Unravelling landscape variables with multiple approaches to overcome scarce species knowledge: a landscape genetic study of the slow worm

Landscape genetics was developed to detect landscape elements shaping genetic population structure, including the effects of fragmentation. Multifarious environmental variables can influence gene flow in different ways and expert knowledge is frequently used to construct friction maps. However, the extent of the migration and the movement of single individuals are frequently unknown, especially for non-model species, and friction maps only based on expert knowledge can be misleading. In this study, we used three different methods: isolation by distance (IBD), least-cost modelling and a strip-based approach to disentangle the human implication in the fragmentation process in the slow worm (Anguis fragilis), as well as the specific landscape elements shaping the genetic structure in a highly anthropized 16 km² area in Switzerland. Friction maps were constructed using expert opinion, but also based on the combination of all possible weightings for all landscape elements. The IBD indicated a significant effect of geographic distance on genetic differentiation. Further approaches demonstrated that highways and railways were the most important elements impeding the gene flow in this area. Surprisingly, we also found that agricultural areas and dense forests seemed to be used as dispersal corridors. These results confirmed that the slow worm has relatively unspecific habitat requirements. Finally, we showed that our models based on expert knowledge performed poorly compared to cautious analysis of each variable. This study demonstrated that landscape genetic analyses should take expert knowledge with caution and exhaustive analyses of each landscape element without a priori knowledge and different methods can be recommended.     

Adaptation with gene flow across the landscape in a dune sunflower

Isolation by adaptation increases divergence at neutral loci when natural selection against immigrants reduces the rate of gene flow between different habitats. This can occur early in the process of adaptive divergence and is a key feature of ecological speciation. Despite the ability of isolation by distance (IBD) and other forms of landscape resistance to produce similar patterns of neutral divergence within species, few studies have used landscape genetics to control for these other forces. We have studied the divergence of Helianthus petiolaris ecotypes living in active sand dunes and adjacent non-dune habitat, using landscape genetics approaches, such as circuit theory and multiple regression of distance matrices, in addition to coalescent modelling. Divergence between habitats was significant, but not strong, and was shaped by IBD. We expected that increased resistance owing to patchy and unfavourable habitat in the dunes would contribute to divergence. Instead, we found that landscape resistance models with lower resistance in the dunes performed well as predictors of genetic distances among subpopulations. Nevertheless, habitat class remained a strong predictor of genetic distance when controlling for isolation by resistance and IBD. We also measured environmental variables at each site and confirmed that specific variables, especially soil nitrogen and vegetation cover, explained a greater proportion of variance in genetic distance than did landscape or the habitat classification alone. Asymmetry in effective population sizes and numbers of migrants per generation was detected using coalescent modelling with Bayesian inference, which is consistent with incipient ecological speciation being driven by the dune habitat.     

Isolation-by-distance in landscapes: considerations for landscape genetics

In landscape genetics, isolation-by-distance (IBD) is regarded as a baseline pattern that is obtained without additional effects of landscape elements on gene flow. However, the configuration of suitable habitat patches determines deme topology, which in turn should affect rates of gene flow. IBD patterns can be characterized either by monotonically increasing pairwise genetic differentiation (for example, F_ST) with increasing interdeme geographic distance (case-I pattern) or by monotonically increasing pairwise genetic differentiation up to a certain geographical distance beyond which no correlation is detectable anymore (case-IV pattern). We investigated if landscape configuration influenced the rate at which a case-IV pattern changed to a case-I pattern. We also determined at what interdeme distance the highest correlation was measured between genetic differentiation and geographic distance and whether this distance corresponded to the maximum migration distance. We set up a population genetic simulation study and assessed the development of IBD patterns for several habitat configurations and maximum migration distances. We show that the rate and likelihood of the transition of case-IV to case-I F_ST–distance relationships was strongly influenced by habitat configuration and maximum migration distance. We also found that the maximum correlation between genetic differentiation and geographic distance was not related to the maximum migration distance and was measured across all deme pairs in a case-I pattern and, for a case-IV pattern, at the distance where the F_ST–distance curve flattens out. We argue that in landscape genetics, separate analyses should be performed to either assess IBD or the landscape effects on gene flow.     

