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Landscape genetics and least-cost path analysis reveal unexpected dispersal routes in the California tiger salamander (Ambystoma californiense)
Abstract
A major goal of landscape genetics is to understand how landscapes structure genetic variation in natural populations. However, landscape genetics still lacks a framework for quantifying the effects of landscape features, such as habitat type, on realized gene flow. Here, we present a methodology for identifying the costs of dispersal through different habitats for the California tiger salamander (Ambystoma californiense), an endangered species restricted to grassland/vernal pool habitat mosaics. We sampled larvae from all 16 breeding ponds in a geographically restricted area of vernal pool habitat at the Fort Ord Natural Reserve, Monterey County, California. We estimated between-pond gene flow using 13 polymorphic microsatellite loci and constructed GIS data layers of habitat types in our study area. We then used least-cost path analysis to determine the relative costs of movement through each habitat that best match rates of gene flow measured by our genetic data. We identified four measurable rates of gene flow between pairs of ponds, with between 10.5% and 19.9% of larvae having immigrant ancestry. Although A. californiense is typically associated with breeding ponds in grassland habitat, we found that dispersal through grassland is nearly twice as costly as dispersal through chaparral and that oak woodland is by far the most costly habitat to traverse. With the increasing availability of molecular resources and GIS data, we anticipate that these methods could be applied to a broad range of study systems, particularly those with cryptic life histories that make direct observation of movement challenging.
... Recent developments have permitted the empirical optimization of resistance values directly from observed genetic distance values thereby avoiding the need to specify candidate surfaces (Peterman, 2018). However, researchers must still select from among many potential spatial scales for each covariate thereby greatly increasing the number of candidate resistance surfaces (Wang et al., 2009). ...
... However, pseudo-optimization may suffer from variable omission as variation in the data due to other covariates is explained by a single covariate when selecting that covariate's scale (Stuber & Gruber, 2020). Full (i.e., true) optimization would simultaneously consider all candidate scales for each covariate yet computational limitations have limited its widespread application (Wang et al., 2009). A potential alternative is to derive multiscale resistance surfaces from multiscale habitat models where some form of scale optimization was used within the multiscale habitat model. ...
... Such bias may at least partly explain our results. While several landscape genetics studies have evaluated the entire parameter space more-or-less thoroughly with regards to resistance values and functional form (Dudaniec et al., 2016;Shirk et al., 2010;Wang et al., 2009), considering scale(s) within the parameter space will markedly increase the parameter space and computational times. However, our approach of combining resistancega optimization with "manual" comparisons across a ...
Landscape features can strongly influence gene flow and the strength and direction of these effects may vary across spatial scales. However, few studies have evaluated methodological approaches for selecting spatial scales in landscape genetics analyses, in part because of computational challenges associated with optimizing landscape resistance surfaces (LRS). We used the federally threatened eastern indigo snake (Drymarchon couperi) in central Florida as a case study with which to compare the importance of landscape features and their scales‐of‐effect in influencing gene flow. We used genetic algorithms (ResistanceGA) to empirically optimize LRS using categorical land cover surfaces, multi‐scale resource selection surfaces (RSS), and four combinations of landscape covariates measured at multiple spatial scales (multi‐surface multi‐scale LRS). We compared LRS where scale was selected using pseudo and full optimization. Multi‐surface multi‐scale LRS received more empirical support than LRS optimized from categorical land cover surfaces or RSS. Multi‐scale LRS with scale selected using full optimization generally outperformed those with scale selected using pseudo optimization. Multi‐scale LRS with large spatial scales (1200‐1800 m) received the most empirical support. Our results highlight the importance of considering landscape features across multiple spatial scales in landscape genetic analyses, particularly broad scales relative to species movement potential. Different effects of scale on home range‐level movements and dispersal could explain weak associations between habitat suitability and gene flow in other studies. Our results also demonstrate the importance of large tracts of undeveloped upland habitat with heterogenous vegetation communities and low urbanization for promoting indigo snake connectivity.
... Environmental distances (e-g) were estimated by taking the absolute difference between values of environmental variables extracted from the occurrences of sequenced individuals. Inset pictures are the adult and larval stage of P. m dodi [Colour figure can be viewed at wileyonlinelibrary.com] therefore intuitive that patterns of genetic divergence are generally best explained by resistance-based geographic distances, as configurations of suitable habitat typically moderate spatial variation in movement and dispersal in such taxa (Broquet, Ray, Petit, Fryxell, & Burel, 2006;Coulon et al., 2004;Crispo et al., 2006;Epps, Wehausen, Bleich, Torres, & Brashares, 2007;McRae, 2006;McRae & Beier, 2007;Sánchez-Ramírez et al., 2018;Vignieri, 2005;Wang, Savage, & Bradley Shaffer, 2009;Wang et al., 2008). But other taxa, including many terrestrial invertebrates, have discrete dispersal life stages (generally the adult) with broader habitat tolerances than larval stages, which may have important consequences for processes affecting genetic divergence (Phillipsen et al., 2015). ...
... Euclidean distances represent minimal distances required to travel between locations and do not account for landscape characteristics. In contrast, leastcost path distances are estimated by searching for single, optimal routes that minimize cumulative costs associated with travelling through heterogeneous landscapes (Wang et al., 2009). Least-cost path analysis thereby assumes that organisms have complete knowledge of such landscapes and are able to consistently navigate optimal routes. ...
... (c.f. Coyne & Orr, 2004;Crispo et al., 2006;McRae, 2006;McRae & Beier, 2007;Thorpe et al., 2008;Wang et al., 2009;Sánchez-Ramírez et al., 2018). ...
Previous work in landscape genetics suggests that geographic isolation is of greater importance to genetic divergence than variation in environmental conditions. This is intuitive when configurations of suitable habitat are the dominant factor limiting dispersal and gene flow, but has not been thoroughly examined for habitat specialists with strong dispersal capability. Here, we evaluate the effects of geographic and environmental isolation on genetic divergence for a vagile invertebrate with high habitat specificity and a discrete dispersal life stage: Dod’s Old World swallowtail butterfly, Papilio machaon dodi. In Canada, P. m. dodi are generally restricted to eroding habitat along major river valleys where their larval host plant occurs. A series of causal and linear mixed effects models indicate that divergence of genome‐wide single nucleotide polymorphisms is best explained by a combination of environmental isolation (variation in summer temperatures) and geographic isolation (Euclidean distance). Interestingly, least‐cost path and circuit distances through a resistance surface parameterized as the inverse of habitat suitability were not supported. This suggests that, although habitat associations of many butterflies are specific due to reproductive requirements, habitat suitability and landscape permeability are not equivalent concepts due to considerable adult vagility. We infer that divergent selection related to variation in summer temperatures has produced two genetic clusters within P. m. dodi, differing in voltinism and diapause propensity. Within the next century, temperatures are predicted to rise by amounts greater than the present‐day difference between regions of the genetic clusters, potentially affecting the persistence of the northern cluster under continued climate change.
... This may be expected for generalist or precocial species that disperse, migrate or otherwise interact with their physical environment in a consistent way across life-history stages and through evolutionary time. On the other hand, it may be that the landscape elements limiting gene flow at large spatial scales are distinct from those that limit gene flow at small spatial scales-as in the case of amphibians with distinct larval and adult life stages (Angelone et al., 2011;Trumbo et al., 2013;Wang et al., 2009). This could also be expected if the landscape features influencing dispersal processes (Wang et al., 2009) are distinct from those affecting range expansions and colonization of new environments, such as interglacial range expansions and contractions (Bouzid et al., 2022). ...
... On the other hand, it may be that the landscape elements limiting gene flow at large spatial scales are distinct from those that limit gene flow at small spatial scales-as in the case of amphibians with distinct larval and adult life stages (Angelone et al., 2011;Trumbo et al., 2013;Wang et al., 2009). This could also be expected if the landscape features influencing dispersal processes (Wang et al., 2009) are distinct from those affecting range expansions and colonization of new environments, such as interglacial range expansions and contractions (Bouzid et al., 2022). Ultimately, developing a deeper understanding of the landscape elements that hinder or promote gene flow at various scales, and relating these patterns to what we understand about species ecology and physiology, will help inform a general understanding of the effect of landscape structure on genetic differentiation across space and time. ...
The field of biogeography unites landscape genetics and phylogeography under a common conceptual framework. Landscape genetics traditionally focuses on recent‐time, population‐based, small geographic scale, spatial genetics processes, while phylogeography typically investigates deep past, lineage‐ and species‐based processes at large geographic scales. Here, we evaluate the link between landscape genetics and phylogeographic methods using the Western Fence lizard (Sceloporus occidentalis) as a model species. First, we conducted replicated landscape genetics studies across several geographic scales to investigate how population genetics inferences change depending on the spatial extent of the study area. Then, we carried out a phylogeographic study of population structure at two evolutionary scales informed by inferences derived from landscape genetics results to identify concordance and conflict between these sets of methods. We found significant concordance in landscape genetics processes at all but the largest geographic scale. Phylogeographic results indicate major clades are restricted to distinct river drainages or distinct hydrologic regions. At a more recent timescale, we find minor clades are restricted to single river canyons in the majority of cases, while the remainder of river canyons include samples from at most two clades. Overall, the broad scale pattern implicating stream and river valleys as key features linking populations in the landscape genetics results, and high degree of clade specificity within major topographic subdivisions in the phylogeographic results, is consistent. As landscape genetics and phylogeography share many of the same objectives, synthesizing theory, models, and methods between these fields will help bring about a better understanding of ecological and evolutionary processes structuring genetic variation across space and time.
... A greater understanding of how amphibians move among breeding ponds and their terrestrial habitats is an informative means to mitigate potential threats, particularly as these animals are often difficult to observe. For instance, previous work on ambystomatid salamanders highlights that combining spatial analyses with population genetics can be informative [14], with a more recent study suggesting that the two methods are congruent [4]. Marbled salamanders, Ambystoma opacum (Gravenhorst), are distributed across eastern North America [12] and are listed as threatened or endangered in several states in the United States [15], often due to habitat destruction or fragmentation. ...
... Many studies use observational data and a priori expectations to measure cost, which may be misleading given that a species may not follow anthropologic assumptions of landscape quality [23,24]. Recent studies combined direct and indirect estimates of gene flow to help assign relative costs of the habitat features and determine the most biologically plausible dispersal routes [14,25]. Notably, habitat costs and associated dispersal routes may differ depending on the type of data (e.g., genetic vs. mark-recapture) used in the initial assessment of the populations, but whether data support this difference needs to be explored. ...
Estimating connectivity is key for maintaining population viability for pond-breeding amphibians, especially in areas where habitat alterations occur. Here, we used genetic data (microsatellites) to estimate connectivity of marbled salamanders, Ambystoma opacum, among three focal ponds and compared it to field data (capture-mark-recapture estimates) of movement among the same ponds. In addition, we derived least-cost dispersal paths from genetic data and compared them to field connectivity estimates. We found that genetic and field estimates of dispersal were generally congruent, but field-based paths were more complex than genetic-based paths. While both methods complement each other in identifying important source-sink metapopulation dynamics to inform efficient conservation management plans, field data provide a more biologically accurate understanding of the spatial movement of individual marbled salamanders.
... Conservation genetic research has examined the effect of straight-line distance on genetic distance of populations, with a non-significant result indicative of features in the landscape that may present barriers or challenges to gene flow and dispersal (Whitlock and Mccauley 1999). Over time, researchers have incorporated spatial data into Mantel tests to examine the effect of least cost path on genetic distances of populations (Wang et al. 2009;Milanesi et al. 2016). This concept of 'landscape genetics' has proved extremely useful for pinpointing barriers to gene flow such as roads (Keller and Largiadèr 2003) and biogeographic barriers (Pérez-Espona et al. 2008;Wei et al. 2012). ...
... This concept of 'landscape genetics' has proved extremely useful for pinpointing barriers to gene flow such as roads (Keller and Largiadèr 2003) and biogeographic barriers (Pérez-Espona et al. 2008;Wei et al. 2012). Additionally, least cost path analyses have identified corridors for priority conservation when paired with genetic data (Wang et al. 2009;Etherington et al. 2014). When investigating the effect of habitat fragmentation on population genetics, it is essential to combine spatial and genetic data to gain a better understanding of genetic structure (Storfer et al. 2007). ...
Arboreal gliders are vulnerable to habitat fragmentation and to barriers that extend their glide distance threshold. Habitat fragmentation through deforestation can cause population isolation and genetic drift in gliding mammals, which in turn can result in a loss of genetic diversity and population long-term persistence. This study utilised next generation sequencing technology to call 8784 genome-wide SNPs from 90 sugar gliders (Petaurus breviceps) sensu stricto. Samples were collected from 12 locations in the Lake Macquarie Local Government Area (New South Wales). The sugar gliders appeared to have high levels of gene flow and little genetic differentiation; however spatial least cost path analyses identified the Pacific Motorway as a potential barrier to their dispersal. This Motorway is still relatively new (<40 years old), so man-made crossing structures should be erected as a management priority to mitigate any long-term effects of population isolation by assisting in the dispersal and gene flow of the species.
... Conservation genetic research has examined the effect of straight-line distance on genetic distance of populations, with a non-signi cant result indicative of features in the landscape that may present barriers or challenges to gene ow and dispersal (Whitlock and Mccauley 1999). Over time, researchers have incorporated spatial data into Mantel tests to examine the effect of least cost path on genetic distances of populations (Wang et al. 2009;Milanesi et al. 2016). This concept of "landscape genetics" has proved extremely useful for pinpointing barriers to gene ow such as roads (Keller and Largiadèr 2003) and biogeographic barriers (Pérez-Espona et al. 2008; Wei et al. 2012). ...
... This concept of "landscape genetics" has proved extremely useful for pinpointing barriers to gene ow such as roads (Keller and Largiadèr 2003) and biogeographic barriers (Pérez-Espona et al. 2008; Wei et al. 2012). Additionally, least cost path analyses have identi ed corridors for priority conservation when paired with genetic data (Wang et al. 2009; Etherington et al. 2014). When investigating the effect of habitat fragmentation on population genetics, it is essential to combine spatial and genetic data to gain a better understanding of genetic structure (Storfer et al. 2007). ...
Arboreal gliders are vulnerable to habitat fragmentation and to barriers that extend their glide distance threshold. Habitat fragmentation through deforestation can cause population isolation and genetic drift in gliding mammals which in turn can result in a loss of genetic diversity and population long-term persistence. This study utilised next generation sequencing technology to call 11, 292 genome-wide SNPs from 90 adult sugar gliders ( Petaurus breviceps ). Samples were collected from 12 locations in the Lake Macquarie Local Government Area (New South Wales), with two of these locations west of the Pacific Motorway, a potential major barrier to their dispersal. Overall, Lake Macquarie sugar gliders appeared to have high levels of gene flow and little genetic differentiation, however spatial least cost path analyses identified the Pacific Motorway as a barrier to their dispersal. This Motorway is still relatively new (< 40 years old), so man-made crossing structures should be erected as a management priority to mitigate any long-term effects of population isolation by assisting in the dispersal and gene flow of the species. This study provides further insight into the sugar glider after it was classed as three separate species in 2020 and could potentially be used as a model for its threatened congener in the area, the squirrel glider ( Petaurus norfolcensis ).
... The reclassification followed the Jenks natural breaks method(De la Torre et al., 2013) providing a common measurement scale of 1 (no resistance) to 10 (highest resistance) for all three cost factors(Hashmi, Frate, Nizami, & Carranza, 2017; Wang, Savage, & Bradley Shaffer, 2009). The three were then combined through a logical overlay operation(Hashmi et al., 2017;Wang et al., 2009). For the LCPs, the starting point was the most eastern ASF-infected wild boar occurrence in the source region (Figure 2; latitude 55.253717 and longitude 46.617933) and the endpoint the most northwestern highly suitable area in the impact region (Figure 2). ...
... For the LCPs, the starting point was the most eastern ASF-infected wild boar occurrence in the source region (Figure 2; latitude 55.253717 and longitude 46.617933) and the endpoint the most northwestern highly suitable area in the impact region (Figure 2). Finally, two least cost paths, LCP-slope and LCP-elevation, were generated(Wang et al., 2009) through alternate input of slope and elevation separately(Figure 2). ...
China has experienced a sudden multi‐focal and multi‐round of African swine fever (ASF) outbreaks during 2018. The subsequent epidemiological survey resulted in a debate including the possibility of a transboundary spread from European Russia to China through wild boar. We contribute to the debate by assessing a hypothetical overland Euro‐Siberian transmission path and its associated ASF arrival dates. We selected the maximum entropy algorithm for spatial modelling of ASF‐infected wild boar and the Spatial Distribution Modeller in ArcGIS to plot Least Cost Paths (LCPs) between Eastern Europe and NE China. The arrival dates of ASF‐infected wild boar have been predicted by cumulative maximum transmission distances per season and cover with their associated minimum time intervals along the LCPs. Our results show high costs for wild boar to cross Kazakhstan, Xinjiang (NW China) and/or Mongolia to reach NE China. Instead, the Paths lead almost straight eastward along the 59.5° northern latitude through Siberia and would have taken a minimum of 219 or 260 days. Therefore, infected wild boar moving all the way along the LCP could not have been the source of the ASF infection in NE China on 2 August 2018.
