Global Gradients in Vertebrate Diversity Predicted by Historical Area-Productivity Dynamics and Contemporary Environment

Broad-scale geographic gradients in species richness have now been extensively documented, but their historical underpinning is still not well understood. While the importance of productivity, temperature, and a scale dependence of the determinants of diversity is broadly acknowledged, we argue here that limitation to a single analysis scale and data pseudo-replication have impeded an integrated evolutionary and ecological understanding of diversity gradients. We develop and apply a hierarchical analysis framework for global diversity gradients that incorporates an explicit accounting of past environmental variation and provides an appropriate measurement of richness. Due to environmental niche conservatism, organisms generally reside in climatically defined bioregions, or evolutionary arenas, characterized by in situ speciation and extinction. These bioregions differ in age and their total productivity and have varied over time in area and energy available for diversification. We show that, consistently across the four major terrestrial vertebrate groups, current-day species richness of the world's main 32 bioregions is best explained by a model that integrates area and productivity over geological time together with temperature. Adding finer scale variation in energy availability as an ecological predictor of within-bioregional patterns of richness explains much of the remaining global variation in richness at the 110 km grain. These results highlight the separate evolutionary and ecological effects of energy availability and provide a first conceptual and empirical integration of the key drivers of broad-scale richness gradients. Avoiding the pseudo-replication that hampers the evolutionary interpretation of non-hierarchical macroecological analyses, our findings integrate evolutionary and ecological mechanisms at their most relevant scales and offer a new synthesis regarding global diversity gradients.