Streams over mountains: influence of riparian connectivity on gene flow in the Pacific jumping mouse (Zapus trinotatus)

In species affiliated with heterogeneous habitat, we expect gene flow to be restricted due to constraints placed on individual movement by habitat boundaries. This is likely to impact both individual dispersal and connectivity between populations. In this study, a GIS-based landscape genetics approach was used, in combination with fine-scale spatial autocorrelation analysis and the estimation of recent intersubpopulation migration rates, to infer patterns of dispersal and migration in the riparian-affiliated Pacific jumping mouse (Zapus trinotatus). A total of 228 individuals were sampled from nine subpopulations across a system of three rivers and genotyped at eight microsatellite loci. Significant spatial autocorrelation among individuals revealed a pattern of fine-scale spatial genetic structure indicative of limited dispersal. Geographical distances between pairwise subpopulations were defined following four criteria: (i) Euclidean distance, and three landscape-specific distances, (ii) river distance (distance travelled along the river only), (iii) overland distance (similar to Euclidean, but includes elevation), and (iv) habitat-path distance (a least-cost path distance that models movement along habitat pathways). Pairwise Mantel tests were used to test for a correlation between genetic distance and each of the geographical distances. Significant correlations were found between genetic distance and both the overland and habitat-path distances; however, the correlation with habitat-path distance was stronger. Lastly, estimates of recent migration rates revealed that migration occurs not only within drainages but also across large topographic barriers. These results suggest that patterns of dispersal and migration in Pacific jumping mice are largely determined by habitat connectivity.     

Preferred habitat and effective population size drive landscape genetic patterns in an endangered species

Landscape genetics provides a framework for pinpointing environmental features that determine the important exchange of migrants among populations. These studies usually test the significance of environmental variables on gene flow, yet ignore one fundamental driver of genetic variation in small populations, effective population size, N_e. We combined both approaches in evaluating genetic connectivity of a threatened ungulate, woodland caribou. We used least-cost paths to calculate matrices of resistance distance for landscape variables (preferred habitat, anthropogenic features and predation risk) and population-pairwise harmonic means of N_e, and correlated them with genetic distances, F_ST and D_c. Results showed that spatial configuration of preferred habitat and N_e were the two best predictors of genetic relationships. Additionally, controlling for the effect of N_e increased the strength of correlations of environmental variables with genetic distance, highlighting the significant underlying effect of N_e in modulating genetic drift and perceived spatial connectivity. We therefore have provided empirical support to emphasize preventing increased habitat loss and promoting population growth to ensure metapopulation viability.     

Population genetic structure and connectivity in the endangered Ethiopian mountain Nyala (Tragelaphus buxtoni): recommending dispersal corridors for future conservation

Habitat fragmentation is an increasing threat to wildlife species across the globe and it has been predicted that future biodiversity will decrease rapidly without the intervention of scientifically-based management. In this study we have applied a landscape genetics approach to suggest a network design that will maintain connectivity among populations of the endangered mountain Nyala (Tragelaphus buxtoni) in the fragmented highlands of Ethiopia. DNA was obtained non-invasively from 328 individuals and genetic population structure and gene flow were estimated using 12 microsatellite markers. In addition, a 475-bp segment of the mitochondrial control region was sequenced for 132 individuals. Potential dispersal corridors were determined from least-cost path analysis based on a habitat suitability map. The genetic data indicated limited gene flow between the sampled mountain Nyala populations of the Bale Massif and the Arsi Massif. The genetic differentiation observed among five sampling areas of the Bale Massif followed a pattern of isolation by distance. We detected no impact of habitat resistance on the gene flow. In the future, however, the current expanding human population in the highlands of Ethiopia may reduce the current mountain Nyala habitat and further limit migration. Hence, maintaining habitat connectivity and facilitating survival of stepping-stone populations will be important for the future conservation of the species. The approach used here may also be useful for the study and conservation of other wildlife species inhabiting areas of increasing human encroachment.     

