DOI: 10.5061/dryad.qrfj6q5r7
title: Environmental heterogeneity, rather than stability, explains spider assemblage differences between ecosystems

description: Open ecosystems (e.g., grasslands, prairies, shrublands) tend to be
 ecologically less stable than closed ones (i.e., forests) and encompass
 higher spatial heterogeneity in terms of environmental diversity. Such
 differences are expected to differentially constrain the diversity and
 structure of the communities that inhabit each of them, but identifying
 the specific processes driving contrasting biodiversity patterns between
 open and closed systems is challenging. In order to understand how
 environmental variability might structure spider assemblages, both between
 and within open and closed ecosystems, we implement a high throughput
 multiplex barcode sequencing approach to generate a dataset for 8585
 specimens representing 168 species, across open ecosystems within the
 Canary Islands. Combining these with spider sequences from closed
 ecosystems within the same islands, we show that spider communities in
 open ecosystems show higher species richness, higher beta diversity, and
 higher proportions of rare species but proportionately lower numbers of
 endemic species than communities in closed ecosystems. We furthermore
 assess if environmental heterogeneity and habitat stability are the major
 drivers of such differences by assessing spatial genetic structuring and
 the influence of bioclimatic variables. Our results point to environmental
 heterogeneity rather than stability as a major driver of spatial patterns
 between open and closed ecosystems.
description: A total of 25 sites were sampled within open shrublands
 characterised by the presence of species of Euphorbia across the four
 western islands of the Canary Archipelago. These data were complemented
 with an existing dataset including 31 closed laurel forest sites across
 the same islands. Sampled individuals were examined under a
 stereomicroscope, and provisional taxonomic assignments were made at the
 species, genus, or family level. Subsequently, all specimens were
 DNA-barcoded using the multiplex barcoding approach. Community
 dissimilarity matrices were estimated at the species level across all
 sites. Matrices were generated using total β diversity (Sørensen index,
 βsor) and its additive turnover (Simpson index, βsim) and nestedness
 (βsne) components. To disentangle the contribution of different factors in
 the βsor within ecosystems, generalised dissimilarity models (GDM) were
 applied. A matrix of dissimilarity across sites was implemented as the
 response variables, and geographical distance and bioclimatic variables
 from the sampling sites were implemented as predictors. Environmental
 variables (the same 19 bioclimatic variables defined in WorldClim, Fick,
 and Hijmans 2017) were downloaded at the finest resolution available (100
 m) from CanaryClim (Patiño et al. 2023). Correlations between predictor
 variables were assessed using Pearson's rank correlation coefficient
 r, establishing r &lt; |0.7| as a threshold to avoid multicollinearity
 among the bioclimatic variables used as predictors. Models were then
 fitted, setting 50 permutations to evaluate the models and variable
 significance (gdm function, gdm package), to estimate variable importance
 (gdm.varImp function, gdm package), and to perform deviance partitioning
 (gdm.partition.deviance function, gdm package). Models were conducted for
 each ecosystem separately, both across and within islands. Finally, the
 overall environmental heterogeneity and the spatial structure of this
 environmental heterogeneity were compared between open and closed
 ecosystems. First, principal component analyses (PCA) (dudi.pca function,
 ade4 package; Dray and Dufour 2007) were conducted both at the archipelago
 and the within-island scale using the 19 bioclimatic variables from Patiño
 et al. (2023) described before. The betadisper function (vegan package;
 Oksanen et al. 2022) was used to evaluate the multivariate homogeneity of
 group dispersions (mean distances to centroid) between ecosystems. To test
 for temporal stability between ecosystems, generalised linear models (glm
 function, stats package) were fitted with a Gaussian error structure
 implementing as dependent variables the following bioclimatic variables
 related to variation: BIO2 (mean diurnal temperature range), BIO7
 (temperature annual range), BIO3 (isothermality), and BIO15 (precipitation
 seasonality). Finally, multiple regression on distance matrices (MRM
 function, ecodist package; Goslee and Urban 2007) was implemented to
 assess the correlation between the geographical and environmental
 (euclidean) distance matrixes for each ecosystem at both archipelago and
 island scales. To test for the geographic structuring of genetic variation
 within PBS, the fixation index NST was calculated using SPAGeDI 1.5. NST
 was calculated both among islands (each island is considered a population)
 and within individual islands (each sampling site is considered a
 population).
