DOI: 10.5061/dryad.nk98sf7r5
title: Climate-diversity relationships underlying cross-taxon diversity of the Africa fauna and their implications for conservation

description: Aim: Many taxa show remarkable similarities in their diversity patterns
 and these similarities are commonly used to define large-scale
 conservation priorities. Here, we investigated the relative importance of
 contemporary climate and climate change since the Last Glacial Maximum for
 determining the species richness and rarity patterns of four animal taxa.
 We assessed the extent to which diversity patterns are congruent across
 taxa because of similar responses to these climatic aspects and we
 identify regions that are disproportionately diverse due to their
 paleoclimatic stability. Location: Sub-Saharan Africa. Time period:
 LGM–contemporary. Major taxa studied: Mammal, bird, amphibian and
 dragonfly species. Methods: Diversity patterns were predicted based on
 their relationships with contemporary climate and Quaternary climate
 change, respectively. Climate-diversity relationships were modelled with
 and without accounting for spatial autocorrelation. For raw and predicted
 diversity patterns, cross-taxon congruence and the coverage of diversity
 hotspots by protected areas were determined. Results: Species richness and
 rarity of all taxa increased with increasing temperature and
 precipitation, but also with increasing paleoclimatic stability.
 Cross-taxon congruence was higher for predictions based on contemporary
 climate than for predictions based on Quaternary climate change. Protected
 areas covered 17–37% of the species richness and rarity hotspots and
 approximately 6% fewer hotspots of the underlying signatures of Quaternary
 climate change (i.e. biodiversity refugia). Main conclusions: Both
 contemporary climate and past climatic changes strongly affect species
 richness and rarity patterns. However, whereas contemporary
 climate-diversity relationships are largely congruent across taxa,
 signatures of Quaternary climate change differ among taxa. Furthermore,
 protected areas emphasise regions with high species richness and rarity
 but fewer biodiversity refugia – even less than expected by random
 placement (&lt; 21%). Our results highlight the importance of
 historical factors for shaping large-scale diversity patterns and the
 potential of using paleoclimatic stability-diversity relationships for
 identifying important conservation areas at the global scale.
description: Our analyses were based on vector maps of the distributions of
 all 1,001 terrestrial mammalian species, 1,942 bird species and 723
 amphibian species (from IUCN, 2016 and BirdLife International and
 NatureServe, 2016) as well as all 731 dragonfly species (from Clausnitzer
 et al., 2012, accessed August 16, 2016) of sub-Saharan Africa. Vector maps
 were reassigned to a grid with a grain size of approximately 50 km × 50 km
 (Lambert azimuthal equal area projection centred on 5°N 20°E) using
 functions provided in the R-package <i>raster</i> (Hijmans et
 al., 2016). Based on the distribution data, we first
 calculated the species richness and rarity of the assemblages (e.g.
 <strong>mamma_rich </strong>and
 <strong>mamma_CWE</strong>). Subsequently, we predicted the
 variation in these diversity patterns based on contemporary climate (e.g.
 pred_mamma_current) and the climatic changes since the LGM (e.g.
 <strong>pred_mamma_current</strong>), respectively (see the
 section “Statistical analysis” for details). Hotspots of diversity
 patterns were arbitrarily defined as the 300 grid cells with the highest
 (raw or predicted) species richness and rarity (e.g.
 <strong>mamma_300</strong>). We evaluated
 the importance of contemporary climate and climatic changes since the LGM
 (PMIP boundary conditions for the LGM, CCSM4 coupled climate model
 prediction) for shaping the species richness and rarity patterns of the
 considered taxa, based on the same 4 bioclimatic variables for each time
 period (for results based on all 19 bioclimatic variables see Supporting
 Information). The environmental variables were downloaded from http://chelsa-climate.org (Karger et al., 2017, 2018). All variables were aggregated to obtain mean values for the grid cells.  To estimate climatic changes since the LGM, for each grid the AMT, TS, AP and PS (i.e. <strong>bio1 </strong>= annual mean temperature, <strong>bio4 </strong>= temperature seasonality, <strong>bio12 </strong>= annual precipitation, <strong>bio15 </strong>= precipitation seasonality) values during the LGM were subtracted from those of the corresponding contemporary climate variables (e.g. <strong>current_bio1 </strong>-<strong> paleo_bio1</strong> = <strong>ano_bio1</strong>).
description: Raw data of species distributions is available at request from
 the above-mentioned institutions and authors.