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Björn Reineking

Researcher at University of Grenoble

Publications -  84
Citations -  14891

Björn Reineking is an academic researcher from University of Grenoble. The author has contributed to research in topics: Climate change & Species richness. The author has an hindex of 32, co-authored 81 publications receiving 11907 citations. Previous affiliations of Björn Reineking include Helmholtz Centre for Environmental Research - UFZ & Harvard University.

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Collinearity: a review of methods to deal with it and a simulation study evaluating their performance

TL;DR: It was found that methods specifically designed for collinearity, such as latent variable methods and tree based models, did not outperform the traditional GLM and threshold-based pre-selection and the value of GLM in combination with penalised methods and thresholds when omitted variables are considered in the final interpretation.
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Methods to account for spatial autocorrelation in the analysis of species distributional data : a review

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.
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Moving in the Anthropocene : global reductions in terrestrial mammalian movements

Marlee A. Tucker, +135 more
- 26 Jan 2018 - 
TL;DR: Using a unique GPS-tracking database of 803 individuals across 57 species, it is found that movements of mammals in areas with a comparatively high human footprint were on average one-half to one-third the extent of their movements in area with a low human footprint.
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Statistical inference for stochastic simulation models – theory and application

TL;DR: Approximate Bayesian Computing and Pattern-Oriented Modelling are discussed, their potential for integrating stochastic simulation models into a unified framework for statistical modelling is demonstrated, and principles and advantages of these methods are discussed.