Using genetic data to improve species distribution models

Tsetse flies (Diptera, Glossinidae) transmit human and animal trypanosomoses in Africa, respectively a neglected human disease (sleeping sickness) and the most important constraint to cattle production in infested countries (nagana). We recently developed a methodology to map landscape friction (i.e. resistance to movement) for tsetse in West Africa. The goal was to identify natural barriers to tsetse dispersal, and potentially isolated tsetse populations for targeting elimination programmes. Most species distribution models neglect landscape functional connectivity whereas environmental factors affecting suitability or abundance are not necessarily the same as those influencing gene flows. Geographic distributions of a given species can be seen as the intersection between biotic (B), abiotic (A) and movement (M) factors (BAM diagram). Here we show that the suitable habitat for Glossina palpalis gambiensis as modelled by Maxent can be corrected by landscape functional connectivity (M) extracted from our friction analysis. This procedure did not degrade the specificity of the distribution model (P = 0.751) whereas the predicted distribution area was reduced. The added value of this approach is that it reveals unconnected habitat patches. The approach we developed on tsetse to inform landscape connectivity (M) is reproducible and does not rely on expert knowledge. It can be applied to any species: we call for a generalization of the use of M to improve distribution models.

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Main Authors: Bouyer, Jérémy, Lancelot, Renaud
Format: article biblioteca
Language:eng
Subjects:L73 - Maladies des animaux, L20 - Écologie animale,
Online Access:http://agritrop.cirad.fr/588126/
http://agritrop.cirad.fr/588126/7/Bouyer_2018_IGE_GeneticsDistributionModels.pdf
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spelling dig-cirad-fr-5881262021-11-09T09:38:27Z http://agritrop.cirad.fr/588126/ http://agritrop.cirad.fr/588126/ Using genetic data to improve species distribution models. Bouyer Jérémy, Lancelot Renaud. 2018. Infection, Genetics and Evolution, 63 : 292-294.https://doi.org/10.1016/j.meegid.2017.03.025 <https://doi.org/10.1016/j.meegid.2017.03.025> Researchers Using genetic data to improve species distribution models Bouyer, Jérémy Lancelot, Renaud eng 2018 Infection, Genetics and Evolution L73 - Maladies des animaux L20 - Écologie animale Tsetse flies (Diptera, Glossinidae) transmit human and animal trypanosomoses in Africa, respectively a neglected human disease (sleeping sickness) and the most important constraint to cattle production in infested countries (nagana). We recently developed a methodology to map landscape friction (i.e. resistance to movement) for tsetse in West Africa. The goal was to identify natural barriers to tsetse dispersal, and potentially isolated tsetse populations for targeting elimination programmes. Most species distribution models neglect landscape functional connectivity whereas environmental factors affecting suitability or abundance are not necessarily the same as those influencing gene flows. Geographic distributions of a given species can be seen as the intersection between biotic (B), abiotic (A) and movement (M) factors (BAM diagram). Here we show that the suitable habitat for Glossina palpalis gambiensis as modelled by Maxent can be corrected by landscape functional connectivity (M) extracted from our friction analysis. This procedure did not degrade the specificity of the distribution model (P = 0.751) whereas the predicted distribution area was reduced. The added value of this approach is that it reveals unconnected habitat patches. The approach we developed on tsetse to inform landscape connectivity (M) is reproducible and does not rely on expert knowledge. It can be applied to any species: we call for a generalization of the use of M to improve distribution models. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/588126/7/Bouyer_2018_IGE_GeneticsDistributionModels.pdf text Cirad license info:eu-repo/semantics/openAccess https://agritrop.cirad.fr/mention_legale.html https://doi.org/10.1016/j.meegid.2017.03.025 10.1016/j.meegid.2017.03.025 info:eu-repo/semantics/altIdentifier/doi/10.1016/j.meegid.2017.03.025 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1016/j.meegid.2017.03.025
institution CIRAD FR
collection DSpace
country Francia
countrycode FR
component Bibliográfico
access En linea
databasecode dig-cirad-fr
tag biblioteca
region Europa del Oeste
libraryname Biblioteca del CIRAD Francia
language eng
topic L73 - Maladies des animaux
L20 - Écologie animale
L73 - Maladies des animaux
L20 - Écologie animale
spellingShingle L73 - Maladies des animaux
L20 - Écologie animale
L73 - Maladies des animaux
L20 - Écologie animale
Bouyer, Jérémy
Lancelot, Renaud
Using genetic data to improve species distribution models
description Tsetse flies (Diptera, Glossinidae) transmit human and animal trypanosomoses in Africa, respectively a neglected human disease (sleeping sickness) and the most important constraint to cattle production in infested countries (nagana). We recently developed a methodology to map landscape friction (i.e. resistance to movement) for tsetse in West Africa. The goal was to identify natural barriers to tsetse dispersal, and potentially isolated tsetse populations for targeting elimination programmes. Most species distribution models neglect landscape functional connectivity whereas environmental factors affecting suitability or abundance are not necessarily the same as those influencing gene flows. Geographic distributions of a given species can be seen as the intersection between biotic (B), abiotic (A) and movement (M) factors (BAM diagram). Here we show that the suitable habitat for Glossina palpalis gambiensis as modelled by Maxent can be corrected by landscape functional connectivity (M) extracted from our friction analysis. This procedure did not degrade the specificity of the distribution model (P = 0.751) whereas the predicted distribution area was reduced. The added value of this approach is that it reveals unconnected habitat patches. The approach we developed on tsetse to inform landscape connectivity (M) is reproducible and does not rely on expert knowledge. It can be applied to any species: we call for a generalization of the use of M to improve distribution models.
format article
topic_facet L73 - Maladies des animaux
L20 - Écologie animale
author Bouyer, Jérémy
Lancelot, Renaud
author_facet Bouyer, Jérémy
Lancelot, Renaud
author_sort Bouyer, Jérémy
title Using genetic data to improve species distribution models
title_short Using genetic data to improve species distribution models
title_full Using genetic data to improve species distribution models
title_fullStr Using genetic data to improve species distribution models
title_full_unstemmed Using genetic data to improve species distribution models
title_sort using genetic data to improve species distribution models
url http://agritrop.cirad.fr/588126/
http://agritrop.cirad.fr/588126/7/Bouyer_2018_IGE_GeneticsDistributionModels.pdf
work_keys_str_mv AT bouyerjeremy usinggeneticdatatoimprovespeciesdistributionmodels
AT lancelotrenaud usinggeneticdatatoimprovespeciesdistributionmodels
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