Type and spatial structure of distribution data and the perceived determinants of geographical gradients in ecology: the species richness of African birds
Article first published online: 14 AUG 2007
© 2007 The Authors. Journal compilation © 2007 Blackwell Publishing Ltd
Global Ecology and Biogeography
Volume 16, Issue 5, pages 657–667, September 2007
How to Cite
McPherson, J. M. and Jetz, W. (2007), Type and spatial structure of distribution data and the perceived determinants of geographical gradients in ecology: the species richness of African birds. Global Ecology and Biogeography, 16: 657–667. doi: 10.1111/j.1466-8238.2007.00318.x
- Issue published online: 14 AUG 2007
- Article first published online: 14 AUG 2007
- Broad-scale ecology;
- distribution models;
- environmental determinants;
- geographical range;
- range porosity;
- spatial autocorrelation;
- species distribution
Aim Studies exploring the determinants of geographical gradients in the occurrence of species or their traits obtain data by: (1) overlaying species range maps; (2) mapping survey-based species counts; or (3) superimposing models of individual species’ distributions. These data types have different spatial characteristics. We investigated whether these differences influence conclusions regarding postulated determinants of species richness patterns.
Location Our study examined terrestrial bird diversity patterns in 13 nations of southern and eastern Africa, spanning temperate to tropical climates.
Methods Four species richness maps were compiled based on range maps, field-derived bird atlas data, logistic and autologistic distribution models. Ordinary and spatial regression models served to examine how well each of five hypotheses predicted patterns in each map. These hypotheses propose productivity, temperature, the heat–water balance, habitat heterogeneity and climatic stability as the predominant determinants of species richness.
Results The four richness maps portrayed broadly similar geographical patterns but, due to the nature of underlying data types, exhibited marked differences in spatial autocorrelation structure. These differences in spatial structure emerged as important in determining which hypothesis appeared most capable of explaining each map's patterns. This was true even when regressions accounted for spurious effects of spatial autocorrelation. Each richness map, therefore, identified a different hypothesis as the most likely cause of broad-scale gradients in species diversity.
Main conclusions Because the ‘true’ spatial structure of species richness patterns remains elusive, firm conclusions regarding their underlying environmental drivers remain difficult. More broadly, our findings suggest that care should be taken to interpret putative determinants of large-scale ecological gradients in light of the type and spatial characteristics of the underlying data. Indeed, closer scrutiny of these underlying data — here the distributions of individual species — and their environmental associations may offer important insights into the ultimate causes of observed broad-scale patterns.