size of asingle sampling unit was 1000, 400, and 225 cm2, in the soft-bottom, natural, and artificial hard-bottom samples, respec-tively. Thus, at comparable species density, a single replicatefor the soft-bottom habitat possibly contained a larger pro-portion of the regional species pool than in the other samples.Also, the suspended PVC panels used for the Madeira data-setcan be considered island communities on patchy substrata,with diversity possibly constrained by habitat (panel) size,whereas the samples from the other two data sets were sub-areas from much larger contiguous habitats. Sample unit sizeand patchiness of habitat may affect the similarity betweenreplicates and, thus, the initial slope of the curve. Moreover,the slow accumulation and consequently, the larger numberof replicates required to reach the plateau in the soft bottomsample may be linked to the number of rare species present, aswell as to the sensitivity of the sampling method.

Despite the extensive differences between the samples cho-sen with regard to size of sampling area, patchiness of habitator age of community, the performance of the estimatorsapplied to the described data sets was comparable. This may beindicative of a remarkable robustness of the observed pattern.The fact that the six estimators underestimated the asymptoticspecies richness is consistent with other studies that use thesame and other estimators (e.g., Petersen and Meier 2003;Brose and Martinez 2004; Cao et al. 2004). Beyond the generalsimilarity among estimator’s performances, Jack2 was moreaccurate and less biased for all habitats in almost all replica-tion levels. In contrast, MM exhibited a high precision in allhabitats. At low sampling effort, MM and Jack2 performed bestin terms of bias, accuracy, and precision for the natural andartificial hard-bottom communities. For the soft-bottom com-munity, Jack2 was clearly the least biased and the most accu-rate estimator at all levels of replication. For estimations basedon larger samples, both Chao1 and ACE seem to performslightly better than MM but worse than Chao2 and both Jack-knifes. These findings are comparable to some previouslyreported results. For instance, the study by Walther and Moore(2005) found that Chao2 (Chao 1987) performed best whileJack2, Jack1, and Chao1 ranked second, third, and fourth,respectively. The MMMean and ACE estimators were reportedto perform worse (Walther and Moore 2005). Although theydid not evaluate the performances of ACE, MM, and Jack2,Foggo et al. (2003) concluded that amongst 6 different tech-niques to estimate marine benthos species richness, Chao1represented the best nonparametric alternative for a limitednumber of survey units. In contrast, Ugland and Gray (2004)argue that Chao1 severely underestimates the true richness in

Fig. 3. Percentages of asymptotic species richness estimated by MM,Chao1, ACE, Chao2, Jack1, and Jack2 using variable replicate numbers forthe (A) soft-bottom, (B) natural hard-bottom, and (C) artificial hard-bot-tom habitats. Means and 95% confidence intervals are indicated (n = 10).Dashed line indicates the asymptote of the SAC given by the UGE index.*In panel B at replicate level 52 confidence intervals are zero as there wasonly one set of 52 replicates.


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benthic assemblages of the Norwegian continental shelf. Intheir study, Chao1 predicted approximately 1100 species froma data-set with 809 species. Nevertheless, when surveyinglarger areas of the shelf than the ones they use in their analy-sis (see their Table 1), over 2500 species were found (Uglandand Gray 2004). The large underestimation by Chao1 iscaused by infrequent species (Ugland and Gray 2004). In arecent evaluation of 15 different estimators using arthropodsabundances, Hortal et al. (2006) concluded that the perform-ance of 10 estimators were highly dependent on the level ofreplication. In that study, Chao1 and ACE showed a higherprecision at low replication but the superiority of these twoestimators over the rest decreases with increasing sample size.Conversely, in a study using Monte Carlo simulations, Broseand Martinez (2004) showed that ACE, Chao1, Chao2, andJack2 were positively biased under high replication. However,in some of the previously mentioned studies, true richness wasestimated based on inventories, experts, simulated landscapemodels, or museum collection data (Brose et al. 2003; Petersenand Meier 2003; Brose and Martinez 2004; Cao et al. 2004;Hortal et al. 2006) and not on real and numerous communitysub-units, as done in this study. If incomplete lists suggest alower-than-real regional richness, apparent overestimations

may result. Only one of the previously mentioned studies hasestimated true richness based on the asymptote of the speciesaccumulation curve (Foggo et al. 2003).

