A hierarchical Bayesian Beta regression approach to study the effects of geographical genetic structure and spatial autocorrelation on species distribution range shifts
Martínez-Minaya, Joaquín (Universitat de València. Departament d'Estadística i Investigació Operativa)
Conesa, David (Universitat de València. Departament d'Estadística i Investigació Operativa)
Fortin, Marie-Josée (University of Toronto. Department of Ecology and Evolutionary Biology)
Alonso-Blanco, Carlos (Centro Nacional de Biotecnología. Departamento de Genética Molecular de Plantas)
Picó, Xavier (Estación Biológica de Doñana. Departamento de Ecología Integrativa)
Marcer, Arnald (Centre de Recerca Ecològica i d'Aplicacions Forestals)

Data:	2019
Resum:	Global climate change (GCC) may be causing distribution range shifts in many organisms worldwide. Multiple efforts are currently focused on the development of models to better predict distribution range shifts due to GCC. We addressed this issue by including intraspecific genetic structure and spatial autocorrelation (SAC) of data in distribution range models. Both factors reflect the joint effect of ecoevolutionary processes on the geographical heterogeneity of populations. We used a collection of 301 georeferenced accessions of the annual plant Arabidopsis thaliana in its Iberian Peninsula range, where the species shows strong geographical genetic structure. We developed spatial and nonspatial hierarchical Bayesian models (HBMs) to depict current and future distribution ranges for the four genetic clusters detected. We also compared the performance of HBMs with Maxent (a presence-only model). Maxent and nonspatial HBMs presented some shortcomings, such as the loss of accessions with high genetic admixture in the case of Maxent and the presence of residual SAC for both. As spatial HBMs removed residual SAC, these models showed higher accuracy than nonspatial HBMs and handled the spatial effect on model outcomes. The ease of modelling and the consistency among model outputs for each genetic cluster was conditioned by the sparseness of the populations across the distribution range. Our HBMs enrich the toolbox of software available to evaluate GCC-induced distribution range shifts by considering both genetic heterogeneity and SAC, two inherent properties of any organism that should not be overlooked.
Ajuts:	Agencia Estatal de Investigación MTM2016-77501-P Agencia Estatal de Investigación CGL2016-77720-P European Commission 612645 Agència de Gestió d'Ajuts Universitaris i de Recerca 2017/SGR-1006
Drets:	Tots els drets reservats.
Llengua:	Anglès
Document:	Article ; recerca ; Versió acceptada per publicar
Matèria:	Arabidopsis thaliana ; Geographic genetic structure ; Global climate change ; Hierarchical Bayesian models ; Maxent ; Spatial autocorrelation
Publicat a:	Molecular Ecology Resources, Vol. 19, Issue 4 (July 2019) , p. 929-943, ISSN 1755-0998

DOI: 10.1111/1755-0998.13024
PMID: 30993910

Postprint 59 p, 2.1 MB

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Documents de recerca > Documents dels grups de recerca de la UAB > Centres i grups de recerca (producció científica) > Ciències > CREAF (Centre de Recerca Ecològica i d'Aplicacions Forestals)
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