Performance of individual vs. group sampling for inferring dispersal under isolation-by-distance

Restricted dispersal in a sea of gene flow

How far do marine larvae disperse in the ocean? Decades of population genetic studies have revealed generally low levels of genetic structure at large spatial scales (hundreds of kilometres). Yet this result, typically based on discrete sampling designs, does not necessarily imply extensive dispersal. Here, we adopt a continuous sampling strategy along 950 km of coast in the northwestern Mediterranean Sea to address this question in four species. In line with expectations, we observe weak genetic structure at a large spatial scale. Nevertheless, our continuous sampling strategy uncovers a pattern of isolation by distance at small spatial scales (few tens of kilometres) in two species. Individual-based simulations indicate that this signal is an expected signature of restricted dispersal. At the other extreme of the connectivity spectrum, two pairs of individuals that are closely related genetically were found more than 290 km apart, indicating long-distance dispersal. Such a combination of restricted dispersal with rare long-distance dispersal events is supported by a high-resolution biophysical model of larval dispersal in the study area, and we posit that it may be common in marine species. Our results bridge population genetic studies with direct dispersal studies and have implications for the design of marine reserve networks.

Sea ice loss increases genetic isolation in a high Arctic ungulate metapopulation

Sea ice loss may have dramatic consequences for population connectivity, extinction-colonization dynamics, and even the persistence of Arctic species subject to climate change. This is of particular concern in face of additional anthropogenic stressors, such as overexploitation. In this study, we assess the population-genetic implications of diminishing sea ice cover in the endemic, high Arctic Svalbard reindeer (Rangifer tarandus platyrhynchus) by analyzing the interactive effects of landscape barriers and reintroductions (following harvest-induced extirpations) on their metapopulation genetic structure. We genotyped 411 wild reindeer from 25 sampling sites throughout the entire subspecies' range at 19 microsatellite loci. Bayesian clustering analysis showed a genetic structure composed of eight populations, of which two were admixed. Overall population genetic differentiation was high (mean F_ST  = 0.21). Genetic diversity was low (allelic richness [AR] = 2.07-2.58; observed heterozygosity = 0.23-0.43) and declined toward the outer distribution range, where populations showed significant levels of inbreeding. Coalescent estimates of effective population sizes and migration rates revealed strong evolutionary source-sink dynamics with the central population as the main source. The population genetic structure was best explained by a landscape genetics model combining strong isolation by glaciers and open water, and high connectivity by dispersal across winter sea ice. However, the observed patterns of natural isolation were strongly modified by the signature of past harvest-induced extirpations, subsequent reintroductions, and recent lack of sea ice. These results suggest that past and current anthropogenic drivers of metapopulation dynamics may have interactive effects on large-scale ecological and evolutionary processes. Continued loss of sea ice as a dispersal corridor within and between island systems is expected to increase the genetic isolation of populations, and thus threaten the evolutionary potential and persistence of Arctic wildlife.

Species-specific genetic variation in response to deep-sea environmental variation amongst Vulnerable Marine Ecosystem indicator taxa

Understanding the ecological processes that shape spatial genetic patterns of population structure is critical for understanding evolutionary dynamics and defining significant evolutionary and management units in the deep sea. Here, the role of environmental factors (topographic, physico-chemical and biological) in shaping the population genetic structure of four deep-sea habitat-forming species (one sponge - Poecillastra laminaris, three corals - Goniocorella dumosa, Madrepora oculata, Solenosmilia variabilis) was investigated using seascape genetics. Genetic data (nuclear and mitochondrial sequences and microsatellite multilocus genotypes) and environmental variables were employed to build individual-based and population-level models. The results indicated that environmental factors affected genetic variation differently amongst the species, as well as at different geographic scales. For individual-based analyses, different environmental variables explained genetic variation in P. laminaris (dissolved oxygen), G. dumosa (dynamic topography), M. oculata (sea surface temperature and surface water primary productivity), and S. variabilis (tidal current speed). At the population level, factors related to current and food source explained the regional genetic structure in all four species, whilst at the geomorphic features level, factors related to food source and topography were most important. Environmental variation in these parameters may be acting as barriers to gene flow at different scales. This study highlights the utility of seascape genetic studies to better understand the processes shaping the genetic structure of organisms, and to identify environmental factors that can be used to locate sites for the protection of deep-sea Vulnerable Marine Ecosystems.