In this study, we estimated true regional richness by extrap-olation of the SAC given by the UGE index using the non-lin-ear Morgan-Mercer-Flodin (MMF) growth model (Morgan et al.1975). The MMF model was previously employed in two sur-veys on the diversity of deep sea and littoral nematodes (Lamb-shead and Boucher 2003; Mundo-Ocampo et al. 2007). Lamb-shead and Boucher (2003) estimated the marine nematodespecies richness in 16 locations. They have compared the esti-mations given by the MMF model with the non-parametricincidence-based coverage estimator (ICE - Lee and Chao 1994;Chazdon et al. 1998; Chao et al. 2000). In 88% of cases, theestimation given by the extrapolation was higher than the esti-mation provided by ICE. In one instance, both methods pro-vided identical estimates of nematodes species, in another oneICE produced higher numbers (Lambshead and Boucher 2003).Mundo-Ocampo et al. (2007) used the same approach to com-pare nematode biodiversity in two shallow, littoral locations ofthe Gulf of California. In both locations, the MMF extrapola-tion gave a higher estimation of nematode richness than ICE(Mundo-Ocampo et al. 2007). Both studies did not attempt toquantify the relationship between estimation error and lowreplication, as we do here. In opposition to these investigationswhere SAC were plotted as a function of the accumulated num-ber of individuals, our study uses SAC plotted against the accu-mulated number of samples. Deciding which type of curves touse depends on the nature of the data available (Gotelli andColwell 2001). If sample-based data are available, a SAC basedon samples is preferable, as it can account for natural levels ofsample patchiness (i.e., heterogeneity between replicates) inthe data (Gotelli and Colwell 2001). A further distinction of thepresent study from the investigations by Lambshead andBoucher (2003) and Mundo-Ocampo et al. (2007) is the use ofthe T-S curve (given by the UGE index) developed by Ugland etal. (2003) followed by the MMF model fitting to it. The result-ing extrapolation of the asymptotic richness is a more realisticestimation than the usual SAC (Ugland et al. 2003).

We demonstrate that the minimum sampling effort toreach a realistic estimation of true regional richness is variableamong communities or sampling methodology. Below thisthreshold sampling effort estimation is negatively biased. Theunavoidable estimation error caused by low replication can,

Fig. 4. Estimation error by Jack2 using variable replicate numbers for thesoft-bottom, natural hard-bottom, and artificial hard-bottom habitatsusing the logarithmic model y = a + b ln(x). Means and 95% confidenceintervals are indicated (n = 10).

Table 3. Quantification of the estimation error by Jack2*

Number of replicates

Habitat 2 4 8 16 32 52 y(0)

Soft-bottom 71.37 64.14 56.9 49.67 42.43 37.37 1865.43

Natural hard-bottom 62.74 54.02 45.31 36.60 27.88 21.78 294.13

Artificial hard-bottom 45.36 36.07 26.71 17.39 8.07 1.54 58.31*Based on the logarithmic model, we calculated the approximate estimation error by Jack2 (%) to compensate the bias caused by low replication. Withthe same model we also calculated for each habitat, the required sampling effort for the Jack2 estimator to be unbiased (y (0)).


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however, be quantified as shown in this paper. In addition, thelogarithmic function used to quantify the estimation erroralso informs at which sampling effort the estimationapproaches the asymptote of the SAC. Consequently, we pre-dict that if a weaker estimator in terms of performance is cho-sen, the logarithmic function will approach the x-axis far later,i.e., a greater amount of replicates would be required to equalthe asymptote of the SAC.

