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Data for: Environmental and anthropogenic constraints on animal space use drive extinction risk worldwide

Citation

Hirt, Myriam R. et al. (2021), Data for: Environmental and anthropogenic constraints on animal space use drive extinction risk worldwide, Dryad, Dataset, https://doi.org/10.5061/dryad.v41ns1rwx

Abstract

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.

Methods

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 et al. 2014; Tamburello et al. 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 et al. 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 et al. 2016). We used the CHELSA database for temperature and precipitation (Karger et al. 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]. NASA EOSDIS Land Processes DAAC.

Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., et al. (2017). Climatologies at high resolution for the earth’s land surface areas. Sci Data, 4, 170122.

Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., et al. (2018). Data from: Climatologies at high resolution for the earth’s land surface areas, Dryad, Dataset. Dryad Digital Repository.

Tamburello, N., Côté, I.M. & Dulvy, N.K. (2015). Energy and the Scaling of Animal Space Use. The American Naturalist, 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. Global Ecology and Biogeography, 23, 1105–1114.

Venter, O., Sanderson, E.W., Magrach, A., Allan, J.R., Beher, J., Jones, K.R., et al. (2016). Global terrestrial Human Footprint maps for 1993 and 2009. Scientific data, 3, 160067.

Funding

German Research Foundation, Award: FZT 118

ERA-Net BiodivERsA - Belmont Forum, Award: FutureWeb

German Research Foundation, Award: FZT 118

ERA-Net BiodivERsA - Belmont Forum, Award: FutureWeb