... Alternatively, cost is parameterized into multiple environmental variables (e.g., vegetation, slope, and proximity to targets) whose relationships may be defined according to expert opinions (e.g., Knaapen et al. 1992, Chardon et al. 2003, Gonzales and Gergel 2007, LaRue and Nielsen 2008, Spear et al. 2010. In another frequently used approach, cost is inversely or negatively related to some advantageous quality generally referred to as 'suitability' (e.g., Ferreras 2001, Wang et al. 2008, Chetkiewicz and Boyce 2009, Poor et al. 2012, Trainor et al. (2013, Reding et al. (2013), Ziółkowska et al. 2014) assuming that if a location is less suitable for a certain use, it will cost more to use that location for that use. In any case, cost is nothing but an estimate from supposedly relevant data with various degrees of uncertainty and subjectivity (see, e.g., Beier et al. (2008), Rayfield et al. (2010), Spear et al. (2010), Zeller et al. (2012), Ligmann-Zielinska and Jankowski (2014) for reviews). ...
... The suitability-to-cost conversion may be done by inversely or negatively relating cost and suitability (e.g. Ferreras 2001, Wang et al. 2008, Chetkiewicz and Boyce 2009). As a simple example, a set, S, of suitability values {1, 5, 9} can be reversed into a set of cost values {9, 5, 1} by the following linear function: ...
Selection of optimal paths or sequences of cells from a grid of cells is one of the most basic functions of raster-based geographic information systems. For this function to work, it is often assumed that the optimality of a path can be evaluated by the sum of the weighted lengths of all its segments – weighted, i.e. by the underlying cell values. The validity of this assumption must be questioned, however, if those values are measured on a scale that does not permit arithmetic operations. Through computational experiments with randomly generated artificial landscapes, this paper compares two models, minisum and minimax path models, which aggregate the values of the cells associated with a path using the sum function and the maximum function, respectively. Results suggest that the minisum path model is effective if the path search can be translated into the conventional least-cost path problem, which aims to find a path with the minimum cost-weighted length between two terminuses on a ratio-scaled raster cost surface. On the other hand, the minimax path model is found mathematically sounder if the cost values are measured on an ordinal scale and practically useful if the problem is concerned not with the minimization of cost but with the maximization of some desirable condition such as suitability.
... The workflow for investigating IBR includes first generating a resistance surface based on prior information or a hypothesis about how landscape elements contribute to differential resistance to movement for the study organism. Resistance distances can then be calculated using least-cost path analysis (Wang et al., 2009;Wang & Shaffer, 2017) or circuit theory analysis (Dickson et al., 2019;McRae & Beier, 2007). Because the parameterization of the resistance surface is highly complex, requiring decisions that often have a significant impact on the estimation of resistance distances (Koen et al., 2012;Spear et al., 2010Spear et al., , 2015, and because the optimal approach to resistance surface parameterization depends strongly on the study design and objectives (Peterman et al., 2019;Spear et al., 2010;Zeller et al., 2012), algatr does not include an automated approach for generating resistance surfaces. ...
Landscape genomics can harness environmental and genetic data to inform conservation decisions by providing essential insights into how landscapes shape biodiversity. The massive increase in genetic data afforded by the genomic era provides exceptional resolution for answering critical conservation genetics questions. The accessibility of genomic data for non‐model systems has also enabled a shift away from population‐based sampling to individual‐based sampling, which now provides accurate and robust estimates of genetic variation that can be used to examine the spatial structure of genomic diversity, population connectivity and the nature of environmental adaptation. Nevertheless, the adoption of individual‐based sampling in conservation genetics has been slowed due, in large part, to concerns over how to apply methods developed for population‐based sampling to individual‐based sampling schemes. Here, we discuss the benefits of individual‐based sampling for conservation and describe how landscape genomic methods, paired with individual‐based sampling, can answer fundamental conservation questions. We have curated key landscape genomic methods into a user‐friendly, open‐source workflow, which we provide as a new R package, A Landscape Genomics Analysis Toolkit in R ( algatr) . The algatr package includes novel added functionality for all of the included methods and extensive vignettes designed with the primary goal of making landscape genomic approaches more accessible and explicitly applicable to conservation biology.
... In practice, this is not usually an issue, and interpretation of the critical variables affecting dispersal and genetic connectivity should be based on the combination of factors present in any given spatial lo- Given these issues of data interpretation, we look forward to continued development and refinement of landscape genetics methods and models. While the field of landscape genetics was conceived from simpler methods, such as least-cost paths (Cushman et al., 2006;Freedman et al., 2010;Richardson et al., 2016;Trumbo et al., 2013Trumbo et al., , 2021van Strien et al., 2012;Wang et al., 2009;Zancolli et al., 2014) and mantel and partial mantel tests (Andrew et al., 2012;Hecht et al., 2015;Krohn et al., 2019;Richardson, 2012;Roffler et al., 2016), it is rapidly gaining ground with more sophisticated spatial models that incorporate increasing complexity in the relationships among environmental variables. resistancega represents one area of rapid growth in model development (Peterman, 2018;Peterman et al., 2019;Winiarski et al., 2020). ...
The degree to which landscape genetics findings can be extrapolated to different areas of a species range is poorly understood. Here, we used a broadly distributed ectothermic lizard (Sceloporus occidentalis, Western Fence lizard) as a model species to evaluate the full role of topography, climate, vegetation, and roads on dispersal and genetic differentiation. We conducted landscape genetics analyses with a total of 119 individuals in five areas within the Sierra Nevada mountain range. Genetic distances calculated from thousands of ddRAD markers were used to optimize landscape resist- ance surfaces and infer the effects of landscape and topographic features on genetic connectivity. Across study areas, we found a great deal of consistency in the primary environmental gradients impacting genetic connectivity, along with some site-specific differences, and a range in the proportion of genetic variance explained by environ- mental factors across study sites. High-elevation colder areas were consistently found to be barriers to gene flow, as were areas of high ruggedness and slope. High tempera- ture seasonality and high precipitation during the winter wet season also presented a substantial barrier to gene flow in a majority of study areas. The effect of other landscape variables on genetic differentiation was more idiosyncratic and depended on specific attributes at each site. Across study areas, canyon valleys were always implicated as facilitators to dispersal and key features linking populations and main- taining genetic connectivity, though the relative importance varied in different areas. We emphasize that spatial data layers are complex and multidimensional, and careful consideration of spatial data correlation structure and robust analytic frameworks will be critical to our continued understanding of spatial genetics processes.
... LCD and CD account for the heterogeneity of landscapes. LCD is the path that minimizes the total cumulative cost between two points through heterogeneous landscapes (Wang et al., 2009). CD is calculated by summarizing the costs of all possible paths between two points (McRae & Beier, 2007). ...
Genomic signatures of local adaptation have been identified in many species but remain sparsely studied in amphibians. Here, we explored genome‐wide divergence within the Asiatic toad, Bufo gargarizans, to study local adaptation and genomic offset (i.e., the mismatch between current and future genotype‐environment relationships) under climate warming scenarios. We obtained high‐quality SNP data for 94 Asiatic toads from 21 populations in China to study spatial patterns of genomic variation, local adaptation, and genomic offset to warming in this wide‐ranging species. Population structure and genetic diversity analysis based on high‐quality SNPs revealed three clusters of B. gargarizans in the western, central‐eastern, and northeastern portions of the species' range in China. Populations generally dispersed along two migration routes, one from the west to the central‐east and one from the central‐east to the northeast. Both genetic diversity and pairwise FST were climatically correlated, and pairwise FST was also correlated with geographic distance. Spatial genomic patterns in B. gargarizans were determined by the local environment and geographic distance. Global warming will increase the extirpation risk of B. gargarizans.
... Within conservation research, DNA sequencing technologies are becoming increasingly popular as these technologies enhance and complement traditional population monitoring techniques (Hohenlohe et al. 2021). Genetic methods typically require lower sampling effort and can provide insight into behaviours, such as dispersal and breeding, making them highly suitable for species that are difficult to observe (Amato et al. 2009;Wang et al. 2009;McCartney-Melstad & Shaffer. 2015). ...
The status of many amphibian populations remains unclear due to undetected declines driven by disease and difficulties in obtaining accurate population estimates. Here, we used genome complexity reduction-based sequencing technology to study the poorly understood Littlejohn’s treefrog, Litoria littlejohni across its fragmented distribution in eastern Australia. We detected five identifiable genetic clusters, with moderate to strong genetic isolation. At a regional scale, population isolation was likely driven by population crashes, resulting in small populations impacted by founder effects. Moderate genetic isolation was detected among populations on the Woronora Plateau despite short distances between population clusters. Evidence of recent declines was apparent in three populations that had very small effective population size, reduced genetic diversity and high inbreeding values. The rates of inbreeding detected in these populations combined with their small size leave these populations at elevated risk of extinction. The Cordeaux Cluster was identified as the most robust population as it was the largest and most genetically diverse. This study exemplifies the value of employing genetic methods to study rare, cryptic species. Despite low recapture rates using traditional capture-recapture demographic methods, we were able to derive population estimates, describe patterns of gene flow, and demonstrate the need for urgent conservation management.
... Exhaustive search approaches based on genetic distance data have been developed to optimize resistance values (Wang et al., 2009). Grid search approaches were first introduced, where a constrained parameter space can be explored, limiting the number of models being assessed (Graves et al., 2013). ...
Understanding landscape connectivity has become a global priority for mitigating the impact of landscape fragmentation on biodiversity. Connectivity methods that use link-based methods traditionally rely on relating pairwise genetic distance between individuals or demes to their landscape distance (e.g., geographic distance, cost distance). In this study, we present an alternative to conventional statistical approaches to refine cost surfaces by adapting the gradient forest approach to produce a resistance surface. Used in community ecology, gradient forest is an extension of random forest, and has been implemented in genomic studies to model species genetic offset under future climatic scenarios. By design, this adapted method, resGF, has the ability to handle multiple environmental predicators and is not subjected to traditional assumptions of linear models such as independence, normality and linearity. Using genetic simulations, resistance Gradient Forest (resGF) performance was compared to other published methods (maximum likelihood population effects model, random forest-based least-cost transect analysis and species distribution model). In univariate scenarios, resGF was able to distinguish the true surface contributing to genetic diversity among competing surfaces better than the compared methods. In multivariate scenarios, the gradient forest approach performed similarly to the other random forest-based approach using least-cost transect analysis but outperformed MLPE-based methods. Additionally, two worked examples are provided using two previously published datasets. This machine learning algorithm has the potential to improve our understanding of landscape connectivity and inform long-term biodiversity conservation strategies.
... Exhaustive search approaches based on genetic distance data have been developed to optimize resistance values (Wang et al., 2009). Grid search approaches were first introduced, where a constrained parameter space can be explored, limiting the number of models being assessed (Graves et al., 2013). ...
Understanding landscape connectivity has become a global priority for mitigating the impact of landscape fragmentation on biodiversity. Link-based methods traditionally rely on relating pairwise genetic distance between individuals or demes to their landscape distance (e.g., geographic distance, cost distance). In this study, we present an alternative to conventional statistical approaches to refine cost surfaces by adapting the Gradient Forest (GF) approach to produce a resistance surface. Used in community ecology, GF is an extension of random forest (RF), and has been implemented in genomic studies to model species genetic offset under future climatic scenarios. By design, this adapted method, resGF, has the ability to handle multiple environmental predicators and is not subjected to traditional assumptions of linear models such as independence, normality and linearity. Using genetic simulations, resGF performance was compared to other published methods. In univariate scenarios, resGF was able to distinguish the true surface contributing to genetic diversity among competing surfaces better than the compared methods. In multivariate scenarios, the GF approach performed similarly to the other RF-based approach using least-cost transect analysis (LCTA). Additionally, two worked examples are provided using two previously published datasets. This machine learning algorithm has the potential to improve our understanding of landscape connectivity and can inform long-term biodiversity conservation strategies.
... Η ανάλυση βασίζεται στην θεώρηση ότι τα είδη πάντα θα προτιμούν να μετακινηθούν με τον τρόπο ο οποίος απαιτεί τη λιγότερη ενέργεια και συνεπώς, ο αλγόριθμος βρίσκει τις διαδρομές με το μικρότερο κόστος οι οποίες αναμένεται να είναι αρκετά όμοιες με αυτές που θα ακολουθήσει ένα είδος σε πραγματικό χώρο, κάτι που επιβεβαιώνεται και από πλήθος μελετών οι οποίες έχουν αξιολογήσει τη μέθοδο ως αξιόπιστη (Sawyer, Epps and Brashares, 2011;Covarrubias, Gonzalez and Gutierrez-Rodriguez, 2020;Wang, Savage and Shaffer, 2009). ...
Using Species Distribution Modeling (SDMs) and Least Cost Path Analysis in order to examine the current and future distribution of the Cyprus grass snake and the effect of climate change on it.
... Increasingly, however, natural conditions are interrupted by natural, technogenic, and anthropogenic obstacles that can result in unsuitable conditions for migrating or foraging urodels (Twitty 1941;Wang et al. 2009;Alvarez et al. 2021). Herein, we report on the rehydration and recovery of an humidity ranged between 36% and 90%. ...
... Next, we randomly paired each origin point with a destination point in a randomly selected grid cell (n = 50) within nonbreeding MCR cores using the relative proportions estimated from the migratory connectivity analysis described above (Fig. 2b). We then computed a probabilistic (i.e., randomized) LCP (Adrianensen et al. 2003, Storfer et al. 2007, Wang et al. 2009) between each breedingnonbreeding core pixel pair that minimized the total cumulative cost, where the cost of moving between paired pixels was determined by the intervening distance weighted by a conductance surface representing average (i.e., arithmetic mean across weeks; see Appendix S1: Table S2) post-breeding occurrence probabilities obtained from eBird (Fig. 2c). Thus, higher occurrence values during migration coincided with higher conductance. ...
For many avian species, spatial migration patterns remain largely undescribed, especially across hemispheric extents. Recent advancements in tracking technologies and high‐resolution species distribution models (i.e., eBird Status and Trends products) provide new insights into migratory bird movements and offer a promising opportunity for integrating independent data sources to describe avian migration. Here, we present a three‐stage modeling framework for estimating spatial patterns of avian migration. First, we integrate tracking and band re‐encounter data to quantify migratory connectivity, defined as the relative proportions of individuals migrating between breeding and nonbreeding regions. Next, we use estimated connectivity proportions along with eBird occurrence probabilities to produce probabilistic least‐cost path (LCP) indices. In a final step, we use generalized additive mixed models (GAMMs) both to evaluate the ability of LCP indices to accurately predict (i.e., as a covariate) observed locations derived from tracking and band re‐encounter data sets versus pseudo‐absence locations during migratory periods and to create a fully integrated (i.e., eBird occurrence, LCP, and tracking/band re‐encounter data) spatial prediction index for mapping species‐specific seasonal migrations. To illustrate this approach, we apply this framework to describe seasonal migrations of 12 bird species across the Western Hemisphere during pre‐ and postbreeding migratory periods (i.e., spring and fall, respectively). We found that including LCP indices with eBird occurrence in GAMMs generally improved the ability to accurately predict observed migratory locations compared to models with eBird occurrence alone. Using three performance metrics, the eBird + LCP model demonstrated equivalent or superior fit relative to the eBird‐only model for 22 of 24 species–season GAMMs. In particular, the integrated index filled in spatial gaps for species with over‐water movements and those that migrated over land where there were few eBird sightings and, thus, low predictive ability of eBird occurrence probabilities (e.g., Amazonian rainforest in South America). This methodology of combining individual‐based seasonal movement data with temporally dynamic species distribution models provides a comprehensive approach to integrating multiple data types to describe broad‐scale spatial patterns of animal movement. Further development and customization of this approach will continue to advance knowledge about the full annual cycle and conservation of migratory birds.
