Bernhard Schmid, Andrea B. Pfisterer, and Patricia Balvanera. 2009. Effects of biodiversity on ecosystem community, and population variables reported 1974–2004. Ecology 90:853.
A. Data set identity:
Title: Effects of biodiversity on ecosystem, community, and population variables.
B. Data set identification code
Suggested Data Set Identity Code: 761BiodiversityEffects1974-2004.txt
C. Data set description
Abstract:
This metadata set contains 761 effects of biodiversity on ecosystem, community, or population response variables reported in 137 publications from 1974 to June 2004. All data were obtained in experimental studies. Relationships between species richness or other biodiversity measures and response variables are described by their shape, direction, significance, and, if available, the simple (regression) or multiple (analysis of variance) correlation coefficient. We classified response variables into groups according to the ecosystem function or service to which they were related, the trophic level at which they were measured, and several other classification schemes. Similarly, we classified studies into groups according to ecosystem type, experimental system (bottle/pot, greenhouse, field), range and trophic level of biodiversity treatments, and further design variables. Covariates include location of study, number of plots, and number of species used in the biodiversity treatments. Analyses of these metadata have shown, for example, that biodiversity effects on community (and ecosystem) variables are often positive, and those on population variables negative, that stocks are more responsive than rates to biodiversity manipulation, and that bottom-up biodiversity effects in multi-trophic studies are often negative. This metadata set gives a representative overview over the results of a first generation of biodiversity experiments and allows a quantitative assessment of the influence of a number of biological and design variables on the significance and shape of the relationship between biodiversity and response variables.
D. Key words: aquatic ecosystems; biodiversity experiments; design variables; ecosystem functioning; ecosystem services; effect size; significance; terrestrial ecosystems; trophic level.
Authors:
Bernhard Schmid1, 3, Andrea B. Pfisterer1, and Patricia Balvanera2
1Institute of Environmental Sciences, Universität Zürich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland
2Centro de Investigaciones en Ecosistemas, Universidad Nacional Autónoma de México, Apdo. Postal 27-3, Sta. Ma. de Guido, Morelia, Michoacán, México 58090
3Corresponding author: Bernhard Schmid ([email protected])
Research Origin Descriptors
Commissioned by the Swiss Office for the Environment, the second author of this data paper started to build up a meta-database of biodiversity studies published between 1974 and June 2004. A working group led by the three authors of this paper discussed which variables to include in this database and how to group studies and reported effects into different categories. The aim was to provide meta-data that would allow a rigorous evaluation of the likely effects of biodiversity loss on a variety of variables relevant to the sustainability of agriculture, forestry, fisheries and conservation. Biodiversity effects were defined broadly as relationships between species richness or other diversity measures and response variables. Biodiversity effects were described by their shape, direction and significance, and, if available, the simple (regression) or multiple (analysis of variance) correlation coefficient. The data were collected from 2002–2004 and analyses with a part of the data were carried out subsequently by the working group (Balvanera et al. 2006). In these and further analyses (Schmid et al., in press), we found for example differences in biodiversity effects among ecosystem types, experimental designs, and ecosystem service groups or other categories of response variables. The database team now consists of the following researchers, who can answer questions pertaining to the collection of data:
Bernhard Schmid ([email protected])
Patricia Balvanera ([email protected])
Data set description
The metadata presented here correspond to the data file named: 761BiodiversityEffects1974-2004.txt. This data set should be cited when data are utilized for any analysis. Missing data are indicated with “NA” in the corresponding field of the data set.
Column headings and descriptions:
r: Simple or multiple correlation coefficient between the explanatory variable or factor biodiversity and the measured response variable (see Methods section).
rType: Type of correlation coefficient: multivar (multiple), none, univar (simple) (see Methods section).
Zr: z-transformed correlation coefficient (see Methods section).
EffShape: Shape of the relationship between biodiversity and the measured response variable. Nine types of shapes were distinguished by inspecting the published material: negative, negative linear, negative log-linear, no, positive, positive linear, positive log-linear, positive not linear (see Methods section).
