Bradley J. Cardinale, Diane S. Srivastava, J. Emmett Duffy, Justin P. Wright, Amy L. Downing, Mahesh Sankaran, Claire Jouseau, Marc W. Cadotte, Ian T. Carroll, Jerome J. Weis, Andy Hector, and Michel Loreau. 2009. Effects of biodiversity on the functioning of ecosystems: A summary of 164 experimental manipulations of species richness. Ecology 90:854.

A. Data set identity:

Title: Effects of biodiversity on the functioning of ecosystems: A summary of 164 experimental manipulations of species richness.

B. Data set identification code

Suggested Data Set Identity Code:

BEF_summary_v2_Aug2008.csv

C. Data set description

Abstract:

Over the past decade, accelerating rates of species extinction have prompted an increasing number of studies to reduce the number of species experimentally in a variety of ecosystems and examine how this aspect of diversity alters the efficiency by which communities capture biologically essential resources and convert them into new tissue. Here we summarize the results of 164 experiments (reported in 84 publications) that have manipulated the richness of primary producers, herbivores, detritivores, or predators in a variety of terrestrial and aquatic ecosystems and examined how this impacts (1) the standing stock abundance or biomass of the focal trophic group, (2) the abundance or biomass of that trophic group's primary resource(s), and/or (3) the extent to which that trophic group depletes its resource(s). Our summary includes studies that have focused on the top-down effects of diversity; whereby researchers have examined how the richness of trophic group t impacts the consumption of a shared resource, and also studies that have focused on the bottom-up effects of diversity, whereby researchers have examined how the richness of trophic group t impacts the consumption of t by the next highest trophic level. The first portion of the data set provides information about the source of data and relevant aspects of the experimental design, including the spatial and temporal scales at which the work was performed. The second portion gives the magnitude of each response variable, the standard deviation, and the level of replication at each level of species richness manipulated. The third portion of the data set summarizes the magnitude of diversity effects in two ways. First, log ratios are used to compare the response variable in the most diverse polyculture to either the mean of all monocultures or the species having the highest/lowest value in monoculture. Second, data from each level of species richness are fit to three nonlinear functions (log, power, and hyperbolic) to assess which best characterizes the shape of diversity effects. The final portion of the data set summarizes any information that helps parse diversity effects into that attributable to species richness vs. that attributable to changes in species composition across levels of richness.

D. Key words: biodiversity; ecosystem efficiency; ecosystem functioning; ecosystem services; productivity; species richness; trophic efficiency.

Authors:

Bradley J. Cardinale^1,12, Diane S. Srivastava², J. Emmett Duffy³, Justin P. Wright⁴, Amy L. Downing⁵, Mahesh Sankaran⁶, Claire Jouseau⁷, Marc W. Caddotte⁸, Ian T. Carroll¹, Jerome J. Weis⁹, Andy Hector¹⁰, Michel Loreau¹¹

¹Department of Ecology, Evolution and Marine Biology, University of California at Santa Barbara, Santa Barbara, California 93106, USA

²Department of Zoology, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada

³Virginia Institute of Marine Science, The College of William and Mary, Gloucester Point, Virginia 23062, USA

⁴Department of Biology, Duke University, Durham, North Carolina 27708, USA

⁵Department of Zoology, Ohio Wesleyan University, Delaware, Ohio 43015, USA

⁶Institute of Integrative & Comparative Biology, Faculty of Biological Sciences, University of Leeds, Leeds LS2 9JT, UK

⁷Department of Ecology, Evolution and Environmental Biology, Columbia University, New York, New York 10027, USA

⁸National Center for Ecological Analysis and Synthesis, University of California at Santa Barbara, Santa Barbara, California 93106, USA

⁹Department of Ecology and Evolutionary Biology, Yale University, New Haven, Connecticut 06511, USA

¹⁰Institute of Environmental Sciences, Universit�t Z�rich, Z�rich, Switzerland

¹¹Department of Biology, McGill University, Montreal, QC, Canada

¹²Corresponding author: Bradley J. Cardinale ([email protected])

