Corridor-based approach with spatial cross-validation reveals scale-dependent effects of geographic distance, human footprint and canopy cover on grizzly bear genetic connectivity

Understanding how human infrastructure and other landscape attributes affect genetic differentiation in animals is an important step for identifying and maintaining dispersal corridors for these species. We built upon recent advances in the field of landscape genetics by using an individual-based and multiscale approach to predict landscape-level genetic connectivity for grizzly bears (Ursus arctos) across ~100,000 km2 in Canada's southern Rocky Mountains. We used a genetic dataset with 1156 unique individuals genotyped at nine microsatellite loci to identify landscape characteristics that influence grizzly bear gene flow at multiple spatial scales and map predicted genetic connectivity through a matrix of rugged terrain, large protected areas, highways and a growing human footprint. Our corridor-based modelling approach used a machine learning algorithm that objectively parameterized landscape resistance, incorporated spatial cross validation and variable selection and explicitly accounted for isolation by distance. This approach avoided overfitting, discarded variables that did not improve model performance across withheld test datasets and spatial predictive capacity compared to random cross-validation. We found that across all spatial scales, geographic distance explained more variation in genetic differentiation in grizzly bears than landscape variables. Human footprint inhibited connectivity across all spatial scales, while open canopies inhibited connectivity at the broadest spatial scale. Our results highlight the negative effect of human footprint on genetic connectivity, provide strong evidence for using spatial cross-validation in landscape genetics analyses and show that multiscale analyses provide additional information on how landscape variables affect genetic differentiation.

EST-Microsatellite Types and Structural Scenarios in European Hake Fisheries

A fishery’s structure and connectivity are priors to its effective management. A successful description of such processes depends on both the sampling design and the choice of adequate genetic markers. EST markers are perfusing the studies of marine metapopulations and are believed to provide access to functional polymorphisms. However, the assumed adaptive role of outlier EST loci might not be generalizable. EST-microsatellites represent the upper polymorphic boundary in these regions because of their high mutation rate. We have subclassified the polymorphisms of EST-microsatellites to assess their structural contribution in the European hake, a paradigmatic and highly mobile marine species (HMMS). Because of the counterbalanced forces between directional markers (15%) and balanced markers (23%), the whole marker set offers the same structural situation as the one observed with neutral markers (62%), i.e., k = 2 gene pools. In contrast to outlier EST- microsatellites, neutral EST subsets allow one to measure crucial population phenomena for fisheries’ management. The high inter-population divergence of outlier EST-microsatellites is compatible with drifted post-selection genomic regions rather than with ongoing local selective pressures. The structural scenario in hake is explainable by a limited gene flow across the Almería-Oran Front (AOF) and by the within-basin IBD pattern of connectivity plus drift-related demographic events. This study highlights how polymorphic properties of EST-microsatellite types can be useful to address mutually excluding research tasks in fisheries, i.e., to address its evolutionary history (directional markers or FAPS: Fossil Adaptive Polymorphic Systems); to delineate management units (neutral markers or NAPS: Non Adaptive Polymorphic Systems); or to ensure sustainability (balanced markers or APS: Adaptive Polymorphic Systems).

Landscape resistance constrains hybridization across contact zones in a reproductively and morphologically polymorphic salamander

Explicitly accounting for phenotypic differentiation together with environmental heterogeneity is crucial to understand the evolutionary dynamics in hybrid zones. Species showing intra-specific variation in phenotypic traits that meet across environmentally heterogeneous regions constitute excellent natural settings to study the role of phenotypic differentiation and environmental factors in shaping the spatial extent and patterns of admixture in hybrid zones. We studied three environmentally distinct contact zones where morphologically and reproductively divergent subspecies of Salamandra salamandra co-occur: the pueriparous S. s. bernardezi that is mostly parapatric to its three larviparous subspecies neighbours. We used a landscape genetics framework to: (i) characterise the spatial location and extent of each contact zone; (ii) assess patterns of introgression and hybridization between subspecies pairs; and (iii) examine the role of environmental heterogeneity in the evolutionary dynamics of hybrid zones. We found high levels of introgression between parity modes, and between distinct phenotypes, thus demonstrating the evolution to pueriparity alone or morphological differentiation do not lead to reproductive isolation between these highly divergent S. salamandra morphotypes. However, we detected substantial variation in patterns of hybridization across contact zones, being lower in the contact zone located on a topographically complex area. We highlight the importance of accounting for spatial environmental heterogeneity when studying evolutionary dynamics of hybrid zones.

