INTEGRATIVE TESTING OF HOW ENVIRONMENTS FROM THE PAST TO THE PRESENT SHAPE GENETIC STRUCTURE ACROSS LANDSCAPES

Tests of the genetic structure of empirical populations typically focus on the correlative relationships between population connectivity and geographic and/or environmental factors in landscape genetics. However, such tests may overlook or misidentify the impact of candidate factors on genetic structure, especially when connectivity patterns differ between past and present populations because of shifting environmental conditions over time. Here we account for the underlying demographic component of population connectivity associated with a temporarily dynamic landscape in tests of the factors structuring population genetic variation in an Australian lizard, Lerista lineopunctulata, from 24 nuclear loci. Correlative tests did not support significant effect from factors associated with a static contemporary landscape. However, spatially explicit demographic modeling of genetic differentiation shows that changes in environmental conditions (as estimated from paleoclimatic data) and corresponding distributional shifts from the past to present landscape significantly structures genetic variation. Results from model‐based inference (i.e., from an integrative modeling approach that generates spatially explicit expectations that are tested with approximate Bayesian computation) contrasts with those from correlative analyses, highlighting the importance of expanding the landscape genetic perspective to tests the links between pattern and process, revealing how factors shape patterns of genetic variation within species.     

The influence of landscape on gene flow in the eastern massasauga rattlesnake (Sistrurus c. catenatus): insight from computer simulations

Understanding how gene flow shapes contemporary population structure requires the explicit consideration of landscape composition and configuration. New landscape genetic approaches allow us to link such heterogeneity to gene flow within and among populations. However, the attribution of cause is difficult when landscape features are spatially correlated, or when genetic patterns reflect past events. We use spatial Bayesian clustering and landscape resistance analysis to identify the landscape features that influence gene flow across two regional populations of the eastern massasauga rattlesnake, Sistrurus c. catenatus. Based on spatially explicit simulations, we inferred how habitat distribution modulates gene flow and attempted to disentangle the effects of spatially confounded landscape features. We found genetic clustering across one regional landscape but not the other, and also local differences in the effect of landscape on gene flow. Beyond the effects of isolation‐by‐distance, water bodies appear to underlie genetic differentiation among individuals in one regional population. Significant effects of roads were additionally detected locally, but these effects are possibly confounded with the signal of water bodies. In contrast, we found no signal of isolation‐by‐distance or landscape effects on genetic structure in the other regional population. Our simulations imply that these local differences have arisen as a result of differences in population density or tendencies for juvenile rather than adult dispersal. Importantly, our simulations also demonstrate that the ability to detect the consequences of contemporary anthropogenic landscape features (e.g. roads) on gene flow may be compromised when long‐standing natural features (e.g. water bodies) co‐exist on the landscape.     

Mechanistic home range models capture spatial patterns and dynamics of coyote territories in Yellowstone

Patterns of space-use by individuals are fundamental to the ecology of animal populations influencing their social organization, mating systems, demography and the spatial distribution of prey and competitors. To date, the principal method used to analyse the underlying determinants of animal home range patterns has been resource selection analysis (RSA), a spatially implicit approach that examines the relative frequencies of animal relocations in relation to landscape attributes. In this analysis, we adopt an alternative approach, using a series of mechanistic home range models to analyse observed patterns of territorial space-use by coyote packs in the heterogeneous landscape of Yellowstone National Park. Unlike RSAs, mechanistic home range models are derived from underlying correlated random walk models of individual movement behaviour, and yield spatially explicit predictions for patterns of space-use by individuals. As we show here, mechanistic home range models can be used to determine the underlying determinants of animal home range patterns, incorporating both movement responses to underlying landscape heterogeneities and the effects of behavioural interactions between individuals. Our analysis indicates that the spatial arrangement of coyote territories in Yellowstone is determined by the spatial distribution of prey resources and an avoidance response to the presence of neighbouring packs. We then show how the fitted mechanistic home range model can be used to correctly predict observed shifts in the patterns of coyote space-use in response to perturbation.     