Research Origin Descriptors

The first version of this data set was compiled as part of the BioMERGE (Biotic Mechanisms of Ecosystem Regulation in the Global Environment) Adaptive Synthesis Workshop III funded by a grant from the U.S. National Science Foundation to Shahid Naeem (DEB 0435178).� After publication of a meta-analysis of version 1 (Cardinale et al. 2006), the data set was updated and extended as part of a grant from the U.S. National Science Foundation to Bradley Cardinale (DEB 0614428).� Two meta-analyses of the extended data set followed, with each focusing on different subsets of the data (Cardinale et al. 2007, Srivastava et al. 2008).� The present version of the data set (version 2) includes 545 measures of how species richness of producers, herbivores, consumers, or detritivores influences the standing stock abundance or biomass of the trophic group, the standing stock abundance or biomass of their primary resource(s), and the extent to which a trophic group depleted its primary resource(s).� Those who have taken lead on data summaries and analyses, and who are in the best position to answer questions pertaining to the data, are Bradley Cardinale ([email protected]) and Diane Srivastava ([email protected]).

Data set description

The metadata presented here correspond to the comma separated value data file named: BEF_summary_v2_Aug2008.csv.�Cells noted with "." indicate that the information is not relevant, not reported, or not available from the study.�Cells that are blank indicate that the information has yet to be collected (i.e., the data may or may not exist).

Column numbers, headings, descriptions, and column sums of numerical data for checking file upload:

Methods

Version 2 of this data set, available as BEF_summary_v2_Aug2008.csv, represents the sum of all data used in the meta-analyses of Cardinale et al. (2006, 2007) and Srivastava et al. (2008).� We encourage users of this data set to read the detailed methods that have also appeared in these other papers, in addition to the combined summary of methods that follows here.

Selection of studies: We collated reference lists from several recent summaries of biodiversity-ecosystem functioning research (Schwartz et al. 2000, Schmid et al. 2001, Covich et al. 2004, Hooper et al. 2005, Srivastava and Vellend 2006), and supplemented these with our own search of the ISI Web of Knowledge database using the keyword sequence species AND (diversity OR richness) AND (community OR ecosystem) AND (function OR functioning OR production OR productivity OR biomass OR predation OR decomposition OR herbivory).� To be included in our analysis, a study had to meet the following criteria:

1.  Study must focus on species richness rather than any other form of biological diversity (genetic, functional group, etc.).

2.  Study must be empirical, and directly manipulate richness as an independent variable.� Observational studies or experiments that indirectly manipulate richness via some additional treatment (e.g., nutrient addition) were not included.

3.  Study must manipulate at least three species within a focal trophic group.� This helped distinguish our summary from those of competition or predator-prey interactions.� If richness was manipulated simultaneously for multiple trophic groups, these manipulations must have been independent so that separate effect sizes could be calculated for each trophic group.

4.  Study must measure a direct effect of species richness in a given trophic group t on (i) the aggregate abundance or biomass (per area or volume) of all species in t, (ii) the aggregate abundance or biomass (per area of volume) of the resource(s) assimilated or consumed by trophic group t, or (iii) the depletion of the resource(s) assimilated or consumed by t.� Resource depletion was estimated as a near instantaneous rate of consumption (e.g., mg O₂ consumed or CO₂ produced per unit area or volume per unit time over short time intervals), through temporal changes by subtracting the amount of a resource available at the end of the experiment from that available at the beginning, or by subtracting resource loss measured in experimental units that have none of the focal species added.

5.  The study must focus on how species richness impacts the magnitude or rate of the response variables.� Other aspects of ecosystem functioning such as temporal stability or invasibility were not considered.

6.  Study must not duplicate data presented in another paper.� When studies overlapped, we chose the paper reporting the most complete information.

7.  If a study used an additive experimental design (i.e., abundance and/or biomass is intentionally confounded with richness so that one can assess non-additive species interactions), authors must specifically account for abundance or biomass as a covariate, or report the observed and expected values so that the difference can be taken as the effect attributable to diversity.

