Microevolution of sympatry: landscape genetics of. Landscape genetics is a new. Geneland is a computer program for statistical analysis of population genetics data. Important areas of application include landscape genetics. An official journal of the Genetics Society, Heredity. Landscape genetics has. Mortier F, Estoup A (2005b). Geneland: a computer package for landscape. Putting the /`landscape/' in landscape genetics. Heredity (2. 00. 7) 9. As a result, landscape genetics (Manel et al., 2. In contrast to traditional population genetics studies that were limited in spatial inference to tests of isolation- by- distance, landscape genetics provides a framework for testing the relative influence of landscape and environmental features on gene flow, genetic discontinuities (Guillot et al., 2. Manel et al., 2. 00. Holderegger and Wagner, 2. Understanding landscape effects on genetic connectivity provides insight into fundamental biological processes such as: metapopulation dynamics, speciation, and ultimately the formation of species' distributions. Landscape genetic analyses can also have great applied scientific value, such as identifying specific anthropogenic barriers that reduce gene flow or genetic diversity, predicting the effects of proposed management alternatives on genetic variation and population connectivity, and identifying potential biological corridors to assist with reserve design. Given these diverse research opportunities, landscape genetics is both challenging and exciting, as it brings together scientists from the broad disciplines of landscape ecology, spatial statistics, geography and population genetics. While several types of spatial statistical analyses have been used in geographical genetics (for review, see Epperson, 2. The vast array of spatial analysis techniques that can be applied to population genetic data make options for designing and conducting a landscape genetics study extremely diverse and potentially confusing. Better communication among landscape ecologists, spatial statisticians, remote- sensing scientists, geographers and population geneticists is key to integrating analysis methods and empirical data. To help bridge communication gaps, we have included a glossary of terminology used in spatial statistics and landscape ecology (Table 1), with the terms denoted in italics when first used in the text. Our goals are to: (a) offer a definition of the term 'landscape genetics'; (b) review questions commonly addressed in the landscape genetics literature, (c) provide guidelines for sampling design, (d) highlight potentially useful analysis techniques; and (e) discuss future directions for the field. The most commonly used molecular tools for landscape genetic studies are neutral, hypervariable markers (e. These studies have varied extensively in their approach to evaluating relationships between landscape variables and genetic variation. We suggest that landscape genetics studies could benefit substantially by including explicit tests of the relative influence of landscape variables on genetic variation by incorporating robust, spatially informed study designs and spatial analyses. Thus, in this review, we define landscape genetics as research that explicitly quantifies the effects of landscape composition, configuration and matrix quality on gene flow and spatial genetic variation. This definition expands on the description of landscape genetics in Holderegger and Wagner (2. Phylogeography (Avise, 2. Manel et al., 2. 00. Top of page. Major research categories in landscape genetics. There are a wide variety of basic and applied research questions that can be addressed using a landscape genetics approach (see Tables 2 and 3). We group these questions under five major research categories: (1) quantifying influence of landscape variables and configuration on genetic variation; (2) identifying barriers to gene flow; (3) identifying source- sink dynamics and movement corridors; (4) understanding the spatial and temporal scale of an ecological process; and (5) testing species- specific ecological hypotheses. Influence of landscape variables and configuration on genetic variation. Quantifying the effect of landscape configuration on gene flow has been a major focus of published landscape genetics studies (Manel et al., 2. Scribner et al., 2. Table 2). Statistical analyses of genetic data have been used to identify the effects of matrix resistance on gene flow and genetic structure, including: cover type (Keyghobadi et al., 1. Spear et al., 2. 00. Roach et al., 2. 00. Antolin et al., 2. Holzhauer et al., 2. Michels et al., 2. Pfenninger, 2. 00. Funk et al., 2. 00. Scribner et al., 2. Ezard and Travis, 2. Recently, Sezen et al. This demonstrates that intrinsic scale, the area encompassed by a population as estimated by a genetic neighborhood, may change across a landscape due to the landscape composition and configuration. As another example, wolverine populations in intact habitats have a larger intrinsic scale than populations in fragmented habitats (Cegelski et al., 2. Identifying barriers. Geneland is a computer program whose main. Important areas of application include landscape genetics, conservation. Landscape genetics is a rapidly evolving interdisciplinary. STRUCTURE software, Pritchard et al. Spatial Pattern Analysis Program for. Estoup, A., Mortier, F. A spatial statistical model for landscape genetics. Geneland : A program for landscape. A computer program to. Geneland: A computer package for landscape genetics. Analysing georeferenced population genetics data with Geneland. Population genetics analysis using R and the Geneland program Gilles Guillot. 