Jana M. McPherson

TL;DR: In this paper, the authors describe six different statistical approaches to infer correlates of species distributions, for both presence/absence (binary response) and species abundance data (poisson or normally distributed response), while accounting for spatial autocorrelation in model residuals: autocovariate regression; spatial eigenvector mapping; generalised least squares; (conditional and simultaneous) autoregressive models and generalised estimating equations.

TL;DR: In this article, the authors examined the influence of range size on the sample size and sampling prevalence of data used to train and test distribution models for 32 bird species endemic to South Africa, Lesotho and Swaziland.

TL;DR: A conceptual and cyber-infrastructure framework for refining species distributional knowledge that is novel in its ability to mobilize and integrate diverse types of data such that their collective strengths overcome individual weaknesses is proposed.

TL;DR: None of the ecological traits tested provides an obvious correlate for environmental niche breadth or intra-specific niche differentiation, and these analyses provide conservation scientists and resource managers with a rule of thumb that helps distinguish between species whose occurrence is reliably or less reliably predicted by distribution models.

TL;DR: The quantitative delineation of biogeographical entities for reef fishes shows a global concordance with recent works based upon endemism, environmental factors, expert knowledge, or their combination and the similarity between the results and those from other phyla suggests that the approach may be of broad utility in describing and understanding global marine biodiversity patterns.