Dataset for the analysis of boundaries between biogeographic regions in amphibians, birds and mammals

Worldwide distribution patterns of living animals are structured in multiple zoogeographical regions, characterized by faunas with homogeneous composition that are separated by sharp boundaries. Tectonic movements, abrupt climatic transitions, and orographic barriers have shaped extant boundaries between these zoogeographical regions. These data allow to assess whether and how the drivers of global zoogeographical boundaries vary between vertebrate clades taxa with different life-history traits.<br>Description of files:<br><br>We provide nine tables reporting data on the global zoogeographical boundaries of mammal, bird and amphibian class. For each class, we provide one table with all the boundaries, one table with the deep boundaries only (i.e. between realms) and with the shallow boundaries only (i.e. between subregions).<br><br>Data are in Mollweide projection, with cell size of 200 km, and can be imported in any GIS software (delimited text).<br><br>The 9 tables include the following column:<br><br>Boundary: 1: the cell is on the boundary; 0: the cell is not on the boundary<br>x: Mollweide x coordinates<br>y: Mollweide y coordinates<br>p_bio1.cv.k: heterogeneity of absolute annual mean temperature<br>p_bio4.cv: heterogeneity of temperature seasonality<br>p_bio12.cv: heterogeneity of annual precipitation<br>p_bio15.cv: heterogeneity of precipitation seasonality<br>p_alt_d: averaged altitude difference (mean of absolute vales) between each cell and the eight neighbouring ones (i.e. orographic barriers)<br>points_stab: tectonic separation, calculated as the variation of geographical distance between each cell and the neighbouring ones during the last 65 million years<br>p_vel: mean velocity of temperature change during the late quaternary for each cell (Sandel et al., 2011)<br><br>Climatic layers were log-transformed to reduce skewness and improve normality, and then scaled and normalized (mean = 0 and variance = 1)<br><br>Furthermore, we provide the R code to run the analyses using hierarchical hierarchical generalised linear mixed models (HGLM) with spatially autocorrelated error. The running example is the analysis on all the geographical boundaries for mammals; the analyses of all the vertebrates can be obtained just by changing the name of the table<br>The analyses of this dataset have been published in the following paper:Ficetola, G. F., F. Mazel, M. Falaschi, S. Marta, and W. Thuiller. 2021. Determinants of zoogeographical boundaries differ between vertebrate groups. Global Ecology and Biogeography 30:1796-1809. https://doi.org/10.1111/geb.13345<br>