description: # Environmental heterogeneity, rather than stability, explains spider
 assemblage differences between ecosystems This README.txt file was
 generated on 2024-08-21 by Daniel Suárez ## GENERAL INFORMATION 1. Author
 Information * Corresponding Investigator Name: Dr. Brent Charles Emerson
 Institution: IPNA-CSIC, San Cristóbal de La Laguna, Canary Islands, Spain
 Email: [bemerson@ipna.csic.es](mailto:bemerson@ipna.csic.es) *
 Co-investigator 1 Name: Daniel Suárez Institution: IPNA-CSIC, San
 Cristóbal de La Laguna, Canary Islands, Spain * Co-investigador 2 Name:
 Paula Arribas Institution: IPNA-CSIC, San Cristóbal de La Laguna, Canary
 Islands, Spain * Co-investigator 4 Name: Amrita Srivathsan Institution:
 Museum für Naturkunde, Leibniz Institute for Evolution and Biodiversity
 Science, Centre for Integrative Biodiversity Discovery, Berlin, Germany *
 Co-investigator 5 Name: Rudolf Meier Institution: Museum für Naturkunde,
 Leibniz Institute for Evolution and Biodiversity Science, Centre for
 Integrative Biodiversity Discovery, Berlin, Germany 2. Date of data
 collection: 2012-2021 3. Geographic location of data collection: Canary
 Islands, Spain 4. Recommended citation for this dataset: Suárez et al.
 (2024). Data from: Environmental heterogeneity, rather than stability,
 explains spider assemblage differences between ecosystems. Dryad Data
 Repository. ## DATA & FILE OVERVIEW These datasets were generated to
 investigate how environmental variability might 8 structure spider
 assemblages, both between and within open and closed ecosystems in a
 community of spiders #### File List: File 1 Name: Climatic_data.xlsx File
 Description: database bioclimatic data for the selected sites. File 2
 Name: input_GDM.csv File Description: database with bioclimatic and
 faunistic data needed for Generalised Dissimilarity Models. File 3 Name:
 Nst_data.csv File Description: a database including fixation indexes (Nst)
 for the studied spider species. File 4 Name: Species_matrix.csv File
 Description: a database including presence-absence data for the spider
 species. ### DATA-SPECIFIC INFORMATION FOR: Climatic\_data.xlsx 1. Number
 of variables: 24 2. Number of cases/rows: 57 3. Variables List: * island:
 code of each island (TF - Tenerife; LG - La Gomera; LP - La Palma; EH - El
 Hierro) * ecosystem: type of each ecosystem (closed or open) * site: code
 of each site * lon: longitude (geographical coordinates) * lat: latitude
 (geographical coordinates) * BIO19: precipitation of the coldest quarter
 (kg/m2) * BIO18: precipitation of the warmest quarter (kg/m2) * BIO17:
 precipitation of the driest quarter (kg/m2) * BIO16: precipitation of the
 wettest quarter (kg/m2) * BIO15: precipitation seasonality (coefficient of
 variation) (kg/m2) * BIO14: precipitation of the driest month (kg/m2) *
 BIO13: precipitation of the wettest month (kg/m2) * BIO12: annual
 precipitation (kg/m2) * BIO11: mean temperature of the coldest quarter
 (ºC) * BIO10: mean temperature of the warmest quarter (ºC) * BIO9: mean
 temperature of the warmest quarter (ºC) * BIO8: mean temperature of the
 wettest quarter (ºC) * BIO7: temperature annual range (BIO5-BIO6) (ºC) *
 BIO6: min temperature of the coldest month (ºC) * BIO5: max temperature of
 the warmest month (ºC) * BIO4: temperature seasonality (standard deviation
 * 100) (ºC/100) * BIO3: isothermality ((BIO2/BI07)*100) (ºC) * BIO2: mean
 diurnal range (mean of monthly (max temp - min temp)) (ºC) * BIO1: annual
 mean temperature (ºC) ### DATA-SPECIFIC INFORMATION FOR: input\_GDM.csv 1.