... Inicialmente se tomaron las Áreas Protegidas y la superficie de fricción para calcular las distancias de costo mínimo entre ellas, ya que al utilizar los valores asignados a cada celda determina cuánto se debe recorrer desde la celda fuente hasta la celda final, la distancia de costo mínimo entre áreas es aquella con menos coste acumulado haciendo alusión al camino más corto con este tipo de distancia o donde son altamente favorecidos los flujos (Etherington, 2016;Richard & Armstrong, 2010;Wang et al., 2009); posteriormente se utilizó la función MK_ProtconnMult donde se definieron los valores para sus diferentes argumentos, por ejemplo los nodos que corresponden a las AP y las regiones o unidades de análisis que corresponden a los municipios, otro argumento importante es definir las AP transfronterizas, estas áreas son aquellas que están fuera del área de estudio pero que influyen en la conectividad de las AP que si lo están, estas fueron seleccionadas mediante un buffer de 165 km al área de estudio obtenido por la fórmula de probabilidad máxima de dispersión de las especies focales (Saura et al., 2017(Saura et al., , 2018. Una vez definidos sus argumentos se procedió a realizar el cálculo del incide ProtConn obteniendo valores de conectividad para cada municipio y para la totalidad del departamento. ...
La pérdida de conectividad del paisaje se traduce en la disminución del funcionamiento conjunto de los flujos o procesos ecológicos que sobre dicho paisaje se encuentren. En el departamento de Caquetá, al sur de Colombia, se localizan 13 áreas protegidas que cubren el 38,50% de su área, su función ecológica está dada gracias a la interacción de los elementos del sistema Andes – Amazonía. Por esto se deseó saber ¿cuál es el estado de la conectividad de las áreas protegidas del departamento de Caquetá? Dar respuesta al interrogativo fue posible gracias al cálculo de un índice de conectividad; producto del análisis y construcción de insumos cartográficos, esto a su vez permitió identificar el comportamiento de diversos elementos agrupados en cinco sistemas planetarios, demostrando así la medida en que cada elemento perturba o favorece la propagación de los flujos en el paisaje de este departamento, es decir, si son favorecidos se entiende que el paisaje se encuentra conectado. Los elementos del paisaje demostraron un comportamiento sistémico dado que se interrelacionan entre ellos y evidencian que en mayor porcentaje los flujos son favorecidos y tienen un movimiento alto y que en menor valor los flujos presentan movimientos moderados o con dificultad, esto fue reflejado en que el 99% de las áreas protegidas se encuentran conectadas, además se identificó que este valor depende casi en su totalidad de la extensión y estado de conservación de los elementos alrededor y dentro del PNN Serranía de Chiribiquete, también que el porcentaje de área protegida desconectada se localiza especialmente en los municipios de San Vicente del Caguán y en Cartagena del Chairá.
... Conservation measures at the regional scale can improve the likelihood of population persistence by focusing on monitoring and maintaining connectivity (Pimm et al. 2006;Gallego-Garcia et al. 2018). Effective management plans that aim to promote movement and gene flow at the regional scale must incorporate spatially-explicit dispersal and reproduction within the specific landscape context (Wang et al. 2009;Harrisson et al. 2012). The combination of landscape genetics and species distribution modeling (SDM) or ecological niche modeling may be invaluable for such efforts (Guisan et al. 2013;Manel and Holderegger 2013). ...
Context
Regional conservation efforts should incorporate fine scale landscape genetic and habitat suitability data for management decisions. This information permits conservation measures to be tailored to a specific landscape.
Objectives
We investigated the landscape determinants of gene flow and habitat suitability for the state-threatened Blanding’s turtle (Emydoidea blandingii) in northeastern New York (NNY). We applied the results from each to examine their complementary contributions to local connectivity and genetic structuring.
Methods
We conducted population and individual-based genetic analyses with microsatellite data to evaluate genetic structuring and landscape genetics in NNY. We coupled these genetic analyses with species distribution modeling (SDM) to estimate the extent of suitable habitat across this important region for species persistence in the state.
Results
Gene flow was strongly associated with open water and cultivated land, indicating the role open water channels play in connecting neighboring activity centers, and the propensity of females to select cultivated land to nest. Species distribution models based on Landsat-derived vegetation indices and percentage of scrub-shrub wetlands accurately identified Blanding’s turtle habitat. Connectivity estimates from our NNY focal area using landscape genetic and SDM resistance surfaces showed potential movement constraints between the two genetic clusters.
Conclusions
Land cover better explained genetic distance data than geographic distance for Blanding’s turtles in our focal area. Accurate SDMs were developed for our focal area with a small number of occurrences (< 50). Using both gene flow and habitat-informed resistance surfaces revealed localized connectivity constraints associated with each, permitting more comprehensive landscape planning.
... This approach assigns different weights to cover types surrounding focal fields to identify spatial arrangements that facilitate or discourage the flow of organisms, and thus it can improve our understanding of how landscape affects ecological processes [13]. Costdistance has been applied to measure landscape connectivity mainly in ecology [14][15][16], with some studies also on forest pests [17,18]. However, to our knowledge, this approach has been used in the field of agricultural pest control only in a study of cotton landscapes [19]. ...
The economic importance of Bactrocera oleae (Rossi) and the problems associated with insecticides make necessary new management approaches, including deeper biological knowledge and its relationship with landscape structure. Landscape complexity reduces B. oleae abundance in late summer–autumn in areas of high dominance of olive groves, but the effect of landscape struc-ture in spring and in areas less dominated by olive groves has not been studied. It is also un-known whether the insect disperses from olive groves, using other land uses as a refugee in summer. This work evaluates the effect of landscape structure on olive fruit fly abundance and movement in spring and autumn, and infestation in autumn, in central Spain, an area where the olive crop does not dominate the landscape. A cost–distance analysis is used to evaluate the movement of the fly, especially trying to know whether the insects move away from olive groves in summer. The results indicate that B. oleae abundance is consistently lower in complex landscapes with high scrubland area (CAS), patch richness (PR) and Simpson landscape diversity index (SIEI), and low olive grove area (CAO). The cost–distance analysis shows that the fly moves mainly in spring, and amongst olive groves, but there is no evidence that land uses other than olive groves serve as a summer refuge. Olive fly infestation decreased with decreasing CAO and increasing CAS and SIEI, accordingly with the effect of landscape on abundance. Thus, mixing olive groves with other land uses, which are not a source of flies, can help improve con-trol of this important pest.
... We agree and suggest that our observation can be connected to many other singular observations that facilitate understanding the natural history of this species. This species has a cryptic natural history, particularly in upland portions of occupied habitats, where it can be difficult to detect (Searcy and Shaffer 2008;Wang et al. 2009). In our case, a pond that is typically a breeding site for the California Tiger Salamander served as upland habitat because it was dry for over a year. ...
Populations of the endangered California Tiger Salamander (Ambystoma californiense) have experienced declines throughout the species range. Although the species is relatively easy to detect in its aquatic breeding habitat, little remains known about the details of the non-breeding upland habitat by this species. We found seven adult California Tiger Salamanders beneath thatch of emergent vegetation at the bottom of a constructed pond that was known to be dry for 15 mo. This represents new information on the adult phase of the use of habitat by this species, and we suggest this behavior might be more widespread, yet undetected.
... Identifying the ecological and evolutionary phenomena underlying spatial patterns of genetic variation in natural populations is of utmost importance to understanding the processes that fuel geographical diversification and speciation , Maier et al. 2019. Contemporary landscape composition (e.g., spatial distribution of suitable habitats and geographical barriers to dispersal; Manel et al. 2003) and selectively driven divergence associated with environmental heterogeneity (e.g., contrasting climates or habitats; Wang and Bradburd 2014) have been identified as important drivers of genetic differentiation among populations in numerous groups of organisms (Wang et al. 2009, Shafer and Wolf 2013, Sexton et al. 2014, González-Serna et al. 2019. However, contemporary patterns of genomic variation often reflect the cues left behind by past demographic dynamics (Lanier et al. 2015, Glover et al. 2018, Branstetter and Longino 2019, Caterino and Langton-Myers 2019. ...
Although the genetic consequences of contemporary landscape composition and range shifts driven Pleistocene climatic oscillations have been studied fairly well in alpine organisms, we know much less about how these factors have shaped the demography of taxa with broader climatic niches and distributions. Here, we use high-throughput sequencing data to study the processes underlying spatial patterns of genomic variation in Omocestus panteli (Bolívar, 1887) (Orthoptera: Acrididae), a common Iberian grasshopper distributed across numerous habitat types and a wide elevational range (from sea level to >2,000 m). Although the species is broadly distributed, our analyses support that its contemporary populations show significant genetic fragmentation that dates back to the last glacial period. Accordingly, spatially explicit testing of alternative gene flow scenarios and demographic inference analyses revealed that genetic differentiation between populations and their long-term effective population sizes are best explained by the spatial configuration of environmentally suitable habitats during the last glacial maximum (ca. 21 ka). At that time, the species experienced net demographic expansions but interspersed unsuitable areas might have disrupted gene flow and created opportunity for geographical diversification. Collectively, our analyses indicate that the genetic makeup of contemporary populations is not well explained by current environmental factors or geographical barriers to dispersal but mostly reflects genetic fragmentation during the last glacial period followed by postglacial admixture among previously isolated gene pools. Taken together, these results support that the Pleistocene ‘species pump’ model might be also useful in explaining demographic dynamics and geographical diversification in taxa characterized by broad climatic niches.
... With the continuing loss and fragmentation of wildlife habitats worldwide over the past decades, landscape connectivity-i.e., "the degree to which the landscape facilitates or impedes movement among resource patches" (Taylor et al. 1993)-has received much attention in ecological research (e.g., Merriam 1984;Taylor et al. 1993;With et al. 1997;Tischendorf and Fahrig 2000;Fletcher Jr et al. 2018;Wilkinson et al. 2018;Sullivan et al. 2019). Since organisms need to move routinely for resource exploitation (Van Dyck and Baguette 2005) and interact with different populations for reproduction (Stevens et al. 2006;Michels et al. 2001;Wang et al. 2009;Cushman and Lewis 2010;Spear et al. 2010), weakened landscape connectivity may lead to the reduction of populations, or even local extinction, of vulnerable native species (Rudnick et al. 2012). ...
Background
Connectivity is an important landscape attribute in ecological studies and conservation practices and is often expressed in terms of effective distance. If the cost of movement of an organism over a landscape is effectively represented by a raster surface, effective distances can be equated with the cost-weighted distance of least-cost paths. It is generally recognized that this measure is sensitive to the grid’s cell size, but little is known if it is always sensitive in the same way and to the same degree and if not, what makes it more (or less) sensitive. We conducted computational experiments with both synthetic and real landscape data, in which we generated and analyzed large samples of effective distances measured on cost surfaces of varying cell sizes derived from those data. The particular focus was on the statistical behavior of the ratio—referred to as ‘accuracy indicator’—of the effective distance measured on a lower-resolution cost surface to that measured on a higher-resolution cost surface.
Results
In the experiment with synthetic cost surfaces, the sample values of the accuracy indicator were generally clustered around 1, but slightly greater with the absence of linear sequences (or barriers) of high-cost or inadmissible cells and smaller with the presence of such sequences. The latter tendency was more dominant, and both tendencies became more pronounced as the difference between the spatial resolutions of the associated cost surfaces increased. When two real satellite images (of different resolutions with fairly large discrepancies) were used as the basis of cost estimation, the variation of the accuracy indicator was found to be substantially large in the vicinity (1500 m) of the source but decreases quickly with an increase in distance from it.
Conclusions
Effective distances measured on lower-resolution cost surfaces are generally highly correlated with—and useful predictors of—effective distances measured on higher-resolution cost surfaces. This relationship tends to be weakened when linear barriers to dispersal (e.g., roads and rivers) exist, but strengthened when moving away from sources of dispersal and/or when linear barriers (if any) are detected by other presumably more accessible and affordable sources such as vector line data. Thus, if benefits of high-resolution data are not likely to substantially outweigh their costs, the use of lower resolution data is worth considering as a cost-effective alternative in the application of least-cost path modeling to landscape connectivity analysis.
... Several investigators have suggested that the decline of the species is related to declines in vernal pools throughout the species' range (Stebbins and Cohen 1995;Loredo and Van Vuren 1996;Fitzpatrick and Shaffer 2004). However, there is an increased understanding of the ability of A. californiense to frequently use perennial waterbodies and seasonal cattle stock ponds as aquatic breeding habitat, which may ultimately contribute to the conservation of the species (Alvarez 2004a,b;Wang et al. 2009;Wilcox et al. 2015). Here we report on observations of additional breeding site plasticity in A. californiense which were found breeding in atypical habitats, such as perennial creeks and anthropogenic structures. ...
... The direct observation of individual movements of N. kaiseri among different breeding stream is impracticable. However, the integration of population genetics and least-cost path (LCP) analysis by a geographic information system is an alternative approach for evaluating the most likely routes (Cushman et al., 2006;Emel & Storfer, 2014;Gowen & de Smet, 2020;Querejeta et al., 2017;Spear et al., 2005;Wang et al., 2009;Yu et al., 2015). For this reason, in this study, we first investigated the population genetic structure of N. kaiseri across the whole distribution range using two mitochondrial DNA markers (NADH dehydrogenase 2 and control region). ...
Variation in landscape features plays an important role in shaping the distribution of species in natural populations. These can influence population connectivity, gene flow, genetic drift, and ultimately the genetic structure and diversity of isolated populations. In this study, we aimed to identify the impact of landscape heterogeneity on the dispersal patterns of the threatened Kaiser’s mountain newt, Neurergus kaiseri. We integrated population genetics and geospatial data to predict the rates and patterns of genetic differentiation as well as to identify potential movement corridors among populations. For this purpose, we used two mitochondrial DNA markers and combined data on genetic subdivision (θST) and least-cost path (LCP) analyses from 15 fragmented highland streams and spring-ponds representing the entire species distribution area. Five possible dispersal routes used in this study were straight-line, stepping-stone, least cost slope, stream likelihood and combination least cost slope/stream likelihood. Genetic and LCP analyses indicated that two clades in the northern and southern distribution range have experienced two differing dispersal routes. The newts identified through the northern populations with high genetic diversity have dispersed with stepping-stone movements. In contrast, the southern populations are more isolated and dispersal might be facilitated by aquatic corridors in the least cost slope. We suggest that this study allows new implications for the conservation priorities of N. kaiseri by estimating the potential dispersal activity of the species in face climate change and ongoing habitat destruction relating to human activities in the southern Zagros mountains of Iran.
... In practice, this is not usually an issue, and interpretation of the critical variables affecting dispersal and genetic connectivity should be based on the combination of factors present in any given spatial lo- Given these issues of data interpretation, we look forward to continued development and refinement of landscape genetics methods and models. While the field of landscape genetics was conceived from simpler methods, such as least-cost paths (Cushman et al., 2006;Freedman et al., 2010;Richardson et al., 2016;Trumbo et al., 2013Trumbo et al., , 2021van Strien et al., 2012;Wang et al., 2009;Zancolli et al., 2014) and mantel and partial mantel tests (Andrew et al., 2012;Hecht et al., 2015;Krohn et al., 2019;Richardson, 2012;Roffler et al., 2016), it is rapidly gaining ground with more sophisticated spatial models that incorporate increasing complexity in the relationships among environmental variables. resistancega represents one area of rapid growth in model development (Peterman, 2018;Peterman et al., 2019;Winiarski et al., 2020). ...
The field of landscape genetics relates habitat features and genetic information to infer dispersal and genetic connectivity between populations or individuals distributed across a landscape. Such studies usually focus on a small portion of a species range, and the degree to which these geographically restricted results can be extrapolated to different areas of a species range remains poorly understood. Studies that have focused on spatial replication in landscape genetics processes either evaluate a small number of sites, are informed by a small set of genetic markers, analyze only a small subset of environmental variables, or implement models that do not fully explore parameter space. Here, we used a broadly distributed ectothermic lizard (Sceloporus occidentalis, Western Fence lizard) as a model species to evaluate the full role of topography, climate, vegetation, and roads on dispersal and genetic differentiation. We conducted landscape genetics analyses in five areas within the Sierra Nevada mountain range, using thousands of ddRAD genetic markers distributed across the genome, implemented in the landscape genetics program ResistanceGA. Across study areas, we found a great deal of consistency in the variables impacting genetic connectivity, but also noted site-specific differences in the factors in each study area. High-elevation colder areas were consistently found to be barriers to gene flow, as were areas of high ruggedness and slope. High temperature seasonality and high precipitation during the winter wet season also presented a substantial barrier to gene flow in a majority of study areas. The effect of other landscape variables on genetic differentiation was more idiosyncratic and depended on specific attributes at each site. Vegetation type was found to substantially affect gene flow only in the southernmost Sequoia site, likely due to a higher proportion of desert habitat here, thereby fragmenting habitats that have lower costs to dispersal. The effect of roads also varied between sites and may be related to differences in road usage and amount of traffic in each area. Across study areas, canyons were always substantially implicated as facilitators to dispersal and key features linking populations and maintaining genetic connectivity across landscapes. We emphasize that spatial data layers are complex and multidimensional, and a careful consideration of associations between variables is vital to form sound conclusions about the critical factors affecting dispersal and genetic connectivity across space.