How does farmer connectivity influence livestock genetic structure? A case‐study in a Vietnamese goat population

Assessing how genes flow across populations is a key component of conservation genetics. Gene flow in a natural population depends on ecological traits and the local environment, whereas for a livestock population, gene flow is driven by human activities. Spatial organization, relationships between farmers and their husbandry practices will define the farmer's network and so determine farmer connectivity. It is thus assumed that farmer connectivity will affect the genetic structure of their livestock. To test this hypothesis, goats reared by four different ethnic groups in a Vietnamese province were genotyped using 16 microsatellites. A Bayesian approach and spatial multivariate analysis (spatial principal component analysis, sPCA) were used to identify subpopulations and spatial organization. Ethnic group frequencies, husbandry practices and altitude were used to create cost maps that were implemented in a least-cost path approach. Genetic diversity in the Vietnamese goat population was low (0.508) compared to other local Asian breeds. Using a Bayesian approach, three clusters were identified. sPCA confirmed these three clusters and also that the genetic structure showed a significant spatial pattern. The least-cost path analysis showed that genetic differentiation was significantly correlated (0.131–0.207) to ethnic frequencies and husbandry practices. In brief, the spatial pattern observed in the goat population was the result of complex gene flow governed by the spatial distribution of ethnic groups, ethnicity and husbandry practices. In this study, we clearly linked the livestock genetic pattern to farmer connectivity and showed the importance of taking into account spatial information in genetic studies.     

Assessing the Permeability of Landscape Features to Animal Movement: Using Genetic Structure to Infer Functional Connectivity

Human-altered environments often challenge native species with a complex spatial distribution of resources. Hostile landscape features can inhibit animal movement (i.e., genetic exchange), while other landscape attributes facilitate gene flow. The genetic attributes of organisms inhabiting such complex environments can reveal the legacy of their movements through the landscape. Thus, by evaluating landscape attributes within the context of genetic connectivity of organisms within the landscape, we can elucidate how a species has coped with the enhanced complexity of human altered environments. In this research, we utilized genetic data from eastern chipmunks (Tamias striatus) in conjunction with spatially explicit habitat attribute data to evaluate the realized permeability of various landscape elements in a fragmented agricultural ecosystem. To accomplish this we 1) used logistic regression to evaluate whether land cover attributes were most often associated with the matrix between or habitat within genetically identified populations across the landscape, and 2) utilized spatially explicit habitat attribute data to predict genetically-derived Bayesian probabilities of population membership of individual chipmunks in an agricultural ecosystem. Consistency between the results of the two approaches with regard to facilitators and inhibitors of gene flow in the landscape indicate that this is a promising new way to utilize both landscape and genetic data to gain a deeper understanding of human-altered ecosystems.     

Landscape configuration determines gene flow and phenotype in a flightless forest-edge ground-dwelling bush-cricket, Pholidoptera griseoaptera

Spatial configuration of habitats influences genetic structure and population fitness whereas it affects mainly species with limited dispersal ability. To reveal how habitat fragmentation determines dispersal and dispersal-related morphology in a ground-dispersing insect species we used a bush-cricket (Pholidoptera griseoaptera) which is associated with forest-edge habitat. We analysed spatial genetic patterns together with variability of the phenotype in two forested landscapes with different levels of fragmentation. While spatial configuration of forest habitats did not negatively affect genetic characteristics related to the fitness of sampled populations, genetic differentiation was found higher among populations from an extensive forest. Compared to an agricultural matrix between forest patches, the matrix of extensive forest had lower permeability and posed barriers for the dispersal of this species. Landscape configuration significantly affected also morphological traits that are supposed to account for species dispersal potential; individuals from fragmented forest patches had longer hind femurs and a higher femur to pronotum ratio. This result suggests that selection pressure act differently on populations from both landscape types since dispersal-related morphology was related to the level of habitat fragmentation. Thus observed patterns may be explained as plastic according to the level of landscape configuration; while anthropogenic fragmentation of habitats for this species can lead to homogenization of spatial genetic structure.     

