Re-evaluating causal modeling with mantel tests in landscape genetics

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 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

Sex-Biased Gene Flow Among Elk in the Greater Yellowstone Ecosystem

We quantified patterns of population genetic structure to help understand gene flow among elk populations across the Greater Yellowstone Ecosystem. We sequenced 596 base pairs of the mitochondrial control region of 380 elk from eight populations. Analysis revealed high mitochondrial DNA variation within populations, averaging 13.0 haplotypes with high mean gene diversity (0.85). The genetic differentiation among populations for mitochondrial DNA was relatively high (FST = 0.161; P = 0.001) compared to genetic differentiation for nuclear microsatellite data (FST = 0.002; P = 0.332), which suggested relatively low female gene flow among populations. The estimated ratio of male to female gene flow (mm/mf = 46) was among the highest we have seen reported for large mammals. Genetic distance (for mitochondrial DNA pairwise FST) was not significantly correlated with geographic (Euclidean) distance between populations (Mantel’s r = 0.274, P = 0.168). Large mitochondrial DNA genetic distances (e.g., FST . 0.2) between some of the geographically closest populations (,65 km) suggested behavioral factors and/or landscape features might shape female gene flow patterns. Given the strong sex-biased gene flow, future research and conservation efforts should consider the sexes separately when modeling corridors of gene flow or predicting spread of maternally transmitted diseases. The growing availability of genetic data to compare male vs. female gene flow provides many exciting opportunities to explore the magnitude, causes, and implications of sex-biased gene flow likely to occur in many species

Mathematical and Computational Applications in Disease and Landscape Ecology

The Individualized Interdisciplinary Program (IIP) at the University of Montana allows students to work with faculty in the design of a graduate curriculum tailored to their unique academic, creative, and professional needs. The principal goal of the National Science Foundation\u27s IGERT: Montana - Ecology of Infectious Diseases (MEID) program is to produce graduates with expertise to lead the collaborative, cross-, and inter-disciplinary efforts in education and research needed to address complex problems as exemplified by the ecology of endemic, epidemic, and emergent infectious diseases. Under the envelope of these two programs, I have developed a Ph.D. program in which I received an interdisciplinary education in applied mathematics and computational ecology. I strongly feel that spatial modeling is one of the most promising approaches to advance the sciences of disease ecology and landscape ecology. Mathematical and computational modeling provide powerful tools for evaluating relationships between mechanisms and responses in a spatially complex environment. Past progress in these fields has been limited by the lack of computational power and flexible mathematical models to simulate the actions of ecosystem and population processes in complex environments. My specific research focus is in the development of mathematical and computational models to synthesize environmental data for describing and predicting the characteristics of population and disease dynamics on the landscape. The results from this research are documented in the following chapters: 1) Mathematical Disease Ecology. This uses numerical and qualitative analysis to study a model for Tick Borne Relapsing Fever in an island ecosystem. 2) Computational Landscape Ecology. The development and applications of a spatially-explicit computer model to predict population connectivity and geneflow on complex landscapes are described

Ecological speciation in the tropics: insights from comparative genetic studies in Amazonia.

Evolution creates and sustains biodiversity via adaptive changes in ecologically relevant traits. Ecologically mediated selection contributes to genetic divergence both in the presence or absence of geographic isolation between populations, and is considered an important driver of speciation. Indeed, the genetics of ecological speciation is becoming increasingly studied across a variety of taxa and environments. In this paper we review the literature of ecological speciation in the tropics. We report on low research productivity in tropical ecosystems and discuss reasons accounting for the rarity of studies. We argue for research programs that simultaneously address biogeographical and taxonomic questions in the tropics, while effectively assessing relationships between reproductive isolation and ecological divergence. To contribute toward this goal, we propose a new framework for ecological speciation that integrates information from phylogenetics, phylogeography, population genomics, and simulations in evolutionary landscape genetics (ELG). We introduce components of the framework, describe ELG simulations (a largely unexplored approach in ecological speciation), and discuss design and experimental feasibility within the context of tropical research. We then use published genetic datasets from populations of five codistributed Amazonian fish species to assess the performance of the framework in studies of tropical speciation. We suggest that these approaches can assist in distinguishing the relative contribution of natural selection from biogeographic history in the origin of biodiversity, even in complex ecosystems such as Amazonia. We also discuss on how to assess ecological speciation using ELG simulations that include selection. These integrative frameworks have considerable potential to enhance conservation management in biodiversity rich ecosystems and to complement historical biogeographic and evolutionary studies of tropical biotas

Looking beyond the mountain: dispersal barriers in a changing world

0000-0001-7279-715X© The Ecological Society of America. The attached file is the published version of the article

Why Did the Bear Cross the Road? Comparing the Performance of Multiple Resistance Surfaces and Connectivity Modeling Methods

There have been few assessments of the performance of alternative resistance surfaces, and little is known about how connectivity modeling approaches differ in their ability to predict organism movements. In this paper, we evaluate the performance of four connectivity modeling approaches applied to two resistance surfaces in predicting the locations of highway crossings by American black bears in the northern Rocky Mountains, USA. We found that a resistance surface derived directly from movement data greatly outperformed a resistance surface produced from analysis of genetic differentiation, despite their heuristic similarities. Our analysis also suggested differences in the performance of different connectivity modeling approaches. Factorial least cost paths appeared to slightly outperform other methods on the movement-derived resistance surface, but had very poor performance on the resistance surface obtained from multi-model landscape genetic analysis. Cumulative resistant kernels appeared to offer the best combination of high predictive performance and sensitivity to differences in resistance surface parameterization. Our analysis highlights that even when two resistance surfaces include the same variables and have a high spatial correlation of resistance values, they may perform very differently in predicting animal movement and population connectivity