... This approach assigns different weights to cover types surrounding focal elds to identify spatial arrangements that facilitate or discourage the ow of organisms, and thus can improve our understanding of how landscapes affect ecological processes (Haan et al. 2020). Cost-distance has been applied to measure landscape connectivity mainly in conservation ecology (Wang et al. 2009;Zeller et al. 2012), with some studies on forest pests (Koch and Smith 2008;Roversi et al. 2013). However, to our knowledge, this approach has been used in the eld of agricultural pest control only in one study in cotton landscapes (Perović et al. 2010). ...
The olive fruit fly, Bactrocera oleae (Rossi) (Diptera: Tephritidae), is a key pest of olive groves. Because of its economic importance and problems associated with chemical control, new approaches to reduce the damage caused by this pest and a deeper knowledge of the biology of the insect and the relationship of landscape structure to different biological parameters are needed. B. oleae can fly long distances and its ability to move within the landscape can determine the damage caused to olive groves. This work evaluates the effect of landscape structure on olive fruit fly abundance, movements and damage at three times of year—spring, early autumn and late autumn—in central Spain. This area is less dominated by olive groves than southern Spain, where the relationship between olive grove area and B. oleae abundance is already known. A cost-distance analysis is used to evaluate the landscape effect on the movement of the fly along the crop cycle. The olive grove area is the landscape composition factor with the greatest effect on the parameters studied, with a decrease in B. oleae abundance in a more complex landscape during spring and early autumn. The cost-distance analysis shows that the olive fruit fly moves mainly in spring, and amongst olive groves. There is no evidence that land uses other than olive groves serve as a summer refuge for B.oleae in the studied landscape context. Olive grove area and land use diversity index had significant effects on olive damage in more than one year.
... According to the concept of functional landscape connectivity (Tischendorf and Fahrig 2000), land use types have different friction values which result in different levels of dispersal inhibition (Zeller et al. 2012). This concept has been mainly applied for organisms of conservation interest and much less for pest insects (Bunn et al. 2000;Ferreras 2001;Wang et al. 2009). In general, it is assumed that land-use types corresponding to the species habitat facilitate dispersal, but several studies indicate the opposite (Lutscher and Musgrave 2017;Crone et al. 2019), because individuals might prefer to stay in favorable habitat patches while moving faster through unfavorable ones. ...
ContextThe pine wood nematode (PWN) is an invasive species which was introduced into Europe in 1999. It represents a major economic and ecological threat to European forests. In Europe, the maritime pine is the main host and Monochamus galloprovinciallis is its only vector.Objectives
Our goal was to analyze the effect of landscape heterogeneity on the vector’s dispersal. We further aimed at developing a new method to locate the origin of insects captured in a systematic network of pheromone traps.MethodsA mark-release-recapture experiment was carried out in a heterogeneous landscape combining maritime pine plantations, clear-cuts and isolated patches of broadleaved and mixed forests in the southwest of France. Least-cost path analysis was used to model dispersal trajectories and assign friction values to each land-use type in the landscape. We used the trap’s geographical coordinates, capture levels and mean friction values of neighbouring patches to calculate a weighed barycentre and the position of the release of marked beetles.ResultsLeast Cost Path modelling revealed the vector’s tendency to avoid habitat patches such as mixed or deciduous forests and not avoid clear-cuts. The weighted barycentre method was greatly improved when the friction values of the trap’s surrounding land-uses were used.Conclusions
Our study demonstrates the value of applying landscape ecology concepts and methods to improve our understanding and prediction of pest invasion processes. A practical application is the design of systematic grids of pheromone traps to locate the infection focus from which PWN vectors originate in a newly colonized area.
... Even species with high dispersal capacity, such as the Stripped Toad (Rhinella ornata), are at risk due to their small populations within these landscapes (Dixo, Metzger, Morgante, & Zamudio, 2009). The negative effect of land-use change on population genetics could be mitigated if patches of natural vegetation are maintained in the landscapes, which facilitates dispersion as has been shown for populations of the California Tiger Salamander (Ambystoma californiense; Wang, Savage, & Shaffer, 2009). Thus, conservation strategies for A. ordinarium populations should consider the maintenance of natural vegetation patches across human modified landscapes, while maintaining optimal levels of water conductivity and dissolved oxygen of streams within the geographic range where the species can thrive. ...
Numerous amphibian species are at risk of extinction worldwide. Therefore, reliable estimations of the distribution and abundance of these species are necessary for their conservation. Generally, amphibians are difficult to detect in the wild, which compromises the accuracy of long-term population monitoring and management. Occupancy models are useful tools to assess how environmental variables, at a local and at a landscape scale, affect the distribution and abundance of organisms taking into account species imperfect detectability. In this study, we evaluated with an environmental multiscale approach the seasonal variation of the occupation area of the threatened salamander, Ambystoma ordinarium along its distribution range. We obtained readings in 60 streams of physicochemical variables associated with habitat quality and landscape features. We found that detection and occupation probability of A. ordinarium are seasonally associated with different environmental variables. During the dry season, detectability was positively associated with temperature and stream depth, whereas occupancy was positively associated with the proportion of crops in the landscape and stream elevation. In the rainy season , the detection probability was not explained by any variable considered, and occupancy was negatively associated with stream's electrical conductivity and dissolved oxygen. Based on the estimation of occupied sites, we showed that A. ordinarium presents a more restricted distribution range than previously projected. Therefore, our results reveal the importance of evaluating the accuracy of distribution estimates for the conservation of threatened species as A. ordinarium.
... The workflow adopted in the FLM is based on the least-cost-path (LCP) algorithm, which calculates a weighted least-cost distance between two pixels in a raster image [59,60]. LCP is commonly used for habitat analysis [61,62] and urban planning [63,64]. However, it is also effective for delineating the path of linear features that are bound by distinctively higher or lower values in a raster image, such as drainage networks [65]. ...
Forest land-use planning and restoration requires effective tools for mapping and attributing linear disturbances such as roads, trails, and asset corridors over large areas. Most existing linear-feature databases are generated by heads-up digitizing. While suitable for cartographic purposes, these datasets often lack the fine spatial details and multiple attributes required for more demanding analytical applications. To address this need, we developed the Forest Line Mapper (FLM), a semi-automated software tool for mapping and attributing linear features using LiDAR-derived canopy height models. Accuracy assessments conducted in the boreal forest of Alberta, Canada showed that the FLM reliably predicts both the center line (polyline) and footprint (extent polygons) of a variety of linear-feature types including roads, pipelines, seismic lines, and power lines. Our analysis showed that FLM outputs were consistently more accurate than publicly available datasets produced by human photo-interpreters, and that the tool can be reliably deployed across large application areas. In addition to accurately delineating linear features, the FLM generates a variety of spatial attributes associated with line geometry and vegetation characteristics from input canopy height data. Our statistical evaluation indicates that spatial attributes generated by the FLM may be useful for studying and classifying linear features based on disturbance type and ground conditions. The FLM is open-source and freely available and is aimed to assist researchers and land managers working in forested environments everywhere.
... There are no conceptual or statistical flaws with this approach, although it typically results in only a limited exploration of possible parameter space. To expand the explored parameter space, various optimization frameworks have been developed(Castillo et al., 2014;Dudaniec et al., 2016;Peterman, 2018;Shirk et al., 2010;Wang et al., 2009). In all these approaches, alternate resistance surfaces are created by transforming and combining individual surfaces. ...
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.
... Before the development of GIS and its application to population genetics, straight line distances between populations were commonly used as a metric through which to analyze genetic similarity of different groups. However, researchers were quick to recognize that the landscape of a particular region has a pronounced effect on the ability of two groups to interact and comingle [5]. Researchers pointed out that straight line distances were not an accurate representation of the geospatial reality in which biological organisms find themselves. ...
Least Cost Path (LCP) analysis allows a user to define a cost parameter through which cost of movement can be assessed using Geographical Information Systems (GIS). These analyses are commonly used to construct theoretical movement through a landscape, which has been useful for creating hypotheses concerning prehistoric archaeology and landscape genomics. However, LCP analysis is commonly employed without testing the generated LCP(s), complicating its usefulness as a methodological tool. This paper proposes a model for analyzing movement in ArcGIS by using topography data to calculate slope. This slope data is then then used to calculate LCPs based on travel time and kilocalorie expenditure. LCPs were constructed in the Nature Preserve at Binghamton University, a 182-acre area that consists of wetland and mountainous terrain, and a Fitbit® Surge activity monitor was used to test the accuracy of the model's predictions. Paired sample t-tests show a lack of significant difference between calculated and walked time in our analysis (p = .953), suggesting that our model can estimate travel time between two points based solely on slope of the region. Paired sample t-tests also show a lack of significant difference between calculated and observed kilocalorie expenditure (p = .930), suggesting that our model is also capable of estimating kilocalorie expenditure associated with movement between two points. Finally, paired sample t-tests confirm that straight line distances do not reflect real movement through a terrain (p = .009), highlighting the need for alternate measures of movement when analyzing the effects of local landscape on movement. Our current model shows strength in its estimations of travel time and kilocalorie expenditure based on topography of a region-future iterations of the model need to establish the statistical similarity between our model's estimations and recorded values for walking time and kilocalorie expenditure.
... It is made available under a The copyright holder for this preprint this version posted August 6, 2020. . https://doi.org/10.1101/2020.08.06.239137 doi: bioRxiv preprint samples (Wang et al., 2009). We calculated LCP using the 'gdistance' package for R and the 270 same resistance matrix used for the previous analysis. ...
The complex topography of the species-rich northern Andes creates heterogeneous environmental landscapes that are hypothesized to have promoted population fragmentation and diversification by vicariance, gradients and/or the adaptation of species. Previous phylogenetic work on the Palm Rocket Frog (Anura: Aromobatidae: Rheobates spp.), endemic to mid-elevation forests of Colombia, suggested valleys were important in promoting divergence between lineages. In this study, we use a spatially, multi-locus population genetic approach of two mitochondrial and four nuclear genes from 25 samples representing the complete geographic range of the genus to delimit species and test for landscape effects on genetic divergence within Rheobates. We tested three landscape genetic models: isolation by distance, isolation by resistance, and isolation by environment. Bayesian species delimitation (BPP) and a Poisson Tree Process (PTP) model both recovered five highly divergent genetic lineages within Rheobates, rather than the three inferred in a previous study. We found that an isolation by environment provided the only variable significantly correlated with genetic distances for both mitochondrial and nuclear genes, suggesting that local adaptation may have a role driving the genetic divergence within this genus of frogs. Thus, genetic divergence in Rheobates may be driven by the local environments where these frogs live, even more so that by the environmental characteristics of the intervening regions among populations (i.e., geographic barriers).
... Thus, these models might be suboptimal for describing landscape complementation in complex landscape compared to the GSM and the PMM. LCP and CD models are routinely and successfully applied to modeling gene flow across heterogeneous landscapes (e.g., Wang et al., 2009). While the use of a HSM to derive a resistance or conductance surface is not new (McRae, 2006;McRae et al., 2008), it differs from the more commonly used assignment of resistance values to landscape features, such as discrete cover types, and we cannot exclude the possibility that a different assignment of resistance values would improve the performance of LCP or CD models. ...
Rapid progression of human socio-economic activities has altered the structure and function of natural landscapes. Species that rely on multiple, complementary habitat types (i.e., landscape complementation) to complete their life cycle may be especially at risk. However, such landscape complementation has received little attention in the context of landscape connectivity modeling. A previous study on flower longhorn beetles (Cerambycidae: Lepturinae) integrated landscape complementation into a continuous habitat suitability ‘surface’, which was then used to quantify landscape connectivity between pairs of sampling sites using gradient-surface metrics. This connectivity model was validated with molecular genetic data collected for the banded longhorn beetle (Typocerus v. velutinus) in Indiana, United States. However, this approach has not been compared to alternative models in a landscape genetics context. Here, we used a discrete land use/land cover map to calculate landscape metrics related to landscape complementation based on a patch mosaic model (PMM) as an alternative to the previously published, continuous habitat suitability model (HSM). We evaluated the HSM surface with gradient surface metrics (GSM) and with two resistance-based models (RBM) based on least cost path (LCP) and commute distance (CD), in addition to an isolation-by-distance (IBD) model based on Euclidean distance. We compared the ability of these competing models of connectivity to explain pairwise genetic distances (RST) previously calculated from ten microsatellite genotypes of 454 beetles collected from 17 sites across Indiana, United States. Model selection with maximum likelihood population effects (MLPE) models found that GSM were most effective at explaining pairwise genetic distances as a proxy for gene flow across the landscape, followed by the landscape metrics calculated from the PMM, whereas the LCP model performed worse than both the CD and the isolation by distance model. We argue that the analysis of a continuous HSM with GSM might perform better because of their combined ability to effectively represent and quantify the continuous degree of landscape complementation (i.e., availability of complementary habitats in vicinity) found at and in-between sites, on which these beetles depend. Our findings may inform future studies that seek to model habitat connectivity in complex heterogeneous landscapes as natural habitats continue to become more fragmented in the Anthropocene.
... Phylogeographic approach integrated with ensemble species distribution modelling (SDM) analysis provides comprehension of how paleo-environmental alterations in landscape and climate have governed species distributions and demographical history (Avise, 2000;Wang and Yan, 2014). In addition, landscape genetics has been widely used for modelling the dispersal corridors of species (Wang et al., 2009;Yu et al., 2017). Least cost path (LCP) uses landscape genetic approach coupled with species distribution models and population genetic data to recognize population genetic connectivity in a spatially explicit framework (Chan et al., 2011;Yu et al., 2015). ...
The genetic architecture within a species in the Himalaya-Hengduan Mountains (HHM) region was considered as the consolidated consequence of historical orogenesis and climatic oscillations. The visualization of dispersal corridors as the function of population genetic connectivity became crucial to elucidate the spatiotemporal dynamics of organisms. However, geodiversity and physical barriers created by paleo geo-climatic events acted vigorously to impact notable alterations in the phylogeographic pattern and dispersal corridors. Therefore, to achieve detailed phylogeography, locate dispersal corridors and estimate genetic connectivity, we integrated phylogeography with species distribution modelling and least cost path of Mirabilis himalaica (Edgew.) Heimerl in the HHM. We amplified four cpDNA regions (petL-psbE, rps16-trnK, rps16 intron, trnS-trnG), and a low copy nuclear gene (G3pdh) from 241 individuals of 29 populations. SAMOVA, genealogical relationships, and phylogenetic analysis revealed four spatially structured phylogroups for M. himalaica with the onset of diversification in late Pliocene (c. 3.64 Ma). No recent demographic growth was supported by results of neutrality tests, mismatch distribution analysis and Bayesian skyline plot. Paleo-distribution modelling revealed the range dynamics of M. himalaica to be highly sensitive to geo-climatic change with limited long-distance dispersal ability and potential evolutionary adaptation. Furthermore, river drainage systems, valleys and mountain gorges were identified as the corridors for population genetic connectivity among the populations. It is concluded that recent intense mountain uplift and subsequent climatic alterations including monsoonal changes since Pliocene or early Pleistocene formulated fragmented habitats and diverse ecology that governed the habitat connectivity, evolutionary and demographic history of M. himalaica. The integrative genetic and geospatial method would bring new implications for the evolutionary process and conservation priority of HHM endemic species.
... For example, genetic markers have been used to estimate both contemporary and historical effective population size [160], assess sex-biased dispersal [161,162,163], identify population bottlenecks [164], and characterize metapopulation dynamics [165]. Genetics can be used to quantify actual functional connectivity either directly or indirectly, and thus provides the means to test hypotheses about how aspects of the intervening landscape matrix support or inhibit dispersal and gene flow [166,167,168]. The results of landscape genetics studies have potentially important applications to conservation biology and land management practices [156]. ...
This article reviews some of the main areas of landscape ecology. It includes the ideas and views of authors to the knowledge and understanding of the interplay between spatial heterogeneity and ecological processes, both in terrestrial and aquatic habitats at their varying scales. Moreover, the ecological consequences of landscape disturbance and landscape fragmentation are reviewed. Man and fire are discussed as the main agents of alternation of the natural landscape both at local and global scales. Studies on landscape restoration and management that emphasize the integration of various stakeholders for effective landscape restoration and management for sustainable biodiversity and socioeconomic benefits to man are as well reviewed. This paper further highlights the integration of the innovative remote sensing and Geographic Information System (GIS) technologies in the generation and analyses of spatial data. The current integration of population genetics in landscape ecology is also visited in this review.
... Vimercati, unpublished data). Analogously, experimental and landscape genetic data obtained in other species of frogs (Cline & Hunter, 2016;Nowakowski et al., 2015;Stevens et al., 2004) and salamanders (Wang, Savage, & Bradley Shaffer, 2009) found that dispersal costs were highest in grassland habitats. Landscapes characterised by permanent grass cover (e.g. ...