High dispersal capacity of Culicoides obsoletus (Diptera: Ceratopogonidae), vector of bluetongue and Schmallenberg viruses, revealed by landscape genetic analyses

BACKGROUND

In the last two decades, recurrent epizootics of bluetongue virus and Schmallenberg virus have been reported in the western Palearctic region. These viruses affect domestic cattle, sheep, goats and wild ruminants and are transmitted by native hematophagous midges of the genus Culicoides (Diptera: Ceratopogonidae). Culicoides dispersal is known to be stratified, i.e. due to a combination of dispersal processes occurring actively at short distances and passively or semi-actively at long distances, allowing individuals to jump hundreds of kilometers.

METHODS

Here, we aim to identify the environmental factors that promote or limit gene flow of Culicoides obsoletus, an abundant and widespread vector species in Europe, using an innovative framework integrating spatial, population genetics and statistical approaches. A total of 348 individuals were sampled in 46 sites in France and were genotyped using 13 newly designed microsatellite markers.

RESULTS

We found low genetic differentiation and a weak population structure for C. obsoletus across the country. Using three complementary inter-individual genetic distances, we did not detect any significant isolation by distance, but did detect significant anisotropic isolation by distance on a north-south axis. We employed a multiple regression on distance matrices approach to investigate the correlation between genetic and environmental distances. Among all the environmental factors that were tested, only cattle density seems to have an impact on C. obsoletus gene flow.

CONCLUSIONS

The high dispersal capacity of C. obsoletus over land found in the present study calls for a re-evaluation of the impact of Culicoides on virus dispersal, and highlights the urgent need to better integrate molecular, spatial and statistical information to guide vector-borne disease control.

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.

Fine-scale landscape genetics unveiling contemporary asymmetric movement of red panda (Ailurus fulgens) in Kangchenjunga landscape, India

Wildlife management in rapid changing landscapes requires critical planning through cross cutting networks, and understanding of landscape features, often affected by the anthropogenic activities. The present study demonstrates fine-scale spatial patterns of genetic variation and contemporary gene flow of red panda (Ailurus fulgens) populations with respect to landscape connectivity in Kangchenjunga Landscape (KL), India. The study found about 1,309.54 km2 area suitable for red panda in KL-India, of which 62.21% area fell under the Protected Area network. We identified 24 unique individuals from 234 feces collected at nine microsatellite loci. The spatially explicit and non-explicit Bayesian clustering algorithms evident to exhibit population structuring and supported red panda populations to exist in meta-population frame work. In concurrence to the habitat suitability and landscape connectivity models, gene flow results supported a contemporary asymmetric movement of red panda by connecting KL-India in a crescent arc. We demonstrate the structural-operational connectivity of corridors in KL-India that facilitated red panda movement in the past. We also seek for cooperation in Nepal, Bhutan and China to aid in preparing for a comprehensive monitoring plan for the long-term conservation and management of red panda in trans-boundary landscapes.

Temporal dynamics of migration-linked genetic variation are driven by streamflows and riverscape permeability

Landscape permeability is often explored spatially, but may also vary temporally. Landscape permeability, including partial barriers, influences migratory animals that move across the landscape. Partial barriers are common in rivers where barrier passage varies with streamflow. We explore the influence of partial barriers on the spatial and temporal distribution of migration‐linked genotypes of Oncorhynchus mykiss, a salmonid fish with co‐occurring resident and migratory forms, in tributaries to the South Fork Eel River, California, USA, Elder and Fox Creeks. We genotyped >4,000 individuals using RAD‐capture and classified individuals as resident, heterozygous or migratory genotypes using life history‐associated loci. Across four years of study (2014–2017), the permeability of partial barriers varied across dry and wet years. In Elder Creek, the largest waterfall was passable for adults migrating up‐river 4–39 days each year. In this stream, the overall spatial pattern, with fewer migratory genotypes above the waterfall, remained true across dry and wet years (67%–76% of migratory alleles were downstream of the waterfall). We also observed a strong relationship between distance upstream and proportion of migratory alleles. In Fox Creek, the primary barrier is at the mouth, and we found that the migratory allele frequency varied with the annual timing of high flow events. In years when rain events occurred during the peak breeding season, migratory allele frequency was high (60%–68%), but otherwise it was low (30% in two years). We highlight that partial barriers and landscape permeability can be temporally dynamic, and this effect can be observed through changing genotype frequencies in migratory animals.