Testing theoretical metapopulation conditions with genotypic data from Boreal Chorus Frogs (Pseudacris maculata)

The metapopulation concept has far-reaching implications in ecology and conservation biology. Hanski’s criteria operationally define metapopulations, yet testing them is hindered by logistical and financial constraints inherent to the collection of long-term demographic data. Hence, ecologists and conservationists often assume metapopulation existence for dispersal-limited species that occupy patchy habitats. To advance understanding of metapopulation theory and improve conservation of metapopulations, we used population and landscape genetic tools to develop a methodological framework for evaluating Hanski’s criteria. We used genotypic data (11 microsatellite loci) from a purported metapopulation of Boreal Chorus Frogs (Pseudacris maculata (Agassiz, 1850)) in Colorado, U.S.A., to test Hanski’s four criteria. We found support for each criterion: (1) significant genetic differentiation between wetlands, suggesting distinct breeding populations; (2) wetlands had small effective population sizes and recent bottlenecks, suggesting populations do not experience long-term persistence; (3) population graphs provided evidence of gene flow between patches, indicating potential for recolonization; and (4) multiscale bottleneck analyses suggest asynchrony, indicating that simultaneous extinction of all populations was unlikely. Our methodological framework provides a logistically and financially feasible alternative to long-term demographic data for identifying amphibian metapopulations.

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.

Using fine-scale spatial genetics of Norway rats to improve control efforts and reduce leptospirosis risk in urban slum environments

The Norway rat (Rattus norvegicus) is a key pest species globally and responsible for seasonal outbreaks of the zoonotic bacterial disease leptospirosis in the tropics. The city of Salvador, Brazil, has seen recent and dramatic increases in human population residing in slums, where conditions foster high rat density and increasing leptospirosis infection rates. Intervention campaigns have been used to drastically reduce rat numbers. In planning these interventions, it is important to define the eradication units - the spatial scale at which rats constitute continuous populations and from where rats are likely recolonizing, post-intervention. To provide this information, we applied spatial genetic analyses to 706 rats collected across Salvador and genotyped at 16 microsatellite loci. We performed spatially explicit analyses and estimated migration levels to identify distinct genetic units and landscape features associated with genetic divergence at different spatial scales, ranging from valleys within a slum community to city-wide analyses. Clear genetic breaks exist between rats not only across Salvador but also between valleys of slums separated by <100 m-well within the dispersal capacity of rats. The genetic data indicate that valleys may be considered separate units and identified high-traffic roads as strong impediments to rat movement. Migration data suggest that most (71-90%) movement is contained within valleys, with no clear source population contributing to migrant rats. We use these data to recommend eradication units and discuss the importance of carrying out individual-based analyses at different spatial scales in urban landscapes.

Environmental and anthropogenic drivers of connectivity patterns: A basis for prioritizing conservation efforts for threatened populations

Ecosystem fragmentation and habitat loss have been the focus of landscape management due to restrictions on contemporary connectivity and dispersal of populations. Here, we used an individual approach to determine the drivers of genetic differentiation in caribou of the Canadian Rockies. We modelled the effects of isolation by distance, landscape resistance and predation risk and evaluated the consequences of individual migratory behaviour (seasonally migratory vs. sedentary) on gene flow in this threatened species. We applied distance-based and reciprocal causal modelling approaches, testing alternative hypotheses on the effects of geographic, topographic, environmental and local population-specific variables on genetic differentiation and relatedness among individuals. Overall, gene flow was restricted to neighbouring local populations, with spatial coordinates, local population size, groups and elevation explaining connectivity among individuals. Landscape resistance, geographic distances and predation risk were correlated with genetic distances, with correlations threefold higher for sedentary than for migratory caribou. As local caribou populations are increasingly isolated, our results indicate the need to address genetic connectivity, especially for populations with individuals displaying different migratory behaviours, whilst maintaining quality habitat both within and across the ranges of threatened populations.

