Can We Compare Effect Size of Spatial Genetic Structure Between Studies and Species Using Moran Eigenvector Maps?

Abstract

As the field of landscape genetics is progressing toward comparative empirical studies and meta-analysis, it is important to know how best to compare the strength of spatial genetic structure between studies and species. Moran’s Eigenvector Maps are a promising method that does not make an assumption of isolation-by-distance in a homogeneous environment but can discern cryptic structure that may result from multiple processes operating in heterogeneous landscapes. MEMgene uses spatial filters from Moran’s Eigenvector Maps as predictor variables to explain variation in a genetic distance matrix, and it returns adjusted R2 as a measure of the amount of genetic variation that is spatially structured. However, it is unclear whether, and under which conditions, this value can be used to compare the degree of spatial genetic structure (effect size) between studies. This study addresses the fundamental question of comparability at two levels: between independent studies (meta-analysis mode) and between species sampled at the same locations (comparative mode). We used published datasets containing 9,900 haploid, biallelic, neutral loci simulated on a quasi-continuous, square landscape under four demographic scenarios (island model, isolation-by-distance, expansion from one or two refugia). We varied the genetic resolution (number of individuals and loci) and the number of random sampling locations. We considered two measures of effect size, the MEMgene adjusted R2 and multivariate Moran’s I, which is related to Moran’s Eigenvector Maps. Both metrics were highly sensitive to the number of locations, even when using standardized effect sizes, SES, and the number of individuals sampled per location, but not to the number of loci. In comparative mode, using the same Moran Eigenvector Maps for all species, even those with missing values at some sampling locations, reduced bias due to the number of locations under isolation-by-distance (stationary process) but increased it under expansion from one or two refugia (non-stationary process). More robust measures of effect size need to be developed before the strength of spatial genetic structure can be accurately compared, either in a meta-analysis of independent empirical studies or within a comparative, multispecies landscape genetic study.

Location and Species Matters: Variable Influence of the Environment on the Gene Flow of Imperiled, Native and Invasive Cottontails

“…Eastern cottontail are thought to be better dispersers than New England cottontail and their movements less restricted by the environment ( Litvaitis et al, 2008 ). The adjusted R-squared values for New England cottontail were higher for two of four comparisons (regional and East), but comparisons can be influenced by uneven sample size and the demographic histories of the compared species ( Hein et al, 2021 ). Two areas showed a similar MEMGENE eigenvector map pattern for both species at the Cape Cod and East subregions.…”

Do habitat fragmentation and degradation influence the strength of fine-scale spatial genetic structure in plants? A global meta-analysis

“… Vekemans and Hardy (2004) recognized the challenge to assess this assumption, which is not specified in empirical FSGS studies. Recently, Hein et al (2021) demonstrated two spatial metrics based on simulated data, MEMgene adjusted R 2 and multivariate Moran’s I , their sensitivity due to demographic history, number of individuals sampled and sampling scheme. Hein et al (2021) concluded that the strength of FSGS cannot be compared using these two metrics and that comparisons among studies and species are not precise and thus not warranted.…”

Spatial genetic structure of two conifers in a highly modified landscape of central Mexico

“…We identified a low contribution of space in genetic variation and weak isolation by distance (IBD) in A. religiosa, indicating that genetic differentiation is barely explained by space and geographic distances (Manel et al 2003;Hein et al 2021). Furthermore, the fine-scale spatial genetic structure was not explained by any resistance surface studied, either in A. religiosa or in P. montezumae, suggesting that the strong environmental changes evaluated (i.e., altitude, orientation, and land use) cause no effect on gene flow among the populations of these species at this spatial scale.…”

“…Environmental conditions, such as environmental gradients, variation in area, and connectivity between habitat patches can affect the spatial genetic structure because allele frequencies depend on the rate of migration (gene flow) and the increase in genetic drift (Wright 1931;Hein et al 2021). Fine-scale spatial genetic structure in wind-pollinated and dispersed tree species is low because they have a high gene flow by pollen dispersal over large geographic distances (Steinitz et al 2011;Kremer et al 2012;Uchiyama et al 2014).…”