“Inferring causalities in landscape genetics: An extension of Wright’s causal modeling to distance matrices”
Lisa Fourtune, Jérôme G. Prunier, Ivan Paz-Vinas, Géraldine Loot, Charlotte Veyssière, and Simon Blanchet (Apr 2018)
The DOI will be http://dx.doi.org/10.1086/696233
Researchers present new methods extending causal modeling (path analysis and the d-sep test) to distance matrices
Identifying landscape features that affect functional connectivity among populations is a major challenge in fundamental and applied sciences. Landscape genetics combines landscape and genetic data to address this issue, with the main objective of disentangling direct and indirect relationships among an intricate set of variables. Causal modeling has strong potential to address the complex nature of landscape genetic datasets. However, this statistical approach was not initially developed to address the pairwise distance matrices commonly used in landscape genetics. Here, we aimed to extend the applicability of two causal modeling methods, i.e., maximum-likelihood path analysis and the directional-separation test, by developing statistical approaches aimed at handling distance matrices and improving functional connectivity inference. Using simulations, we showed that these approaches greatly improved the robustness of the absolute (using a frequentist approach) and relative (using an information-theoretic approach) fit of the tested models. We used an empirical dataset combining genetic information on a freshwater fish species (Gobio occitaniae) and detailed landscape descriptors to demonstrate the usefulness of causal modeling to identify functional connectivity in wild populations. Specifically, we demonstrated how direct and indirect relationships involving altitude, temperature and oxygen concentration influenced within- and between-population genetic diversity of G. occitaniae.