Ecological resistance surfaces predict fine‐scale genetic differentiation in a terrestrial woodland salamander

Landscape genetics has seen tremendous advances since its introduction, but parameterization and optimization of resistance surfaces still poses significant challenges. Despite increased availability and resolution of spatial data, few studies have integrated empirical data to directly represent ecological processes as genetic resistance surfaces. In our study, we determine the landscape and ecological factors affecting gene flow in the western slimy salamander (Plethodon albagula). We used field data to derive resistance surfaces representing salamander abundance and rate of water loss through combinations of canopy cover, topographic wetness, topographic position, solar exposure and distance from ravine. These ecologically explicit composite surfaces directly represent an ecological process or physiological limitation of our organism. Using generalized linear mixed‐effects models, we optimized resistance surfaces using a nonlinear optimization algorithm to minimize model AIC. We found clear support for the resistance surface representing the rate of water loss experienced by adult salamanders in the summer. Resistance was lowest at intermediate levels of water loss and higher when the rate of water loss was predicted to be low or high. This pattern may arise from the compensatory movement behaviour of salamanders through suboptimal habitat, but also reflects the physiological limitations of salamanders and their sensitivity to extreme environmental conditions. Our study demonstrates that composite representations of ecologically explicit processes can provide novel insight and can better explain genetic differentiation than ecologically implicit landscape resistance surfaces. Additionally, our study underscores the fact that spatial estimates of habitat suitability or abundance may not serve as adequate proxies for describing gene flow, as predicted abundance was a poor predictor of genetic differentiation.     

Using simulations to evaluate Mantel‐based methods for assessing landscape resistance to gene flow

Mantel‐based tests have been the primary analytical methods for understanding how landscape features influence observed spatial genetic structure. Simulation studies examining Mantel‐based approaches have highlighted major challenges associated with the use of such tests and fueled debate on when the Mantel test is appropriate for landscape genetics studies. We aim to provide some clarity in this debate using spatially explicit, individual‐based, genetic simulations to examine the effects of the following on the performance of Mantel‐based methods: (1) landscape configuration, (2) spatial genetic nonequilibrium, (3) nonlinear relationships between genetic and cost distances, and (4) correlation among cost distances derived from competing resistance models. Under most conditions, Mantel‐based methods performed poorly. Causal modeling identified the true model only 22% of the time. Using relative support and simple Mantel r values boosted performance to approximately 50%. Across all methods, performance increased when landscapes were more fragmented, spatial genetic equilibrium was reached, and the relationship between cost distance and genetic distance was linearized. Performance depended on cost distance correlations among resistance models rather than cell‐wise resistance correlations. Given these results, we suggest that the use of Mantel tests with linearized relationships is appropriate for discriminating among resistance models that have cost distance correlations <0.85 with each other for causal modeling, or <0.95 for relative support or simple Mantel r. Because most alternative parameterizations of resistance for the same landscape variable will result in highly correlated cost distances, the use of Mantel test‐based methods to fine‐tune resistance values will often not be effective.     

Watershed‐level brook trout genetic structuring: Evaluation and application of riverscape genetics models

相似文献   

Contrasting the seasonal and elevational prevalence of generalist avian haemosporidia in co‐occurring host species

Understanding the ecology and evolution of parasites is contingent on identifying the selection pressures they face across their infection landscape. Such a task is made challenging by the fact that these pressures will likely vary across time and space, as a result of seasonal and geographical differences in host susceptibility or transmission opportunities. Avian haemosporidian blood parasites are capable of infecting multiple co‐occurring hosts within their ranges, yet whether their distribution across time and space varies similarly in their different host species remains unclear. Here, we applied a new PCR method to detect avian haemosporidia (genera Haemoproteus, Leucocytozoon, and Plasmodium) and to determine parasite prevalence in two closely related and co‐occurring host species, blue tits (Cyanistes caeruleus, N = 529) and great tits (Parus major, N = 443). Our samples were collected between autumn and spring, along an elevational gradient in the French Pyrenees and over a three‐year period. Most parasites were found to infect both host species, and while these generalist parasites displayed similar elevational patterns of prevalence in the two host species, this was not always the case for seasonal prevalence patterns. For example, Leucocytozoon group A parasites showed inverse seasonal prevalence when comparing between the two host species, being highest in winter and spring in blue tits but higher in autumn in great tits. While Plasmodium relictum prevalence was overall lower in spring relative to winter or autumn in both species, spring prevalence was also lower in blue tits than in great tits. Together, these results reveal how generalist parasites can exhibit host‐specific epidemiology, which is likely to complicate predictions of host–parasite co‐evolution.     

