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A hierarchical Bayesian Beta regression approach to study the effects of geographic genetic structure and spatial autocorrelation on species distribution range shifts

Name: A hierarchical Bayesian Beta regression approach to study the effects of geographic genetic structure and spatial autocorrelation on species distribution range shifts
Keywords: Arabidopsis thaliana, geographic genetic structure, global climate change, hierarchical Bayesian models, Maxent, spatial autocorrelation

integration_instructionsResearch softwarekeyboard_double_arrow_right Software 02 Apr 2019 English Publisher:Zenodo

Authors: Martínez-Minaya, Joaquín; Conesa, David; Fortin, Marie-Josée; Alonso-Blanco, Carlos; Picó, F. Xavier; Marcer, Arnald;

doi: 10.5281/zenodo.2552025 , 10.5281/zenodo.2552024

A hierarchical Bayesian Beta regression approach to study the effects of geographic genetic structure and spatial autocorrelation on species distribution range shifts

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Abstract

This repository contains the data and code for the paper: Martínez-Minaya J., Conesa D., Fortin M.J., Alonso-Blanco C., Picó, F.X. and Marcer A. (2019). A hierarchical Bayesian Beta regression approach to study the effects of geographic genetic structure and spatial autocorrelation on species distribution range shifts. Molecular Ecology Resources. In order to use the data and code please read the README.pdf file. Code and data are also available at https://github.com/arnaldmarcer/GenAthHBM

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Arabidopsis thaliana, geographic genetic structure, global climate change, hierarchical Bayesian models, Maxent, spatial autocorrelation

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