DirSign5: Significance level and direction of biodiversity effect: -2 (negative effect, P < 0.01), -1 (negative effect, P < 0.05), 0, 1 (positive effect, P < 0.05), 2 (positive effect, P < 0.01).
AuthorDate: Identifies the publication from which the data were extracted (see Methods section).
StudyID: A unique number for every study (see Methods section).
SiteID: A unique number with the prefix “S” for each study that was carried out at a separate single site, or a separate set of multiple sites (see Methods section).
EcosysType: Type of ecosystem used in the study: aquatic fresh, aquatic marine, bacterial microcosm, crop/successional, forest, grassland, ruderal/salt marsh, soil community.
ExpSyst: Type of experimental system: bottle/pot, field, greenhouse (see Methods section).
Manip: Manipulation of biodiversity levels: direct, indirect.
Design: Type of experiment (species densities): substitutive, additive, none (= indirect manipulations) (see Methods section).
IndDivCause: Cause of biodiversity gradient (indirect manipulations): management, natural, nutrient, succession, none (= direct manipulations) (see Methods section).
EstablFrom: Way experimental systems were established: juveniles, propagules/colonization, removal, none (see Methods section).
DivMeas: Type of the explanatory biodiversity variable: SpRichn (species richness), FgRichn (number of functional groups), SpPerFg (number of species per functional group), TrophRich (number of trophic levels), evenness, diversity (diversity index) (see Methods section).
FgLevels: Number of “number of functional groups” levels.
SpLevels: Number of species richness levels.
MaxSpNo: Species richness of highest biodiversity level: low, intermediate, high (see Methods section).
NoPlots: Number of experimental units.
NoSpec: Number of species in experimental pool.
TL manip: Trophic level where biodiversity varied: 1°Producer, 1°Consumer, 2°Consumer, Detritivores, Mycorrhiza, Multitrophic (see Methods section).
TL measur: Trophic level where response was measured: 1°Producer, 1°Consumer, 2°Consumer, Detritivores, Mycorrhiza, Multitrophic, Ecosystem (see Methods section).
TrophRel: Trophic level of response relative to level where biodiversity varied: above, below, eco (ecosystem), same, symbiont, within (level where biodiversity was varied multitrophic).
Variable: Description of response variable measured (see Methods section).
GroupVar: 30 response groups into which response variables were classified.
Service: Ecosystem service type to which response variable was assigned: p (provisioning), r (regulating), s (supporting) (see Methods section).
EcoServ: Ecosystem service to which response variable was assigned: bioregulation, climate regulation, nutrient cycling, prim prod (primary production), soil fertility, water cycl (water cycle), water quality (see Methods section).
ResidInvad: Response variable measured for resident (1) or invader (2) species.
StatDyn: Distinguishes between static and dynamic responses: static, variable (see Methods section).
RateStock: Response variable assigned to rates (r) or standing stocks (s) (see Methods section).
RespLevel: Response variable measured at community (com), ecosystem (eco), or population (pop) level (see Methods section).
Methods
The description of how data were extracted from the publications closely follows Balvanera et al. (2006). With the exception of data from three observational studies, all data analyzed by Balvanera et al. (2006) are contained in the meta-database presented here. This new meta-database contains 324 additional records for a new total of 761 records. We re-checked all the data, including those analyzed by Balvanera et al. (2006), and changed some signs of biodiversity effects (significances and correlation coefficients), assignments of measurements to particular categories of response variables or ecosystem services, and assignments of studies to particular categories of ecosystem type or design specifics.