Habitat distribution influences dispersal and fine-scale genetic population structure of eastern foxsnakes (Mintonius gloydi) across a fragmented landscape

Dispersal is a fundamental attribute of species in nature and shapes population dynamics, evolutionary trajectories and genetic variation across spatial and temporal scales. It is increasingly clear that landscape features have large impacts on dispersal patterns. Thus, understanding how individuals and species move through landscapes is essential for predicting impacts of landscape alterations. Information on dispersal patterns, however, is lacking for many taxa, particularly reptiles. Eastern foxsnakes (Mintoinus gloydi) are marsh and prairie specialists that avoid agricultural fields, but they have persisted across a fragmented region in southwestern Ontario and northern Ohio. Here, we combined habitat suitability modelling with population genetic analyses to infer how foxsnakes disperse through a habitat mosaic of natural and altered landscape features. Boundary regions between the eight genetic clusters, identified through assignment tests, were comprised of low suitability habitat (e.g. agricultural fields). Island populations were grouped into a single genetic cluster, and comparatively low F(ST) values between island and mainland populations suggest open water presents less of a barrier than nonsuitable terrestrial habitat. Isolation by resistance and least-cost path analysis produced similar results with matrices of pairwise individual genetic distance significantly more correlated to matrices of resistance values derived from habitat suitability than models with an undifferentiated landscape. Spatial autocorrelation results matched better with assignment results when incorporating resistance values rather than straight-line distances. All analyses used in our study produced similar results suggesting that habitat degradation limits dispersal for foxsnakes, which has had a strong effect on the genetic population structure across this region.     

Landscape Genetics of Leaf-Toed Geckos in the Tropical Dry Forest of Northern Mexico

Habitat fragmentation due to both natural and anthropogenic forces continues to threaten the evolution and maintenance of biological diversity. This is of particular concern in tropical regions that are experiencing elevated rates of habitat loss. Although less well-studied than tropical rain forests, tropical dry forests (TDF) contain an enormous diversity of species and continue to be threatened by anthropogenic activities including grazing and agriculture. However, little is known about the processes that shape genetic connectivity in species inhabiting TDF ecosystems. We adopt a landscape genetic approach to understanding functional connectivity for leaf-toed geckos (Phyllodactylus tuberculosus) at multiple sites near the northernmost limit of this ecosystem at Alamos, Sonora, Mexico. Traditional analyses of population genetics are combined with multivariate GIS-based landscape analyses to test hypotheses on the potential drivers of spatial genetic variation. Moderate levels of within-population diversity and substantial levels of population differentiation are revealed by F _ST and D _est. Analyses using structure suggest the occurrence of from 2 to 9 genetic clusters depending on the model used. Landscape genetic analysis suggests that forest cover, stream connectivity, undisturbed habitat, slope, and minimum temperature of the coldest period explain more genetic variation than do simple Euclidean distances. Additional landscape genetic studies throughout TDF habitat are required to understand species-specific responses to landscape and climate change and to identify common drivers. We urge researchers interested in using multivariate distance methods to test for, and report, significant correlations among predictor matrices that can impact results, particularly when adopting least-cost path approaches. Further investigation into the use of information theoretic approaches for model selection is also warranted.     

Environmental factors affecting population level genetic divergence of the striped field mouse (<Emphasis Type="Italic">Apodemus agrarius</Emphasis>) in South Korea

