DOI: 10.5061/dryad.wwpzgmsv0
title: Data from: Large-scale variation in biodiversity–ecosystem functioning (BEF) relationships in aquatic metacommunities on terrestrial islands

description: Recent work has shown that the biodiversity of potential colonists in a
 landscape (the local species pool) may be more important for ecosystem
 functioning than the biodiversity in local habitat patches. However, it is
 unknown how such biodiversity-ecosystem functioning (BEF) relationships
 may change across different biomes. To explore such patterns, nested
 insular ecosystems where variation in local biodiversity and local species
 pool biodiversity can be reliably quantified can provide important
 insights. Study locations were rock pool metacommunities on isolated rocky
 outcrops (i.e., inselbergs) in Africa, Australia, Europe, and North
 America. The sampling time period was 2011-2019. Major taxa studied were
 freshwater invertebrates. We assembled a large-scale dataset of
 invertebrate metacommunities from replicated rock pool clusters on
 inselbergs as a model system to test the ability of local biodiversity and
 local species pool biodiversity to explain community biomass in organisms
 with different survival strategies (active or passive dispersers). To test
 our hypotheses, we used a combination of directed acrylic graph based path
 analyses and general linear mixed-effects models. The biodiversity of the
 local species pool was influenced by climate but did not significantly
 impact community biomass. Instead, local environmental gradients seem to
 override any species pool effects on community biomass. However, in line
 with expectations, the relationship between local biodiversity and biomass
 varied across inselbergs. Contrary to expectations, inselberg prominence
 did not influence the BEF slope. However, in drier conditions, the BEF
 relationship weakened for active dispersers, likely reflecting
 environmental limits on recolonisation. Thus, climate and dispersal
 strategy jointly shaped how biodiversity influenced community biomass.
 This study illustrates that even in a simple ecosystem there can be
 substantial geographical variation in the relationship between
 biodiversity and ecosystem functioning that may be partially explained by
 environmental conditions and by the survival strategy of the organisms
 considered.
description: The dataset consists of invertebrate communities sampled in 238
 rock pools from ten different inselbergs across four continents. On each
 inselberg site, between 12 and 34 pools were sampled. After sampling, the
 invertebrates from each rock pool were identified to the lowest possible
 taxonomic resolution in the laboratory (genus or species, when possible),
 and the number of individuals of each group was counted. The
 community-level invertebrate biomass per litre was calculated for each
 pool by multiplying species-specific biomass estimates by density. Alpha
 diversity was calculated as the local-scale species richness of each rock
 pool, and gamma diversity was calculated as the inselberg-scale species
 richness representing the diversity of the local species pool.
description: # Large-scale variation in BEF relationships in rock pools
 [https://doi.org/10.5061/dryad.wwpzgmsv0](https://doi.org/10.5061/dryad.wwpzgmsv0) ## Description of the data and file structure All code and data to reproduce the analysis reported in the following publication can be found in this repository: "Large-scale variation in biodiversity–ecosystem functioning (BEF) relationships in aquatic metacommunities on terrestrial islands." We assembled a global dataset of invertebrate metacommunities from replicated rock pool clusters on inselbergs as a convenient natural model system to test the ability of local biodiversity and local species pool biodiversity to explain community biomass in organisms with different survival strategies (active or passive dispersers). To test our macro-ecological hypotheses, we used a combination of hierarchical piecewise structural equation models (SEM) and general linear models. ### Files and variables #### File: active-passive-split-taxa.csv **Description:** The list of taxa names split into two columns, one for active dispersers and one for passive dispersers.  #### Files: alpha-all-raw.csv, alpha-active-raw.csv, alpha-passive-raw.csv **Description:** Alpha diversities for each rock pool in three datasets - a full dataset that contains all taxa and two subsets of the data that contain only actively or passively dispersing taxa, respectively. ##### Variables * Inselberg: three letter code specifying the inselberg from which the data were gathered.  * Pool: identity of the specific rock pool sampled.  * Alpha: alpha diversity for each rock pool as estimated using the Chao-1 estimator based on abundance. * ASE: standard error corresponding to the alpha Chao diversity.  * Biomass: community-level invertebrate biomass per litre.  * Gamma: gamma diversity for each inselberg as estimated using the Chao-1 estimator based on incidence frequency.  * GSE: standard error corresponding to the gamma Chao diversity.  * Depth: estimated depth for each rock pool (cm).  * PC1: PC1 scores of the principal component analysis (PCA) of 19 different bioclimatic variables for each inselberg.  * PC2: PC2 scores of the principal component analysis (PCA) of 19 different bioclimatic variables for each inselberg.  #### File: bioclim-data.csv **Description:** For each inselberg, 19 different bioclimatic variables were extracted from the WorldClim database (Fick & Hijmans, 2017) at a resolution of 0.04167°. More information about the bioclimatic variables can be found on the WorldClim website ([www.worldclim.org](http://www.worldclim.org)).  #### File: bioclim-pca-scores.csv **Description:** PC1 and PC2 scores for the principal component analysis (PCA) conducted on the 19 bioclimatic variables at each inselberg location.  #### Files: gamma-all-raw.csv, gamma-active-raw.csv, gamma-passive-raw.csv **Description:** Gamma diversities for each inselberg in three datasets - a full dataset that contains all taxa and two subsets of the data that contain only actively or passively dispersing taxa, respectively. ##### Variables * Inselberg : three letter code specifying the inselberg from which the data were gathered.  * Gamma: gamma diversity for each inselberg as estimated using the Chao-1 estimator based on incidence frequency. * Biomass (median): meadian value of the community-level invertebrate biomass (per litre) value of each of the pools on an inselberg.  * PC1: PC1 scores of the Principal component analysis (PCA) of 19 different bioclimatic variables for each inselberg.  * PC2: PC2 scores of the Principal component analysis (PCA) of 19 different bioclimatic variables for each inselberg.  * Depth (median): median value of the depths of all rock pools on an inselberg * SSE: the standard error corresponding each slope coefficient.  * GSE: standard error corresponding to the gamma Chao diversity. * Prominence: height of an inselberg above the surrounding landscape.  #### File: pool-community-abundance-data.csv, pool-community-biomass-data.csv **Description:** Abundances values and biomass per litre for each taxa in each of the rock pools. Rows correspond to individual rock pools. Column headings correspond to individual taxon.  #### File: pool-sediment-data.csv **Description:** Sediment cover and depth for individual rock pools.  ##### Variables * Location: three letter code specifying the inselberg from which the data were gathered.  * Poolname: identity of the specific rock pool sampled.  * Lndepth: Depth of the rock pools. The values are ln-transformed to improve linearity. * LnSedPerVRatio: Sediment cover measured as the percentage of the basin covered with sediment and is standardised by the pool volume. The values are ln-transformed to improve linearity. #### File: raw-data-alpha.csv **Description:** Raw data at individual rock pool scale with depth values for each rock pool.  ##### Variables * Inselberg: three letter code specifying the inselberg from which the data were gathered.  * Pool: identity of the specific rock pool sampled.  * Depth: estimated depth for each rock pool (cm).  #### File: raw-data-gamma.csv **Description:** Raw data at inselberg scale with data on inselberg prominence (m) and location.  ##### Variables * Inselberg: three letter code specifying the inselberg from which the data were gathered.  * Prominence: height of an inselberg above the surrounding landscape. * Longitude: Longitude value for the inselberg expressed in decimal degrees. * Latitude: Latitude value for the inselberg expressed in decimal degrees. ## Code/software To run the files you need the software R. Download all the data and scripts and save it locally in a folder - this will be your working directory. Within the working directory there should be three sepparate folders labelled exactly as follows (without the invered commas) (1) "Data-raw", (2) "Code" and (3) "Figures". Store all the downloaded data files in the 'Data' folder and all the R scripts in the 'Code' folder.  To run the code correctly, it is important to create a R-Project. In R-Studio go to File &gt; New Project... &gt; Existing Directory &gt; Choose the extracted directory. Download the relevant packages For package version control, we used *renv* (Ushey and Wickham 2023). Therefore, in the repository, there is a renv lockfile (renv.lock) that contains information about the versions of all packages used in the analysis. To download the correct versions of all packages that we used in the analysis, you need to install the renv package: `+ install.packages("renv")` Once renv is installed, in the R-console, run the following command: `+ renv::restore()` The `restore()` command will download the correct versions of all packages used in the analysis into a local folder in the repository called 'renv'. Run the scripts In the 'Code' folder, there are 15 scripts numbered 01-15 that need to be run in order. If you correctly downloaded and stored the files as described above, then the scripts should run smoothly. File '00-prepare-RAW-data' is the file used to prepare the data used for analysis. This is already uploaded into this repository and thus, this script doesn't need to be run.