We searched publications in the ISI Web of Science and in the Biological Abstracts database using the following key words: biodiversity, species richness, stability, ecosystem function, productivity, yield, food web. From a list of >1800 papers published from 1954–2004 we selected 137 articles according to a set of exclusion criteria. These 137 articles were published from 1974–June 2004 and contained 761 measurements of response variables potentially affected by biodiversity as explanatory variable or factor. Observational studies and modeling studies were excluded first. The further exclusion criteria were: duplicate or repeated measurements (for example, the same experiment and same measurement reported in a different publication or measured in a different year, in which case we entered the first measurement into the data base [excluding measurements from the establishment phase of an experiment]), studies that compared monocultures with mixtures of a single higher biodiversity level, or single-species removal experiments. We chose the first post-establishment measure among repeated measures to avoid confounding effects of study duration or study age (in the case where studies were still ongoing). Furthermore, biodiversity experiments in our own experience often achieve their best match between planned and realized biodiversity levels shortly after establishment. Where appropriate, we contacted authors to obtain additional information and additional publications. We entered information about specifics of experimental designs, the response variables measured and the direction, significance, size, and shape of reported effects into our database.
The size of biodiversity effects was measured as simple (regression analysis with biodiversity as explanatory variable) or multiple (analysis of variance [ANOVA] with biodiversity as multi-level explanatory factor) correlation coefficient, r (r and rType). We inspected material in text, tables and figures of publications to assign a positive or negative sign to r in cases where we had to obtain it from ANOVA or R2 values. If no direction could be assigned to a multiple correlation coefficient, we did not use it. Correlation coefficients were adjusted for the degrees of freedom used to fit a model. The correlation coefficients were z-transformed to obtain a measure of effect size, Zr, that better follows the normal distribution (r is bound between –1 and +1): Zr = 0.5 * ln ((1 + r) / (1–r)) (Zr).
We described the shape of relationships between biodiversity and response variables (EffShape) as precisely as possible by inspecting material in text, tables and figures of publications. In some cases the relationships showed “no” discernibly shape and in some cases it could only be classified as “negative” or “positive”. In other cases, it was possible to assign linear (“negative linear” or “positive linear”)or log-linear (“negative log-linear” or “positive log-linear”) shapes. Some positive relationships could be distinguished from linear but not be assigned to log-linear and where thus labeled “positive not linear.” Note that shapes were assigned independently of significance or size of biodiversity effects.
Each publication has a unique code made up from author names, publication year, and letter “a” or “b” if necessary (AuthorDate). Using this code the publications can be looked up in the Literature Cited section below. Because two publications reported more than one study each, we included a further column which was also used to sort the data set (StudyID). This factor with 142 levels should be used as random-effects term when testing effects of fixed terms which do not vary within studies. Study site (SiteID) refers to the location or environmental conditions of a particular study.
ExpSyst indicates whether a study was carried out in bottles (microcosm studies) or pots, in the greenhouse (including climate chambers), or in the field. Pot and greenhouse systems differ from field systems in that the latter experience natural climate and light regimes. Greenhouse and field systems included studies that directly and indirectly manipulated biodiversity (Manip). Direct manipulations of biodiversity were subdivided into those which were set up so that total density remained constant (“substitutive” experiments) and others (“additive” experiments), leaving the label “none” for observed densities in studies with indirect biodiversity manipulations (Design). Indirect manipulations of biodiversity were achieved in several studies using a variety of causes (IndDivCause), i.e. different kinds of “management”, “natural” environmental variation, “nutrient” application, or different stages of “succession”, leaving the label “none” for studies with direct biodiversity manipulations. Whereas indirect manipulations always resulted in non-randomly assembled communities, almost all direct manipulations resulted in randomly-assembled communities (exception Fridley 2002: “combinations were chosen to provide a range of differences in species sizes (based on anticipated mature height), with the constraint that each species be represented in two compositions”). None of the studies with direct manipulations of biodiversity used specific assembly rules such as predicted non-random extinction scenarios. The studies with indirect manipulations of biodiversity allowed species immigration. In almost all studies with direct manipulations of biodiversity, species immigration was prevented by using closed systems or by weeding. We know of one exception where a field experiment in Switzerland was not weeded (Stocker et al. 1999, Niklaus et al. 2001). Experiments where species immigration was deliberately allowed, to follow convergence of biodiversity levels, were carried out only recently (Pfisterer et al. 2004, Rixen et al. 2008).