The cognizing of connectivity among small mammal populations across heterogeneous landscapes is complicated due to complex influences of landscape and anthropogenic factors on gene flow. A landscape genetics approach offers inferences on how landscape features drive population structure. Through a landscape genetics approach, we investigated influences of geographical, environmental, and anthropogenic features on populations of Apodemus agrarius, the striped field mouse, the prime vector of hemorrhagic fever by a landscape genetic approach. We identified landscape features that might affect the population structure of striped field mice by analyzing microsatellite markers of 197 striped field mice from 21 populations throughout South Korea. We developed Maximum-likelihood population effects models based on landscape distances and resistance matrices and pairwise F_ST values for meta-populations of striped field mouse. We also conducted Mantel and partial Mantel tests to investigate geographic patterns of genetic similarities. In Mantel and partial Mantel tests, the F_ST was significantly correlated with all three models of movement; movement cost, Euclidian distance and least-cost distance, although the magnitudes of correlations varied. The 4 top-ranked models included three variables; temperature, precipitation and one human disturbance factor (population). We did not attain a significant effect for anthropogenic factors on genetic similarities among populations in the Korean striped field mouse, but we confirmed a significant association for genetic similarity with climatic features (temperature and precipitation).     

Ecological metrics predict connectivity better than geographic distance

We use microsatellite loci to examine genetic structure of the Florida scrub lizard (Sceloporus woodi) and test for the effects of landscape variables at the scale of neighboring patches. We evaluate ecological metrics of connectivity with genetics data, which to our knowledge is the first application of these particular metrics to landscape-level genetics studies in Florida scrub. Florida scrub is a highly threatened ecosystem in which habitat patches are remnants of a previously widespread xeric landscape. Analysis of mitochondrial DNA (mtDNA) has shown that landscape structure influenced the evolutionary history of the Florida scrub lizard (S. woodi) across its range. Our results concur with these mtDNA studies in documenting divergence between xeric ridge systems and also demonstrate divergence at very local scales. Both least-cost distance and pairwise isolation (a metric used in ecological studies that includes patch size, quality and a modified isolation index) were better predictors of genetic distance than Euclidean distance, indicating that mesic and hydric habitat influence spatial patterns in genetic variation. Our results support the need for focusing on spatial distribution of scrub habitat at the scale of neighboring patches, as well as regionally, in conservation management and restoration. Also, our study points to the value of integrating landscape ecology metrics into landscape genetics.     

基于景观遗传学的滇金丝猴栖息地连接度分析

结合景观遗传学,应用最小费用距离模型对物种栖息地进行连接度分析,能够为生物多样性保护和自然保护区管理提供更加真实准确及可实践操作的指导。选取滇金丝猴这一珍稀濒危物种,结合景观遗传学,应用最小费用距离模型对其栖息地进行了连接度和潜在扩散廊道分析。并且通过连接度的分析和制图绘制出了更为准确的种群间潜在扩散廊道,确定了受人工障碍影响的廊道及敏感区域。结果表明,研究区内的5个亚群中,仅S3亚群内的5个猴群保持着较好的连接度,总体来说,各亚群内的连接度相对于各亚群间连接度保持的较好。除S3亚群中猴群间的潜在扩散廊道较为理想外,其余种群间的潜在扩散廊道均受人工斑块的影响,多数廊道被人工障碍阻断,或面临即将被阻断的情况,对于滇金丝猴的扩散交流影响较大。敏感区域多集中在中南部的3个亚群间,这些敏感区域应作为景观恢复及保护区规划的重要优先区域。     

Effects of the landscape on boreal toad gene flow: does the pattern–process relationship hold true across distinct landscapes at the northern range margin?

Understanding the impact of natural and anthropogenic landscape features on population connectivity is a major goal in evolutionary ecology and conservation. Discovery of dispersal barriers is important for predicting population responses to landscape and environmental changes, particularly for populations at geographic range margins. We used a landscape genetics approach to quantify the effects of landscape features on gene flow and connectivity of boreal toad (Bufo boreas) populations from two distinct landscapes in south-east Alaska (Admiralty Island, ANM, and the Chilkat River Valley, CRV). We used two common methodologies for calculating resistance distances in landscape genetics studies (resistance based on least-cost paths and circuit theory). We found a strong effect of saltwater on genetic distance of CRV populations, but no landscape effects were found for the ANM populations. Our discordant results show the importance of examining multiple landscapes that differ in the variability of their features, to maximize detectability of underlying processes and allow results to be broadly applicable across regions. Saltwater serves as a physiological barrier to boreal toad gene flow and affects populations on a small geographic scale, yet there appear to be few other barriers to toad dispersal in this intact northern region.     