sGD: software for estimating spatially explicit indices of genetic diversity

Anthropogenic landscape changes have greatly reduced the population size, range and migration rates of many terrestrial species. The small local effective population size of remnant populations favours loss of genetic diversity leading to reduced fitness and adaptive potential, and thus ultimately greater extinction risk. Accurately quantifying genetic diversity is therefore crucial to assessing the viability of small populations. Diversity indices are typically calculated from the multilocus genotypes of all individuals sampled within discretely defined habitat patches or larger regional extents. Importantly, discrete population approaches do not capture the clinal nature of populations genetically isolated by distance or landscape resistance. Here, we introduce spatial Genetic Diversity (sGD), a new spatially explicit tool to estimate genetic diversity based on grouping individuals into potentially overlapping genetic neighbourhoods that match the population structure, whether discrete or clinal. We compared the estimates and patterns of genetic diversity using patch or regional sampling and sGD on both simulated and empirical populations. When the population did not meet the assumptions of an island model, we found that patch and regional sampling generally overestimated local heterozygosity, inbreeding and allelic diversity. Moreover, sGD revealed fine-scale spatial heterogeneity in genetic diversity that was not evident with patch or regional sampling. These advantages should provide a more robust means to evaluate the potential for genetic factors to influence the viability of clinal populations and guide appropriate conservation plans.     

Climate change,genetic markers and species distribution modelling

Ecologists and biogeographers are currently expending great effort forecasting shifts in species geographical ranges that may result from climate change. However, these efforts are problematic because they have mostly relied on presence‐only data that ignore within‐species genetic diversity. Technological advances in high‐throughput sequencing have now made it cost‐effective to survey the genetic structure of populations sampled throughout the range of a species. These data can be used to delineate two or more genetic clusters within the species range, and to identify admixtures of individuals within genetic clusters that reflect different patterns of ancestry. Species distribution models (SDMs) applied to the presence and absence of genetic clusters should provide more realistic forecasts of geographical range shifts that take account of genetic variability. High‐throughput sequencing and spatially explicit models may be used to further refine these projections.     

RAPD variation and population genetic structure of Physalaemus cuvieri (Anura: Leptodactylidae) in Central Brazil

Studies about the organization of the genetic variability and population structure in natural populations are used either to understand microevolutionary processes or the effects of isolation by human-inducted landscape modifications. In this paper, we analyzed patterns of genetic population structure using 126 RAPD loci scored for 214 individuals of Physalaemus cuvieri, sampled from 18 local populations. Around 97% of these loci were polymorphic. The among-population variation component (Φ_ST) obtained by AMOVA was equal to 0.101 and θ ^B obtained using a Bayesian approach for dominant markers was 0.103. Genetic divergence, analyzed by Mantel spatial correlogram, revealed only a short-distance significant correlation between genetic and geographic distances. This is expected if low levels of population differentiation, due to high abundance buffering the effect of stochastic processes, are combined with low spatially restricted gene flow. Although this may be consistent with the current knowledge of species’ biology, the spatial distribution of local populations observed in this study also suggest that, at least in part, recent human occupation and habitat fragmentation may also explain part of the interpopulational component of the genetic variation.     

Fine-scale analysis reveals cryptic landscape genetic structure in desert tortoises

Characterizing the effects of landscape features on genetic variation is essential for understanding how landscapes shape patterns of gene flow and spatial genetic structure of populations. Most landscape genetics studies have focused on patterns of gene flow at a regional scale. However, the genetic structure of populations at a local scale may be influenced by a unique suite of landscape variables that have little bearing on connectivity patterns observed at broader spatial scales. We investigated fine-scale spatial patterns of genetic variation and gene flow in relation to features of the landscape in desert tortoise (Gopherus agassizii), using 859 tortoises genotyped at 16 microsatellite loci with associated data on geographic location, sex, elevation, slope, and soil type, and spatial relationship to putative barriers (power lines, roads). We used spatially explicit and non-explicit Bayesian clustering algorithms to partition the sample into discrete clusters, and characterize the relationships between genetic distance and ecological variables to identify factors with the greatest influence on gene flow at a local scale. Desert tortoises exhibit weak genetic structure at a local scale, and we identified two subpopulations across the study area. Although genetic differentiation between the subpopulations was low, our landscape genetic analysis identified both natural (slope) and anthropogenic (roads) landscape variables that have significantly influenced gene flow within this local population. We show that desert tortoise movements at a local scale are influenced by features of the landscape, and that these features are different than those that influence gene flow at larger scales. Our findings are important for desert tortoise conservation and management, particularly in light of recent translocation efforts in the region. More generally, our results indicate that recent landscape changes can affect gene flow at a local scale and that their effects can be detected almost immediately.     

