Skip to main content

Climate-diversity relationships underlying cross-taxon diversity of the Africa fauna and their implications for conservation

Cite this dataset

Pinkert, Stefan et al. (2021). Climate-diversity relationships underlying cross-taxon diversity of the Africa fauna and their implications for conservation [Dataset]. Dryad.


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 (< 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.


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 raster (Hijmans et al., 2016).

Based on the distribution data, we first calculated the species richness and rarity of the assemblages (e.g. mamma_rich and mamma_CWE). 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. pred_mamma_current), 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. mamma_300).

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 (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. bio1 = annual mean temperature, bio4 = temperature seasonality, bio12 = annual precipitation, bio15 = precipitation seasonality) values during the LGM were subtracted from those of the corresponding contemporary climate variables (e.g. current_bio1 - paleo_bio1 = ano_bio1).

Usage notes

Raw data of species distributions is available at request from the above-mentioned institutions and authors.