Relationships between migration rates and landscape resistance assessed using individual-based simulations

Linking landscape effects on gene flow to processes such as dispersal and mating is essential to provide a conceptual foundation for landscape genetics. It is particularly important to determine how classical population genetic models relate to recent individual-based landscape genetic models when assessing individual movement and its influence on population genetic structure. We used classical Wright-Fisher models and spatially explicit, individual-based, landscape genetic models to simulate gene flow via dispersal and mating in a series of landscapes representing two patches of habitat separated by a barrier. We developed a mathematical formula that predicts the relationship between barrier strength (i.e., permeability) and the migration rate (m) across the barrier, thereby linking spatially explicit landscape genetics to classical population genetics theory. We then assessed the reliability of the function by obtaining population genetics parameters (m, F(ST) ) using simulations for both spatially explicit and Wright-Fisher simulation models for a range of gene flow rates. Next, we show that relaxing some of the assumptions of the Wright-Fisher model can substantially change population substructure (i.e., F(ST) ). For example, isolation by distance among individuals on each side of a barrier maintains an F(ST) of ～0.20 regardless of migration rate across the barrier, whereas panmixia on each side of the barrier results in an F(ST) that changes with m as predicted by classical population genetics theory. We suggest that individual-based, spatially explicit modelling provides a general framework to investigate how interactions between movement and landscape resistance drive population genetic patterns and connectivity across complex landscapes.     

Predicting landscape-genetic consequences of habitat loss, fragmentation and mobility for multiple species of woodland birds

Inference concerning the impact of habitat fragmentation on dispersal and gene flow is a key theme in landscape genetics. Recently, the ability of established approaches to identify reliably the differential effects of landscape structure (e.g. land-cover composition, remnant vegetation configuration and extent) on the mobility of organisms has been questioned. More explicit methods of predicting and testing for such effects must move beyond post hoc explanations for single landscapes and species. Here, we document a process for making a priori predictions, using existing spatial and ecological data and expert opinion, of the effects of landscape structure on genetic structure of multiple species across replicated landscape blocks. We compare the results of two common methods for estimating the influence of landscape structure on effective distance: least-cost path analysis and isolation-by-resistance. We present a series of alternative models of genetic connectivity in the study area, represented by different landscape resistance surfaces for calculating effective distance, and identify appropriate null models. The process is applied to ten species of sympatric woodland-dependant birds. For each species, we rank a priori the expectation of fit of genetic response to the models according to the expected response of birds to loss of structural connectivity and landscape-scale tree-cover. These rankings (our hypotheses) are presented for testing with empirical genetic data in a subsequent contribution. We propose that this replicated landscape, multi-species approach offers a robust method for identifying the likely effects of landscape fragmentation on dispersal.     

Using resistance distance from circuit theory to model dispersal through habitat corridors

Aims Resistance distance (RD), based on circuit theory, is a promising metric for modelling effects of landscape configuration on dispersal of organisms and the resulting population and community patterns. The values of RD reflect the likelihood of a random walker to reach from a source to a certain destination in the landscape. Although it has successfully been used to model genetic structures of animal populations, where it most often outperforms other isolation metrics, there are hardly any applications to plants and, in particular, to plant community data. Our aims were to test if RD was a suitable metric for studying dispersal processes of plants in narrow habitat corridors (linear landscape elements [LLE]). This would be the case, if dispersal processes (seed dispersal and migration) resembled random walks. Further, we compared the model performance of RD against least-cost distance (LCD) and Euclidean distance (ED). Finally, we tested the suitability of different cost surfaces for calculations of LCD and RD.Methods We used data from 50 vegetation plots located on semi-natural LLE (field margins, ditches, road verges) in eight agricultural landscapes of Northwest Germany. We mapped LLE, including hedges and tree rows, from aerial images in a Geographic Information System, converted the maps into raster layers, and assigned resistance values to the raster cells, where all cells outside of LLE received infinite resistance and, thus, represented barriers to dispersal. For all pairs of plots within study areas, we calculated Jaccard similarity assuming that it was a proxy (or correlate) of dispersal events between plots. Further, we calculated RD and LCD of the network of LLE and ED between the plots. We modelled the effects of distance metrics on community similarity using binomial generalized linear mixed models.Important findings ED was clearly the least suitable isolation metrics. Further, we found that RD performed better than LCD at modelling Jaccard similarity. Predictions varied markedly between the two distance metrics suggesting that RD comprises additional information about the landscape beyond spatial distance, such as the possible presence of multiple pathways between plots. Cost surfaces with equal cell-level resistances for all types of LLE performed better than more complex ones with habitat-specific resistances. We conclude that RD is a highly suitable measure of isolation or, inversely, connectivity for studying dispersal processes of plants within habitat corridors. It is likely also suitable for assessing landscape permeability in other landscape types with areal habitats instead of narrow corridors. RD holds the potential to improve assessments of isolation (or connectivity) for models of regional population and meta-community dynamics.     