Sampling related individuals within ponds biases estimates of population structure in a pond‐breeding amphibian

Effective conservation and management of pond‐breeding amphibians depends on the accurate estimation of population structure, demographic parameters, and the influence of landscape features on breeding‐site connectivity. Population‐level studies of pond‐breeding amphibians typically sample larval life stages because they are easily captured and can be sampled nondestructively. These studies often identify high levels of relatedness between individuals from the same pond, which can be exacerbated by sampling the larval stage. Yet, the effect of these related individuals on population genetic studies using genomic data is not yet fully understood. Here, we assess the effect of within‐pond relatedness on population and landscape genetic analyses by focusing on the barred tiger salamanders (Ambystoma mavortium) from the Nebraska Sandhills. Utilizing genome‐wide SNPs generated using a double‐digest RADseq approach, we conducted standard population and landscape genetic analyses using datasets with and without siblings. We found that reduced sample sizes influenced parameter estimates more than the inclusion of siblings, but that within‐pond relatedness led to the inference of spurious population structure when analyses depended on allele frequencies. Our landscape genetic analyses also supported different models across datasets depending on the spatial resolution analyzed. We recommend that future studies not only test for relatedness among larval samples but also remove siblings before conducting population or landscape genetic analyses. We also recommend alternative sampling strategies to reduce sampling siblings before sequencing takes place. Biases introduced by unknowingly including siblings can have significant implications for population and landscape genetic analyses, and in turn, for species conservation strategies and outcomes.     

The use and misuse of regression models in landscape genetic analyses

The field of landscape genetics has been rapidly evolving, adopting and adapting analytical frameworks to address research questions. Current studies are increasingly using regression‐based frameworks to infer the individual contributions of landscape and habitat variables on genetic differentiation. This paper outlines appropriate and inappropriate uses of multiple regression for these purposes, and demonstrates through simulation the limitations of different analytical frameworks for making correct inference. Of particular concern are recent studies seeking to explain genetic differences by fitting regression models with effective distance variables calculated independently on separate landscape resistance surfaces. When moving across the landscape, organisms cannot respond independently and uniquely to habitat and landscape features. Analyses seeking to understand how landscape features affect gene flow should model a single conductance or resistance surface as a parameterized function of relevant spatial covariates, and estimate the values of these parameters by linking a single set of resistance distances to observed genetic dissimilarity via a loss function. While this loss function may involve a regression‐like step, the associated nuisance parameters are not interpretable in terms of organismal movement and should not be conflated with what is actually of interest: the mapping between spatial covariates and conductance/resistance. The growth and evolution of landscape genetics as a field has been rapid and exciting. It is the goal of this paper to highlight past missteps and demonstrate limitations of current approaches to ensure that future use of regression models will appropriately consider the process being modeled, which will provide clarity to model interpretation.     

Landscape characteristics influencing the genetic structure of greater sage‐grouse within the stronghold of their range: a holistic modeling approach

