landgenreport: a new r function to simplify landscape genetic analysis using resistance surface layers

We describe functions recently added to the r package popgenreport that can be used to perform a landscape genetic analysis (LGA) based on landscape resistance surfaces, which aims to detect the effect of landscape features on gene flow. These functions for the first time implement a LGA in a single framework. Although the approach has been shown to be a valuable tool to study gene flow in landscapes, it has not been widely used to date, despite the type of data being widely available. In part, this is likely due to the necessity to use several software packages to perform landscape genetic analyses. To apply LGA functions, two types of data sets are required: a data set with spatially referenced and genotyped individuals, and a resistance layer representing the effect of the landscape. The function outputs three pairwise distance matrices from these data: a genetic distance matrix, a cost distance matrix and a Euclidean distance matrix. Statistical tests are performed to test whether the cost matrix contributes to the understanding of the observed population structure. A full report on the analysis and outputs in the form of plots and tables of all intermediate steps of the LGA is produced. It is possible to customize the LGA to allow for different cost path approaches and measures of genetic distances. The package is written in the r language and is available through the Comprehensive r Archive. Comprehensive tutorials and information on how to install and use the package are provided at the authors’ website ( www.popgenreport.org ).     

Choosing appropriate genetic markers and analytical methods for testing landscape genetic hypotheses

Landscape genetics and phylogeography both examine population‐level microevolutionary processes, such as population structure and gene flow, in the context of environmental and geographic variation. They differ in terms of the spatial and temporal scales they typically investigate, meaning that different genetic markers and analytical methods are better suited for testing the different hypotheses typically posed by each discipline. In a recent comment, Bohonak & Vandergast (2011) argue that I overlooked the value of mtDNA for landscape genetics in an article I published last year in Molecular Ecology (Wang 2010) and that a gap between landscape genetics and phylogeography, which I outlined, does not exist. Here, I clarify several points in my original article and summarize the commonly held viewpoint that different genetic markers are appropriate for drawing inferences at different temporal scales.     

Inferring landscape resistance to gene flow when genetic drift is spatially heterogeneous

In connectivity models, land cover types are assigned cost values characterizing their resistance to species movements. Landscape genetic methods infer these values from the relationship between genetic differentiation and cost distances. The spatial heterogeneity of population sizes, and consequently genetic drift, is rarely included in this inference although it influences genetic differentiation. Similarly, migration rates and population spatial distributions potentially influence this inference. Here, we assessed the reliability of cost value inference under several migration rates, population spatial patterns and degrees of population size heterogeneity. Additionally, we assessed whether considering intra-population variables, here using gravity models, improved the inference when drift is spatially heterogeneous. We simulated several gene flow intensities between populations with varying local sizes and spatial distributions. We then fit gravity models of genetic distances as a function of (i) the ‘true’ cost distances driving simulations or alternative cost distances, and (ii) intra-population variables (population sizes, patch areas). We determined the conditions making the identification of the ‘true’ costs possible and assessed the contribution of intra-population variables to this objective. Overall, the inference ranked cost scenarios reliably in terms of similarity with the ‘true’ scenario (cost distance Mantel correlations), but this ‘true’ scenario rarely provided the best model goodness of fit. Ranking inaccuracies and failures to identify the ‘true’ scenario were more pronounced when migration was very restricted (<4 dispersal events/generation), population sizes were most heterogeneous and some populations were spatially aggregated. In these situations, considering intra-population variables helps identify cost scenarios reliably, thereby improving cost value inference from genetic data.     

A comparison of regression methods for model selection in individual‐based landscape genetic analysis

Anthropogenic migration barriers fragment many populations and limit the ability of species to respond to climate‐induced biome shifts. Conservation actions designed to conserve habitat connectivity and mitigate barriers are needed to unite fragmented populations into larger, more viable metapopulations, and to allow species to track their climate envelope over time. Landscape genetic analysis provides an empirical means to infer landscape factors influencing gene flow and thereby inform such conservation actions. However, there are currently many methods available for model selection in landscape genetics, and considerable uncertainty as to which provide the greatest accuracy in identifying the true landscape model influencing gene flow among competing alternative hypotheses. In this study, we used population genetic simulations to evaluate the performance of seven regression‐based model selection methods on a broad array of landscapes that varied by the number and type of variables contributing to resistance, the magnitude and cohesion of resistance, as well as the functional relationship between variables and resistance. We also assessed the effect of transformations designed to linearize the relationship between genetic and landscape distances. We found that linear mixed effects models had the highest accuracy in every way we evaluated model performance; however, other methods also performed well in many circumstances, particularly when landscape resistance was high and the correlation among competing hypotheses was limited. Our results provide guidance for which regression‐based model selection methods provide the most accurate inferences in landscape genetic analysis and thereby best inform connectivity conservation actions.     