Representing genetic variation as continuous surfaces: an approach for identifying spatial dependency in landscape genetic studies

Landscape genetics, an emerging field integrating landscape ecology and population genetics, has great potential to influence our understanding of habitat connectivity and distribution of organisms. Whereas typical population genetics studies summarize gene flow as pairwise measures between sampling localities, landscape characteristics that influence population genetic connectivity are often continuously distributed in space. Thus, there are currently gaps in both the ability to analyze genotypic data in a continuous spatial context and our knowledge of expected of landscape genetic structure under varying conditions. We present a framework for generating continuous “genetic surfaces”, evaluate their statistical properties, and quantify statistical behavior of landscape genetic structure in a simple landscape. We simulated microsatellite genotypes under varying parameters (time since vicariance, migration, effective population size) and used ancestry (q) values from STRUCTURE to interpolate a genetic surface. Using a spatially adjusted Pearson's correlation coefficient to test the significance of landscape variable(s) on genetic structure we were able to detect landscape genetic structure on a contemporary time scale (≥5 generations post vicariance, migration probability ≤0.10) even when population differentiation was minimal (F_ST≥0.00015). We show that genetic variation can be significantly correlated with geographic distance even when genetic structure is due to landscape variable(s), demonstrating the importance of testing landscape influence on genetic structure. Finally, we apply genetic surfacing to analyze an empirical dataset of black bears from northern Idaho USA. We find black bear genetic variation is a function of distance (autocorrelation) and habitat patch (spatial dependency), consistent with previous results indicating genetic variation was influenced by landscape by resistance. These results suggest genetic surfaces can be used to test competing hypotheses of the influence of landscape characteristics on genetic structure without delineation of categorical groups.     

The influence of contemporary and historic landscape features on the genetic structure of the sand dune endemic,Cirsium pitcheri (Asteraceae)

Narrow endemics are at risk from climate change because of their restricted habitat preferences, lower colonization ability and dispersal distances. Landscape genetics combines new tools and analyses that allow us to test how both past and present landscape features have facilitated or hindered previous range expansion and local migration patterns, and thereby identifying potential limitations to future range shifts. We have compared current and historic habitat corridors in Cirsium pitcheri, an endemic of the linear dune ecosystem of the Great Lakes, to determine the relative contributions of contemporary migration and post-glacial range expansion on genetic structure. We used seven microsatellite loci to characterize the genetic structure for 24 populations of Cirsium pitcheri, spanning the center to periphery of the range. We tested genetic distance against different measures of geographic distance and landscape permeability, based on contemporary and historic landscape features. We found moderate genetic structure (Fst=0.14), and a north–south pattern to the distribution of genetic diversity and inbreeding, with northern populations having the highest diversity and lowest levels of inbreeding. High allelic diversity, small average pairwise distances and mixed genetic clusters identified in Structure suggest that populations in the center of the range represent the point of entry to the Lake Michigan and a refugium of diversity for this species. A strong association between genetic distances and lake-level changes suggests that historic lake fluctuations best explain the broad geographic patterns, and sandy habitat best explains local patterns of movement.     