Given the significance of animal dispersal to population dynamics and geographic variability, understanding how dispersal is impacted by landscape patterns has major ecological and conservation importance. Speaking to the importance of dispersal, the use of linear mixed models to compare genetic differentiation with pairwise resistance derived from landscape resistance surfaces has presented new opportunities to disentangle the menagerie of factors behind effective dispersal across a given landscape. Here, we combine these approaches with novel resistance surface parameterization to determine how the distribution of high‐ and low‐quality seasonal habitat and individual landscape components shape patterns of gene flow for the greater sage‐grouse (Centrocercus urophasianus) across Wyoming. We found that pairwise resistance derived from the distribution of low‐quality nesting and winter, but not summer, seasonal habitat had the strongest correlation with genetic differentiation. Although the patterns were not as strong as with habitat distribution, multivariate models with sagebrush cover and landscape ruggedness or forest cover and ruggedness similarly had a much stronger fit with genetic differentiation than an undifferentiated landscape. In most cases, landscape resistance surfaces transformed with 17.33‐km‐diameter moving windows were preferred, suggesting small‐scale differences in habitat were unimportant at this large spatial extent. Despite the emergence of these overall patterns, there were differences in the selection of top models depending on the model selection criteria, suggesting research into the most appropriate criteria for landscape genetics is required. Overall, our results highlight the importance of differences in seasonal habitat preferences to patterns of gene flow and suggest the combination of habitat suitability modeling and linear mixed models with our resistance parameterization is a powerful approach to discerning the effects of landscape on gene flow.     

Evaluating methods to visualize patterns of genetic differentiation on a landscape

With advances in sequencing technology, research in the field of landscape genetics can now be conducted at unprecedented spatial and genomic scales. This has been especially evident when using sequence data to visualize patterns of genetic differentiation across a landscape due to demographic history, including changes in migration. Two recent model‐based visualization methods that can highlight unusual patterns of genetic differentiation across a landscape, SpaceMix and EEMS, are increasingly used. While SpaceMix's model can infer long‐distance migration, EEMS’ model is more sensitive to short‐distance changes in genetic differentiation, and it is unclear how these differences may affect their results in various situations. Here, we compare SpaceMix and EEMS side by side using landscape genetics simulations representing different migration scenarios. While both methods excel when patterns of simulated migration closely match their underlying models, they can produce either un‐intuitive or misleading results when the simulated migration patterns match their models less well, and this may be difficult to assess in empirical data sets. We also introduce unbundled principal components (un‐PC), a fast, model‐free method to visualize patterns of genetic differentiation by combining principal components analysis (PCA), which is already used in many landscape genetics studies, with the locations of sampled individuals. Un‐PC has characteristics of both SpaceMix and EEMS and works well with simulated and empirical data. Finally, we introduce msLandscape, a collection of tools that streamline the creation of customizable landscape‐scale simulations using the popular coalescent simulator ms and conversion of the simulated data for use with un‐PC, SpaceMix and EEMS.     

Genetic effects of landscape,habitat preference and demography on three co‐occurring turtle species

Expanding the scope of landscape genetics beyond the level of single species can help to reveal how species traits influence responses to environmental change. Multispecies studies are particularly valuable in highly threatened taxa, such as turtles, in which the impacts of anthropogenic change are strongly influenced by interspecific differences in life history strategies, habitat preferences and mobility. We sampled approximately 1500 individuals of three co‐occurring turtle species across a gradient of habitat change (including varying loss of wetlands and agricultural conversion of upland habitats) in the Midwestern USA. We used genetic clustering and multiple regression methods to identify associations between genetic structure and permanent landscape features, past landscape composition and landscape change in each species. Two aquatic generalists (the painted turtle, Chrysemys picta, and the snapping turtle Chelydra serpentina) both exhibited population genetic structure consistent with isolation by distance, modulated by aquatic landscape features. Genetic divergence for the more terrestrial Blanding's turtle (Emydoidea blandingii), on the other hand, was not strongly associated with geographic distance or aquatic features, and Bayesian clustering analysis indicated that many Emydoidea populations were genetically isolated. Despite long generation times, all three species exhibited associations between genetic structure and postsettlement habitat change, indicating that long generation times may not be sufficient to delay genetic drift resulting from recent habitat fragmentation. The concordances in genetic structure observed between aquatic species, as well as isolation in the endangered, long‐lived Emydoidea, reinforce the need to consider both landscape composition and demographic factors in assessing differential responses to habitat change in co‐occurring species.     

