Unveiling geographical gradients of species richness from scant occurrence data

Abstract

Aim Despite longstanding investigation, the gradients of species richness remain unknown for most taxa because of shortfalls in knowledge regarding the quantity and distribution of species. Here, we explore the ability of a geostatistical interpolation model, regression‐kriging, to recover geographical gradients of species richness. We examined the technique with an in silico gradient of species richness and evaluated the effect of different configurations of knowledge shortfalls. We also took the same approach for empirical data with large knowledge gaps, the infraorder Furnariides of suboscine birds. Innovation Regression‐kriging builds upon two cornerstones of geographical gradients of biodiversity, the spatial autocorrelation of species richness and the conspicuous association of species with environmental factors. With this technique, we recovered a simulated gradient of richness using Main conclusions Geostatistical interpolation, such as regression‐kriging, might be a useful tool to overcome shortfalls in knowledge that plague our understanding of geographical gradients of biodiversity, with many applications in ecology, palaeoecology and conservation.

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