Navigating the pitfalls and promise of landscape genetics

The field of landscape genetics has been evolving rapidly since its emergence in the early 2000s. New applications, techniques and criticisms of techniques appear like clockwork with each new journal issue. The developments are an encouraging, and at times bewildering, sign of progress in an exciting new field of study. However, we suggest that the rapid expansion of landscape genetics has belied important flaws in the development of the field, and we add an air of caution to this breakneck pace of expansion. Specifically, landscape genetic studies often lose sight of the fundamental principles and complex consequences of gene flow, instead favouring simplistic interpretations and broad inferences not necessarily warranted by the data. Here, we describe common pitfalls that characterize such studies, and provide practical guidance to improve landscape genetic investigation, with careful consideration of inferential limits, scale, replication, and the ecological and evolutionary context of spatial genetic patterns. Ultimately, the utility of landscape genetics will depend on translating the relationship between gene flow and landscape features into an understanding of long-term population outcomes. We hope the perspective presented here will steer landscape genetics down a more scientifically sound and productive path, garnering a field that is as informative in the future as it is popular now.

Comparative spatial genetics and epigenetics of plant populations: heuristic value and a proof of concept

Despite the recent upsurge of interest on natural epigenetic variation of nonmodel organisms, factors conditioning the spatial structure of epigenetic diversity in wild plant populations remain virtually unexplored. We propose that information on processes shaping natural epigenetic variation can be gained using the spatial structure of genetic diversity as null model. Departures of epigenetic isolation-by-distance (IBD) patterns from genetic IBD patterns for the same sample, particularly differences in slope of similarity-distance regressions, will reflect the action of factors that operate specifically on epigenetic variation, including imperfect transgenerational inheritance and responsiveness to environmental factors of epigenetic marks. As a proof of concept, we provide a comparative analysis of spatial genetic and epigenetic structure of 200 mapped individuals of the perennial herb Helleborus foetidus. Plants were fingerprinted using nuclear microsatellites, amplified fragment length polymorphisms (AFLP) and methylation-sensitive AFLP markers. Expectations from individual-level IBD patterns were tested by means of kinship-distance regressions. Both genetic and epigenetic similarity between H. foetidus individuals conformed to theoretical expectations under individual-level IBD models. Irrespective of marker type, there were significant negative linear relationships between the kinship coefficient for plant pairs and their spatial separation. Regression slopes were significantly steeper for epigenetic markers. Epigenetic similarity between individuals was much greater than genetic similarity at shortest distances, such epigenetic 'kinship excess' tending to decrease as plant separation increased. Results suggest that moderate-to-high heritability and responsiveness to local environments are major drivers of epigenetic spatial structure in H. foetidus, and illustrate the heuristic value of comparing genetic and epigenetic spatial structure for formulating and testing hypotheses on forces shaping epigenetic diversity in wild plant populations.

Multicollinearity in spatial genetics: separating the wheat from the chaff using commonality analyses

Direct gradient analyses in spatial genetics provide unique opportunities to describe the inherent complexity of genetic variation in wildlife species and are the object of many methodological developments. However, multicollinearity among explanatory variables is a systemic issue in multivariate regression analyses and is likely to cause serious difficulties in properly interpreting results of direct gradient analyses, with the risk of erroneous conclusions, misdirected research and inefficient or counterproductive conservation measures. Using simulated data sets along with linear and logistic regressions on distance matrices, we illustrate how commonality analysis (CA), a detailed variance-partitioning procedure that was recently introduced in the field of ecology, can be used to deal with nonindependence among spatial predictors. By decomposing model fit indices into unique and common (or shared) variance components, CA allows identifying the location and magnitude of multicollinearity, revealing spurious correlations and thus thoroughly improving the interpretation of multivariate regressions. Despite a few inherent limitations, especially in the case of resistance model optimization, this review highlights the great potential of CA to account for complex multicollinearity patterns in spatial genetics and identifies future applications and lines of research. We strongly urge spatial geneticists to systematically investigate commonalities when performing direct gradient analyses.