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Local versus broad scale environmental drivers of continental beta diversity patterns in subterranean spider communities across Europe

Citation

Mammola, Stefano et al. (2019), Local versus broad scale environmental drivers of continental beta diversity patterns in subterranean spider communities across Europe, Dryad, Dataset, https://doi.org/10.5061/dryad.qz612jm8z

Abstract

Macroecologists seek to identify drivers of community turnover (β-diversity) through broad spatial scales. Yet, the influence of local habitat features in driving broad-scale β-diversity patterns remains largely untested, due to the objective challenges of associating local-scale variables to continental-framed datasets. We examined the relative contribution of local- versus broad-scale drivers of continental β-diversity patterns, using a uniquely suited dataset of cave-dwelling spider communities across Europe (35–70° latitude). Generalized dissimilarity modeling showed that geographical distance, mean annual temperature, and size of the karst area in which caves occurred drove most of β-diversity, with differential contributions of each factor according to the level of subterranean specialization. Highly specialized communities were mostly influenced by geographical distance, while less specialized communities were mostly driven by mean annual temperature. Conversely, local-scale habitat features turned out to be meaningless predictors of community change, which emphasizes the idea of caves as the human accessible fraction of the extended network of fissures that more properly represents the elective habitat of the subterranean fauna. To the extent that the effect of local features turned to be inconspicuous, caves emerge as experimental model systems in which to study broad biological patterns without the confounding effect of local habitat features.

Methods

We compiled what we believe to be the first continental-scale geo-referenced dataset of subterranean spider communities across Europe. The dataset comprises data from 475 caves from 27 European countries, and covers a latitudinal range from 35° to 70°. In constructing the dataset, we deliberately choose caves for which we deemed the spider fauna to be exhaustively known and for which the morphological and environmental features were available, thus minimizing the number of missing data (‘NA’) in the dataset. Although we acknowledge that different sampling bias exists when it comes to estimate the diversity of species within subterranean habitats, by selecting only well-studied caves we assumed the sampling bias to be homogeneous within the caves included in the dataset. We associated high-resolution data to each cave, namely spider community composition along with information on local geomorphological and environmental features. The full dataset will be made available in a datapaper that is currently under review:

Mammola S, Cardoso P, Angyal D, Balázs G, Blick T, Brustel H, Carter J, Ćurčić S, Danflous S, Dányi D, Déjean S, Deltshev C, Elverici M, Fernández J, Gasparo F, Komnenov M, Komposch C, Kováč L, Kunt K B, Mock A, Moldovan O T, Naumova M, Pavlek M, Prieto C E, Ribera C, Rozwałka R, Růžička V, Vargovitsh R S, Zaenker S, Isaia M. Continental data on cave-dwelling spider communities across Europe (Arachnida: Araneae). Biodivers. Data J., submitted.

Here, we upload the databases elaborated for performing Generalized Dissimilarity Modellingin R, namelysite-by-environment (data_env.txt), and site-by-species matrices for troglophile (data_troglophiles.txt) and troglobiont (data_troglobionts.txt ) spiders, as well as the R code for generating the analyses (R_code.r).

Usage Notes

Datasets:

-data_env.txt =Environmental predictors formatted for generalized dissimilarity modeling.

-data_troglophiles.txt = ‘gdm’ Site-Pair Table for troglophiles.

-data_troglobionts.txt = ‘gdm’ Site-Pair Table for troglobionts.

Data are provide as tab-delimited txt. Please refer to the main publication for a full description of the variables (Appendix S1) and analyses (Appendix S2).

 

Codes:

R_code.r = R code for generating the analyses.