Translocation, genetic structure and homing ability confirm geographic barriers disrupt saltwater crocodile movement and dispersal

Translocated saltwater crocodiles (Crocodylus porosus) in the Northern Territory (NT) of Australia often return to their original capture sites, which complicates management interventions aimed at reducing human-crocodile conflict. We examined the spatial events implicated in this homing ability, using ARGOS satellite tracking devices. Five large male C. porosus (3.03 m to 4.02 m TL) were shifted and released 100-320 km from their capture sites, and 3 additional ones (3.67 m to 4.23 m TL) were released at their site of capture as controls. Translocated crocodiles were more mobile than the controls, and moved at sea in the direction of their original capture site. However, they were unable or unwilling to swim around a geographic structure, Cobourg Peninsula, which prevented homing being achieved in all five cases. Two control crocodiles remained near their capture sites, but one, after the first year, made a 900km journey for six months, before returning to its original capture and release site. Genetic analysis of tissue samples from nests across the NT coast demonstrated significant genetic structure across the coast, and confirmed that Cobourg Peninsula contributes to genetic differentiation among populations along the NT coast. These results provide new insights into C. porosus movements, which have management significance for the maintenance of public safety.

Contrasting support for alternative models of genomic variation based on microhabitat preference: species-specific effects of climate change in alpine sedges

Deterministic processes may uniquely affect codistributed species' phylogeographic patterns such that discordant genetic variation among taxa is predicted. Yet, explicitly testing expectations of genomic discordance in a statistical framework remains challenging. Here, we construct spatially and temporally dynamic models to investigate the hypothesized effect of microhabitat preferences on the permeability of glaciated regions to gene flow in two closely related montane species. Utilizing environmental niche models from the Last Glacial Maximum and the present to inform demographic models of changes in habitat suitability over time, we evaluate the relative probabilities of two alternative models using approximate Bayesian computation (ABC) in which glaciated regions are either (i) permeable or (ii) a barrier to gene flow. Results based on the fit of the empirical data to data sets simulated using a spatially explicit coalescent under alternative models indicate that genomic data are consistent with predictions about the hypothesized role of microhabitat in generating discordant patterns of genetic variation among the taxa. Specifically, a model in which glaciated areas acted as a barrier was much more probable based on patterns of genomic variation in Carex nova, a wet-adapted species. However, in the dry-adapted Carex chalciolepis, the permeable model was more probable, although the difference in the support of the models was small. This work highlights how statistical inferences can be used to distinguish deterministic processes that are expected to result in discordant genomic patterns among species, including species-specific responses to climate change.

Multiscale connectivity and graph theory highlight critical areas for conservation under climate change

Conservation planning and biodiversity management require information on landscape connectivity across a range of spatial scales from individual home ranges to large regions. Reduction in landscape connectivity due changes in land use or development is expected to act synergistically with alterations to habitat mosaic configuration arising from climate change. We illustrate a multiscale connectivity framework to aid habitat conservation prioritization in the context of changing land use and climate. Our approach, which builds upon the strengths of multiple landscape connectivity methods, including graph theory, circuit theory, and least-cost path analysis, is here applied to the conservation planning requirements of the Mohave ground squirrel. The distribution of this threatened Californian species, as for numerous other desert species, overlaps with the proposed placement of several utility-scale renewable energy developments in the American southwest. Our approach uses information derived at three spatial scales to forecast potential changes in habitat connectivity under various scenarios of energy development and climate change. By disentangling the potential effects of habitat loss and fragmentation across multiple scales, we identify priority conservation areas for both core habitat and critical corridor or stepping stone habitats. This approach is a first step toward applying graph theory to analyze habitat connectivity for species with continuously distributed habitat and should be applicable across a broad range of taxa.

