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. Used in community ecology, GF 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
predicators and is not subjected to traditional assumptions of linear
models such as independence, normality and linearity. Using genetic
simulations, resGF performance was compared to other published methods.
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 GF approach
performed similarly to the other RF-based approach using least-cost
transect analysis (LCTA). Additionally, two worked examples are provided
using two previously published datasets. This machine learning algorithm
has the potential to improve our understanding of landscape connectivity
and can inform long-term biodiversity conservation strategies.