Landscape genetics of a specialized grasshopper inhabiting highly fragmented habitats: a role for spatial scale

Aim The study of geographical discontinuities in the distribution of genetic variability in natural populations is a central topic in both evolutionary and conservation research. In this study, we aimed to analyse (1) the factors associated with genetic diversity at the landscape spatial scale in the highly specialized grasshopper Mioscirtus wagneri and (2) to identify the relative contribution of alternative factors to the observed patterns of genetic structure in this species. Location La Mancha region, Central Spain. Methods We sampled 28 populations of the grasshopper M. wagneri and genotyped 648 individuals at seven microsatellite loci. We employed a causal modelling approach to identify the most influential variables associated with genetic differentiation within a multiple hypothesis‐testing framework. Results We found that genetic diversity differs among populations located in different river basins and decreases with population isolation. Causal modelling analyses showed variability in the relative influence of the studied landscape features across different spatial scales. When a highly isolated population is considered, the analyses suggested that geographical distance is the only factor explaining the genetic differentiation between populations. When that population is excluded, the causal modelling analysis revealed that elevation and river basins are also relevant factors contributing to explaining genetic differentiation between the studied populations. Main conclusions These results indicate that the spatial scale considered and the inclusion of outlier populations may have important consequences on the inferred contribution of alternative landscape factors on the patterns of genetic differentiation even when all populations are expected to similarly respond to landscape structure. Thus, a multiscale perspective should also be incorporated into the landscape genetics framework to avoid biased conclusions derived from the spatial scale analysed and/or the geographical distribution of the studied populations.     

Genes mirror geography in Daphnia magna

Identifying the presence and magnitude of population genetic structure remains a major consideration in evolutionary biology as doing so allows one to understand the demographic history of a species as well as make predictions of how the evolutionary process will proceed. Next‐generation sequencing methods allow us to reconsider previous ideas and conclusions concerning the distribution of genetic variation, and what this distribution implies about a given species evolutionary history. A previous phylogeographic study of the crustacean Daphnia magna suggested that, despite strong genetic differentiation among populations at a local scale, the species shows only moderate genetic structure across its European range, with a spatially patchy occurrence of individual lineages. We apply RAD sequencing to a sample of D. magna collected across a wide swath of the species' Eurasian range and analyse the data using principle component analysis (PCA) of genetic variation and Procrustes analytical approaches, to quantify spatial genetic structure. We find remarkable consistency between the first two PCA axes and the geographic coordinates of individual sampling points, suggesting that, on a continent‐wide scale, genetic differentiation is driven to a large extent by geographic distance. The observed pattern is consistent with unimpeded (i.e. no barriers, landscape or otherwise) migration at large spatial scales, despite the fragmented and patchy nature of favourable habitats at local scales. With high‐resolution genetic data similar patterns may be uncovered for other species with wide geographic distributions, allowing an increased understanding of how genetic drift and selection have shaped their evolutionary history.     

A spatial statistical model for landscape genetics

Landscape genetics is a new discipline that aims to provide information on how landscape and environmental features influence population genetic structure. The first key step of landscape genetics is the spatial detection and location of genetic discontinuities between populations. However, efficient methods for achieving this task are lacking. In this article, we first clarify what is conceptually involved in the spatial modeling of genetic data. Then we describe a Bayesian model implemented in a Markov chain Monte Carlo scheme that allows inference of the location of such genetic discontinuities from individual geo-referenced multilocus genotypes, without a priori knowledge on populational units and limits. In this method, the global set of sampled individuals is modeled as a spatial mixture of panmictic populations, and the spatial organization of populations is modeled through the colored Voronoi tessellation. In addition to spatially locating genetic discontinuities, the method quantifies the amount of spatial dependence in the data set, estimates the number of populations in the studied area, assigns individuals to their population of origin, and detects individual migrants between populations, while taking into account uncertainty on the location of sampled individuals. The performance of the method is evaluated through the analysis of simulated data sets. Results show good performances for standard data sets (e.g., 100 individuals genotyped at 10 loci with 10 alleles per locus), with high but also low levels of population differentiation (e.g., F_ST < 0.05). The method is then applied to a set of 88 individuals of wolverines (Gulo gulo) sampled in the northwestern United States and genotyped at 10 microsatellites.     