Morphometric and genetic differentiation among populations of flat‐headed cusimanse (Crossarchus platycephalus) in Nigeria

Geographic barriers can partition genetic diversity among populations and drive evolutionary divergence between populations, promoting the speciation process and affecting conservation goals. We integrated morphological and genomic data to assess the distribution of variation in the flat‐headed cusimanse (Crossarchus platycephalus), a species of least conservation concern, on either side of the River Niger in Nigeria. Ecological disturbances affect the conservation status of many other animals in this region. The two populations were differentiated in the snout and fore limbs, with greater morphological diversity in the western population. We used Restriction site Associated DNA sequencing (RAD‐seq) and identified two genotypic clusters in a STRUCTURE analysis. Individuals from the eastern population are almost entirely assigned to one cluster, whereas genotypes from the western population are a mixture of the two clusters. The population from west of the River Niger also had higher heterozygosity. The morphological and population genetic data are therefore in agreement that the population from west of the River Niger is more diverse than the eastern population, and the eastern population contains a subset of the genetic variation found in the western population. Our results demonstrate that combining morphological and genotypic measures of diversity can provide a congruent picture of the distribution of intraspecific variation. The results also suggest that future work should explore the role of the River Niger as a natural barrier to migration in Nigeria.     

Population genetic patterns among social groups of the endangered Central American spider monkey (Ateles geoffroyi) in a human‐dominated landscape

Spider monkeys (Genus: Ateles) are a widespread Neotropical primate with a highly plastic socioecological strategy. However, the Central American species, Ateles geoffroyi, was recently re‐listed as endangered due to the accelerated loss of forest across the subcontinent. There is inconsistent evidence that spider monkey populations could persist when actively protected, but their long‐term viability in unprotected, human‐dominated landscapes is not known. We analyzed noninvasive genetic samples from 185 individuals in 14 putative social groups on the Rivas Isthmus in southwestern Nicaragua. We found evidence of weak but significant genetic structure in the mitochondrial control region and in eight nuclear microsatellite loci plus negative spatial autocorrelation in Fst and kinship. The overall pattern suggests strong localized mating and at least historical female‐biased dispersal, as is expected for this species. Heterozygosity was significantly lower than expected under random mating and lower than that found in other spider monkey populations, possibly reflecting a recent decline in genetic diversity and a threat from inbreeding. We conclude that despite a long history of human disturbance on this landscape, spider monkeys were until recently successful at maintaining gene flow. We consider the recent decline to be further indication of accelerated anthropogenic disturbance, but also of an opportunity to conserve native biodiversity. Spider monkeys are one of many wildlife species in Central America that is threatened by land cover change, and an apt example of how landscape‐scale conservation planning could be used to ensure long‐term persistence.     

Optimizing the trade‐off between spatial and genetic sampling efforts in patchy populations: towards a better assessment of functional connectivity using an individual‐based sampling scheme

Genetic data are increasingly used in landscape ecology for the indirect assessment of functional connectivity, that is, the permeability of landscape to movements of organisms. Among available tools, matrix correlation analyses (e.g. Mantel tests or mixed models) are commonly used to test for the relationship between pairwise genetic distances and movement costs incurred by dispersing individuals. When organisms are spatially clustered, a population‐based sampling scheme (PSS) is usually performed, so that a large number of genotypes can be used to compute pairwise genetic distances on the basis of allelic frequencies. Because of financial constraints, this kind of sampling scheme implies a drastic reduction in the number of sampled aggregates, thereby reducing sampling coverage at the landscape level. We used matrix correlation analyses on simulated and empirical genetic data sets to investigate the efficiency of an individual‐based sampling scheme (ISS) in detecting isolation‐by‐distance and isolation‐by‐barrier patterns. Provided that pseudo‐replication issues are taken into account (e.g. through restricted permutations in Mantel tests), we showed that the use of interindividual measures of genotypic dissimilarity may efficiently replace interpopulation measures of genetic differentiation: the sampling of only three or four individuals per aggregate may be sufficient to efficiently detect specific genetic patterns in most situations. The ISS proved to be a promising methodological alternative to the more conventional PSS, offering much flexibility in the spatial design of sampling schemes and ensuring an optimal representativeness of landscape heterogeneity in data, with few aggregates left unsampled. Each strategy offering specific advantages, a combined use of both sampling schemes is discussed.     