1 Overview 6 1.1 About Geneland. Santos Using AFLP markers and the Geneland program. Santos A computer program. Geneland: A program for landscape genetics. Identifying potential gene flow barriers is a major focus of landscape genetics research. While all landscape features affect gene flow, particular structures such as roads (Riley et al., 2. Antolin et al., 2. Funk et al., 2. 00. Genetic data have been used to identify abrupt breaks in gene flow (Dupanloup et al., 2. Manni et al., 2. 00. Geffen et al., 2. Barriers may also consist of microhabitats that prevent gene flow because they exceed a threshold for moisture, temperature or chemical tolerance for particular species (Palo et al., 2. Therefore, barrier identification has important implications for ecological (Walker et al., 2. Kreyer et al., 2. Funk et al., 2. 00. Bhattacharya et al., 2. Miller and Waits, 2. Dodd et al., 2. 00. Castella et al., 2. Broderick et al., 2. Cicero, 2. 00. 4; Gee, 2. One distinct benefit of a landscape genetics approach is that spatially explicit techniques can allow researchers to identify barriers not detectable by traditional population genetic methods (Guillot et al., 2. Coulon et al., 2. For example, Coulon et al. Landscape genetics can also be used to quantify the cumulative impact of a particular barrier type distributed across the landscape. For example, Epps et al. They also estimated a 'barrier effect distance' and suggested that any one of these barriers would create the same decrease in gene flow as 4. Source- sink dynamics. Understanding source- sink dynamics (Pulliam, 1. Dias et al., 1. 99. Genetic data have been used to identify source and sink habitats for populations by identifying asymmetric gene flow using private alleles (Kennington et al., 2. Beerli and Felsenstein, 2. Paetkau et al., 1. Wilson and Rannala, 2. Theoretical population models suggest evaluations of linkage disequilibrium can be used to detect sink habitats because disequilibrium is predicted to be higher in individuals from sinks due to immigrants from different sources (Nei and Le, 1. This study also helped detect a dispersal threshold for male A. Least- cost analysis also has been valuable in identifying landscape variables that facilitate gene flow and may function as corridors (Spear et al., 2. Vignieri, 2. 00. 5). For example, Vignieri (2. Pacific jumping mouse (Zapus trinotatus). Spatial and temporal scales. Genetic variation may respond differently over varying spatial or temporal scales, which is a critical issue in defining research questions and subsequent study design in landscape ecology and spatial statistics (for review, see Gardner, 2. The scale at which particular landscape variables have the greatest influence on gene flow (i. For example, Trapnell and Hamrick (2. Central American epiphytic orchid, Laelia rubescens, were scale- dependent. Primary factors governing gene flow were seed gravity (seed dispersal) at the finest spatial scale, hummingbird behavior (pollen dispersal) at the intermediate scale, and wind (occasional seed dispersal) at the broadest scale. Temporal scale may also have a significant impact on landscape genetics. For example, Ramstad et al. They found that temporal isolation based on spawning time and founder effects associated with ongoing glacial retreat and colonization of new spawning habitats contributed significantly to genetic population structure, while geographic distance and spawning habitat differences did not have significant influence. Species- specific hypothesis testing. Landscape genetics offers new approaches for testing hypotheses specifically related to how the ecology of the study species shapes patterns of genetic variation, such as identification of bioregions (Sacks et al., 2. Rehfeldt et al., 1. J. For example, Sacks et al. In a study of herring (Clupea harengus) in the Baltic Sea, J. Opportunistic sampling may fail to capture the spatial variation or spatial dependency of the system, resulting in difficulty detecting spatial relationships or erroneous model inferences (Legendre, 2. Fortin and Dale, 2. Thus, studies should be designed to sample the variable(s) of interest within the scale of spatial dependency (Coulon et al., 2. In addition, population genetics studies have been traditionally designed to collect samples from a minimum of 2. Nei, 1. 97. 8). However, a priori delineation is no longer necessary due to the development of genetic clustering algorithms, such as assignment tests (Pritchard et al., 2. Wilson and Rannala, 2. Manel et al., 2. 00. In addition, models of landscape influence on genetic variation often require more continuously distributed sampling, which can be analyzed with a wide variety of spatial analysis techniques reviewed herein (see Table 3). Careful consideration of scale in study design is also critical because arbitrarily defined scales may lead to erroneous conclusions or fail to capture appropriate variability in the landscape (King, 1. Gardner, 2. 00. 1). We refer to scale as the appropriate spatial or temporal dimensions at which processes can be observed and quantified (for review, see Dungan et al., 2. Under this definition, scale has two relevant components: 'grain' and 'extent' (O'Neill et al., 1. Grain is the minimum resolution of the data and extent is the total area of interest.
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