Frank M. Schurr

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: This review proposes two main avenues to progress the understanding and prediction of the different processes occurring on the leading and trailing edge of the species' distribution in response to any global change phenomena and concludes with clear guidelines on how such modelling improvements will benefit conservation strategies in a changing world.

TL;DR: The extent of the trait data compiled in TRY is evaluated and emerging patterns of data coverage and representativeness are analyzed to conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements.

TL;DR: To advance the understanding of LDD, this work advocates a vector-based research approach that identifies the significant LDD vectors and quantifies how environmental conditions modify their actions.

TL;DR: This synthesis outlines the development of ‘species interaction distribution models’ (SIDMs), which aim to incorporate multispecies interactions at large spatial extents using interaction matrices using error matrices in multivariate regression models.