Species distributional or trait data based on range map (extent‐of‐occurrence) or atlas
survey data often display spatial autocorrelation, ie locations close to each other exhibit …

Landscape genetics has emerged as a new research area that integrates population
genetics, landscape ecology and spatial statistics. Researchers in this field can combine the …

This book provides a first synthetic view of an emerging area of ecology and biogeography,
linking individual-and population-level processes to geographic distributions and …

Maps of species' distributions or habitat suitability are required for many aspects of
environmental research, resource management and conservation planning. These include …

The vast scope of conservation problems has forced biologists and managers to rely on"
surrogate" species to serve as shortcuts to guide their decision making. These species …

Ecological data often exhibit spatial pattern, which can be modeled as autocorrelation.
Conditional autoregressive (CAR) and simultaneous autoregressive (SAR) models are …

Aim Spatial autocorrelation is a frequent phenomenon in ecological data and can affect
estimates of model coefficients and inference from statistical models. Here, we test the …

Spatial autocorrelation is a well‐recognized concern for observational data in general, and
more specifically for spatial data in ecology. Generalized linear mixed models (GLMMs) with …

Aim Spatial autocorrelation (SAC) in data, ie the higher similarity of closer samples, is a
common phenomenon in ecology. SAC is starting to be considered in the analysis of …

Recently, analytical approaches based on the eigenfunctions of spatial configuration
matrices have been proposed in order to consider explicitly spatial predictors. The present …