Science Center Objects
Landscape genetics is a recently developed discipline that involves the merger of molecular population genetics and landscape ecology. The goal of this new field of study is to provide information about the interaction between landscape features and microevolutionary processes such as gene flow, genetic drift, and selection allowing for the understanding of processes that generate genetic structure across space.
Landscape Genetics - Current Research
Developing Best Practices for Linear Mixed Modelling in Landscape Genetics Through Landscape-directed Dispersal Simulations - Principal Investigator - Sara Oyler McCance
Mixed models that account for the error structure of pairwise datasets are being utilized to compare models relating genetic differentiation with pairwise measures of landscape resistance. A model selection framework based on information criteria metrics or explained variance may help disentangle the ecological and landscape factors influencing spatial genetic structure, yet there are currently no tests of the error rates of this approach, or a consensus for the best protocols for minimizing them. The goal of this project is to develop and test a landscape-directed dispersal model to simulate a series of replicates that emulate independent empirical datasets of two species with vastly different life history and habitat use characteristics (Greater Sage-grouse and eastern fox snakes). This study develops best practices for using linear mixed models to identify the features underlying patterns of dispersal across a variety of landscapes. This research is in collaboration with University of Waterloo and is supported by Wyoming Game and Fish Department and BLM.
Latent Spatial Models and Sampling Design for Landscape Genetics - Prinicipal Investigator - Sara Oyler McCance
The goal of this study is to develop a spatially-explicit approach for modeling genetic variation across space and to illustrate how this approach can be used to optimize spatial prediction and sampling design for landscape genetic data. We are using a multinomial data model for categorical microsatellite allele data and introduced a latent spatial random effect to allow for spatial correlation between genetic observations. We illustrate how modern dimension reduction approaches to spatial statistics can allow for efficient computation in landscape genetic statistical models covering large spatial domains. We apply our approach to propose a retrospective spatial sampling design for Greater Sage-grouse. This research is in collaboration with Pennsylvania State University, USGS, Colorado State University, USFS, and the University of Montana.
Landscape Genetic Assessment of Gunnison Sage-grouse - Principal Investigator - Sara Oyler-McCance
The range of the Gunnison Sage-grouse has been fragmented into geographically and genetically distinct populations. The viability of the individual populations and long-term persistence of the species may be impacted by the ability of individual birds to move between populations. We are using genetic samples to infer connectivity across the species range and between leks within the Gunnison Basin to gain insight on which landscape or habitat features are contributing to the fragmentation of the species range. Our connectivity analysis within the basin will provide insight at a manageable scale and ultimately aims to inform current and future management possibilities by delineating corridors of movement and barriers to movement.
Rangewide Connectivity and Landscape Genetic Assessment for Greater Sage-grouse - Principal Investigator - Sara Oyler McCance
Greater Sage-grouse consist primarily of a few large core populations surrounded by numerous small populations. The viability of these small populations is sustained by dispersing individuals from neighboring populations. Development that causes habitat loss or creates barriers to dispersal between core areas restricts movements important to maintain genetic diversity, augment small populations, or recolonize extirpated populations. The goal of this study is to assess connectivity among core areas, and to identify features that may act as barriers to movement. In addition to defining populations and assessing connectivity, this study uses genetic approaches to address many other relevant questions including the conservation of genetic diversity, the impacts of inbreeding, and the association among landscape and geographic characteristics, habitats, and genetics. This research is in collaboration with USGS, USFS, and the University of Montana and is supported by Natural Resources Conservation Service, USFWS, and 11 US state fish and wildlife agencies.
Landscape Genetics - Completed Research
Sample Design Effects in Landscape Genetics - Principal Investigator - Sara Oyler McCance
This study addressed an important research gap in landscape genetics by examining the impact of different sampling designs on the ability to detect the effects of landscape pattern on gene flow. The study evaluated how five different (commonly employed) sampling regimes affected the probability of correctly identifying the generating landscape process of population structure. It also examined the impact of using different number of microsatellite loci, with differing levels of polymorphism, as well different number of individuals sampled. This study suggests that random, linear, and systematic sampling regimes performed well with high sample sizes (200), levels of polymorphism (10 alleles per locus), and number of molecular markers (20). This paper emphasizes the importance of sampling data at ecologically appropriate spatial and temporal scales and suggests careful consideration for sampling near landscape components that are likely to most influence the genetic structure of the species. This research was in collaboration with the University of Waterloo and the University of Montana.
Effects of Sample Size, Number of Markers, and Allelic Richness on the Detection of Spatial Genetic Pattern - Sara Oyler McCance
This study investigated the effect of study design on landscape genetics inference using a spatially-explicit, individual-based program to simulate gene flow. The study simulated a wide range of combinations of number of loci, number of alleles per locus and number of individuals sampled from the population and assessed how these three aspects of study design influenced the statistical power to successfully identify the generating process among competing hypotheses of isolation-by-distance, isolation-by-barrier, and isolation-by-landscape resistance using a causal modelling approach with partial Mantel tests. The study concluded that amplifying more (and more variable) loci is likely to increase the power of landscape genetic inferences more than increasing number of individuals. This research resulted from a Distributed Graduate Seminar (Developing Best Practices for Testing Landscape Effects on Gene Flow), conducted through the National Center for Ecological Analysis and Synthesis).
Landscape Genetic Analysis of Greater Sage-grouse in Wyoming - Principal Investigator - Sara Oyler McCance
This study compared the genetic differences between Greater Sage-grouse breeding areas with seasonal habitat distributions or combinations of landscape factors – such as amount of sagebrush habitat, agriculture fields or roads – to understand how each factor or combination of factors influence effective dispersal of sage-grouse across the state. The study revealed that the juxtaposition and quality of nesting and winter seasonal habitats were the greatest predictors of gene flow for Greater Sage-grouse in Wyoming. Furthermore, the combinations of high levels of forest cover and highly rugged (steep and uneven) terrain or low levels of sagebrush cover and highly rugged terrain were correlated with low levels of gene flow among sage-grouse populations. This research is in collaboration with the University of Waterloo, supported by Wyoming Game and Fish Department and BLM.