In total, we reviewed more than 200 papers that were published, or known to be accepted and in press, as of February 2006.� Of these, 85 papers reporting results from 164 independent experiments met the criteria above.� The full reference for each paper reviewed is given in the literature cited below.� The author and year of publication is cross-referenced in column four (Ref), and the source of extracted data shown in column 5 (Source) of BEF_summary_v2_Aug2008.csv.� The remaining columns of the dataset can be broadly divided into four general categories that we describe next.

Descriptors of the experiment: Columns 6 through 37 of the dataset provide descriptive information about each experiment.� We first report whether the study measured a top-down or bottom-up effect of diversity.� By top-down effect of diversity, we refer to the effect of a manipulation of species richness at trophic level t on the consumption of resources used by t, or on the standing stock abundance or biomass of t.� By bottom-up effects of diversity, we instead refer to the effect of a manipulation of the richness of resources on the consumption of the collective resource pool by t, or on the standing stock abundance or biomass of t.� We then outline whether the manipulated trophic level was producers, herbivores, higher consumers or detritivores, and detail whether the response variable was a standing stock of the focal trophic level, standing stock of the focal resource, or depletion of the focal resource.� Specific taxonomic information about the focal group of consumers and resource pool are provided.� Studies are then characterized according to the type of ecosystem they were performed in � first by aquatic verses terrestrial, and then more finely divided into 14 types of ecosystems � and then according to the experimental setting (e.g., lab/greenhouse, field, etc.).� We distinguish between several types of experimental designs, including how species pools were chosen and assembled, how many species and levels of richness were included in the experiment, and whether the initial biomass or densities of species were standardized according to a substitutive or additive experimental design.� For each study, we also recorded the size of the experimental units (area or volume) and duration of the experiment (in days), and standardized these relative to the average body mass and generation time of the species used in the study to obtain comparative estimates of the spatial and temporal scales at which each experiment performed (i.e., size of experimental unit per mean body mass � or � number of generations of the focal organisms).� The generation time and body size at age of reproduction for each species used each experiment was obtained using a hierarchy of five sources. First, if generation time or body size were included in the original publication for the experiment, we included those values.� Second, if these values were not present, we searched for publications in the primary literature using the ISI Web of Knowledge database, using each genus and species epithet as the keyword, and extracted values from these publications.� Third, if no primary papers were found, we search through physiological, taxonomic or life-history book treatments of larger taxonomic groups (i.e., phylum) for information on the species of interest.� Fourth, if book information was unavailable we searched governmental or non-governmental natural history websites.� Lastly, if generation time or body size information was not available through these sources, we queried authors of the original experiments for any unpublished data.

Response variables: Columns 40 through 126 of the data set provide information about each response variable at each level of species richness manipulated in the experiment.� This includes

(i)  the average value of the response variable across all replicate experimental units at a given level of species richness S,

(ii)  the standard deviation of the response variable across all replicate experimental units at a given level of species richness S,

(iii)  the number of replicate experimental units run for each level of species richness.� In most instances, replicates represent different species combinations run for a given level of richness.� In some instances, replicates represent identical species combinations run in different experimental units.� The nature of the replicates can only be understood in the context of the experimental design given in Descriptors of the Experiment above.

Diversity effect sizes: Columns 126 through 145 of the dataset summarize the effect of species richness on the response variables in two general ways.� First, we used two log response ratios to characterize the proportional change in the response variable between the highest vs. lowest levels of richness used in a study.� The first response ratio, LRR1, is calculated as the natural logarithm of the ratio of the mean value of the response variable measured in all replicates in the highest level of richness to the mean value of the response variable measured in all the species grown in monoculture.� The second log ratio, LRR2, tests whether the average response of the most species rich treatment was different than the species having the highest (if LRR1 > 0) or lowest (if LRR1 < 0) value in monoculture.� The advantage of log ratios is that they can be computed for most all experiments included in the dataset.� The disadvantage is that they only compare the highest to the lowest levels of richness used in a study, which can be misleading when the highest levels of richness vary widely across studies.� Therefore, for the subset of studies having a sufficient number of levels of species richness, we fit data from each study to three non-linear functions that have previously been used to describe diversity-function relationships in the literature.� These include the log function Y = b + m*log(S), the power function Y = m*S^b, and the Michaelis-Menton version of a hyperbolic function Y = Ymax* S/(K + S).� In each of these functions, Y is the value of the response variable, S is the number of species in the experimental unit, and b, m, Ymax and K are all fitted parameters that were estimated by maximum likelihood, and which describe the form of changes in Y as a function of S in a given experiment.� Goodness of fit, measured by R^2 values, is used to note the function that explains the greatest fraction of variation.� We caution, however, that in many cases the R^2 values are very close for different functions, and that this measure of fit does not discount for the parsimony (no. parameters) of a model.��

