Fast and flexible estimation of effective migration surfaces

Abstract

Spatial population genetic data often exhibits ‘isolation-by-distance,’ where genetic similarity tends to decrease as individuals become more geographically distant. The rate at which genetic similarity decays with distance is often spatially heterogeneous due to variable population processes like genetic drift, gene flow, and natural selection. Petkova et al., 2016 developed a statistical method called Estimating Effective Migration Surfaces (EEMS) for visualizing spatially heterogeneous isolation-by-distance on a geographic map. While EEMS is a powerful tool for depicting spatial population structure, it can suffer from slow runtimes. Here, we develop a related method called Fast Estimation of Effective Migration Surfaces (FEEMS). FEEMS uses a Gaussian Markov Random Field model in a penalized likelihood framework that allows for efficient optimization and output of effective migration surfaces. Further, the efficient optimization facilitates the inference of migration parameters per edge in the graph, rather than per node (as in EEMS). With simulations, we show conditions under which FEEMS can accurately recover effective migration surfaces with complex gene-flow histories, including those with anisotropy. We apply FEEMS to population genetic data from North American gray wolves and show it performs favorably in comparison to EEMS, with solutions obtained orders of magnitude faster. Overall, FEEMS expands the ability of users to quickly visualize and interpret spatial structure in their data.

Demographic and Selection Histories of Populations Across the Sahel/Savannah Belt

“…To investigate patterns of migration in the Sahel/Savannah belt, we used Fast Estimation of Effective Migration Surfaces software (FEEMS) which allows for depicting spatial population structure and migration surfaces ( Petkova et al 2016 ; Marcus et al 2021 ). The estimated effective migration rates evidenced a very low migration rates between Sahelian and North African populations due to the presence of the geographical barrier represented by the Sahara Desert that limits gene-flow between populations ( fig.…”

“…To further investigate spatial population structure in the Sahel/Savannah belt, we used FEEMS ( Petkova et al 2016 ; Marcus et al 2021 ). Briefly, FEEMS applies a Gaussian Markov random field model in a penalized-likelihood-based framework to infer whether populations are exchanging gene flow with neighboring populations in a spatial graph of a “stepping-stone” model of migration and genetic drift.…”

Biogeographic inferences across spatial and evolutionary scales

“…A limitation, however, is that phylogeographical methods do not inherently link isolation by landscape features to evolutionary dissimilarity (Rissler, 2016). While we have focused here on the union of landscape genetics and phylogeography, other spatial population genetics models such as Estimated Effective Migration Surfaces (FEEMS; Marcus et al, 2021), which visualizes areas of the landscape relative to isolation by distance (IBD), and Migration and Population Site Surfaces (MAPS; Al‐Asadi et al, 2019) methods, which uses identity‐by‐descent tracks from genomic data to model migration rates and population sizes, may both further contribute to biogeographical inferences in uniquely advantageous ways.…”

slendr: a framework for spatio-temporal population genomic simulations on geographic landscapes

“…Moving forward, we expect that the slendr framework will become a useful tool for comparing and benchmarking inference methods for modeling spatial genomic processes (Peter and Slatkin, 2013; Petkova, Novembre and Stephens, 2016; Marcus et al ., 2021; Muktupavela et al ., 2021). It will also enable the development of new approaches to spatial problems in population genomics.…”