DOI: 10.5061/dryad.v41ns1rwx
title: Data for: Environmental and anthropogenic constraints on animal space use drive extinction risk worldwide

description: Animals require a certain amount of habitat to persist and thrive, and
 habitat loss is one of the most critical drivers of global biodiversity
 decline. While habitat requirements have been predicted by relationships
 between species traits and home range size, little is known about
 constraints imposed by environmental conditions and human impacts on a
 global scale. Our meta-analysis of 395 vertebrate species shows that
 global climate gradients in temperature and precipitation exert indirect
 effects via primary productivity, generally reducing space requirements.
 Human pressure, however, reduces realized space use due to ensuing
 limitations in available habitat, particularly for large carnivores. We
 show that human pressure drives extinction risk by increasing the mismatch
 between space requirements and availability. We use large-scale climate
 gradients to predict current species extinction risk across global
 regions, which also offers an important tool for predicting future
 extinction risk due to ongoing space loss and climate change.
description: We used published data on home range sizes and species traits
 such as body mass, thermoregulation (endotherm, ectotherm), trophic guild
 (carnivore, herbivore, omnivore), and locomotion mode (running, flying,
 swimming) of terrestrial and semi-aquatic amniotes by combining the most
 extensive databases to date (Tucker <i>et al.</i> 2014;
 Tamburello <i>et al.</i> 2015). These databases included home
 range estimates from different tracking and analysis methods. As a prior
 study showed that these methodological differences do not affect the
 scaling relationships (Tucker <i>et al.</i> 2014), and we have
 put the highest priority on estimating the slopes accurately (which is at
 risk in mixed models with insufficient replication of the independent
 variables such as body mass or temperature within each random block such
 as methodology), we have refrained from using them in our study. We then
 extracted the geographic location of each study site from the original
 publications. At best, the paper directly provided the longitude and
 latitude of the study site. However, we also used studies where a detailed
 description gave the location (e.g., naming nearby cities or national
 parks). If there were several study sites in the same reference, we
 extracted the home range size and geographic location for each study site
 separately, if possible. We excluded all studies where only one individual
 home range was measured, and all studies where the geographic location was
 not precisely described. For all semi-aquatic animals, we chose the
 nearest point on land to obtain the environmental variables. We used the
 center of the study location as an approximation to the home range's
 geographic location. We then extracted the corresponding data on
 temperature, precipitation, Normalized Difference Vegetation Index (NDVI),
 and Human Footprint Index (HFI) from global databases (Table S1). The
 Human Footprint Index cumulates different proxies of human pressure on the
 environment, such as the amount of built environments, croplands, pasture
 lands, population density, nightlights, railways, major roadways, and
 navigable waterways (Venter <i>et al.</i> 2016). We used the
 CHELSA database for temperature and precipitation (Karger <i>et
 al.</i> 2017, 2018) and MODIS data for NDVI (MOD13C2; Didan 2015).
 We extracted the annual mean data of the corresponding study year. While
 temperature extremes should also have an effect on the metabolism,
 behavior and feeding performance of animals, we focused on the annual
 averages of temperature to test for relationships with the average NDVI
 and the long-term averages of metabolic rates because (1) all of these
 measures integrate over time periods, and (2) we could establish causal
 relationships for our SEM analysis (see below). For HFI, only maps from
 either 1993 or 2009 were available. Thus, we used the HFI from 1993 for
 studies before 1999, the mean HFI from 1993 and 2009 for studies from
 1999-2003, and the HFI from 2009 for studies after 2003. Didan, K. (2015). MOD13C2 MODIS/Terra
 Vegetation Indices Monthly L3 Global 0.05Deg CMG V006 [Data set].
 <i>NASA EOSDIS Land Processes DAAC.</i> Karger, D.N., Conrad, O., Böhner, J.,
 Kawohl, T., Kreft, H., Soria-Auza, R.W., <i>et al.</i> (2017).
 Climatologies at high resolution for the earth’s land surface areas.
 <i>Sci Data</i>, 4, 170122. Karger, D.N., Conrad, O., Böhner, J.,
 Kawohl, T., Kreft, H., Soria-Auza, R.W., <i>et al.</i> (2018).
 Data from: Climatologies at high resolution for the earth’s land surface
 areas, Dryad, Dataset. <i>Dryad Digital
 Repository</i>. Tamburello, N., Côté, I.M. &
 Dulvy, N.K. (2015). Energy and the Scaling of Animal Space Use.
 <i>The American Naturalist</i>, 186, 196–211. Tucker, M.A., Ord, T.J. &
 Rogers, T.L. (2014). Evolutionary predictors of mammalian home range size:
 body mass, diet and the environment. <i>Global Ecology and
 Biogeography</i>, 23, 1105–1114. Venter, O., Sanderson, E.W., Magrach,
 A., Allan, J.R., Beher, J., Jones, K.R., <i>et al.</i> (2016).
 Global terrestrial Human Footprint maps for 1993 and 2009.
 <i>Scientific data</i>, 3, 160067.