• Environmental DNA (eDNA) is becoming an essential tool for detecting aquatic invasive species and investigating their spread. Surprisingly, this technique has been very rarely used to investigate habitat selection, site occupancy, and colonisation despite its higher capacity to detect many species.
• The African clawed frog (Xenopus laevis) is a principally aquatic amphibian introduced in several continents from South Africa. In western France, no recent systematic survey of the invasion range has been attempted, mainly because of the elusive nature of the species. Furthermore, the influence of landscape features on invasion has never been investigated, even if adults and juveniles are known to disperse overland and along river networks.
• Using presence–absence data generated by an eDNA survey conducted across the known invasion front of X. laevis in western France, we aimed to determine whether and how the landscape features surrounding a pond influence the probability that a pond is colonised.
• Xenopus laevis was detected well beyond the formerly known invasive distribution and at the outward end of some transects, suggesting that we did not reach the actual invasion front in these parts of the range. The landscape variables that best predicted the presence of X. laevis in a pond were topographic wetness index and grass cover within a buffer of 250 m.
• Higher values of both topographic wetness index and grass cover were negatively related to the occurrence probability. The effects of these two variables more likely to reflect dispersal behaviour than habitat preferences at the pond scale.
• By combining the high detection probability of eDNA survey techniques and a landscape ecology approach, we may gain valuable insight into the colonisation process of water bodies by elusive invasive species. Such information is crucial to prevent access to specific sites and locate invasion front areas where connectivity can be disrupted, thus increasing the effectiveness of management countermeasures.
Characterizing spatial patterns in allele frequencies is fundamental to evolutionary biology because these patterns contain evidence of underlying processes. However, the spatial scales at which gene flow, changing selection, and drift act are often unknown. Many of these processes can operate inconsistently across space, causing nonstationary patterns. We present a wavelet approach to characterize spatial pattern in allele frequency that helps solve these problems. We show how our approach can characterize spatial patterns in relatedness at multiple spatial scales, i.e. a multilocus wavelet genetic dissimilarity. We also develop wavelet tests of spatial differentiation in allele frequency and quantitative trait loci (QTL). With simulation, we illustrate these methods under different scenarios. We also apply our approach to natural populations of Arabidopsis thaliana to characterize population structure and identify locally adapted loci across scales. We find, for example, that Arabidopsis flowering time QTL show significantly elevated genetic differentiation at 300–1,300 km scales. Wavelet transforms of allele frequencies offer a flexible way to reveal geographic patterns and underlying evolutionary processes.
The status of many amphibian populations remains unclear due to undetected declines driven by disease and difficulties in obtaining accurate population estimates. Here, we used genetic data (Single Nucleotide Polymorphisms) to investigate Australia’s poorly understood Littlejohn’s treefrog , Litoria littlejohni across its fragmented distribution. We detected five identifiable genetic clusters, with moderate to strong genetic isolation. At a regional scale, population isolation likely of population crashes, resulting in small populations with evidence of founder effects. Moderate genetic isolation was detected among populations on the Woronora Plateau despite short distances between population clusters. Evidence of recent declines was apparent in three populations that had very small effective population size, reduced genetic diversity and high inbreeding values. The rates of inbreeding detected in these populations combined with their small size leave these populations at elevated risk of extinction. The Cordeaux Cluster was identified as the most robust population as it was the largest and most genetically diverse. This study exemplifies the value of employing genetic methods to study rare, cryptic species. Despite low recapture rates using traditional capture-recapture demographic methods, we were able to derive population estimates, describe patterns of gene flow, and demonstrate the need for urgent conservation management.
Theory predicts that the distribution of genetic diversity in a landscape is strongly dependent on the connectivity of the metapopulation and the dispersal of individuals between patches. However, the influence of explicit spatial configurations such as dendritic landscapes on the genetic diversity of metapopulations is still understudied, and theoretical corroborations of empirical patterns are largely lacking. Here, we used microsatellite data and stochastic simulations of two metapopulations of freshwater amphipods in a 28,000 km² riverine network to study the influence of spatial connectivity and dispersal strategies on the spatial distribution of their genetic diversity. We found a significant imprint of the effects of riverine network connectivity on the local and global genetic diversity of both amphipod species. Data from 95 sites showed that allelic richness significantly increased towards more central nodes of the network. This was also seen for observed heterozygosity, yet not for expected heterozygosity. Genetic differentiation increased with instream distance. In simulation models, depending on the mutational model assumed, upstream movement probability and dispersal rate, respectively, emerged as key factors explaining the empirically observed distribution of local genetic diversity and genetic differentiation. Surprisingly, the role of site-specific carrying capacities, for example by assuming a direct dependency of population size on local river size, was less clear-cut: while our best fitting model scenario included this feature, over all simulations, scaling of carrying capacities did not increase data-model fit. This highlights the importance of dispersal behaviour along spatial networks in shaping population genetic diversity.
Background: Species interactions may affect spatial dynamics when the movement of one species is determined by the presence of another one. The most direct species-dependence of dispersal is vectored, usually cross-kingdom, movement of immobile parasites, diseases or seeds by mobile animals. Joint movements of species should, however, not be vectored by definition, as even mobile species are predicted to move together when they are tightly connected in symbiont communities.
Methods: We studied concerted movements in a diverse and heterogeneous community of arthropods (myrmecophiles) associated with red wood ants. We questioned whether joint-movement strategies eventually determine and speed-up community succession.
Results: We recorded an astonishingly high number of obligate myrmecophiles outside red wood ant nests. They preferentially co-moved with the host ants as the highest densities were found in locations with the highest density of foraging red wood ants, such as along the network of ant trails. These observations suggest that myrmecophiles resort to the host to move away from the nest, and this to a much higher extent than hitherto anticipated. Interestingly, functional groups of symbionts displayed different dispersal kernels, with predatory myrmecophiles moving more frequently and further from the nest than detritivorous myrmecophiles. We discovered that myrmecophile diversity was lower in newly founded nests than in mature red wood ant nests. Most myrmecophiles, however, were able to colonize new nests fast suggesting that the heterogeneity in mobility does not affect community assembly.
Conclusions: We show that co-movement is not restricted to tight parasitic, or cross-kingdom interactions. Movement in social insect symbiont communities may be heterogeneous and functional group-dependent, but clearly affected by host movement. Ultimately, this co-movement leads to directional movement and allows a fast colonisation of new patches, but not in a predictable way. This study highlights the importance of spatial dynamics of local and regional networks in symbiont metacommunities, of which those of symbionts of social insects are prime examples.
Internationally a growing body of literature has interrogated the vulnerability, risk, resilience, and adaptation of Indigenous peoples to climate change. Key traits in this literature synthesis point to the impacts of climate change on sovereignty, culture, health, and economies that are currently being experienced by Indigenous (First Nations) communities globally. While knowledge and science of how climate change impacts are affecting Indigenous peoples can contribute to the formulation of policies, plans, designs and programs for climate change adaptation, settlement resilience planning and greenhouse gas emission reductions, little research has validated this knowledge as well as its potential. In Australia, climate change is expected to have social, economic and environmental impacts on urban Indigenous communities inhabiting coastal areas throughout south-eastern Australia. These impacts include a loss of community and environmental assets, cultural heritage sites, significant impacts on the quality of life of populations, and the establishment of favourable conditions for the spread of plant diseases, weeds and pests. Over most of south-eastern Australia, including southern Victoria and the Brisbane region, climate change is expected to lead to increased risk of heatwaves, longer drought periods, increased bushfire risk, increased risks of flood events and more frequent coastal inundation and associated impacts such as coastal erosion.
This paper defines and describes the key frameworks that inform Australian Aboriginal (and Torres Strait Islander) understandings of climate change impacts and pathways for adaptation and mitigation, including, sovereignty and self-determination, culture and cultural identity, and Aboriginals community health indicators, with an emphasis upon Geelong and Brisbane urban areas. It also provides a synthesis of climate knowledge, science, and strategies that Aboriginal communities are exploring, and their understanding of the gaps in research on these issues. A review of the literature examining the impacts of climate change on urban Aboriginal people found that there is limited research on the topic in Australia and globally. The review identified that lower socio-economic members of this group are more vulnerable to climate change compared to the general Australian population. Their adaptive capacity is low as a result of the same systemic issues confronting Aboriginal people that have led to disadvantage. As such, research on climate change adaptation positions climate change as one of the many issues facing Aboriginal people and needs to be addressed collaboratively and not in isolation. Research from other more remote regions in Australia and abroad indicate collaborative community-based approaches are needed for effective climate change vulnerability assessments and the building of individual and collective adaptive capacity. The evidence emerging from this research clearly demonstrates that Aboriginal people’s consideration of the future, even with the overlay of climate change and the requirements for serious considerations of adaptation, are significantly influenced and dominated by economic aspirations which are seen as fundamental survival strategies for their communities.
Theory predicts that the distribution of genetic diversity in a landscape is strongly dependent on the connectivity of the metapopulation and the dispersal of individuals between patches. However, the influence of explicit spatial configurations such as dendritic landscapes on the genetic diversity and structure of metapopulations is still understudied, and theoretical corroborations of empirical patterns are largely lacking. Here, we used real-world microsatellite data and stochastic simulations of two metapopulations of freshwater amphipods in a 28,000 km2 riverine network to study the influence of spatial connectivity and dispersal strategies on their spatial genetic diversity and structure. We found a significant imprint of the riverine network connectivity on the genetic diversity of both amphipod species. Data from 95 sites showed that allelic richness and observed heterozygosity significantly increased towards more central nodes of the network. In simulation models, dispersal rate was suggested to be the key factor explaining the empirically observed distribution of genetic diversity. Contrary to often-claimed expectations, however, the relevance of directionality of dispersal was only minor. Surprisingly, also the consideration of site-specific carrying capacities, for example by assuming a direct dependency of population size with local river size, substantially decreased the model fit to empirical data. This highlights that directional dispersal and the spatial arrangement of population sizes may have a smaller relevance in shaping population genetic diversity of riverine organisms than previously thought, and that dispersal along the river network is the single-most important determinant of population genetic diversity.
Populations of the western spadefoot (Spea hammondii) in southern California occur in one of the most urbanized and fragmented landscapes on the planet and have lost up to 80% of their native habitat. Orange County is one of the last strongholds for this pond-breeding amphibian in the region, and ongoing restoration efforts targeting S. hammondii have involved habitat protection and the construction of artificial breeding ponds. These efforts have successfully increased breeding activity, but genetic characterization of the populations, including estimates of effective population size and admixture between the gene pools of constructed artificial and natural ponds, has never been undertaken. Using thousands of genome-wide single-nucleotide polymorphisms, we characterized the population structure, genetic diversity, and genetic connectivity of spadefoots in Orange County to guide ongoing and future management efforts. We identified at least two, and possibly three major genetic clusters, with additional substructure within clusters indicating that individual ponds are often genetically distinct. Estimates of landscape resistance suggest that ponds on either side of the Los Angeles Basin were likely interconnected historically but intense urban development has rendered them essentially isolated, and the resulting risk of interruption to natural metapopulation dynamics appears to be high. Resistance surfaces show that the existing artificial ponds were well-placed and connected to natural populations by low-resistance corridors. Toad samples from all ponds (natural and artificial) returned extremely low estimates of effective population size, possibly due to a bottleneck caused by a recent multi-year drought. Management efforts should focus on maintaining gene flow among natural and artificial ponds by both assisted migration and construction of new ponds to bolster the existing pond network in the region.
Roads fragment landscapes and can cause the loss of metapopulation dynamics in threatened species, but as relatively new landscape features, few studies have had the statistical power to genetically examine road effects. We used DNA sequence data from thousands of nuclear loci to characterize the population structure of New York-endangered Eastern tiger salamanders (Ambystoma tigrinum) on Long Island and quantify the impacts of roads on population fragmentation. We uncovered highly genetically structured populations over an extremely small spatial scale (approximately 40 km ² ) in an increasingly human-modified landscape. Geographic distance and the presence of roads between ponds are both strong predictors of genetic divergence, suggesting that both natural and anthropogenic factors are responsible for the observed patterns of genetic variation. Our study demonstrates the value of genomic approaches in molecular ecology, as these patterns did not emerge in an earlier study of the same system using microsatellite loci. Ponds supported small effective population sizes, and pond surface area showed a strong positive correlation with salamander population size. When combined with the high degree of structuring in this heavily modified landscape, our study indicates that these endangered amphibians require management at the individual pond, or pond cluster, level. Particular efforts should be made to preserve large vernal pools, which harbor the greatest genetic diversity, and their surrounding upland habitat. Contiguous upland landscapes between ponds that facilitate natural metapopulation connectivity and demographic rescue from future local extirpations should also be protected.
Examining how the landscape may influence gene flow is at the forefront of understanding population differentiation and adaptation. Such understanding is crucial in light of ongoing environmental changes and the elevated risk of ecosystems alteration. In particular, knowledge of how humans may influence the structure of populations is imperative to allow for informed decisions in management and conservation as well as to gain a better understanding of anthropogenic impacts on the interplay between gene flow, genetic drift and selection. Here we use genome-wide molecular markers to characterize the population genetic structure and connectivity of Ipomoea purpurea , a noxious invasive weed. We likewise assess the interaction between natural and human-driven influences on genetic differentiation among populations. Our analyses find that human population density is an important predictor of pairwise population differentiation, suggesting that the agricultural and/or horticultural trade may be involved in maintaining some level of connectivity across distant agricultural fields. Climatic variation appears as an additional predictor of genetic connectivity in this species. We discuss the implications of these results and highlight future research needed to disentangle the mechanistic processes underlying population connectivity of weeds.
Understanding how landscape features affect gene flow has implications for numerous fields including molecular and evolutionary ecology. Despite this, modeling landscape resistance surfaces has remained a significant challenge. The R package ResistanceGA was developed to provide a framework for optimizing landscape resistance surfaces. In this study, we assessed ResistanceGA’s ability to recover the true resistance surface under a variety of scenarios, including when the underlying surface: (i) had different levels of spatial autocorrelation and (ii) was transformed into a resistance surface using different functional transformations. These scenarios were evaluated with regard to varying sample size and varying levels of variance in the measure of genetic distance. We also assessed the ability of ResistanceGA to identify the true resistance surface among alternative correlated surfaces. In univariate simulations, correlation between the true and optimized resistance surfaces remained high with increased variance in genetic distance, but only when sample size was moderate to high (≥50). Model selection error was also driven by sample size with low type I error when simulations had moderate to high sample sizes, even with moderate to high variance in genetic distance and correlated alternative surfaces. ResistanceGA also performed well in multivariate simulations, but had more difficulty identifying the true data generating surfaces when genetic data was simulated using an agent‐based approach (especially with individual based genetic data). Overall, our simulations highlight the ability of ResistanceGA to accurately optimize resistance surfaces but also underscore challenges in optimizing landscape resistance surfaces, especially with highly stochastic individual based data.
In the current era, habitat degradation and fragmentations are a severe threat to the survival of the species in natural habitats. It is caused by ever-growing anthropogenic activities leading to an unprecedented rate of climate change. The red panda as an endangered species is no exception. However, limited studies have been done in the context of the spatial distribution of habitats for red panda and their habitat connectivity in Sakteng Wildlife Sanctuary. Lack of such information remains a challenge while implementing effective and holistic conservation initiatives. Therefore, this study attempts to identify the distribution of potential habitats and their connectivity under different climate scenarios using the maxent and linkage mapper algorithms respectively. The model predicted 260km2 of potential habitat (fundamental niche) under the current climate scenario which is unequally distributed across Merak (54.5%), Sakteng (33.4%) and Joenkhar (12.2%) ranges connected by a least-cost corridor (length µ= 2.91 km) with several pinch points in it. Out of the total predicted habitat, more than 75% falls outside the designated core zones where the likelihood of anthropogenic disturbance is relatively high. With climate change, it is predicted that there will be an expansion in suitable habitat (up to ca. 26.5 percent) towards relatively higher elevation. However, predicted expansion is likely to make red panda more vulnerable to disturbances from seminomadic communities who practice extensive grazing in the higher elevation during the summer season. Climate change is predicted to increase the number of habitat fragmentations (up to ca. 13%) and linkages (up to ca. 29%). However; there won't be much impact on the quality and functionality of the predicted connectivity, except change in the centrality scores of few habitats. This indicates that connectivity with current climate scenarios will potentially facilitate the movement of red panda and will be also useful in the event of future climate change. Therefore, the current conservation initiatives should not be restricted to only habitats where the red panda occurs today but should be also extended to predicted future potential habitats. Such initiatives would enhance the capability of the red panda to adapt to future climate change; ensuring their long term persistence.
