Which global circulation model works best for my region? Concordance with genetic data for a Neotropical shrew
Por:
Guevara L., Zugasti-Mateos A., Pinilla-Buitrago G.E., León-Tapia M.Á., Vázquez-Domínguez E., Anderson R.P.
Publicada:
1 ene 2025
Ahead of Print:
1 oct 2025
Resumen:
© 2025 The Author(s). Ecography published by John Wiley & Sons Ltd on behalf of Nordic Society Oikos.Copious questions in global change biology require estimates of climatic suitability for species in the past or future, often via transfers of ecological niche models (ENMs) using outputs from global circulation models (GCMs). However, available GCMs differ markedly, affecting hindcasts and forecasts of species potential distributions. We propose using demographic inferences based on genetic data (indicative of either population-level continuous occupation or postglacial colonization) to test which GCM leads to a better match with reality for ENM hindcasting. We implement an intuitive worked example for four isolated focal populations of a montane shrew Cryptotis mexicanus in central-eastern Mexico, by comparing suitability maps at the Last Glacial Maximum (LGM) and today. We built an optimized Maxent niche model and transferred it to the LGM based on four GCMs (CCSM4, IPSL-CM5A-LR, MIROC-ESM, MPI-ESM-P), followed by phylogeographic analyses to test hypotheses of changes in distribution according to each GCM. CCSM4 and IPSL-CM5A-LR indicated an LGM suitability area for C. mexicanus mainly in the southern portion of its range, suggesting that extant focal populations to the north result from postglacial colonization. In contrast, MIROC-ESM and MPI-ESM-P indicated LGM suitability for three or all the populations, respectively. Genetic results for the four focal populations showed high genetic diversity and signals of constant population size. Because only the hindcast based on MPI-ESM-P generated the prediction of stable occupation for all four sites, we interpret that its estimate (a cold and wet LGM climate) best approximates reality for this system. Future studies can apply this framework using more extensive genetic or genomic data and finer temporal resolutions, also exploring differences in the assumptions and methodologies underlying the various GCMs.
Filiaciones:
Guevara L.:
Colección Nacional de Mamíferos, Departamento de Zoología, Instituto de Biología, Universidad Nacional Autónoma de México, Mexico City, Mexico
Laboratorio Nacional Conahcyt de Biología del Cambio Climático, Instituto de Biología, Universidad Nacional Autónoma de México, Mexico City, Mexico
Zugasti-Mateos A.:
Colección Nacional de Mamíferos, Departamento de Zoología, Instituto de Biología, Universidad Nacional Autónoma de México, Mexico City, Mexico
Posgrado en Ciencias Biológicas, Universidad Nacional Autónoma de México, Mexico City, Mexico
Pinilla-Buitrago G.E.:
Program in Biology, Graduate Center, City University of New York, New York, NY, USA
Department of Biology, City College of New York, City University of New York, New York, NY, USA
León-Tapia M.Á.:
Colección Nacional de Mamíferos, Departamento de Zoología, Instituto de Biología, Universidad Nacional Autónoma de México, Mexico City, Mexico
Vázquez-Domínguez E.:
Departamento de Ecología de la Biodiversidad, Instituto de Ecología, Universidad Nacional Autónoma de México, México City, Mexico
Anderson R.P.:
Program in Biology, Graduate Center, City University of New York, New York, NY, USA
Department of Biology, City College of New York, City University of New York, New York, NY, USA
gold
|