W. Daniel Kissling

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 biotic interactions have clearly left their mark on species distributions and realised assemblages of species across all spatial extents, and is called for for accelerated collection of spatially and temporally explicit species data.

TL;DR: In this article, the performance of three different simultaneous autoregressive (SAR) model types (spatial error = SAR err, lagged = SAR lag and mixed = SAR mix ) and common ordinary least squares (OLS) regression when accounting for spatial autocorrelation in species distribution data using four artificial data sets with known (but different) spatial auto-correlation structures.

TL;DR: Spatial and ecological networks need to be combined to explore the effects of dispersal, colonisation, extinction and habitat fragmentation on network structure and coevolutionary dynamics and embed network approaches more explicitly within applied ecology in general.

TL;DR: A demographic research agenda is formulated that entails advances in incorporating process-based models of demographic responses and range dynamics into a statistical framework, systematic collection of data on temporal changes in distribution and abundance and on the response of demographic rates to environmental variation, and improved theoretical understanding of the scaling of demographics rates and the dynamics of spatially coupled populations.