Landscape connectivity refers to the functional relationship among habitat patches, owing to the spatial contagion of habitat and the movement responses of organisms to landscape structure. Heterogeneous landscapes provide a particular challenge for modelling population-level responses to habitat fragmentation, because individuals may be utilizing multiple habitats to varying degrees across the landscape. We apply neutral landscape models to understand how species' habitat affinities interacted with landscape structure (i.e., habitat abundance, distribution, and quality as measured by carrying capacity) to affect the redistribution of individuals. Two types of neutral models are presented: random maps, in which the distribution of habitat is spatially independent, and fractal maps, in which habitat exhibits an intermediate level of spatial dependence. The neutral landscapes comprised varying proportions of three habitat types, for which species exhibited a preference gradient (high, medium, low). We performed a series of simulation experiments as a factorial design of parameter states to tease apart the underlying factors responsible for population distributional patterns (random vs clumped) in spatially complex mosaics. Landscape connectivity is a threshold phenomenon, in which even a minimal loss of habitat near the critical threshold (p_c) is likely to disconnect the landscape, and which may have consequences for population distributions. The exact value of p_c depends upon the spatial arrangement of habitat; fractal landscapes exhibited connectivity across a greater range of habitat abundance (p) than random maps (fractal p_c = 0.29-0.50, random p_c >= 0.59). Although the spatial arrangement of habitat (random vs fractal) was the most important determinant of population distributional patterns, different landscape factors were important in structuring populations in the two types of maps. The relative abundance of habitat had the greatest effect on populations in random landscapes, whereas scale-dependent patterns were evident in fractal landscapes. At fine scales, population dispersion was determined by habitat abundance in both random and fractal maps, although populations were more aggregated (as measured by Morisita's Index, I_m) at this scale in random landscapes. But at coarse scales on fractal maps, population distribution was primarily influenced by species' habitat affinities. Assessment of the independent effects of habitat affinity and habitat carrying capacity on population distributions revealed that the differential interaction of species with landscape structure (i.e., different residence probabilities in each habitat type) was the primary determinant of distributional patterns. Neutral landscape models thus provide a useful tool for determining the relative importance of various components of landscape structure that affect population distributions.
In December 1991, we initiated a long-term study of the California Tiger Sala- mander (Ambystoma californiense) at a breeding pond in Monterey County, California. Because of habitat loss, this species is a candidate for federal endangered species status, but many basic features of its life history and demography have not been studied in detail. During the first seven years of this study, we captured, measured, individually marked, and released 657 breeding adults and 1895 newly metamor- phosed juveniles at the drift fence encircling this pond. We also used skeletochron- ology to investigate age structure in cohorts of breeding adults. Numbers of breed- ing adults varied by more than a factor of four among years, and annual juvenile production ranged from 121-775 metamorphs. Contrary to the results of related studies, total juvenile production was positively related to the total biomass of breed- ing females. Both skeletochronology and mark-recapture data indicate that most individuals do not reach sexual maturity until 4-5 years of age, and, although indi- vidual longevity can exceed 10 years, less than 50% of individuals returned to breed a second time. These results suggest that this breeding population was a reproduc- tive sink during the period of this study and that isolated breeding ponds may be insufficient for the long-term maintenance of viable populations of A. californiense.
The regional dynamics of pond-breeding amphibians are often discussed in the context of metapopulations, under the assumption that individual ponds support distinct subpopulations. We used a combination of indirect and direct methods to assess the spatial population structure of California tiger salamanders (Ambystoma californiense) relative to two basic requirements of metapopulation models: (1) that patches support somewhat independent populations linked by dispersal, and (2) that interpatch dispersal probabilities decline as distance increases. Over three consecutive field seasons we captured, marked, and released adult California tiger salamanders at 10 breeding ponds. We observed interpond dispersal by these experienced breeders, and also by first-time breeders marked as newly metamorphosed juveniles at one pond. Spatial autocorrelation of pond-specific demographic parameters suggests that these ponds meet both of these requirements of metapopulation theory. Direct observations of interpond movement by marked individuals support the conclusions of the autocorrelation analyses but reveal relatively high probabilities of interpond movement by both first-time and experienced breeders. We conclude that the subpopulations utilizing these ponds are too closely linked by dispersal for classical extinction–colonization metapopulation dynamics to apply. High probabilities of dispersal are predicted to constantly supply less isolated ponds in this system with dispersers such that local extinctions will be rare. Based on population genetic theory, the high probability of interpond movement is also predicted to prevent significant genetic divergence among ponds over large areas.
A new Bayesian method that uses individual multilocus genotypes to estimate rates of recent immigration (over the last several generations) among populations is presented. The method also estimates the posterior probability distributions of individual immigrant ancestries, population allele frequencies, population inbreeding coefficients, and other parameters of potential interest. The method is implemented in a computer program that relies on Markov chain Monte Carlo techniques to carry out the estimation of posterior probabilities. The program can be used with allozyme, microsatellite, RFLP, SNP, and other kinds of genotype data. We relax several assumptions of early methods for detecting recent immigrants, using genotype data; most significantly, we allow genotype frequencies to deviate from Hardy-Weinberg equilibrium proportions within populations. The program is demonstrated by applying it to two recently published microsatellite data sets for populations of the plant species Centaurea corymbosa and the gray wolf species Canis lupus. A computer simulation study suggests that the program can provide highly accurate estimates of migration rates and individual migrant ancestries, given sufficient genetic differentiation among populations and sufficient numbers of marker loci.
Bayesian methods have been proposed for estimating hidden population substructures in closely
related populations. A sampling of 64 Duroc and 170 Iberian pigs, assigned to two strains (Torbiscal and
Guadyerbas), two red varieties (Retinto, Entrepelado) and one black hairless variety (Lampiño) was genotyped
for 36 microsatellites. An optimal partition of only five clusters was estimated, when the previous assignation to
six pig groups was taken into account. The analysis without pre-assigned groups showed a more complex
partition: (i) most of the Duroc pigs were grouped into the same cluster; (ii) the cluster of greater size included
entirely the Entrepelado variety and most of the Retinto and Lampiño pigs; and (iii) the remaining seven clusters
grouped pigs from five small isolated herds or from Torbiscal and Guadyerbas closed strains. Genotypes of
MC1R coat color gene also revealed a large intercross between black and red Iberian varieties. The future
definition of conservation units in the Iberian breed should consider these results.
Keywords: Iberian, Duroc, varieties, genetic differentiation, clustering.
Both the ability to generate DNA data and the variety of analytical methods for conservation genetics are expanding at an ever-increasing pace. Analytical approaches are now possible that were unthinkable even five years ago due to limitations in computational power or the availability of DNA data, and this has vastly expanded the accuracy and types of information that may be gained from population genetic data. Here we provide a guide to recently developed methods for population genetic analysis, including identification of population structure, quantification of gene flow, and inference of demographic history. We cover both allele-frequency and sequence-based approaches, with a special focus on methods relevant to conservation genetic applications. Although classical population genetic approaches such as F
st (and its derivatives) have carried the field thus far, newer, more powerful, methods can infer much more from the data, rely on fewer assumptions, and are appropriate for conservation genetic management when precise estimates are needed.
Amphibians are frequently characterized as having limited dispersal abilities, strong site fidelity and spatially disjunct breeding habitat. As such, pond-breeding species are often alleged to form metapopulations. Amphibian species worldwide appear to be suffering population level declines caused, at least in part, by the degradation and fragmentation of habitat and the intervening areas between habitat patches. If the simplification of amphibians occupying metapopulations is accurate, then a regionally based conservation strategy, informed by metapopulation theory, is a powerful tool to estimate the isolation and extinction risk of ponds or populations. However, to date no attempt to assess the class-wide generalization of amphibian populations as metapopulations has been made. We reviewed the literature on amphibians as metapopulations (53 journal articles or theses) and amphibian dispersal (166 journal articles or theses for 53 anuran species and 37 salamander species) to evaluate whether the conditions for metapopulation structure had been tested, whether pond isolation was based only on the assumption of limited dispersal, and whether amphibian dispersal was uniformly limited. We found that in the majority of cases (74%) the assumptions of the metapopulation paradigm were not tested. Breeding patch isolation via limited dispersal and/or strong site fidelity was the most frequently implicated or tested metapopulation condition, however we found strong evidence that amphibian dispersal is not as uniformly limited as is often thought. The frequency distribution of maximum movements for anurans and salamanders was well described by an inverse power law. This relationship predicts that distances beneath 11–13 and 8–9 km, respectively, are in a range that they may receive one emigrating individual. Populations isolated by distances approaching this range are perhaps more likely to exhibit metapopulation structure than less isolated populations. Those studies that covered larger areas also tended to report longer maximum movement distances – a pattern with implications for the design of mark-recapture studies. Caution should be exercised in the application of the metapopulation approach to amphibian population conservation. Some amphibian populations are structured as metapopulations – but not all.
Both the ability to generate DNA data and the variety of analytical methods for conservation genetics are expanding at an ever-increasing pace. Analytical approaches are now possible that were unthinkable even five years ago due to limitations in computational power or the availability of DNA data, and this has vastly expanded the accuracy and types of information that may be gained from population genetic data. Here we provide a guide to recently developed methods for population genetic analysis, including identification of population structure, quantification of gene flow, and inference of demographic history. We cover both allele-frequency and sequence-based approaches, with a special focus on methods relevant to conservation genetic applications. Although classical population genetic approaches such as Fst (and its derivatives) have carried the field thus far, newer, more powerful, methods can infer much more from the data, rely on fewer assumptions, and are appropriate for conservation genetic management when precise estimates are needed.
Understanding how changes in land-use affect the distribution and abundance of organisms is an increasingly important question in landscape ecology. Amphibians may be especially prone to local extinction resulting from human-caused transformation and fragmentation of their habitats owing to the spatially and temporally dynamic nature of their populations. In this study, distributions of five species of woodland amphibians with differing life histories were surveyed along a 10 km, spatially continuous gradient of forest fragmentation in southern Connecticut, U.S.A. Redback salamanders (Plethodon cinereus) and northern spring peepers (Pseudacris c. crucifer) occupied available habitat along the gradient''s length. Wood frogs (Rana sylvatica) and spotted salamanders (Ambystoma maculatum) were absent from portions of the gradient where forest cover was reduced to below about 30%. Red-spotted newts (Notophthalmus v. viridescens) did not persist below a forest cover threshold of about 50%. Correlations between species'' biological traits and their fragmentation tolerance imply that low density, population variability, and high mobility coupled with restricted habitat needs predispose woodland amphibians to local extinction caused by habitat fragmentation. These patterns are in contrast to the widely held notion that populations of the best dispersers are those most tolerant of habitat fragmentation.
A grid-based random walk model has been developed to simulate animal dispersal, taking landscape heterogeneity and linear barriers such as roads and rivers into account. The model can be used to estimate connectivity and has been parameterized for thebadger in the central part of the Netherlands. The importance of key parameters was evaluated by means of sensitivity analysis. Results agree with field observations, and give interesting insight into the isolation of populations and potential populations. The model can be applied to obtain knowledge of dispersal processes in complex landscapes.
Recent technical advances allow straightforward access to genetic information directly drawn
from DNA. The present article highlights the suitability of high variation molecular genetic
markers, such as microsatellites, for studies relevant to amphibian conservation. Molecular
markers appear particularly useful for i) measuring local gene flow and migration, ii) assigning
individuals to their most likely population of origin, iii) measuring effective population size
through the between-generation comparison of allele frequencies, and iv) detecting past
demographic bottlenecks through allele frequency distortions. We demonstrate the use of some
newly developed analytical tools on newt (Triturus sp.) microsatellite data, discuss practical
aspects of using microsatellites for amphibians, and outline potential future research directions.
Geographic patterns of genetic diversity allow us to make inferences about population histories and the evolution of inherited disease. The statistical methods describing genetic variation in space, such as estimation of genetic variances, mapping of allele frequencies, and principal components analysis, have opened up the possibility to reconstruct demographic processes whose effects have been tested by a variety of approaches, including spatial autocorrelation, cladistic analyses, and simulations. These studies have significantly contributed to our understanding of human genetic variation; however, the molecular data that have accumulated since the mid-1980s have also created new complications. Reasons include the generally limited sample sizes, but, more generally, it is the nature of molecular variation itself that makes it necessary to develop and apply specific models and methods for the treatment of DNA data. The foreseeable diffusion of laboratory techniques for the rapid typing of many DNA markers will force us to change our approach to the study of human variation anyway, moving from the gene level toward the genome level. Because extensive variation among loci is the rule rather than the exception, an important practical tip is to be skeptical of inferences based on single-locus diversity.
We introduce a Bayesian method for estimating hidden population substructure using multilocus molecular markers and geographical information provided by the sampling design. The joint posterior distribution of the substructure and allele frequencies of the respective populations is available in an analytical form when the number of populations is small, whereas an approximation based on a Markov chain Monte Carlo simulation approach can be obtained for a moderate or large number of populations. Using the joint posterior distribution, posteriors can also be derived for any evolutionary population parameters, such as the traditional fixation indices. A major advantage compared to most earlier methods is that the number of populations is treated here as an unknown parameter. What is traditionally considered as two genetically distinct populations, either recently founded or connected by considerable gene flow, is here considered as one panmictic population with a certain probability based on marker data and prior information. Analyses of previously published data on the Moroccan argan tree (Argania spinosa) and of simulated data sets suggest that our method is capable of estimating a population substructure, while not artificially enforcing a substructure when it does not exist. The software (BAPS) used for the computations is freely available from http://www.rni.helsinki.fi/~mjs.
A new Bayesian method that uses individual multilocus genotypes to estimate rates of recent immigration (over the last several generations) among populations is presented. The method also estimates the posterior probability distributions of individual immigrant ancestries, population allele frequencies, population inbreeding coefficients, and other parameters of potential interest. The method is implemented in a computer program that relies on Markov chain Monte Carlo techniques to carry out the estimation of posterior probabilities. The program can be used with allozyme, microsatellite, RFLP, SNP, and other kinds of genotype data. We relax several assumptions of early methods for detecting recent immigrants, using genotype data; most significantly, we allow genotype frequencies to deviate from Hardy-Weinberg equilibrium proportions within populations. The program is demonstrated by applying it to two recently published microsatellite data sets for populations of the plant species Centaurea corymbosa and the gray wolf species Canis lupus. A computer simulation study suggests that the program can provide highly accurate estimates of migration rates and individual migrant ancestries, given sufficient genetic differentiation among populations and sufficient numbers of marker loci.
Genetic assignment methods use genotype likelihoods to draw inference about where individuals were or were not born, potentially allowing direct, real-time estimates of dispersal. We used simulated data sets to test the power and accuracy of Monte Carlo resampling methods in generating statistical thresholds for identifying F0 immigrants in populations with ongoing gene flow, and hence for providing direct, real-time estimates of migration rates. The identification of accurate critical values required that resampling methods preserved the linkage disequilibrium deriving from recent generations of immigrants and reflected the sampling variance present in the data set being analysed. A novel Monte Carlo resampling method taking into account these aspects was proposed and its efficiency was evaluated. Power and error were relatively insensitive to the frequency assumed for missing alleles. Power to identify F0 immigrants was improved by using large sample size (up to about 50 individuals) and by sampling all populations from which migrants may have originated. A combination of plotting genotype likelihoods and calculating mean genotype likelihood ratios (DLR) appeared to be an effective way to predict whether F0 immigrants could be identified for a particular pair of populations using a given set of markers.
A genetic study of the European tree frog, Hyla arborea, in Denmark was undertaken to examine the population structure on mainland Jutland and the island of Lolland after a period of reduction in suitable habitat and population sizes. The two regions have experienced the same rate of habitat loss but fragmentation has been more severe on Lolland. Genetic variation based on 12 polymorphic DNA microsatellites was analysed in 494 tree frogs sampled from two ponds in Jutland and 10 ponds on Lolland. A significant overall deviation from Hardy-Weinberg expectations could be attributed to three ponds, all on Lolland. This was most probably caused by an inbreeding effect reducing fitness, which was supported by the observed significant negative correlation between larva survival and mean F(IS) value and mean individual inbreeding coefficient. A significant reduction in genetic variation (bottleneck) was detected in most of the ponds on Lolland. Population-structure analysis suggested the existence of at least 11 genetically different populations, corresponding to most of the sampled population units. The results indicated that the populations were unique genetic units and could be used to illustrate the migration pattern between newly established ponds arisen either by natural colonization of tree frogs or by artificial introduction. A high degree of pond fidelity in the tree frogs was suggested. A severe fragmentation process reducing population size and fitness within some of the populations probably caused the significant reduction in genetic variation of tree frog populations on Lolland.
