Pedro R. Peres-Neto

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: It is shown that variation partitioning as currently applied in canonical analysis is biased, and appropriate unbiased estimators are presented to consider so that comparisons between fractions or, eventually, between different canonical models are meaningful.

TL;DR: The Principal Coordinates of Neighbors of Neighbor Matrices (PCNM) approach as discussed by the authors was proposed to create spatial predictors that can be easily incorporated into regression or canonical analysis models, providing a flexible tool especially when contrasted to the family of autoregressive models and trend surface analysis which are of common use in ecological and geographical analysis.

TL;DR: In this article, the authors compare two statistical methods, namely, canonical ordination and variation partitioning on distance matrices (Mantel approach), to test the origin and maintenance of community diversity among sites.

TL;DR: Evidence for the structuring of fish communities from stream and lake systems and the roles of biotic, abiotic, and spatial factors in determining the species composition are examined.