Partitioning of diversity effects: Columns 146 through 170 of the data set summarize any attempts in a study to partition the effects of species diversity into that portion attributable to species richness verses that portion attributable to changes in the composition or identity of species that accompany changes in richness.� These attempts to partition diversity effects take three general forms.� First, a number of studies have used nested ANOVAs to test the significance of, and partition the sums-of-squares attributable to, various combinations of species that are nested within levels of species richness.� This type of analysis is commonly used in studies where it is not possible to assess species-specific contributions to the response variable in polycultures (e.g., the contributions of different fungal species to decomposition).� In studies where it is possible to assess species-specific contributions to the response variable, Loreau (1998) summarized a sequence of metrics that help differentiation the contributions of single versus multiple species to an ecological process, with these metrics all based on the relative contributions of species to the response variable in a polyculture versus their contribution in a monoculture.� Loreau and Hector (2001) then devised a statistical method to differentiate two general mechanisms that contribute to the net diversity effect � selection effects (SE) and complementarity effects (CE).� SE represent changes in a response variable in polyculture that can be attributed to an individual species, such as can occur when the most productive species come to dominate the biomass of diverse polycultures.� In contrast, CE represent that portion of a diversity effect that cannot be attributed to by any single species.� Although positive values of CE are often taken as evidence for �niche complementarity� (e.g., resource partitioning or positive species interactions), CE actually represent the balance of all forms of niche partitioning that might influence biomass, as well as all forms of indirect and non-additive species interactions (Petchey 2003).� While this makes it impossible to equate CE to any single biological mechanism, CE and SE do quantify ecologically distinct factors that contribute to diversity effects (single vs. multi-species processes).

The calculations for each metric are given in Loreau (1998) and Loreau and Hector (2001), and are not repeated here.� We only caution that all metrics that attempt to partition diversity effects post-hoc are subject to certain limitations in their interpretation, and should not be falsely equated with any particular biological mechanism.� Users of the data should be aware of these limitations as discussed in Loreau (1998), Loreau and Hector (2001), Petchey (2003), and Cardinale et al. (2007).� We should also point out that since this data set was assembled, Fox (2005, 2006) has improved on several of these metrics, and users may want to be aware of these improvements as they become increasingly used in the literature.

Data-use policy

The data presented here are publicly available. We have spent ca. two years compiling, categorizing, and checking these data for the purpose of meta-analyses and encourage others to utilize the meta-database for further analyses. Those wishing to publish results from such analyses should read this meta-data document and consult the original publications from which they were collated.� The data set should be cited as:

Bradley J. Cardinale, Diane S. Srivastava, J. Emmett Duffy, Justin P. Wright, Amy L. Downing, Mahesh Sankaran, Claire Jouseau, Marc W. Cadotte, Ian T. Carroll, Jerome J. Weis, Andy Hector, and Michel Loreau. 2009. Effects of biodiversity on the functioning of ecosystems: A summary of 164 experimental manipulations of species richness. Ecology 90:854.

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ACKNOWLEDGMENTS

We wish to thank the numerous researchers who graciously shared their original datasets with us and then reviewed the summaries for accuracy. This work was supported by United States National Science Foundation Grants DEB 0614428 (to B.J.C.) and DEB 0435178 (to S. Naeem�s BioMERGE network).� We also thank Diversitas for financial support of the 2006 joint meeting with BioMERGE.

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