Changes in agricultural practices and forest fragmentation can have a dramatic effect on landscape connectivity and the dispersal of animals, potentially reducing gene flow within populations. In this study, we assessed the influence of woodland connectivity on gene flow in a traditionally forest-dwelling species--the European roe deer--in a fragmented landscape. From a sample of 648 roe deer spatially referenced within a study area of 55 x 40 km, interindividual genetic distances were calculated from genotypes at 12 polymorphic microsatellite loci. We calculated two geographical distances between each pair of individuals: the Euclidean distance (straight line) and the 'least cost distance' (the trajectory that maximizes the use of wooded corridors). We tested the correlation between genetic pairwise distances and the two types of geographical pairwise distance using Mantel tests. The correlation was better using the least cost distance, which takes into account the distribution of wooded patches, especially for females (the correlation was stronger but not significant for males). These results suggest that in a fragmented woodland area roe deer dispersal is strongly linked to wooded structures and hence that gene flow within the roe deer population is influenced by the connectivity of the landscape.
Functional connectivity is a key factor for the persistence of many specialist species in fragmented landscapes. However, connectivity estimates have rarely been validated by the observation of dispersal movements. In this study, we estimated functional connectivity of a real landscape by modelling dispersal for the endangered natterjack toad (Bufo calamita) using cost distance. Cost distance allows the evaluation of 'effective distances', which are distances corrected for the costs involved in moving between habitat patches in spatially explicit landscapes. We parameterized cost-distance models using the results of our previous experimental investigation of natterjack's movement behaviour. These model predictions (connectivity estimates from the GIS study) were then confronted to genetic-based dispersal rates between natterjack populations in the same landscape using Mantel tests. Dispersal rates between the populations were inferred from variation at six microsatellite loci. Based on these results, we conclude that matrix structure has a strong effect on dispersal rates. Moreover, we found that cost distances generated by habitat preferences explained dispersal rates better than did the Euclidian distances, or the connectivity estimate based on patch-specific resistances (patch viscosity). This study is a clear example of how landscape genetics can validate operational functional connectivity estimates.
Predicting population-level effects of landscape change depends on identifying factors that influence population connectivity in complex landscapes. However, most putative movement corridors and barriers have not been based on empirical data. In this study, we identify factors that influence connectivity by comparing patterns of genetic similarity among 146 black bears (Ursus americanus), sampled across a 3,000-km(2) study area in northern Idaho, with 110 landscape-resistance hypotheses. Genetic similarities were based on the pairwise percentage dissimilarity among all individuals based on nine microsatellite loci (average expected heterozygosity=0.79). Landscape-resistance hypotheses describe a range of potential relationships between movement cost and land cover, slope, elevation, roads, Euclidean distance, and a putative movement barrier. These hypotheses were divided into seven organizational models in which the influences of barriers, distance, and landscape features were statistically separated using partial Mantel tests. Only one of the competing organizational models was fully supported: patterns of genetic structure are primarily related to landscape gradients of land cover and elevation. The alternative landscape models, isolation by barriers and isolation by distance, are not supported. In this black bear population, gene flow is facilitated by contiguous forest cover at middle elevations.
Gene flow is an important determinant of population genetic structure, and understanding patterns of gene flow is especially meaningful in amphibians to estimate their dispersal capabilities in the face of current wetland destruction. In this study, I investigated gene flow using nine polymorphic allozyme loci in samples from 15 stream sites inhabited by the streamside salamander, Ambystoma barbouri. Among all 15 sites considered together, gene flow was generally low and populations were subdivided genetically. However, on a local level, gene flow was significantly higher between some pairs of streams and within some neighborhoods (groups of populations within 5 km of each other). Samples from all sites showed sufficient genetic subdivision to warrant salamander populations from each site to be considered as separate genetic populations. Sunfish that are major predators of larval streamside salamanders appear to act as a barrier to gene flow. This result was supported by significant genetic subdivision between two sites sampled within the same stream and separated by fish pools and two adjacent streams separated by areas with high fish densities but a geographic distance of less than a kilometer. As predicted by isolation-by-distance models, gene flow was significantly negatively correlated with geographic distance.
I examined the expected detrimental effects of restricted gene flow, a consequence of population fragmentation, through a population genetics model. The model predicts the frequency of deleterious mutations and the viability of the individuals in a set of populations, depending on the number of migrants per generation per population. When the number of migrants is lower than one per generation, a large increase in the viability of the individuals along with the number of migrants is expected. With one migrant per generation, detrimental genetic effects on population survival are expected for low population size or low population growth rate. Finally, the negative effects of restricted gene flow depend not only on the number of migrants entering the population considered but also on the number of migrants entering the populations where the migrants came from.
Resumen: Examiné los efectos nocivos del flujo restringido de genes, una consecuencia de la fragmentación poblacional, empleando un modelo de genética poblacional. El modelo predijo la frecuencia de las mutaciones nocivas y la viabilidad de los individuos de una población determinada, dependiendo del número de emigrantes por generación por población. Cuando el número de emigrantes fue menor que uno por generación, se espera un incremento grande en la viabilidad de los individuos junto con el número de emigrantes. Con un emigrante por generación, se esperan efectos genéticos nocivos en la supervivencia de la población cuando el tamaño poblacional es pequeño o cuando la tasa de crecimiento poblacional es baja. Finalmente, los efectos negativos del flujo restringido de genes no dependen solamente del número de emigrantes entrando a la población, sino también del número de emigrantes que ingresan a la población de donde los emigrantes provienen.
Populations of the amphibian Bufo calamita were sampled for genetic analysis in eleven areas distributed across its biogeographical range in Europe. Genetic diversity estimates across eight microsatellite loci showed a decline in polymorphism, numbers of alleles and heterozygosity as a function of distance from the presumed ice-age refugium in Iberia. Trials with a selection of tree-building algorithms indicated mat UPGMA of Cavalli-Sforza chord distances (Dc) generated the tree topology most easily reconciled with other biogeographical information. Genetic distance measures were also calibrated against a postglacial event from which the separation of extant populations could be estimated in real time. Dc again outperformed two other measures (Nei's standard distance, Ds, and δμ2) in producing realistic correlations with minimal variance. The genetic analysis was consistent wim die hypodiesis that B. calamita survived in a single refugium (Iberia) during the Pleistocene glaciation and indicated that it spread north and east from there during the last interstadial which commenced about 14 000 years before present (BP). Microsatellites should provide useful tools for biogeographical investigations of other species, especially with respect to patterns of population dispersal.
To predict the effects of habitat alteration on population size and viability, data describing the landscape-scale distribution of individuals are needed. Many amphibians breed in wetland habitats and spend the vast majority of their lives in nearby upland habitats. However, for most species, the spatial distribution of individuals in upland habitats is poorly understood. To estimate the upland distribution of subadult and adult California tiger sal-amanders (Ambystoma californiense), we used a novel trapping approach that allowed us to model the spatial variation in capture rates in the landscape surrounding an isolated breeding pond. As expected, we found that captures of adults declined with distance from the breeding pond. However, captures of subadults increased steadily from 10 to 400 m from the breeding site, but there were no captures at 800 m. A negative exponential function fit to the adult capture data suggested that 50%, 90%, and 95% were within 150, 490, and 620 m of the pond, respectively. For subadults, the quadratic function fit to the data similarly suggested that 95% were within 630 m of the pond, but that 85% of this life stage was concentrated between 200 and 600 m from the pond. To investigate the population-level consequences of reducing the amount of suitable upland habitat around breeding ponds, we used a stage-based stochastic population model with subadult and adult survival pa-rameters modified according to our empirical observations of upland distribution. Model simulations suggested that substantial reductions in population size are less likely if upland habitats extending at least 600 m from the pond edge are maintained. Model elasticities indicated that quasi-extinction probabilities are more sensitive to reductions in subadult and adult survivorship than reproductive parameters. These results indicate that under-standing the upland ecology of pond-breeding amphibians, especially the distribution and survivorship of subadults, may be critical for designing protective reserves and land use plans.
Landscapes can be described by two essential features: the composition and spatial arrangement of patches. We considered the roles of these basic landscape descriptors by examining how the occurrence of nine amphibian species in breeding ponds was associated with the area of forested habitat and the proximity of ponds to forested habitat. We used visual and call surveys to compare the composition of amphibian assemblages in 116 ponds adjacent to or separated from forest and surrounded by different amounts of forested land. The area of forest and pond adjacency to forest were not associated ( t = −0.13, nisolated = 64, nconnected = 52, p = 0.21), which means these factors can manifest their effects separately. We used logistic regression to test predictions about associations between each species and forest area and to test for associations with pond-forest adjacency. Seven of nine species were associated with forest area. Wood frogs ( Rana sylvatica), green frogs ( Rana clamitans), eastern newts ( Notopthalmus viridescens), spotted salamanders (Ambystoma maculatum), and salamanders of the blue-spotted/Jefferson's complex (Ambystoma laterale/A. jeffersonianum) were more likely to occupy ponds in more forested areas, whereas leopard frogs ( Rana pipiens) and American toads ( Bufo americanus) were negatively associated with forest area. Three species were associated with pond-forest adjacency. Spotted salamanders and salamanders of the blue-spotted/Jefferson's complex were more likely to occupy ponds that were adjacent to forest. In areas with little forest, leopard frogs were more likely to occur in adjacent ponds, but the reverse was true for areas with extensive forests. Our results suggest that the composition of the landscape surrounding breeding ponds is associated with the likelihood of occurrence of most of the species examined and that landscape configuration is also important for a smaller subset of species.
Declines in amphibian populations are rarely reported on the community or ecosystem level. We combined broad-scale field sampling with historical analyses of museum records to quantify amphibian declines in California’s Great Central Valley. Overall, amphibians showed an unambiguous pattern of decline, although the intensity of decline varied both geographically and taxonomically. The greatest geographical decline was detected in the counties of the Sacramento and San Joaquin Valleys. Two species, Rana aurora and Bufo boreas were identified as the most affected by decline, whereas Pseudacris regilla was the least affected. The Coast Range counties had little or no detectable decline. We provide new evidence implicating introduced predators as a primary threat. Introduced predators occur at lower elevations than native species, and our data indicate that for some native species there has been significant restriction to higher elevation sites from a formerly broader distribution. Our historical approach provides a strategy for identifying declining amphibian communities that complements more detailed, long-term monitoring programs and provides an assessment of the pattern of change that is a necessary prerequisite for the development of field experiments that test hypothesized mechanisms of change.
Reports of malformed amphibians and global amphibian declines have led to public concern, particularly because amphibians are thought to be indicator species of overall environmental health. The topic also draws scientific attention because there is no obvious, simple answer to the question of what is causing amphibian declines? Complex interactions of several anthropogenic factors are probably at work, and understanding amphibian declines may thus serve as a model for understanding species declines in general. While we have fewer answers than we would like, there are six leading hypotheses that we sort into two classes. For class I hypotheses, alien species, over-exploitation and land use change, we have a good understanding of the ecological mechanisms underlying declines; these causes have affected amphibian populations negatively for more than a century. However, the question remains as to whether the magnitude of these negative effects increased in the 1980s, as scientists began to notice a global decline of amphibians. Further, remedies for these problems are not simple. For class II hypotheses, global change (including UV radiation and global climate change), contaminants and emerging infectious diseases we have a poor, but improving understanding of how each might cause declines. Class II factors involve complex and subtle mechanistic underpinnings, with probable interactions among multiple ecological and evolutionary variables. They may also interact with class I hypotheses. Suspected mechanisms associated with class II hypotheses are relatively recent, dating from at least the middle of the 20th century. Did these causes act independently or in concert with pre-existing negative forces of class I hypotheses to increase the rate of amphibian declines to a level that drew global attention? We need more studies that connect the suspected mechanisms underlying both classes of hypotheses with quantitative changes in amphibian population sizes and species numbers. An important step forward in this task is clarifying the hypotheses and conditions under which the various causes operate alone or together.
Habitat loss is the single greatest threat to persistence of the critically threatened California tiger salamander (Ambystoma californiense). To aid management plans that designate critical habitat for this species, I developed and characterized 21 tetranucleotide
microsatellite markers using two native populations in Santa Barbara and Alameda Counties. Allelic variation and average heterozygosities
were lower in the endangered Santa Barbara population (allele range 1–4, mean 2.4; H
O=0.308 H
E=0.288) compared with the threatened Alameda population (allele range 2–10, mean 6.7; H
O=0.712, H
E=0.722). In-depth population studies using these markers will provide vital information for plans to assign critical habitat
that optimize gene flow among breeding populations, as well as for identifying non-native hybrid genotypes that threaten native
A. californiense stocks. Beyond the conservation goals for A. californiense, the close phylogenetic relationships within the tiger salamander complex also suggest a broad utility for population studies
using these markers.
The spatial genetic structuring of the land snail Helix aspersa was investigated for 32 colonies within an intensive agricultural area, the polders of the Bay of Mont-Saint-Michel (France). Given the habitat patchiness and environmental instability, the setting of H.aspersa colonies meets the broader view of a metapopulation structure. The identification of extrinsic barriers to migration and their impact on the genetic distribution was addressed through the genotyping of 580 individuals using a combined set of enzyme and microsatellite loci. To evaluate the distance as well as the direction over which the spatial genetic arrangement occurs, two-dimensional spatial autocorrelation analyses, Mantel tests of association and multivariate Mantel correlograms were used. Different connectivity networks and geographical distances based on landscape features were constructed to evaluate the effect of environmental heterogeneity and to test the adequacy of an isolation by distance model on the distribution of the genetic variability. Genetic divergence was assessed using either classical IAM-based statistics, or SMM-based genetic distances specifically designed to accommodate the mutational processes thought to fit microsatellite evolution (IAM: Infinite Allele Model; SMM: Stepwise Mutation Model). Genetic distances based only on genetic drift yielded the most plausible biologically meaningful interpretation of the observed spatial structure. Applying a landscape-based geographical distance which postulates that migration arises along roadside verges, hedges or irrigation canal embankments gave a better fit to an isolation by distance model than did a simple Euclidean distance. The progressive decline of genetic similarity with physical distance appeared to be environmentally induced, leading to functional migration pathways.
GENALEX is a user-friendly cross-platform package that runs within Microsoft Excel, enabling population genetic analyses of codominant, haploid and binary data. Allele frequency-based analyses include heterozygosity, F statistics, Nei's genetic distance, population assignment, probabilities of identity and pairwise relatedness. Distance-based calculations include AMOVA, principal coordinates analysis (PCA), Mantel tests, multivariate and 2D spatial autocorrelation and TWOGENER. More than 20 different graphs summarize data and aid exploration. Sequence and genotype data can be imported from automated sequencers, and exported to other software. Initially designed as tool for teaching, GENALEX 6 now offers features for researchers as well. Documentation and the program are available at http://www.anu.edu.au/BoZo/GenAlEx/
DNA degradation, low DNA concentrations and primer-site mutations may result in the incorrect assignment of microsatellite genotypes, potentially biasing population genetic analyses. MICRO-CHECKER is WINDOWS(R)-based software that tests the genotyping of microsatellites from diploid populations. The program aids identification of genotyping errors due to nonamplified alleles (null alleles), short allele dominance (large allele dropout) and the scoring of stutter peaks, and also detects typographic errors. MICRO-CHECKER estimates the frequency of null alleles and, importantly, can adjust the allele and genotype frequencies of the amplified alleles, permitting their use in further population genetic analysis.
The persistence of amphibian populations in fragmented landscapes requires dispersal and recolonization of habitat patches after local extinction. These processes entail individuals crossing habitat edges. Edge permeability integrates the behavior of individuals with the vegetative structure of the habitat edge and may influence the dispersal rates between habitat patches. We used drift fences, radio telemetry, and an experimental displacement to examine the movement behavior of juvenile and adult spotted salamanders (Ambystoma maculatum) at a pond located in continuous forest and a pond located on a distinct forest–grassland edge. At the pond on the habitat edge, adult salamanders migrated to and from the forested side of the pond. Resident adults with transmitters migrated to forested habitat without approaching the habitat edge. Displaced adults with transmitters halted emigration movements when they approached the habitat edge. None of the radio-tagged adults were observed more than a few meters into the grassland. Recently metamorphosed juveniles exiting the grassland side of the pond changed their direction of movement and migrated into the forested habitat. We conclude that salamanders oriented movements with respect to features in the terrestrial habitat, detected the habitat edge, and behaviorally avoided the grassland. Exploring the permeability of habitat edges will improve our understanding of dispersal within fragmented landscapes and enhance efforts to conserve regional populations of amphibians.
Understanding the processes and patterns of gene flow and local adaptation requires a detailed knowledge of how landscape characteristics structure populations. This understanding is crucial, not only for improving ecological knowledge, but also for managing properly the genetic diversity of threatened and endangered populations. For nearly 80 years, population geneticists have investigated how physiognomy and other landscape features have influenced genetic variation within and between populations. They have relied on sampling populations that have been identified beforehand because most population genetics methods have required discrete populations. However, a new approach has emerged for analyzing spatial genetic data without requiring that discrete populations be identified in advance. This approach, landscape genetics, promises to facilitate our understanding of how geographical and environmental features structure genetic variation at both the population and individual levels, and has implications for ecology, evolution and conservation biology. It differs from other genetic approaches, such as phylogeography, in that it tends to focus on processes at finer spatial and temporal scales. Here, we discuss, from a population genetic perspective, the current tools available for conducting studies of landscape genetics.
