Landscape genetics and genetic structure of the southern torrent salamander, Rhyacotriton variegatus

Urbanization reduces gene flow but not genetic diversity of stream salamander populations in the New York City metropolitan area

Natural landscape heterogeneity and barriers resulting from urbanization can reduce genetic connectivity between populations. The evolutionary, demographic, and ecological effects of reduced connectivity may lead to population isolation and ultimately extinction. Alteration to the terrestrial and aquatic environment caused by urban influence can affect gene flow, specifically for stream salamanders who depend on both landscapes for survival and reproduction. To examine how urbanization affects a relatively common stream salamander species, we compared genetic connectivity of Eurycea bislineata (northern two-lined salamander) populations within and between streams in an urban, suburban, and rural habitat around the New York City (NYC) metropolitan area. We report reduced genetic connectivity between streams within the urban landscape found to correspond with potential barriers to gene flow, that is, areas with more dense urbanization (roadways, industrial buildings, and residential housing). The suburban populations also exhibited areas of reduced connectivity correlated with areas of greater human land use and greater connectivity within a preserve protected from development. Connectivity was relatively high among neighboring rural streams, but a major roadway corresponded with genetic breaks even though the habitat contained more connected green space overall. Despite greater human disturbance across the landscape, urban and suburban salamander populations maintained comparable levels of genetic diversity to their rural counterparts. Yet small effective population size in the urban habitats yielded a high probability of loss of heterozygosity due to genetic drift in the future. In conclusion, urbanization impacted connectivity among stream salamander populations where its continual influence may eventually hinder population persistence for this native species in urban habitats.

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 genetics of northern crested newt Triturus cristatus populations in a contrasting natural and human-impacted boreal forest

Among vertebrates, amphibians currently have the highest proportion of threatened species worldwide, mainly through loss of habitat, leading to increased population isolation. Smaller amphibian populations may lose more genetic diversity, and become more dependent on immigration for survival. Investigations of landscape factors and patterns mediating migration and population genetic differentiation are fundamental for knowledge-based conservation. The pond-breeding northern crested newt (Triturus cristatus) populations are decreasing throughout Europe, and are a conservation concern. Using microsatellites, we studied the genetic structure of the northern crested newt in a boreal forest ecosystem containing two contrasting landscapes, one subject to recent change and habitat loss by clear-cutting and roadbuilding, and one with little anthropogenic disturbance. Newts from 12 breeding ponds were analyzed for 13 microsatellites and 7 landscape and spatial variables. With a Maximum-likelihood population-effects model we investigated important landscape factors potentially explaining genetic patterns. Results indicate that intervening landscape factors between breeding ponds, explain the genetic differentiation in addition to an isolation-by-distance effect. Geographic distance, gravel roads, and south/south-west facing slopes reduced landscape permeability and increased genetic differentiation for these newts. The effect was opposite for streams, presumably being more favorable for newt dispersal. Populations within or bordering on old growth forest had a higher allelic richness than populations in managed forest outside these areas. Old growth forest areas may be important source habitats in the conservation of northern crested newt populations.

Landscape genetic inferences vary with sampling scenario for a pond-breeding amphibian

A critical decision in landscape genetic studies is whether to use individuals or populations as the sampling unit. This decision affects the time and cost of sampling and may affect ecological inference. We analyzed 334 Columbia spotted frogs at 8 microsatellite loci across 40 sites in northern Idaho to determine how inferences from landscape genetic analyses would vary with sampling design. At all sites, we compared a proportion available sampling scheme (PASS), in which all samples were used, to resampled datasets of 2-11 individuals. Additionally, we compared a population sampling scheme (PSS) to an individual sampling scheme (ISS) at 18 sites with sufficient sample size. We applied an information theoretic approach with both restricted maximum likelihood and maximum likelihood estimation to evaluate competing landscape resistance hypotheses. We found that PSS supported low-density forest when restricted maximum likelihood was used, but a combination model of most variables when maximum likelihood was used. We also saw variations when AIC was used compared to BIC. ISS supported this model as well as additional models when testing hypotheses of land cover types that create the greatest resistance to gene flow for Columbia spotted frogs. Increased sampling density and study extent, seen by comparing PSS to PASS, showed a change in model support. As number of individuals increased, model support converged at 7-9 individuals for ISS to PSS. ISS may be useful to increase study extent and sampling density, but may lack power to provide strong support for the correct model with microsatellite datasets. Our results highlight the importance of additional research on sampling design effects on landscape genetics inference.