The extrapolation of the SAC is a computation and theshape of the curve is affected by the presence/absence of rarespecies in the samples as well as the amount of samples usedin the model. To assess how well the plateau reflects the “real”regional richness, we compared the plateau values obtained byour approach to the numbers provided by existing compre-hensive inventories in the three systems. (i) A 30-year-longsurvey of the soft-bottom macrofauna in the Kiel Bay, WesternBaltic Sea lists 184 species at the Station “Millionenviertel 14”(Rumohr, pers com). (ii) On Helgoland island, three extensivestudies in the same sub-habitats we used here, reported 53 ses-sile animal species (Janke 1986; Reichert 2003) and 39 speciesof macroalgae (Inka Bartsch, unpublished data). (iii) Finally,studies on the diversity of hard-bottom communities growingon artificial substrata conducted during three consecutiveyears in the south coast of Madeira Island (Jochimsen 2007;Canning-Clode et al. 2008, Manfred Kaufmann, unpublisheddata) reported a total of 44 species growing on the same typeof substrata, depth, and study site as the artificial hard-bottomdata-set in this analysis. Compared to these values, our extrap-olation still underestimates the “real” richness of the investi-gated habitats by 44% for the soft-bottom, 29% for the natu-ral hard-bottom, and 14% for the artificial hard-bottomhabitats. However, it should be noted that the reference listsinclude species from several seasons, years, and successionalstages, which, in contrast to our data set, do not necessarilyco-exist at the local scale. Regional species pools based on suchinventories may include species not susceptible to recruit intothe community considered because they are restricted to dif-ferent habitats and seasons.

We conclude that regional richness can be estimated fromsub-samples, that the quality of the estimation increases withsample size, and that the magnitude of the unavoidable esti-mation error can be quantified and, thus, corrected to someextent. We encourage further studies to include data frommore locations and then provide more robust correction val-ues to compensate the bias caused by low replication.

ReferencesBaltanas, A. 1992. On the use of some methods for the esti-

mation of species richness. Oikos 65:484-492.Brose, U., and N. D. Martinez. 2004. Estimating the richness of

species with variable mobility. Oikos 105:292-300.———, ———, and R. J. Williams. 2003. Estimating species

richness: Sensitivity to sample coverage and insensitivity tospatial patterns. Ecology 84:2364-2377.

Burnham, K. P., and W. S. Overton. 1979. Robust estimation ofpopulation-size when capture probabilities vary among ani-mals. Ecology 60:927-936.

Canning-Clode, J., M. Kaufmann, M. Wahl, M. Molis, and M.Lenz. 2008. Influence of disturbance and nutrient enrich-ment on early successional fouling communities in an olig-otrophic marine system. Mar. Ecol. Evol. Persp. 29:115-124.

Cao, Y., D. P. Larsen, and D. White. 2004. Estimating regionalspecies richness using a limited number of survey units.Ecoscience 11:23-35.

Chao, A. 1984. Non-parametric estimation of the number ofclasses in a population. Scand. J. Stat. 11:265-270.

———. 1987. Estimating the population size for capture-recap-ture data with unequal catchability. Biometrics. 43:783-791.

———, W. H. Hwang, Y. C. Chen, and C. Y. Kuo. 2000. Esti-mating the number of shared species in two communities.Stat. Sinica 10:227-246.

——— and S. M. Lee. 1992. Estimating the number of classesvia sample coverage. J. Am. Stat. Assoc. 87:210-217.

Chazdon, R. L., R. K. Colwell, J. S. Denslow, and M. R. Guar-iguata. 1998. Statistical methods for estimating species rich-ness of woody regeneration in primary and secondary rainforests of Northeastern Costa Rica, p. 285-309. In F.Dallmeier and J. A. Comiskey [eds.], Forest biodiversityresearch, monitoring and modeling: conceptual back-ground and old world case studies. Parthenon Publishing.

Clarke, K. R., and R. N. Gorley. 2006. PRIMER v6. User man-ual/tutorial. Plymouth routine in mulitvariate ecologicalresearch. Plymouth Marine Laboratory.

Colwell, R. K. 2005. EstimateS: Statistical estimation of speciesrichness and shared species from samples. Version 7.5.User’s guide and application published at: http://purl.oclc.org/estimates. (accessed October 2007)

——— and J. A. Coddington. 1994. Estimating terrestrial biodiver-sity through extrapolation. Philos. T. Roy. Soc. B. 345:101-118.

Cornell, H. V. 1999. Unsaturation and regional influences onspecies richness in ecological communities: A review of theevidence. Ecoscience 6:303-315.

——— and R. H. Karlson. 1997. Local and regional processesas controls of species richness, p. 250-268. In D. Tilman andP. Kareiva [eds.], Spatial ecology. Princeton Univ. Press.

——— and J. H. Lawton. 1992. Species interactions, local andregional processes, and limits to the richness of ecologicalcommunities: a theoretical perspective. J. Anim. Ecol. 61:1-12.