A primary goal of molecular ecology is to understand the influence of abiotic factors on the spatial distribution of genetic variation. Features including altitudinal clines, topography and landscape characteristics affect the proportion of suitable habitat, influence dispersal patterns, and ultimately structure genetic differentiation among populations. We studied the effects of altitude and topography on genetic variation of long-toed salamanders (Ambystoma macrodactylum), a geographically widespread amphibian species throughout northwestern North America. We focused on 10 low altitude sites (< 1200 m) and 11 high-altitude sites in northwestern Montana and determined multilocus genotypes for 549 individuals using seven microsatellite loci. We tested four hypotheses: (1) gene flow is limited between high- and low-altitude sites; and, (2) gene flow is limited among high-altitude sites due to harsh habitat and extreme topographical relief between sites; (3) low-altitude sites exhibit higher among-site gene flow due to frequent flooding events and low altitudinal relief; and (4) there is a negative correlation between altitude and genetic variation. Overall F(ST) values were moderate (0.08611; P < 0.001). Pairwise F(ST) estimates between high and low populations and a population graphing method supported the hypothesis that low-altitude and high-altitude sites, taken together, are genetically differentiated from each other. Also as predicted, gene flow is more prominent among low-altitude sites than high-altitude sites; low-altitude sites had a significantly lower F(ST) (0.03995; P < 0.001) than high altitude sites (F(ST) = 0.10271; P < 0.001). Use of Bayesian analysis of population structure (BAPS) resulted in delineation of 10 genetic groups, two among low-altitude populations and eight among high-altitude populations. In addition, within high altitude populations, basin-level genetic structuring was apparent. A nonequilibrium algorithm for detecting current migration rates supported these population distinctions. Finally, we also found a significant negative correlation between genetic diversity and altitude. These results are consistent with the hypothesis that topography and altitudinal gradients shape the spatial distribution of genetic variation in a species with a broad geographical range and diverse life history. Our study sheds light on which key factors limit dispersal and ultimately species' distributions.
Natural populations of living organisms often have complex histories consisting of phases of expansion and decline, and the
migratory patterns within them may fluctuate over space and time. When parts of a population become relatively isolated, e.g.,
due to geographical barriers, stochastic forces reshape certain DNA characteristics of the individuals over generations such
that they reflect the restricted migration and mating/reproduction patterns. Such populations are typically termed as genetically
structured and they may be statistically represented in terms of several clusters between which DNA variations differ clearly
from each other. When detailed knowledge of the ancestry of a natural population is lacking, the DNA characteristics of a
sample of current generation individuals often provide a wealth of information in this respect. Several statistical approaches
to model-based clustering of such data have been introduced, and in particular, the Bayesian approach to modeling the genetic
structure of a population has attained a vivid interest among biologists. However, the possibility of utilizing spatial information
from sampled individuals in the inference about genetic clusters has been incorporated into such analyses only very recently.
While the standard Bayesian hierarchical modeling techniques through Markov chain Monte Carlo simulation provide flexible
means for describing even subtle patterns in data, they may also result in computationally challenging procedures in practical
data analysis. Here we develop a method for modeling the spatial genetic structure using a combination of analytical and stochastic
methods. We achieve this by extending a novel theory of Bayesian predictive classification with the spatial information available,
described here in terms of a colored Voronoi tessellation over the sample domain. Our results for real and simulated data
sets illustrate well the benefits of incorporating spatial information to such an analysis.
There is abundant geographic variation in both morphology and gene frequency in most species. The extent of geographic variation
results from a balance of forces tending to produce local genetic differentiation and forces tending to produce genetic homogeneity.
Mutation, genetic drift due to finite population size, and natural selection favoring adaptations to local environmental conditions
will all lead to the genetic differentiation of local populations, and the movement of gametes, individuals, and even entire
populations--collectively called gene flow--will oppose that differentiation. Gene flow may either constrain evolution by
preventing adaptation to local conditions or promote evolution by spreading new genes and combinations of genes throughout
a species' range. Several methods are available for estimating the amount of gene flow. Direct methods monitor ongoing gene
flow, and indirect methods use spatial distributions of gene frequencies to infer past gene flow. Applications of these methods
show that species differ widely in the gene flow that they experience. Of particular interest are those species for which
direct methods indicate little current gene flow but indirect methods indicate much higher levels of gene flow in the recent
past. Such species probably have undergone large-scale demographic changes relatively frequently.
The accuracy of gene flow estimates is unknown in most natural populations because direct estimates of dispersal are often not possible. These estimates can be highly imprecise or even biased because population genetic structure reflects more than a simple balance between genetic drift and gene flow. Most of the models used to estimate gene flow also assume very simple patterns of movement. As a result, multiple interpretations of population structure involving contemporary gene flow, departures from equilibrium, and other factors are almost always possible. One way to isolate the relative contribution of gene flow to population genetic differentiation is to utilize comparative methods. Population genetic statistics such as FST, heterozygosity and Nei's D can be compared between species with differing dispersal abilities if these species are otherwise phylogenetically, geographically and demographically comparable. Accordingly, the available literature was searched for all groups that meet these criteria to determine whether broad conclusions regarding the relationships between dispersal, population genetic structure, and gene flow estimates are possible. Allozyme and mtDNA data were summarized for 27 animal groups in which dispersal differences can be characterized. In total, genetic data were obtained for 333 species of vertebrates and invertebrates from terrestrial, freshwater and marine habitats. Across these groups, dispersal ability was consistently related to population structure, with a mean rank correlation of -0.72 between ranked dispersal ability and FST. Gene flow estimates derived from private alleles were also correlated with dispersal ability, but were less widely available. Direct-count heterozygosity and average values of Nei's D showed moderate degrees of correlation with dispersal ability. Thus, despite regional, taxonomic and methodological differences among the groups of species surveyed, available data demonstrate that dispersal makes a measurable contribution to population genetic differentiation in the majority of animal species in nature, and that gene flow estimates are rarely so overwhelmed by population history, departures from equilibrium, or other microevolutionary forces as to be uninformative.
Population genetic theory predicts that plant populations will exhibit internal spatial autocorrelation when propagule flow is restricted, but as an empirical reality, spatial structure is rarely consistent across loci or sites, and is generally weak. A lack of sensitivity in the statistical procedures may explain the discrepancy. Most work to date, based on allozymes, has involved pattern analysis for individual alleles, but new PCR-based genetic markers are coming into vogue, with vastly increased numbers of alleles. The field is badly in need of an explicitly multivariate approach to autocorrelation analysis, and our purpose here is to introduce a new approach that is applicable to multiallelic codominant, multilocus arrays. The procedure treats the genetic data set as a whole, strengthening the spatial signal and reducing the stochastic (allele-to-allele, and locus-to-locus) noise. We (i) develop a very general multivariate method, based on genetic distance methods, (ii) illustrate it for multiallelic codominant loci, and (iii) provide nonparametric permutational testing procedures for the full correlogram. We illustrate the new method with an example data set from the orchid Caladenia tentaculata, for which we show (iv) how the multivariate treatment compares with the single-allele treatment, (v) that intermediate frequency alleles from highly polymorphic loci perform well and rare alleles poorly, (vi) that a multilocus treatment provides clearer answers than separate single-locus treatments, and (vii) that weighting alleles differentially improves our resolution minimally. The results, though specific to Caladenia, offer encouragement for wider application.
We present a comprehensive survey of genetic variation across the range of the narrowly distributed endemic Yosemite toad Bufo canorus, a declining amphibian restricted to the Sierra Nevada of California. Based on 322 bp of mitochondrial cytochrome b sequence data, we found limited support for the monophyly of B. canorus and its closely related congener B. exsul to the exclusion of the widespread western toad B. boreas. However, B. exsul was always phylogenetically nested within B. canorus, suggesting that the latter may not be monophyletic. SSCP (single-strand conformation polymorphism) analysis of 372 individual B. canorus from 28 localities in Yosemite and Kings Canyon National Parks revealed no shared haplotypes among these two regions and lead us to interpret these two parks as distinct management units for B. canorus. Within Yosemite, we found significant genetic substructure both at the level of major drainages and among breeding ponds. Kings Canyon samples show a different pattern, with substantial variation among breeding sites, but no substructure among drainages. Across the range of B. canorus as well as among Yosemite ponds, we found an isolation-by-distance pattern suggestive of a stepping stone model of migration. However, in Kings Canyon we found no hint of such a pattern, suggesting that movement patterns of toads may be quite different in these nearby parklands. Our data imply that management for B. canorus should focus at the individual pond level, and effective management may necessitate reintroductions if local extirpations occur. A brief review of other pond-breeding anurans suggests that highly structured populations are often the case, and thus that our results for B. canorus may be general for other species of frogs and toads.
We investigated genetic population structure in wood frogs (Rana sylvatica) from a series of Prairie Pothole wetlands in the northern Great Plains. Amphibians are often thought to exist in demographic metapopulations, which require some movement between populations, yet genetic studies have revealed strong subdivision among populations, even at relatively fine scales (several km). Wood frogs are highly philopatric and studies of dispersal suggest that they may exhibit subdivision on a scale of approximately 1-2 km. We used microsatellites to examine population structure among 11 breeding assemblages separated by as little as 50 m up to approximately 5.5 km, plus one population separated from the others by 20 km. We found evidence for differentiation at the largest distances we examined and among a few neighbouring ponds, but most populations were strikingly similar in allele frequencies, suggesting high gene flow among all but the most distant populations. We hypothesize that the few significant differences among neighbouring populations at the finest scale may be a transient effect of extinction-recolonization founder events, driven by periodic drying of wetlands in this hydrologically dynamic landscape.
In systems of interconnected ponds or lakes, the dispersal of zooplankton may be mediated by the active population component, with rivulets and overflows functioning as dispersal pathways. Using a landscape-based approach, we modelled the effective geographical distance among a set of interconnected ponds (De Maten, Genk, Belgium) in a Geographic Information System (GIS) environment. The first model (the Landscape Model; LM) corrects for the presence of direct connections among ponds and was based on the existing landscape structure (i.e. network of connecting elements among ponds, travelling distance and direction of the current). A second model (the Flow Rate Model; FRM) also incorporated field data on flow rates in the connecting elements as the driving force for the passive dispersal of the active zooplankton population component. Finally, the third model (the Dispersal Rate Model; DRM) incorporated field data on zooplankton dispersal rates. An analysis of the pattern of genetic differentiation among Daphnia ambigua populations inhabiting 10 ponds in the pond complex reveals that the effective geographical distance as modelled by the flow rate and the dispersal rate model provide a better approximation of the true rates of genetic exchange among populations than mere Euclidean geographical distances or the landscape model that takes into account solely the presence of physical connections.
Individual-based assignment tests are now standard tools in molecular ecology and have several applications, including the study of dispersal. The measurement of natal dispersal is vital to understanding the ecology of many species, yet the accuracy of assignment tests in situations where natal dispersal is common remains untested in the field. We studied a metapopulation of the grand skink, Oligosoma grande, a large territorial lizard from southern New Zealand. Skink populations occur on isolated, regularly spaced rock outcrops and are characterized by frequent interpopulation dispersal. We examined the accuracy of assignment tests at four replicate sites by comparing long-term mark-and-recapture records of natal dispersal with the results of assignment tests based on microsatellite DNA data. Assignment tests correctly identified the natal population of most individuals (65-100%, depending on the method of assignment), even when interpopulation dispersal was common (5-20% dispersers). They also provided similar estimates of the proportions of skinks dispersing to those estimated by the long-term mark-and-recapture data. Fully and partially Bayesian assignment methods were equally accurate but their accuracy depended on the stringency applied, the degree of genetic differentiation between populations, and the number of loci used. In addition, when assignments required high confidence, the method of assignment (fully or partially Bayesian) had a large bearing on the number of individuals that could be assigned. Because assignment tests require significantly less fieldwork than traditional mark-and-recapture approaches (in this study < 3 months vs. > 7 years), they will provide useful dispersal data in many applied and theoretical situations.
We reanalysed published data to evaluate whether climate and habitat are barriers to dispersal in one of the most mobile and widely distributed mammals, the grey wolf (Canis lupus). Distance-based redundancy analysis (dbRDA) was used to examine the amount of variation in genetic distances that could be explained by an array of environmental factors, including geographical distance. Patterns in genetic variation were also examined using MDS plots among populations and relationships between genetic structure and individual environmental variables were further explored using the BIOENV procedure. We found that, contrary to a previous report, a pattern of isolation with distance is evident on a continental scale in the North American wolf population. This pattern is apparently related to climate and habitat. Specifically, vegetation types appear to play a role in the genetic dissimilarities among populations. When we controlled for the effect of spatial variation, climate was still associated with genetic distance. Further, partitioning of geographical distances into latitudinal and longitudinal axes revealed that the east-west gradient had the strongest relationship with genetic distance. We suggest two possible mechanisms by which environmental conditions may influence the dispersal decisions made by wolves.
A primary goal of conservation genetics is the discovery, delimitation and protection of phylogenetic lineages within sensitive or endangered taxa. Given the importance of lineage protection, a combination of phylogeography, historical geology and molecular clock analyses can provide an important historical context for overall species conservation. We present the results of a range-wide survey of genetic variation in the California tiger salamander, Ambystoma californiense, as well as a summary of the past several million years of inundation and isolation of the Great Central Valley and surrounding uplands that constitute its limited range. A combination of population genetic and phylogenetic analyses of mitochondrial DNA variation among 696 samples from 84 populations revealed six well-supported genetic units that are geographically discrete and characterized by nonoverlapping haplotype distributions. Populations from Santa Barbara and Sonoma Counties are particularly well differentiated and geographically isolated from all others. The remaining units in the Southern San Joaquin Valley, Central Coast Range, Central Valley and Bay Area are separated by geological features, ecological zone boundaries, or both. The geological history of the California landscape is consistent with molecular clock evidence suggesting that the Santa Barbara unit has been isolated for at least 0.74-0.92 Myr, and the Sonoma clade is equally ancient. Our work places patterns of genetic differentiation into both temporal- and landscape-level contexts, providing important insights into the conservation genetics of the California tiger salamander.
Landscape features such as mountains, rivers, and ecological gradients may strongly affect patterns of dispersal and gene flow among populations and thereby shape population dynamics and evolutionary trajectories. The landscape may have a particularly strong effect on patterns of dispersal and gene flow in amphibians because amphibians are thought to have poor dispersal abilities. We examined genetic variation at six microsatellite loci in Columbia spotted frogs (Rana luteiventris) from 28 breeding ponds in western Montana and Idaho, USA, in order to investigate the effects of landscape structure on patterns of gene flow. We were particularly interested in addressing three questions: (i) do ridges act as barriers to gene flow? (ii) is gene flow restricted between low and high elevation ponds? (iii) does a pond equal a 'randomly mating population' (a deme)? We found that mountain ridges and elevational differences were associated with increased genetic differentiation among sites, suggesting that gene flow is restricted by ridges and elevation in this species. We also found that populations of Columbia spotted frogs generally include more than a single pond except for very isolated ponds. There was also evidence for surprisingly high levels of gene flow among low elevation sites separated by large distances. Moreover, genetic variation within populations was strongly negatively correlated with elevation, suggesting effective population sizes are much smaller at high elevation than at low elevation. Our results show that landscape features have a profound effect on patterns of genetic variation in Columbia spotted frogs.
The field of landscape genetics has great potential to identify habitat features that influence population genetic structure. To identify landscape correlates of genetic differentiation in a quantitative fashion, we developed a novel approach using geographical information systems analysis. We present data on blotched tiger salamanders (Ambystoma tigrinum melanostictum) from 10 sites across the northern range of Yellowstone National Park in Montana and Wyoming, USA. We used eight microsatellite loci to analyse population genetic structure. We tested whether landscape variables, including topographical distance, elevation, wetland likelihood, cover type and number of river and stream crossings, were correlated with genetic subdivision (F(ST)). We then compared five hypothetical dispersal routes with a straight-line distance model using two approaches: (i) partial Mantel tests using Akaike's information criterion scores to evaluate model robustness and (ii) the BIOENV procedure, which uses a Spearman rank correlation to determine the combination of environmental variables that best fits the genetic data. Overall, gene flow appears highly restricted among sites, with a global F(ST) of 0.24. While there is a significant isolation-by-distance pattern, incorporating landscape variables substantially improved the fit of the model (from an r2 of 0.3 to 0.8) explaining genetic differentiation. It appears that gene flow follows a straight-line topographic route, with river crossings and open shrub habitat correlated with lower F(ST) and thus, decreased differentiation, while distance and elevation difference appear to increase differentiation. This study demonstrates a general approach that can be used to determine the influence of landscape variables on population genetic structure.