Geographic separation and genetic differentiation of populations are not coupled with niche differentiation in threatened Kaiser's spotted newt (Neurergus kaiseri)

The combination of niche modelling and landscape genetics (genomics) helps to disentangle processes that have shaped population structure in the evolutionary past and presence of species. Herein, we integrate a comprehensive genomic dataset with ecological parameters and niche modelling for the threatened Kaiser's newt, a newt species adapted to mountain spring-ponds in Iran. Genomic analysis suggests the existence of two highly differentiated clades North and South of the Dez River. Genetic variation between the two clades (76.62%) was much greater than within clades (16.25%), suggesting that the Dez River prevented gene flow. River disconnectivity, followed by geographic distance, contributed mostly to genetic differentiation between populations. Environmental niche and landscape resistance had no significant influence. Though a significant difference between climatic niches occupied by each clade at the landscape-scale, habitat niches at the local-scale were equivalent. 'Niche similarity analysis' supported niche conservatism between the two clades despite the southward shift in the climatic niche of the Southern clade. Accordingly, populations of different clades may occupy different climatic niches within their ancestral niche. Our results indicate that the change of climatic conditions of geographically and genetically separated populations does not necessarily result in the shift of an ecological niche.

Consequences of population topology for studying gene flow using link-based landscape genetic methods

Many landscape genetic studies aim to determine the effect of landscape on gene flow between populations. These studies frequently employ link‐based methods that relate pairwise measures of historical gene flow to measures of the landscape and the geographical distance between populations. However, apart from landscape and distance, there is a third important factor that can influence historical gene flow, that is, population topology (i.e., the arrangement of populations throughout a landscape). As the population topology is determined in part by the landscape configuration, I argue that it should play a more prominent role in landscape genetics. Making use of existing literature and theoretical examples, I discuss how population topology can influence results in landscape genetic studies and how it can be taken into account to improve the accuracy of these results. In support of my arguments, I have performed a literature review of landscape genetic studies published during the first half of 2015 as well as several computer simulations of gene flow between populations. First, I argue why one should carefully consider which population pairs should be included in link‐based analyses. Second, I discuss several ways in which the population topology can be incorporated in response and explanatory variables. Third, I outline why it is important to sample populations in such a way that a good representation of the population topology is obtained. Fourth, I discuss how statistical testing for link‐based approaches could be influenced by the population topology. I conclude the article with six recommendations geared toward better incorporating population topology in link‐based landscape genetic studies.

Developing approaches for linear mixed modeling in landscape genetics through landscape-directed dispersal simulations

Dispersal can impact population dynamics and geographic variation, and thus, genetic approaches that can establish which landscape factors influence population connectivity have ecological and evolutionary importance. Mixed models that account for the error structure of pairwise datasets are increasingly used to compare models relating genetic differentiation to 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 genetic structure, yet there are currently no consensus for the best protocols. Here, we develop landscape-directed simulations and test a series of replicates that emulate independent empirical datasets of two species with different life history characteristics (greater sage-grouse; eastern foxsnake). We determined that in our simulated scenarios, AIC and BIC were the best model selection indices and that marginal R2 values were biased toward more complex models. The model coefficients for landscape variables generally reflected the underlying dispersal model with confidence intervals that did not overlap with zero across the entire model set. When we controlled for geographic distance, variables not in the underlying dispersal models (i.e., nontrue) typically overlapped zero. Our study helps establish methods for using linear mixed models to identify the features underlying patterns of dispersal across a variety of landscapes.