ArticleEvolutionary applications2024
Integrating very high resolution environmental proxies in genotype-environment association studies.
Article in Evolutionary applications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Use of drone-derived high-resolution elevation data to improve species distribution models for a primate in mountainous areas.Conservation biology : the journal of the Society for Conservation Biology · 2026Article
- Unveiling the ecology and spatial dynamics of Trypanosoma cruzi, its DTUs and Triatoma vitticeps in the Atlantic Forest of south-eastern Espírito Santo State, Brazil.PLoS neglected tropical diseases · 2026Article
- Harnessing Multiscale Topographic Environmental Variables for Regional Coral Species Distribution Models.Ecology and evolution · 2025Article
- Integrating very high resolution environmental proxies in genotype-environment association studies.Evolutionary applications · 2024Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Landscape genomic analyses associating genetic variation with environmental variables are powerful tools for studying molecular signatures of species' local adaptation and for detecting candidate genes under selection. The development of landscape genomics over the past decade has been spurred by improvements in resolutions of genomic and environmental datasets, allegedly increasing the power to identify putative genes underlying local adaptation in non-model organisms. Although these associations have been successfully applied to numerous species across a diverse array of taxa, the spatial scale of environmental predictor variables has been largely overlooked, potentially limiting conclusions to be reached with these methods. To address this knowledge gap, we systematically evaluated performances of genotype-environment association (GEA) models using predictor variables at multiple spatial resolutions. Specifically, we used multivariate redundancy analyses to associate whole-genome sequence data from the plant
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