Re-Evaluating Causal Modeling with Mantel Tests in Landscape Genetics

Abstract

Abstract:The predominant analytical approach to associate landscape patterns with gene flow processes is based on the association of cost distances with genetic distances between individuals. Mantel and partial Mantel tests have been the dominant statistical tools used to correlate cost distances and genetic distances in landscape genetics. However, the inherent high correlation among alternative resistance models results in a high risk of spurious correlations using simple Mantel tests. Several refinements, including causal modeling, have been developed to reduce the risk of affirming spurious correlations and to assist model selection. However, the evaluation of these approaches has been incomplete in several respects. To demonstrate the general reliability of the causal modeling approach with Mantel tests, it must be shown to be able to correctly identify a wide range of landscape resistance models as the correct drivers relative to alternative hypotheses. The objectives of this study were to (1) evaluate the effectiveness of the originally published causal modeling framework to support the correct model and reject alternative hypotheses of isolation by distance and isolation by barriers and to (2) evaluate the effectiveness of causal modeling involving direct competition of all hypotheses to support the correct model and reject all alternative landscape resistance models. We found that partial Mantel OPEN ACCESSDiversity 2013, 5 52 tests have very low Type II error rates, but elevated Type I error rates. This leads to frequent identification of support for spurious correlations between alternative resistance hypotheses and genetic distance, independent of the true resistance model. The frequency in which this occurs is directly related to the degree of correlation between true and alternative resistance models. We propose an improvement based on the relative support of the causal modeling diagnostic tests.

Estimating effective landscape distances and movement corridors: comparison of habitat and genetic data

“…Specifically, we used genetic samples of brown bears genotyped at 17 polymorphic microsatellite loci to quantify genetic structure and measured the genetic distance among samples as the proportion of shared alleles (Bowcock et al 1994). The relationship between the genetic structure observed within the bear population and likely drivers of landscape resistance was systematically evaluated through v www.esajournals.org reciprocal causal modeling (Cushman et al 2006(Cushman et al , 2013b and the multi-model optimization approach developed by Shirk et al (2010). The resulting resistance models included variables of landscape composition (percentage of landscape cover by mixed forest and agricultural lands), landscape configuration (cohesion of mixed forest and shrubland) and canopy cover.…”

Beyond the snapshot: Landscape genetic analysis of time series data reveal responses of American black bears to landscape change

“…Lastly, due to the inherent risk of false positives using Mantel and partial Mantel tests, we implemented an analytical framework proposed by Cushman et al. (2013) that is based on the relative support of each candidate model to separate the true resistance model from a range of erroneous alternative resistance models.…”

“…Due to the inherent risk of false positives using Mantel tests, we used an additional analytical framework proposed by Cushman et al. (2013) that is based on the relative support of each candidate model to separate the true resistance model from a range of erroneous alternative resistance models. The relative support for each model was based on the difference in the partial Mantel correlation after partialling out the resistance distance of the other model.…”

“…The relative support for each model was based on the difference in the partial Mantel correlation after partialling out the resistance distance of the other model. The model with significant and positive relative support values for all comparisons was considered the best candidate model (Cushman et al., 2006, 2013). …”

“…These “snapshots” of data only measure populations in their current state (Anderson et al., 2010; Martensen, Saura, & Fortin, 2017; Schwartz, Luikart, & Waples, 2007). Therefore, inferences can be problematic when studying long‐lived and iteroparous species such as black bears ( Ursus americanus ) that are sensitive to landscape features (i.e., land cover) that are expected to undergo future modification (Cushman, McKelvey, Hayden, & Schwartz, 2006; Cushman, Wasserman, Landguth, & Shirk, 2013). Thus, incorporating a time series approach becomes necessary to make inferences about how a species responds to landscape or environmental change (Sun & Hedgecock, 2017).…”