Spatial heterogeneity in landscape structure influences dispersal and genetic structure: empirical evidence from a grasshopper in an agricultural landscape

Dispersal may be strongly influenced by landscape and habitat characteristics that could either enhance or restrict movements of organisms. Therefore, spatial heterogeneity in landscape structure could influence gene flow and the spatial structure of populations. In the past decades, agricultural intensification has led to the reduction in grassland surfaces, their fragmentation and intensification. As these changes are not homogeneously distributed in landscapes, they have resulted in spatial heterogeneity with generally less intensified hedged farmland areas remaining alongside streams and rivers. In this study, we assessed spatial pattern of abundance and population genetic structure of a flightless grasshopper species, Pezotettix giornae, based on the surveys of 363 grasslands in a 430-km² agricultural landscape of western France. Data were analysed using geostatistics and landscape genetics based on microsatellites markers and computer simulations. Results suggested that small-scale intense dispersal allows this species to survive in intensive agricultural landscapes. A complex spatial genetic structure related to landscape and habitat characteristics was also detected. Two P. giornae genetic clusters bisected by a linear hedged farmland were inferred from clustering analyses. This linear hedged farmland was characterized by high hedgerow and grassland density as well as higher grassland temporal stability that were suspected to slow down dispersal. Computer simulations demonstrated that a linear-shaped landscape feature limiting dispersal could be detected as a barrier to gene flow and generate the observed genetic pattern. This study illustrates the relevance of using computer simulations to test hypotheses in landscape genetics studies.

Simulating population genetics of pathogen vectors in changing landscapes: guidelines and application with Triatoma brasiliensis

Background

Understanding the mechanisms that influence the population dynamics and spatial genetic structure of the vectors of pathogens infecting humans is a central issue in tropical epidemiology. In view of the rapid changes in the features of landscape pathogen vectors live in, this issue requires new methods that consider both natural and human systems and their interactions. In this context, individual-based model (IBM) simulations represent powerful yet poorly developed approaches to explore the response of pathogen vectors in heterogeneous social-ecological systems, especially when field experiments cannot be performed.

Methodology/Principal Findings

We first present guidelines for the use of a spatially explicit IBM, to simulate population genetics of pathogen vectors in changing landscapes. We then applied our model with Triatoma brasiliensis, originally restricted to sylvatic habitats and now found in peridomestic and domestic habitats, posing as the most important Trypanosoma cruzi vector in Northeastern Brazil. We focused on the effects of vector migration rate, maximum dispersal distance and attraction by domestic habitat on T. brasiliensis population dynamics and spatial genetic structure. Optimized for T. brasiliensis using field data pairwise fixation index (FST) from microsatellite loci, our simulations confirmed the importance of these three variables to understand vector genetic structure at the landscape level. We then ran prospective scenarios accounting for land-use change (deforestation and urbanization), which revealed that human-induced land-use change favored higher genetic diversity among sampling points.

Conclusions/Significance

Our work shows that mechanistic models may be useful tools to link observed patterns with processes involved in the population genetics of tropical pathogen vectors in heterogeneous social-ecological landscapes. Our hope is that our study may provide a testable and applicable modeling framework to a broad community of epidemiologists for formulating scenarios of landscape change consequences on vector dynamics, with potential implications for their surveillance and control.

Worldwide, humans are modifying landscapes at an unprecedented rate. These modifications have an influence on the ecology of pathogen vectors, yet this issue has received relatively little input from modeling research. The current study presents guidelines for the use of a modeling framework for the representation of the dynamics and spatial genetic structure of pathogen vectors. It allows considering spatiotemporal landscape modifications explicitly, to represent human-altered modifications and consequences. We applied this modeling framework to Triatoma brasiliensis, vector of the pathogen Trypanosoma cruzi responsible for the Chagas disease, in the semi-arid Northeastern Brazil. Using field data of pairwise fixation index (FST) from microsatellite loci, we found that migration rate, maximum dispersal distance and attraction by domestic habitat were all key parameters to understand vector spatial genetic structure at the landscape level. At the interface across disciplines, this study provides to the community of epidemiologists a testable and applicable framework to foresee landscape modification consequences on vector dynamics and genetic structure, with potential implications for their surveillance and control.

