Long‐term cloud forest response to climate warming revealed by insect speciation history

Montane cloud forests are areas of high endemism, and are one of the more vulnerable terrestrial ecosystems to climate change. Thus, understanding how they both contribute to the generation of biodiversity, and will respond to ongoing climate change, are important and related challenges. The widely accepted model for montane cloud forest dynamics involves upslope forcing of their range limits with global climate warming. However, limited climate data provides some support for an alternative model, where range limits are forced downslope with climate warming. Testing between these two models is challenging, due to the inherent limitations of climate and pollen records. We overcome this with an alternative source of historical information, testing between competing model predictions using genomic data and demographic analyses for a species of beetle tightly associated to an oceanic island cloud forest. Results unequivocally support the alternative model: populations that were isolated at higher elevation peaks during the Last Glacial Maximum are now in contact and hybridizing at lower elevations. Our results suggest that genomic data are a rich source of information to further understand how montane cloud forest biodiversity originates, and how it is likely to be impacted by ongoing climate change.

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Bibliographic Details
Main Authors: Salces-Castellano, Antonia, Stankowski, Sean, Arribas, Paula, Patiño, Jairo, Karger, Dirk N., Butlin, Roger, Emerson, Brent C.
Other Authors: Agencia Estatal de Investigación (España)
Format: artículo biblioteca
Language:English
Published: Wiley-VCH 2021-02
Subjects:Coleoptera, Hybridization, Last Glacial Maximum, Quaternary climate, Speciation, Trade‐wind inversion,
Online Access:http://hdl.handle.net/10261/223937
http://dx.doi.org/10.13039/501100011033
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spelling dig-ipna-es-10261-2239372022-06-30T07:30:00Z Long‐term cloud forest response to climate warming revealed by insect speciation history Salces-Castellano, Antonia Stankowski, Sean Arribas, Paula Patiño, Jairo Karger, Dirk N. Butlin, Roger Emerson, Brent C. Agencia Estatal de Investigación (España) Ministerio de Ciencia, Innovación y Universidades (España) Cabildo de Tenerife Coleoptera Hybridization Last Glacial Maximum Quaternary climate Speciation Trade‐wind inversion Montane cloud forests are areas of high endemism, and are one of the more vulnerable terrestrial ecosystems to climate change. Thus, understanding how they both contribute to the generation of biodiversity, and will respond to ongoing climate change, are important and related challenges. The widely accepted model for montane cloud forest dynamics involves upslope forcing of their range limits with global climate warming. However, limited climate data provides some support for an alternative model, where range limits are forced downslope with climate warming. Testing between these two models is challenging, due to the inherent limitations of climate and pollen records. We overcome this with an alternative source of historical information, testing between competing model predictions using genomic data and demographic analyses for a species of beetle tightly associated to an oceanic island cloud forest. Results unequivocally support the alternative model: populations that were isolated at higher elevation peaks during the Last Glacial Maximum are now in contact and hybridizing at lower elevations. Our results suggest that genomic data are a rich source of information to further understand how montane cloud forest biodiversity originates, and how it is likely to be impacted by ongoing climate change. This work was financed by the Spanish Agencia Estatal de Investigación (CGL2017‐85718‐P), awarded to BCE, and co‐financed by FEDER. It was also supported by the Spanish Ministerio de Ciencia, Innovación y Universidades (EQC2018‐004418‐P), awarded to BCE. AS‐C was funded by the Spanish Ministerio de Ciencia, Innovación y Universidades through an FPU PhD fellowship (FPU014/02948). The authors thank Instituto Tecnológico y de Energías Renovables (ITER), S.A for providing access to the Teide High‐Performance Computing facility (Teide‐HPC). Fieldwork was supported by collecting permit AFF 107/17 (sigma number 2017‐00572) kindly provided by the Cabildo of Tenerife. Peer reviewed 2020-11-27T14:40:33Z 2020-11-27T14:40:33Z 2021-02 artículo http://purl.org/coar/resource_type/c_6501 Evolution 75(2): 231-244 (2021) 0014-3820 http://hdl.handle.net/10261/223937 10.1111/evo.14111 1558-5646 http://dx.doi.org/10.13039/501100011033 en #PLACEHOLDER_PARENT_METADATA_VALUE# MICIU/EQC2018/004418‐P Postprint https://doi.org/10.1111/evo.14111 Sí open Wiley-VCH Society for the Study of Evolution
institution IPNA ES
collection DSpace
country España
countrycode ES
component Bibliográfico
access En linea
databasecode dig-ipna-es
tag biblioteca
region Europa del Sur
libraryname Biblioteca del IPNA España
language English
topic Coleoptera
Hybridization
Last Glacial Maximum
Quaternary climate
Speciation
Trade‐wind inversion
Coleoptera
Hybridization
Last Glacial Maximum
Quaternary climate
Speciation
Trade‐wind inversion
spellingShingle Coleoptera
Hybridization
Last Glacial Maximum
Quaternary climate
Speciation
Trade‐wind inversion
Coleoptera
Hybridization
Last Glacial Maximum
Quaternary climate
Speciation
Trade‐wind inversion
Salces-Castellano, Antonia
Stankowski, Sean
Arribas, Paula
Patiño, Jairo
Karger, Dirk N.
Butlin, Roger
Emerson, Brent C.
Long‐term cloud forest response to climate warming revealed by insect speciation history
description Montane cloud forests are areas of high endemism, and are one of the more vulnerable terrestrial ecosystems to climate change. Thus, understanding how they both contribute to the generation of biodiversity, and will respond to ongoing climate change, are important and related challenges. The widely accepted model for montane cloud forest dynamics involves upslope forcing of their range limits with global climate warming. However, limited climate data provides some support for an alternative model, where range limits are forced downslope with climate warming. Testing between these two models is challenging, due to the inherent limitations of climate and pollen records. We overcome this with an alternative source of historical information, testing between competing model predictions using genomic data and demographic analyses for a species of beetle tightly associated to an oceanic island cloud forest. Results unequivocally support the alternative model: populations that were isolated at higher elevation peaks during the Last Glacial Maximum are now in contact and hybridizing at lower elevations. Our results suggest that genomic data are a rich source of information to further understand how montane cloud forest biodiversity originates, and how it is likely to be impacted by ongoing climate change.
author2 Agencia Estatal de Investigación (España)
author_facet Agencia Estatal de Investigación (España)
Salces-Castellano, Antonia
Stankowski, Sean
Arribas, Paula
Patiño, Jairo
Karger, Dirk N.
Butlin, Roger
Emerson, Brent C.
format artículo
topic_facet Coleoptera
Hybridization
Last Glacial Maximum
Quaternary climate
Speciation
Trade‐wind inversion
author Salces-Castellano, Antonia
Stankowski, Sean
Arribas, Paula
Patiño, Jairo
Karger, Dirk N.
Butlin, Roger
Emerson, Brent C.
author_sort Salces-Castellano, Antonia
title Long‐term cloud forest response to climate warming revealed by insect speciation history
title_short Long‐term cloud forest response to climate warming revealed by insect speciation history
title_full Long‐term cloud forest response to climate warming revealed by insect speciation history
title_fullStr Long‐term cloud forest response to climate warming revealed by insect speciation history
title_full_unstemmed Long‐term cloud forest response to climate warming revealed by insect speciation history
title_sort long‐term cloud forest response to climate warming revealed by insect speciation history
publisher Wiley-VCH
publishDate 2021-02
url http://hdl.handle.net/10261/223937
http://dx.doi.org/10.13039/501100011033
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