Estimating resistance surfaces using gradient forest and allelic frequencies.
functional connectivity
gradient forest
isolation by resistance
landscape genetics
machine learning
resistance surface
Journal
Molecular ecology resources
ISSN: 1755-0998
Titre abrégé: Mol Ecol Resour
Pays: England
ID NLM: 101465604
Informations de publication
Date de publication:
27 Feb 2023
27 Feb 2023
Historique:
revised:
06
02
2023
received:
24
05
2022
accepted:
22
02
2023
pubmed:
28
2
2023
medline:
28
2
2023
entrez:
27
2
2023
Statut:
aheadofprint
Résumé
Understanding landscape connectivity has become a global priority for mitigating the impact of landscape fragmentation on biodiversity. Connectivity methods that use link-based methods traditionally rely on relating pairwise genetic distance between individuals or demes to their landscape distance (e.g., geographic distance, cost distance). In this study, we present an alternative to conventional statistical approaches to refine cost surfaces by adapting the gradient forest approach to produce a resistance surface. Used in community ecology, gradient forest is an extension of random forest, and has been implemented in genomic studies to model species genetic offset under future climatic scenarios. By design, this adapted method, resGF, has the ability to handle multiple environmental predicators and is not subjected to traditional assumptions of linear models such as independence, normality and linearity. Using genetic simulations, resistance Gradient Forest (resGF) performance was compared to other published methods (maximum likelihood population effects model, random forest-based least-cost transect analysis and species distribution model). In univariate scenarios, resGF was able to distinguish the true surface contributing to genetic diversity among competing surfaces better than the compared methods. In multivariate scenarios, the gradient forest approach performed similarly to the other random forest-based approach using least-cost transect analysis but outperformed MLPE-based methods. Additionally, two worked examples are provided using two previously published data sets. This machine learning algorithm has the potential to improve our understanding of landscape connectivity and inform long-term biodiversity conservation strategies.
Identifiants
pubmed: 36847356
doi: 10.1111/1755-0998.13778
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Interreg France-Channel-England
Informations de copyright
© 2023 John Wiley & Sons Ltd.
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