The trajectory of dispersal research in conservation biology. Systematic review

Dispersal knowledge is essential for conservation management, and demand is growing. But are we accumulating dispersal knowledge at a pace that can meet the demand? To answer this question we tested for changes in dispersal data collection and use over time. Our systematic review of 655 conservation-related publications compared five topics: climate change, habitat restoration, population viability analysis, land planning (systematic conservation planning) and invasive species. We analysed temporal changes in the: (i) questions asked by dispersal-related research; (ii) methods used to study dispersal; (iii) the quality of dispersal data; (iv) extent that dispersal knowledge is lacking, and; (v) likely consequences of limited dispersal knowledge. Research questions have changed little over time; the same problems examined in the 1990s are still being addressed. The most common methods used to study dispersal were occupancy data, expert opinion and modelling, which often provided indirect, low quality information about dispersal. Although use of genetics for estimating dispersal has increased, new ecological and genetic methods for measuring dispersal are not yet widely adopted. Almost half of the papers identified knowledge gaps related to dispersal. Limited dispersal knowledge often made it impossible to discover ecological processes or compromised conservation outcomes. The quality of dispersal data used in climate change research has increased since the 1990s. In comparison, restoration ecology inadequately addresses large-scale process, whilst the gap between knowledge accumulation and growth in applications may be increasing in land planning. To overcome apparent stagnation in collection and use of dispersal knowledge, researchers need to: (i) improve the quality of available data using new approaches; (ii) understand the complementarities of different methods and; (iii) define the value of different kinds of dispersal information for supporting management decisions. Ambitious, multi-disciplinary research programs studying many species are critical for advancing dispersal research.

Simulating Pattern-Process Relationships to Validate Landscape Genetic Models

Landscapes may resist gene flow and thereby give rise to a pattern of genetic isolation within a population. The mechanism by which a landscape resists gene flow can be inferred by evaluating the relationship between landscape models and an observed pattern of genetic isolation. This approach risks false inferences because researchers can never feasibly test all plausible alternative hypotheses. In this paper, rather than infer the process of gene flow from an observed genetic pattern, we simulate gene flow and determine if the simulated genetic pattern is related to the observed empirical genetic pattern. This is a form of inverse modeling and can be used to independently validate a landscape genetic model. In this study, we used this approach to validate a model of landscape resistance based on elevation, landcover, and roads that was previously related to genetic isolation among mountain goats (Oreamnos americanus) inhabiting the Cascade Range, Washington (USA). The strong relationship between the empirical and simulated patterns of genetic isolation we observed provides independent validation of the resistance model and demonstrates the utility of this approach in supporting landscape genetic inferences.

Quantifying the lag time to detect barriers in landscape genetics

Understanding how spatial genetic patterns respond to landscape change is crucial for advancing the emerging field of landscape genetics. We quantified the number of generations for new landscape barrier signatures to become detectable and for old signatures to disappear after barrier removal. We used spatially explicit, individual-based simulations to examine the ability of an individual-based statistic [Mantel's r using the proportion of shared alleles' statistic (Dps)] and population-based statistic (FST ) to detect barriers. We simulated a range of movement strategies including nearest neighbour dispersal, long-distance dispersal and panmixia. The lag time for the signal of a new barrier to become established is short using Mantel's r (1-15 generations). FST required approximately 200 generations to reach 50% of its equilibrium maximum, although G'ST performed much like Mantel's r. In strong contrast, FST and Mantel's r perform similarly following the removal of a barrier formerly dividing a population. Also, given neighbour mating and very short-distance dispersal strategies, historical discontinuities from more than 100 generations ago might still be detectable with either method. This suggests that historical events and landscapes could have long-term effects that confound inferences about the impacts of current landscape features on gene flow for species with very little long-distance dispersal. Nonetheless, populations of organisms with relatively large dispersal distances will lose the signal of a former barrier within less than 15 generations, suggesting that individual-based landscape genetic approaches can improve our ability to measure effects of existing landscape features on genetic structure and connectivity.