A new individual-based spatial approach for identifying genetic discontinuities in natural populations

The population concept is central in evolutionary and conservation biology, but identifying the boundaries of natural populations is often challenging. Here, we present a new approach for assessing spatial genetic structure without the a priori assumptions on the locations of populations made by adopting an individual-centred approach. Our method is based on assignment tests applied in a moving window over an extensively sampled study area. For each individual, a spatially explicit probability surface is constructed, showing the estimated probability of finding its multilocus genotype across the landscape, and identifying putative migrants. Population boundaries are localized by estimating the mean slope of these probability surfaces over all individuals to identify areas with genetic discontinuities. The significance of the genetic discontinuities is assessed by permutation tests. This new approach has the potential to reveal cryptic population structure and to improve our ability to understand gene flow dynamics across landscapes. We illustrate our approach by simulations and by analysing two empirical datasets: microsatellite data of Ursus arctos in Scandinavia, and amplified fragment length polymorphism (AFLP) data of Rhododendron ferrugineum in the Alps.     

广东省中山市五桂山土沉香空间遗传结构

为了解五桂山的土沉香(Aquilaria sinensis)种群空间遗传结构,利用18个微卫星位点对143株胸高直径大于5 cm的土沉香个体基因型进行了检测。结果表明,五桂山土沉香种群的观测杂合度和期望杂合度均为0.534;近交系数为0.000 1,说明五桂山土沉香遗传多样性并不低,且种群处于随机交配状况。空间遗传结构分析结果表明,五桂山土沉香缺乏空间遗传结构,不同遗传背景的个体相互混杂,导致相邻个体遗传差异较大。这可能是五桂山土沉香种群发育过程中自疏所致,也可能是这一地区土沉香为人工种植,而非自然发育形成而造成的。     

Influence of habitat fragmentation on population structure of red deer in Croatia

The genetic structure of red deer populations is under strong influence of human activities such as game management and habitat fragmentation. Using multilocus genotypes from 193 geo-referenced individuals, we evaluated the population genetic structure of three red deer populations in Croatia. The effect of habitat fragmentation on genetic structure was tested using Bayesian non-spatial and spatial clustering methods. Our results indicate levels of genetic diversity similar to the ones previously reported by other authors for stable and appropriately managed populations within all populations analyzed. The spatial clustering model was able to detect the effect of habitat fragmentation on population differentiation, supporting the use of spatially explicit methods in landscape genetics, and giving important guidelines for future road planning.     

Environmental niche factor analysis (ENFA) relates environmental parameters to abundance and genetic diversity in an endangered amphibian, the fire-bellied-toad (Bombina bombina)

Increasing attempts are made to understand the factors responsible for both the demographic and genetic depletion in amphibian populations. Landscape genetics aims at a spatially explicit correlation of genetic population parameters to landscape features. Using data from the endangered fire-bellied toad Bombina bombina in Brandenburg (Northeastern Germany), we performed an environmental niche factor analysis (ENFA), relating demographic (abundance) and genetic (diversity at 17 microsatellite loci and partial sequences of the mitochondrial control region in 434 individuals from 16 populations) parameters to ecological and anthropogenic variables such as temperature, precipitation, soil wetness, water runoff, vegetation density, and road/traffic impact. We found significant correlations between road disturbance and observed heterozygosity and between soil wetness and mitochondrial diversity. As the influences of the environmental variables can differ between different indicators for genetic diversity, population size and abundance data, our ENFA-based landscape genetics approach allows us to put various aspects of long- versus short term effective population size and genetic connectivity into an ecological and spatially explicit context, enabling potentially even forecast assessment under future environmental scenarios.     

Inferring colonization history from analyses of spatial genetic structure within populations of Pinus strobus and Quercus rubra

