Estimating resistance surface on multilayers landscapes using gradient
forest and allelic frequencies
Understanding landscape connectivity has become a global priority for
mitigating the impact of landscape fragmentation on biodiversity.
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 (GF) approach to produce a
resistance surface based on multiple environmental variables. Used in
community ecology, Gradient Forest is an extension of random forest
(RF), 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
predictors and is not subjected to traditional assumptions of linear
models such as independence, normality and linearity. Using genetic
simulations, our RF-based approach was compared to other published
methods using univariate and multivariate scenarios. Additionally, two
worked examples are provided using two previously published datasets.
This machine learning algorithm has the potential improve our
understanding of landscape connectivity and can inform long-term
biodiversity conservation strategies.