DOI: 10.5061/dryad.g4f4qrfw2
title: A global synthesis and meta-analysis of the environmental heterogeneity effects on the freshwater biodiversity

description: In this study, we aimed to synthesize the global knowledge about the
 relationship between spatial Environmental Heterogeneity (EH) and
 freshwater biodiversity (i.e., taxonomic and functional diversity, and
 their respective α and β components). Through a systematic review, we
 integrated results from 98 studies, published in 33 different countries,
 about the role of spatial EH – biodiversity relationship in freshwater
 ecosystems. Following the PRISMA-ecoevol criteria, we generated a dataset
 with 276 observations of the effect of EH – biodiversity relationship. For
 the meta-analysis, we extracted the effect size from 74 studies that
 effectively tested the effect of EH over any metric of biodiversity.
 Through this subset, we provided 242 individual effect sizes with their
 respective variances and sample sizes.
description: Data collection We searched for studies that
 investigated the effect of EH over any metric of biological diversity
 (e.g., richness, taxonomic diversity indexes, functional diversity, or
 beta diversity). We applied three different approaches of data searching,
 aiming to detect as many studies as possible and, consequently, avoiding
 data bias. The first approach consisted of searching for terms in the
 three search engines ISI Web of Science, Scopus and Scielo, from 1945 to
 2020. We used 46 terms that are frequently used to refer to EH (e.g.,
 "habitat* heterogen*" OR "habitat* diversity" OR
 "habitat* complexity" OR "structural complexity" OR
 “fractal heterogen*” OR "biotop* heterogen*" OR
 "environment* complexity"). The second approach consisted of a
 general search for studies on the Google Scholar webpage, using the four
 most used terms to refer to EH according to a previous study. In this
 case, we established a maximum of 677 articles to screen because the
 number of results was excessive (1,370) and overestimated. The third
 approach consisted of searching the references of previous reviews and
 opinion articles that included freshwater ecosystems in the central theme.
 Through this, we added 29 studies that were not captured by the search
 engines and Google Scholar. After duplicate exclusion, we obtained 2,381
 studies. To select studies we considered the following
 criteria: (1) encompassed freshwater ecosystems and their associated
 organisms; (2) were either observational, experimental or both; (3)
 considered only spatial EH (temporal EH was not included); (4) explicitly
 investigated the relationship between EH and any biological metric of
 taxonomic and functional diversity; (5) were published in a peer-reviewed
 journal. In the title and abstract screening stage 2,176 studies we
 excluded, thus a total of 205 studies were fully read and evaluated. After
 this second screening, we selected 98 studies to be included in our
 dataset.  Qualitative data
 extraction To conduct a systematic integrative review
 we extracted information from the 98 studies  about: 1) geographical
 location; 2) type of freshwater ecosystem; 3) study type (i.e.,
 observational or experimental); 4) most cited term in the title and/or
 abstract used to indicate EH (e.g., “habitat heterogeneity”,
 “environmental complexity”); 5) environmental variables used as proxy of
 EH (e.g., substrate, flow velocity); 6) methodological approaches to
 manipulated EH proxies (e.g., qualitative variation, roughness); 7)
 calculation methods to measure EH (e.g., indices, coefficient of
 variation); 8) biological group evaluated; 9) zone of communities’
 occurrence (i.e., nekton, plankton, benthos or in the riparian
 meta-ecosystem); 10) type of biological metric considered as a response
 variable (i.e., functional or taxonomic, and alpha or beta), 11) whether
 experimental design controlled for the effect of surface area, and 12) the
 mechanisms invoked by authors to discuss patterns or used to test the
 relationship between EH and the biological metric response. We only
 considered the mechanisms that were explicitly cited by authors.
 Occasionally, studies encompassed more than one taxonomic group, at
 different occurrence zones, with different EH proxies, and used different
 methodological approaches and calculation methods. We incorporated this
 information in separate outcomes from the same study, which generated 276
 comparisons or independent registers. EH proxy denotes
 the element that authors actually quantified in the ecosystem aiming to
 evaluate the effect of EH over biological metrics of diversity. During
 data extraction, we identified several EH proxies used by authors, which
 were then sorted into 12 categories: aquatic vegetation; artificial
 substrates; natural substrates; channel morphology; elevation; food
 resources; land cover; riparian vegetation; water flow; water chemistry;
 wood debris; and mixed. The mixed category included studies that used more
 than one category of proxy to manipulate EH in the experiments
 The methodological approach denotes the way authors manipulated
 the EH proxies to create EH gradients or EH levels, aiming to test the
 effects of spatial EH on a biological metric. In the same way, we created
 eight categories of methodological approach: density; mixed; presence vs.
 absence; qualitative variation; quantitative variation; roughness;
 structure; and size variation. Quantitative data extraction For a meta-analytical study, we used 74 studies
 within our dataset that reported statistical results (e.g., correlation
 coefficient, mean and deviation values between treatments), variance and
 sample size which estimated the relationship between EH and any biological
 metric. When authors reported results in figures, we extracted information
 using the software ImageJ®. When effects were not provided in the text,
 table, or figures, we requested data and/or statistical results from the
 corresponding author of the study. Every statistical result reported in those studies
 was converted into a correlation coefficient (r). To estimate the
 magnitude of the overall effect of EH on taxonomic and functional alpha
 and beta diversity, we used Fisher’s z-value as the metric of effect size.
 We derive z values using the formula: z = 0.5 x ln [(1 + r) / (1 - r)].
 For each z value, we calculate its respective variance (vz) based on the
 sample size (n) of the test performed in the study: vz = 1 / (n - 3).
 Based on z variance and sample size, we calculated the 95% confidence
 intervals around each z value. Negative values of z represent a negative
 effect of the EH over the biological metric whereas positive z values
 represent a positive effect, and z = 0 represents no effect. If confidence
 intervals did not overlap zero, the estimate of the true effect size is
 considered significant. From the 74 studies, we
 recorded 242 individual effect sizes. We separated effect sizes into four
 distinct categories according to the type of biological diversity metric
 considered as the response variable: taxonomic alpha, taxonomic beta,
 functional alpha and functional beta. Among these, 200 referred to the
 effect size on taxonomic alpha, 20 on taxonomic beta, 16 on functional
 alpha and six on functional beta diversity. 
description: # A global synthesis and meta-analysis of the environmental heterogeneity
 effects on the freshwater biodiversity
 [https://doi.org/10.5061/dryad.g4f4qrfw2](https://doi.org/10.5061/dryad.g4f4qrfw2) The present database comes from a review and meta-analysis research that aimed to elucidate the overall effect of the environmental heterogeneity over biodiversity in freshwater ecosystems. The alpha and beta facets of taxonomic and functional diversity were considered, as biodiversity metrics. The research covers data from articles published in English between 1975 and 2020 around the world. The R code to run the meta-analysis and bias analysis is also available. ## Description of the data and file structure **1\. data\.csv file** \- is a database in which each line corresponds to an observation of the relationship between environmental heterogeneity and a biodiversity metric\. **2\. meta\_data\.csv** **file** \- describes each attribute of data\.csv file **3\. R\_scrip\.R file** \- provides the full code to calculate the z\-Fisher and variance\, as effects sizes\. Also\, we provide code to run four random\-effect meta\-analysis for each metric of biodiversity \(alpha and beta taxonomic and functional diversity\) and mixed\-effects meta\-analysis models that incorporated moderators to investigate variations on the overall mean effect size of EH on taxonomic alpha diversity\. ###