Studies differed in the way experimental systems were established (EstablFrom). Synthetic experiments were started with young plants or animals (“juveniles”) or with seeds, spores, eggs etc. (“propagules/colonization”). Contrasted with these were “removal” experiments and studies which could not be classified (“none”). The explanatory biodiversity term in most studies was species richness (“SpRichn”); in few cases it was one of the following: number of functional groups (“FgRichn”), number of species per functional group (“SpPerFg”), number of trophic levels (“TrophRich”), species evenness, or a biodiversity index calculated from richness and abundance (“diversity”) (DivMeas). Three levels of maximum biodiversity were recognized: low (≤10 species), intermediate (11-20 species), and high (≥20 species) (MaxSpNo).
Studies that manipulated biodiversity (TL manip) or measured response variables (TL measur) at different trophic levels were categorized into: primary producer (“1°Producer”), primary consumer (“1°Consumer”), secondary consumer (“2°Consumer”), “detritivores”, “mycorrhiza”, “multitrophic”, and “ecosystem” (this only for TL measur). “Multitrophic” refers to studies where biodiversity was manipulated on more than one trophic level, or where the response variable involves more than one trophic level (e.g. total macrofaunal biomass). “Ecosystem” refers to response variables measured in the entire ecosystem within the abiotic compartment (e.g. nutrient loss from the system).
The different response variables reported in the 137 publications are briefly described in the column Variable. These descriptions are self-explanatory, but for detailed descriptions the original publications should be consulted. To allow a better analysis of biodiversity effects we spent much time on assigning the response variables to groups according to different classification schemes. First, we classified similar response variables into 30 response groups with self-explanatory labels to facilitate comparisons among contrasting types of properties (GroupVar). Next, we classified response variables into the three categories of ecosystem services proposed by the Millennium Ecosystem Assessment (MA 2003) (Service): provisioning (“p”), regulating (“r”), supporting (“s”). Then we classified response variables into groups considered to underpin each ecosystem service (EcoServ): bioregulation (e.g. biodiversity effects on pests and pathogens), climate regulation, nutrient cycling, primary production, soil fertility, water cycle, water quality.
The last four columns in the data set represent contrasts between different groups of response variables. We distinguished between response variables that could be assigned to resident species (“1”) vs. those that could be assigned to invader (“2”) species (ResidInvad); an invader species was defined as any species added after the establishment of a community. We were not able to distinguish between invaders belonging to the species pool used in an experiment and invaders external to that species pool or between native invaders and exotic invaders, because these distinctions were often not made in the original publications.
We also distinguished between effects on means (“static”) vs. changes or variances (“variable”) of response variables (StatDyn). Response variables related to fluxes or rates (“r”) were distinguished from those related to pools or standing stocks (“s”) (RateStock). Finally, we distinguished between population-level responses (“pop”) recorded for individual target species (density, cover, biomass, etc.), community-level responses (“com”) recorded for multi-species assemblages (density, biomass, consumption, diversity, etc.), and ecosystem-level responses (“eco”), which could not be assigned to population- or community-level (nutrients, water, CO2/O2, etc.) (RespLevel).
Data-use policy
The data presented here are publicly available data collected from the literature by the authors. We have spent about two person-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 metadata document and if necessary consult the original publications from which they were collected. The data set should be cited as follows:
Schmid, B., A. B. Pfisterer, and P. Balvanera. 2009. Effects of biodiversity on ecosystem, community, and population variables reported 1974–2004. Ecology 90:853.
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ACKNOWLEDGMENTS
We are grateful to our collaborators Nina Buchmann, Jing-Sheng He, Tohru Nakashizuka, and David Raffaelli who participated in a workshop and discussions to build the meta-database presented here and who coauthored together with us the first analysis paper using a part of these data. We thank the Swiss Biodiversity Forum for administrative support and guidance and SCOPE, with Chris Field, Carlo Heip and Osvaldo Sala for suggestions about the project design. We also thank two anonymous reviewers for their helpful comments. Finally, we thank the Swiss Agency for the Environment, Forests and Landscape (SAEFL) and the University of Zurich for financial support.