Dennis, J. G., and M. A. Ruggiero. 1996. Biodiversity inventory:building an inventory at scales from local to global, p. 149-156. In R. C. Szaro and D. W. Johnston [eds.], Biodiversity inmanaged landscapes. Oxford Univ. Press.

Flather, C. H. 1996. Fitting species-accumulation functionsand assessing regional land use impacts on avian diversity.J. Biogeogr. 23:155-168.

Foggo, A., M. J. Attrill, M. T. Frost, and A. A. Rowden. 2003.Estimating marine species richness: an evaluation of sixextrapolative techniques. Mar. Ecol. Prog. Ser. 248:15-26.


589

Gaston, K. J. 1996. Species richness: Measures and mea-surement. In K. J. Gaston [ed.], Biodiversity: a biology ofnumber and differences. Blackwell Sciences.

Gotelli, N. J., and R. K. Colwell. 2001. Quantifying biodiver-sity: procedures and pitfalls in the measurement and com-parison of species richness. Ecol. Lett. 4:379-391.

Harrison, S., H. D. Safford, J. B. Grace, J. H. Viers, and K. F.Davies. 2006. Regional and local species richness in aninsular environment: Serpentine plants in California. Ecol.Monogr. 76:41-56.

Hellmann, J. J., and G. W. Fowler. 1999. Bias, precision, andaccuracy of four measures of species richness. Ecol. Appl.9:824-834.

Heltshe, J. F., and N. E. Forrester. 1983. Estimating species rich-ness using the jackknife procedure. Biometrics. 39:1-11.

Hillebrand, H., and T. Blenckner. 2002. Regional and localimpact on species diversity - from pattern to processes.Oecologia 132:479-491.

Hortal, J., P. A. V. Borges, and C. Gaspar. 2006. Evaluating theperformance of species richness estimators: sensitivity tosample grain size. J. Anim. Ecol. 75:274-287.

Hugueny, B., and D. Paugy. 1995. Unsaturated fish communi-ties in African rivers. Am. Nat. 146:162-169.

Hyams, D. G. 2005. CurveExpert 1.3: A comprehensive curvefitting system for Windows. http://curveexpert.webhop.biz/(accessed October 2007)

Janke, K. 1986. Die Makrofauna und ihre Verteilung im Nor-dost-Felswatt von Helgoland. Helgol Meeresunters 40:1–55.

Jimenez-Valverde, A., S. J. Mendoza, J. M. Cano, and M. L.Munguira. 2006. Comparing relative model fit of severalspecies-accumulation functions to local Papilionoidea andHesperioidea butterfly inventories of Mediterranean habi-tats. Biodivers. Conserv. 15:177-190.

Jochimsen, M. C. 2007. Role of community structure for inva-sion dynamics. Master thesis. University of Osnabrück.

Lambshead, P. J. D., and G. Boucher. 2003. Marine nematodedeep-sea biodiversity - hyperdiverse or hype? J. Biogeogr.30:475-485.

Lawes, M. J., H. A. C. Eeley, and S. E. Piper. 2000. The rela-tionship between local and regional diversity of indigenousforest fauna in KwaZulu-Natal Province, South Africa. Bio-divers. Conserv. 9:683-705.

Lee, S. M., and A. Chao. 1994. Estimating population-size viasample coverage for closed capture-recapture models. Bio-metrics 50:88-97.

Loreau, M. 2000. Are communities saturated? On the relation-ship between alpha, beta and gamma diversity. Ecol. Lett.3:73-76.

Magurran, A. 1988. Ecological diversity and its measurement.Princeton Univ. Press.

———. 2004. Measuring biological diversity. Blackwell Pub-lishing.

Melo, A. S., and C. G. Froehlich. 2001. Evaluation of methodsfor estimating macroinvertebrate species richness using

individual stones in tropical streams. Freshwater Biol.46:711-721.

Moreno, C. E., and G. Halffter. 2000. Assessing the complete-ness of bat biodiversity inventories using species accumula-tion curves. J. Appl. Ecol. 37:149-158.

Morgan, P. H., L. P. Mercer, and N. W. Flodin. 1975. Generalmodel for nutritional responses of higher organisms. Proc.Natl. Acad. Sci. USA 72:4327-4331.

