Michael K. Schwartz

Landscape genetics: combining landscape ecology and population genetics

Understanding the processes and patterns of gene flow and local adaptation requires a detailed knowledge of how landscape characteristics structure populations. This understanding is crucial, not only for improving ecological knowledge, but also for managing properly the genetic diversity of threatened and endangered populations. For nearly 80 years, population geneticists have investigated how physiognomy and other landscape features have influenced genetic variation within and between populations. They have relied on sampling populations that have been identified beforehand because most population genetics methods have required discrete populations. However, a new approach has emerged for analyzing spatial genetic data without requiring that discrete populations be identified in advance. This approach, landscape genetics, promises to facilitate our understanding of how geographical and environmental features structure genetic variation at both the population and individual levels, and has implications for ecology, evolution and conservation biology. It differs from other genetic approaches, such as phylogeography, in that it tends to focus on processes at finer spatial and temporal scales. Here, we discuss, from a population genetic perspective, the current tools available for conducting studies of landscape genetics.

Gene Flow in Complex Landscapes: Testing Multiple Hypotheses with Causal Modeling

Predicting population‐level effects of landscape change depends on identifying factors that influence population connectivity in complex landscapes. However, most putative movement corridors and barriers have not been based on empirical data. In this study, we identify factors that influence connectivity by comparing patterns of genetic similarity among 146 black bears (Ursus americanus), sampled across a 3,000‐km2 study area in northern Idaho, with 110 landscape‐resistance hypotheses. Genetic similarities were based on the pairwise percentage dissimilarity among all individuals based on nine microsatellite loci (average expected \documentclass{aastex} \usepackage{amsbsy} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{bm} \usepackage{mathrsfs} \usepackage{pifont} \usepackage{stmaryrd} \usepackage{textcomp} \usepackage{portland,xspace} \usepackage{amsmath,amsxtra} \usepackage[OT2,OT1]{fontenc} \newcommand\cyr{ \renewcommand\rmdefault{wncyr} \renewcommand\sfdefault{wncyss} \renewcommand\encodingdefault{OT2} \normalfont \selectfont} \DeclareTextFontCommand{\textcyr}{\cyr} \pagestyle{empty} \DeclareMathSizes{10}{9}{7}{6} \begin{document} \landscape

Why sampling scheme matters: the effect of sampling scheme on landscape genetic results

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Compromising genetic diversity in the wild: unmonitored large-scale release of plants and animals

\end{document} ). Landscape‐resistance hypotheses describe a range of potential relationships between movement cost and land cover, slope, elevation, roads, Euclidean distance, and a putative movement barrier. These hypotheses were divided into seven organizational models in which the influences of barriers, distance, and landscape features were statistically separated using partial Mantel tests. Only one of the competing organizational models was fully supported: patterns of genetic structure are primarily related to landscape gradients of land cover and elevation. The alternative landscape models, isolation by barriers and isolation by distance, are not supported. In this black bear population, gene flow is facilitated by contiguous forest cover at middle elevations.

Advancing ecological understandings through technological transformations in noninvasive genetics

Population census size (NC) and effective population sizes (Ne) are two crucial parameters that influence population viability, wildlife management decisions, and conservation planning. Genetic estimators of both NC and Ne are increasingly widely used because molecular markers are increasingly available, statistical methods are improving rapidly, and genetic estimators complement or improve upon traditional demographic estimators. We review the kinds and applications of estimators of both NC and Ne, and the often undervalued and misunderstood ratio of effective-to-census size (Ne/NC). We focus on recently improved and well evaluated methods that are most likely to facilitate conservation. Finally, we outline areas of future research to improve Ne and NC estimation in wild populations.

Robust detection of rare species using environmental DNA: the importance of primer specificity.

There has been a recent trend in genetic studies of wild populations where researchers have changed their sampling schemes from sampling pre-defined populations to sampling individuals uniformly across landscapes. This reflects the fact that many species under study are continuously distributed rather than clumped into obvious “populations”. Once individual samples are collected, many landscape genetic studies use clustering algorithms and multilocus genetic data to group samples into subpopulations. After clusters are derived, landscape features that may be acting as barriers are examined and described. In theory, if populations were evenly sampled, this course of action should reliably identify population structure. However, genetic gradients and irregularly collected samples may impact the composition and location of clusters. We built genetic models where individual genotypes were either randomly distributed across a landscape or contained gradients created by neighbor mating for multiple generations. We investigated the influence of six different sampling protocols on population clustering using program STRUCTURE, the most commonly used model-based clustering method for multilocus genotype data. For models where individuals (and their alleles) were randomly distributed across a landscape, STRUCTURE correctly predicted that only one population was being sampled. However, when gradients created by neighbor mating existed, STRUCTURE detected multiple, but different numbers of clusters, depending on sampling protocols. We recommend testing for fine scale autocorrelation patterns prior to sample clustering, as the scale of the autocorrelation appears to influence the results. Further, we recommend that researchers pay attention to the impacts that sampling may have on subsequent population and landscape genetic results.

Understanding and Estimating Effective Population Size for Practical Application in Marine Species Management

Understanding how spatial genetic patterns respond to landscape change is crucial for advancing the emerging field of landscape genetics. We quantified the number of generations for new landscape barrier signatures to become detectable and for old signatures to disappear after barrier removal. We used spatially explicit, individual‐based simulations to examine the ability of an individual‐based statistic [Mantel’s r using the proportion of shared alleles’ statistic (Dps)] and population‐based statistic (FST) to detect barriers. We simulated a range of movement strategies including nearest neighbour dispersal, long‐distance dispersal and panmixia. The lag time for the signal of a new barrier to become established is short using Mantel’s r (1–15 generations). FST required approximately 200 generations to reach 50% of its equilibrium maximum, although G’ST performed much like Mantel’s r. In strong contrast, FST and Mantel’s r perform similarly following the removal of a barrier formerly dividing a population. Also, given neighbour mating and very short‐distance dispersal strategies, historical discontinuities from more than 100 generations ago might still be detectable with either method. This suggests that historical events and landscapes could have long‐term effects that confound inferences about the impacts of current landscape features on gene flow for species with very little long‐distance dispersal. Nonetheless, populations of organisms with relatively large dispersal distances will lose the signal of a former barrier within less than 15 generations, suggesting that individual‐based landscape genetic approaches can improve our ability to measure effects of existing landscape features on genetic structure and connectivity.

Genomics and the challenging translation into conservation practice

Large-scale exploitation of wild animals and plants through fishing, hunting and logging often depends on augmentation through releases of translocated or captively raised individuals. Such releases are performed worldwide in vast numbers. Augmentation can be demographically and economically beneficial but can also cause four types of adverse genetic change to wild populations: (1) loss of genetic variation, (2) loss of adaptations, (3) change of population composition, and (4) change of population structure. While adverse genetic impacts are recognized and documented in fisheries, little effort is devoted to actually monitoring them. In forestry and wildlife management, genetic risks associated with releases are largely neglected. We outline key features of programs to effectively monitor consequences of such releases on natural populations.