 Number of variables: 23 2. Number of cases/rows: 1960 3. Variables List: *
 site: code of each site * PBS: probable biological species (PBS) code *
 lon: longitude (geographical coordinates) * lat: latitude (geographical
 coordinates) * BIO19: precipitation of the coldest quarter (kg/m2) *
 BIO18: precipitation of the warmest quarter (kg/m2) * BIO17: precipitation
 of the driest quarter (kg/m2) * BIO16: precipitation of the wettest
 quarter (kg/m2) * BIO15: precipitation seasonality (coefficient of
 variation) (kg/m2) * BIO14: precipitation of the driest month (kg/m2) *
 BIO13: precipitation of the wettest month (kg/m2) * BIO12: annual
 precipitation (kg/m2) * BIO11: mean temperature of the coldest quarter
 (ºC) * BIO10: mean temperature of the warmest quarter (ºC) * BIO9: mean
 temperature of the warmest quarter (ºC) * BIO8: mean temperature of the
 wettest quarter (ºC) * BIO7: temperature annual range (BIO5-BIO6) (ºC) *
 BIO6: min temperature of the coldest month (ºC) * BIO5: max temperature of
 the warmest month (ºC) * BIO4: temperature seasonality (standard deviation
 * 100) (ºC/100) * BIO3: isothermality ((BIO2/BI07)*100) (ºC) * BIO2: mean
 diurnal range (mean of monthly (max temp - min temp)) (ºC) * BIO1: annual
 mean temperature (ºC) ### DATA-SPECIFIC INFORMATION FOR: Nst\_data.csv 1.
 Number of variables: 25 1. Number of cases/rows: 149 2. Variables List: *
 Ecosystem: type of each ecosystem (closed or open) * PBS: Probable
 Biological Species (PBS) code * Taxonomy: PBS taxonomical classification *
 Dispersal: dispersal ability (NoBallooning; poor dispersal ability -
 Ballooning; good dispersal ability) * Nseq_AR: number of sequences at the
 archipelago scale * Gst_AR: Gst value at the archipelago scale *
 Gst_pval_AR: p-value for Gst value at the archipelago scale * Nst_AR: Nst
 value at the archipelago scale * Nst_pval_AR: p-value for Nst value at the
 archipelago scale * Gst_TF: Gst value at the within island (Tenerife)
 scale * Gst_pval_TF: p-value for Gst value at the within island (Tenerife)
 scale * Nst_TF: Nst value at the within island (Tenerife) scale *
 Nst_pval_TF: p-value for Nst value at the within the island (Tenerife)
 scale * Gst_LG: Gst value at the within the island (La Gomera) scale *
 Gst_pval_LG: p-value for Gst value at the within island (La Gomera) scale
 * Nst_LG: Nst value at the within island (La Gomera) scale * Nst_pval_LG:
 p-value for Nst value at the within island (La Gomera) scale * Gst_LP: Gst
 value at the within the island (La Palma) scale * Gst_pval_LP: p-value for
 Gst value at the within the island (La Palma) scale * Nst_LP: Nst value at
 the within island (La Palma) scale * Nst_pval_LP: p-value for Nst value at
 the within the island (La Palma) scale * Gst_EH: Gst value at the within
 island (El Hierro) scale * Gst_pval_EH: p-value for Gst value at the
 within island (El Hierro) scale * Nst_EH: Nst value at the within island
 (El Hierro) scale * Nst_pval_EH: p-value for Nst value at the within
 island (El Hierro) scale Nst and Gst values range from 0 (no geographic
 structuring) to 1 (geographic structuring). NA values indicate that Nst
 and/or Gst were not calculated for a given PBS. ### DATA-SPECIFIC
 INFORMATION FOR: Species\_matrix.csv 1. Number of variables: 248 2. Number
 of cases/rows: 57 3. Variables List: * site: code of each site *
 PBS001-247: remaining rows (PBS001 to PBS247) refers to values of presence
 (1) or absence (0) in a given site (rows)