Mundo-Ocampo, M., and others. 2007. Biodiversity of littoralnematodes from two sites in the Gulf of California. Hydro-biologia 586:179-189.

O’hara, R. B. 2005. Species richness estimators: how manyspecies can dance on the head of a pin? J. Anim. Ecol.74:375-386.

Petersen, F. T., and R. Meier. 2003. Testing species-richness esti-mation methods on single-sample collection data using theDanish Diptera. Biodivers. Conserv. 12:667-686.

Raaijmakers, J. G. W. 1987. Statistical-analysis of theMichaelis-Menten equation. Biometrics. 43:793-803.

Reichert, K. 2003. Die Makrofauna und ihre räumlicheVerteilung und saisonale Veränderung im Felswatt von Hel-goland - Ein Vergleich zur Untersuchung 1984. UniversitätHamburg.

Ricklefs, R. E. 2004. A comprehensive framework for globalpatterns in biodiversity. Ecol. Lett. 7:1-15.

Rosenzweig, M. L., W. R. Turner, J. G. Cox, and T. H. Ricketts.2003. Estimating diversity in unsampled habitats of a bio-geographical province. Conserv. Biol. 17:864-874.

Rumohr, H. 1999. Soft bottom macrofauna: collection, treat-ment and quality assurance of samples. ICES Tech. Mar.Environ. Sci. 27:1-19.

———, I. Karakassis, and J. N. Jensen. 2001. Estimating speciesrichness, abundance and diversity with 70 macrobenthicreplicates in the Western Baltic Sea. Mar. Ecol. Prog. Ser.214:103-110.

Smith, E. P., and G. Van Belle. 1984. Nonparametric-estima-tion of species richness. Biometrics. 40:119-129.

Soberon, J., and J. Llorente. 1993. The use of species accumu-lation functions for the prediction of species richness. Con-serv. Biol. 7:480-488.

Srivastava, D. S. 1999. Using local-regional richness plots totest for species saturation: Pitfalls and potential. J. Anim.Ecol. 68:1-16.

Tjørve, E. 2003. Shapes and functions of species-area curves: areview of possible models. J. Biogeogr. 30:827-835.

Ugland, K. I., and J. S. Gray. 2004. Estimation of species rich-ness: analysis of the methods developed by Chao andKarakassis. Mar. Ecol. Progr. Ser. 284:1-8.

———, ———, and K. E. Ellingsen. 2003. The species-accumu-lation curve and estimation of species richness. J. Anim.Ecol. 72(5):888-897.

Walther, B. A., and J. L. Martin. 2001. Species richness estima-tion of bird communities: how to control for samplingeffort? Ibis 143:413-419.


590

——— and J. L. Moore. 2005. The concepts of bias, precisionand accuracy, and their use in testing the performance ofspecies richness estimators, with a literature review of esti-mator performance. Ecography 28:815-829.

——— and S. Morand. 1998. Comparative performance of speciesrichness estimation methods. Parasitology 116:395-405.

Whittaker, R. H. 1972. Evolution and measurement of speciesdiversity. Taxon 21:213-251.

Williams, B. K., J. D. Nichols, and M. J. Conroy. 2002. Analy-

sis and management of animal populations: modeling, esti-mation, and decision making. Academic Press.

Witman, J. D., R. J. Etter, and F. Smith. 2004. The relationshipbetween regional and local species diversity in marine ben-thic communities: A global perspective. Proc. Natl. Acad.Sci. USA 111(44):15664-15669.

Submitted 17 June 2008Revised 6 October 2008

Accepted 22 October 2008

ANLAGE ZUR DISSERTATION

ANLAGE ZUR DISSERTATION

Name: Nelson Valdivia

Anschrift: An der Sapskuhle 516

27498 Helgoland

Helgoland, 11.12.08

Erklärung

Gem.§6 (5) Nr.1-3 PromO

Ich erkläre, dass ich NELSON VALDIVIA

1. die Arbeit ohne unerlaubte fremde Hilfe angefertigt habe,

2. keine anderen, als die von mir angegebenen Quellen und Hilfsmittel benutzt

habe

3. die den benutzten Werken wörtlich oder inhaltlich entnommenen Stellen als

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