Geography best explains global patterns of genetic diversity and postglacial co‐expansion in marine turtles

For many species, climate oscillations drove cycles of population contraction during cool glacial periods followed by expansion during interglacials. Some groups, however, show evidence of uniform and synchronous expansion, while others display differences in the timing and extent of demographic change. We compared demographic histories inferred from genetic data across marine turtle species to identify responses to postglacial warming shared across taxa and to examine drivers of past demographic change at the global scale. Using coalescent simulations and approximate Bayesian computation (ABC), we estimated demographic parameters, including the likelihood of past population expansion, from a mitochondrial data set encompassing 23 previously identified lineages from all seven marine turtle species. For lineages with a high posterior probability of expansion, we conducted a hierarchical ABC analysis to estimate the proportion of lineages expanding synchronously and the timing of synchronous expansion. We used Bayesian model averaging to identify variables associated with expansion and genetic diversity. Approximately 60% of extant marine turtle lineages showed evidence of expansion, with the rest mainly exhibiting patterns of genetic diversity most consistent with population stability. For lineages showing expansion, there was a strong signal of synchronous expansion after the Last Glacial Maximum. Expansion and genetic diversity were best explained by ocean basin and the degree of endemism for a given lineage. Geographic differences in sensitivity to climate change have implications for prioritizing conservation actions in marine turtles as well as for identifying areas of past demographic stability and potential resilience to future climate change for broadly distributed taxa.     

Overlapping trophic niches among co‐occurring amphipods from a cryptic species complex

相似文献   

Limited influence of local and landscape factors on finescale gene flow in two pond‐breeding amphibians

Dispersal and gene flow within animal populations are influenced by the composition and configuration of the landscape. In this study, we evaluated hypotheses about the impact of natural and anthropogenic factors on genetic differentiation in two amphibian species, the spotted salamander (Ambystoma maculatum) and the wood frog (Lithobates sylvaticus) in a commercial forest in central Maine. We conducted this analysis at two scales: a local level, focused on factors measured at each breeding pond, and a landscape level, focused on factors measured between ponds. We investigated the effects of a number of environmental factors in six categories including Productivity, Physical, Land Composition, Land Configuration, Isolation and Location. Embryos were sampled from 56 spotted salamander breeding ponds and 39 wood frog breeding ponds. We used a hierarchical Bayesian approach in the program GESTE at each breeding pond and a random forest algorithm in conjunction with a network analysis between the ponds. We found overall high genetic connectivity across distances up to 17 km for both species and a limited effect of natural and anthropogenic factors on gene flow. We found the null models best explained patterns of genetic differentiation at a local level and found several factors at the landscape level that weakly influenced gene flow. This research indicates multiscale investigations that incorporate local and landscape factors are valuable for understanding patterns of gene flow. Our findings suggest that dispersal rates in this system are high enough to minimize genetic structuring and that current forestry practices do not significantly impede dispersal.     

Node‐based measures of connectivity in genetic networks

At‐site environmental conditions can have strong influences on genetic connectivity, and in particular on the immigration and settlement phases of dispersal. However, at‐site processes are rarely explored in landscape genetic analyses. Networks can facilitate the study of at‐site processes, where network nodes are used to model site‐level effects. We used simulated genetic networks to compare and contrast the performance of 7 node‐based (as opposed to edge‐based) genetic connectivity metrics. We simulated increasing node connectivity by varying migration in two ways: we increased the number of migrants moving between a focal node and a set number of recipient nodes, and we increased the number of recipient nodes receiving a set number of migrants. We found that two metrics in particular, the average edge weight and the average inverse edge weight, varied linearly with simulated connectivity. Conversely, node degree was not a good measure of connectivity. We demonstrated the use of average inverse edge weight to describe the influence of at‐site habitat characteristics on genetic connectivity of 653 American martens (Martes americana) in Ontario, Canada. We found that highly connected nodes had high habitat quality for marten (deep snow and high proportions of coniferous and mature forest) and were farther from the range edge. We recommend the use of node‐based genetic connectivity metrics, in particular, average edge weight or average inverse edge weight, to model the influences of at‐site habitat conditions on the immigration and settlement phases of dispersal.