Many factors interact to determine genetic structure within populations including adult density, the mating system, colonization history, natural selection, and the mechanism and spatial patterns of gene dispersal. We examined spatial genetic structure within colonizing populations of Quercus rubra seedlings and Pinus strobus juveniles and adults in an aspen-white pine forest in northern Michigan, USA. A 20-year spatially explicit demographic study of the forest enables us to interpret the results in light of recent colonization of the site for both species. We assayed 217 Q. rubra seedlings and 171 P. strobus individuals at 11 polymorphic loci using nine allozyme systems. Plant genotypes and locations were used in an analysis of spatial genetic structure. Q. rubra and P. strobus showed similar observed levels of heterozygosity, but Q. rubra seedlings have less heterozygosity than expected. Q. rubra seedlings show spatial genetic clumping of individuals on a scale to 25 m and levels of genetic relatedness expected from the clumped dispersion of half-siblings. In contrast, P. strobus has low levels of genetic relatedness at the smallest distance class and positive spatial genetic structure at scales < 10 m within the plot. The low density of adult Q. rubra outside the study plot and limited, spatially clumped rodent dispersal of acorns is likely responsible for the observed pattern of spatial genetic structure and the observed heterozygote deficit (i.e. a Wahlund effect). We attribute weaker patterns observed in P. strobus to the longer dispersal distance of seeds and the historical overlap of seed shadows from adults outside of the plot coupled with the overlap of seed shadows from younger, more recently established reproductive adults. The study demonstrates the utility of long-term demographic data in interpreting mechanisms responsible for generating contemporary patterns of genetic structure within populations.     

Isolation by distance,resistance and/or clusters? Lessons learned from a forest‐dwelling carnivore inhabiting a heterogeneous landscape

Landscape genetics provides a valuable framework to understand how landscape features influence gene flow and to disentangle the factors that lead to discrete and/or clinal population structure. Here, we attempt to differentiate between these processes in a forest‐dwelling small carnivore [European pine marten (Martes martes)]. Specifically, we used complementary analytical approaches to quantify the spatially explicit genetic structure and diversity and analyse patterns of gene flow for 140 individuals genotyped at 15 microsatellite loci. We first used spatially explicit and nonspatial Bayesian clustering algorithms to partition the sample into discrete clusters and evaluate hypotheses of ‘isolation by barriers’ (IBB). We further characterized the relationships between genetic distance and geographical (‘isolation by distance’, IBD) and ecological distances (‘isolation by resistance’, IBR) obtained from optimized landscape models. Using a reciprocal causal modelling approach, we competed the IBD, IBR and IBB hypotheses with each other to unravel factors driving population genetic structure. Additionally, we further assessed spatially explicit indices of genetic diversity using sGD across potentially overlapping genetic neighbourhoods that matched the inferred population structure. Our results revealed a complex spatial genetic cline that appears to be driven jointly by IBD and partial barriers to gene flow (IBB) associated with poor habitat and interspecific competition. Habitat loss and fragmentation, in synergy with past overharvesting and possible interspecific competition with sympatric stone marten (Martes foina), are likely the main factors responsible for the spatial genetic structure we observed. These results emphasize the need for a more thorough evaluation of discrete and clinal hypotheses governing gene flow in landscape genetic studies, and the potential influence of different limiting factors affecting genetic structure at different spatial scales.     

Spatial population genomics of the brown rat (Rattus norvegicus) in New York City

Human commensal species such as rodent pests are often widely distributed across cities and threaten both infrastructure and public health. Spatially explicit population genomic methods provide insights into movements for cryptic pests that drive evolutionary connectivity across multiple spatial scales. We examined spatial patterns of neutral genomewide variation in brown rats (Rattus norvegicus) across Manhattan, New York City (NYC), using 262 samples and 61,401 SNPs to understand (i) relatedness among nearby individuals and the extent of spatial genetic structure in a discrete urban landscape; (ii) the geographic origin of NYC rats, using a large, previously published data set of global rat genotypes; and (iii) heterogeneity in gene flow across the city, particularly deviations from isolation by distance. We found that rats separated by ≤200 m exhibit strong spatial autocorrelation (r = .3, p = .001) and the effects of localized genetic drift extend to a range of 1,400 m. Across Manhattan, rats exhibited a homogeneous population origin from rats that likely invaded from Great Britain. While traditional approaches identified a single evolutionary cluster with clinal structure across Manhattan, recently developed methods (e.g., fineSTRUCTURE, sPCA, EEMS) provided evidence of reduced dispersal across the island's less residential Midtown region resulting in fine‐scale genetic structuring (F_ST = 0.01) and two evolutionary clusters (Uptown and Downtown Manhattan). Thus, while some urban populations of human commensals may appear to be continuously distributed, landscape heterogeneity within cities can drive differences in habitat quality and dispersal, with implications for the spatial distribution of genomic variation, population management and the study of widely distributed pests.