Identifying potential conservation areas in the Cuitzeo Lake basin,Mexico by multitemporal analysis of landscape connectivity

Lake Cuitzeo basin is an important ecological area subjected to strong human pressure on forest covers that are key elements for the long-term support of biodiversity. We studied landscape connectivity changes for the years 1975, 1996, 2000, 2003 and 2008 to identify potential conservation areas in the basin. We modeled potential distributions of the Mexican bobcat (Lynx rufus escuinapae) and the ringtail (Bassariscus astutus) – two terrestrial mammal focal species with contrasting dispersal capacities – and we determined their habitat availability and suitability in the basin. We then identified their optimal habitat patches and produced landscape cumulative resistance maps, estimated least-cost paths (graph theory approach), and elaborated current flow maps (circuit theory approach). For evaluation of landscape connectivity, we applied an integral index of connectivity (IIC) to each study period, and determined individual habitat patch contribution to the overall landscape connectivity. The IIC index had very low values associated with reduced availability of focal species habitat. However, our study showed the conservation importance of the surface of optimal habitat patch areas. The combined application of a graph-based approach and current flow mapping were useful, and complementary both in terms of estimating potential dispersal corridors and identifying high probability dispersal areas. This indicated that landscape connectivity analysis is a useful tool for identification of potential conservation areas and for local landscape planning.     

Expert-based versus habitat-suitability models to develop resistance surfaces in landscape genetics

Landscape genetics aims to investigate functional connectivity among wild populations by evaluating the impact of landscape features on gene flow. Genetic distances among populations or individuals are generally better explained by least-cost path (LCP) distances derived from resistance surfaces than by simple Euclidean distances. Resistance surfaces reflect the cost for an organism to move through particular landscape elements. However, determining the effects of landscape types on movements is challenging. Because of a general lack of empirical data on movements, resistance surfaces mostly rely on expert knowledge. Habitat-suitability models potentially provide a more objective method to estimate resistance surfaces than expert opinions, but they have rarely been applied in landscape genetics so far. We compared LCP distances based on expert knowledge with LCP distances derived from habitat-suitability models to evaluate their performance in landscape genetics. We related all LCP distances to genetic distances in linear mixed effect models on an empirical data set of wolves (Canis lupus) from Italy. All LCP distances showed highly significant (P ≤ 0.0001) standardized β coefficients and R ² values, but LCPs from habitat-suitability models generally showed higher values than those resulting from expert knowledge. Moreover, all LCP distances better explained genetic distances than Euclidean distances, irrespective of the approaches used. Considering our results, we encourage researchers in landscape genetics to use resistance surfaces based on habitat suitability which performed better than expert-based LCPs in explaining patterns of gene flow and functional connectivity.     

Using a genetic network to parameterize a landscape resistance surface for fishers, Martes pennanti

Knowledge of dispersal-related gene flow is important for addressing many basic and applied questions in ecology and evolution. We used landscape genetics to understand the recovery of a recently expanded population of fishers (Martes pennanti) in Ontario, Canada. An important focus of landscape genetics is modelling the effects of landscape features on gene flow. Most often resistance surfaces in landscape genetic studies are built a priori based upon nongenetic field data or expert opinion. The resistance surface that best fits genetic data is then selected and interpreted. Given inherent biases in using expert opinion or movement data to model gene flow, we sought an alternative approach. We used estimates of conditional genetic distance derived from a network of genetic connectivity to parameterize landscape resistance and build a final resistance surface based upon information-theoretic model selection and multi-model averaging. We sampled 657 fishers from 31 landscapes, genotyped them at 16 microsatellite loci, and modelled the effects of snow depth, road density, river density, and coniferous forest on gene flow. Our final model suggested that road density, river density, and snow depth impeded gene flow during the fisher population expansion demonstrating that both human impacts and seasonal habitat variation affect gene flow for fishers. Our approach to building landscape genetic resistance surfaces mitigates many of the problems and caveats associated with using either nongenetic field data or expert opinion to derive resistance surfaces.