The use and misuse of regression models in landscape genetic analyses

The field of landscape genetics has been rapidly evolving, adopting and adapting analytical frameworks to address research questions. Current studies are increasingly using regression‐based frameworks to infer the individual contributions of landscape and habitat variables on genetic differentiation. This paper outlines appropriate and inappropriate uses of multiple regression for these purposes, and demonstrates through simulation the limitations of different analytical frameworks for making correct inference. Of particular concern are recent studies seeking to explain genetic differences by fitting regression models with effective distance variables calculated independently on separate landscape resistance surfaces. When moving across the landscape, organisms cannot respond independently and uniquely to habitat and landscape features. Analyses seeking to understand how landscape features affect gene flow should model a single conductance or resistance surface as a parameterized function of relevant spatial covariates, and estimate the values of these parameters by linking a single set of resistance distances to observed genetic dissimilarity via a loss function. While this loss function may involve a regression‐like step, the associated nuisance parameters are not interpretable in terms of organismal movement and should not be conflated with what is actually of interest: the mapping between spatial covariates and conductance/resistance. The growth and evolution of landscape genetics as a field has been rapid and exciting. It is the goal of this paper to highlight past missteps and demonstrate limitations of current approaches to ensure that future use of regression models will appropriately consider the process being modeled, which will provide clarity to model interpretation.     

Combining the least cost path method with population genetic data and species distribution models to identify landscape connectivity during the late Quaternary in Himalayan hemlock

Himalayan hemlock (Tsuga dumosa) experienced a recolonization event during the Quaternary period; however, the specific dispersal routes are remain unknown. Recently, the least cost path (LCP) calculation coupled with population genetic data and species distribution models has been applied to reveal the landscape connectivity. In this study, we utilized the categorical LCP method, combining species distribution of three periods (the last interglacial, the last glacial maximum, and the current period) and locality with shared chloroplast, mitochondrial, and nuclear haplotypes, to identify the possible dispersal routes of T. dumosa in the late Quaternary. Then, both a coalescent estimate of migration rates among regional groups and establishment of genetic divergence pattern were conducted. After those analyses, we found that the species generally migrated along the southern slope of Himalaya across time periods and genomic makers, and higher degree of dispersal was in the present and mtDNA haplotype. Furthermore, the direction of range shifts and strong level of gene flow also imply the existence of Himalayan dispersal path, and low area of genetic divergence pattern suggests that there are not any obvious barriers against the dispersal pathway. Above all, we inferred that a dispersal route along the Himalaya Mountains could exist, which is an important supplement for the evolutionary history of T. dumosa. Finally, we believed that this integrative genetic and geospatial method would bring new implications for the evolutionary process and conservation priority of species in the Tibetan Plateau.     

Grains of connectivity: analysis at multiple spatial scales in landscape genetics

Landscape genetic analyses are typically conducted at one spatial scale. Considering multiple scales may be essential for identifying landscape features influencing gene flow. We examined landscape connectivity for woodland caribou (Rangifer tarandus caribou) at multiple spatial scales using a new approach based on landscape graphs that creates a Voronoi tessellation of the landscape. To illustrate the potential of the method, we generated five resistance surfaces to explain how landscape pattern may influence gene flow across the range of this population. We tested each resistance surface using a raster at the spatial grain of available landscape data (200 m grid squares). We then used our method to produce up to 127 additional grains for each resistance surface. We applied a causal modelling framework with partial Mantel tests, where evidence of landscape resistance is tested against an alternative hypothesis of isolation-by-distance, and found statistically significant support for landscape resistance to gene flow in 89 of the 507 spatial grains examined. We found evidence that major roads as well as the cumulative effects of natural and anthropogenic disturbance may be contributing to the genetic structure. Using only the original grid surface yielded no evidence for landscape resistance to gene flow. Our results show that using multiple spatial grains can reveal landscape influences on genetic structure that may be overlooked with a single grain, and suggest that coarsening the grain of landcover data may be appropriate for highly mobile species. We discuss how grains of connectivity and related analyses have potential landscape genetic applications in a broad range of systems.     

Different landscape effects on the genetic structure of two broadly distributed woody legumes,Acacia salicina and A. stenophylla (Fabaceae)

Restoring degraded landscapes has primarily focused on re‐establishing native plant communities. However, little is known with respect to the diversity and distribution of most key revegetation species or the environmental and anthropogenic factors that may affect their demography and genetic structure. In this study, we investigated the genetic structure of two widespread Australian legume species (Acacia salicina and Acacia stenophylla) in the Murray–Darling Basin (MDB), a large agriculturally utilized region in Australia, and assessed the impact of landscape structure on genetic differentiation. We used AFLP genetic data and sampled a total of 28 A. salicina and 30 A. stenophylla sampling locations across southeastern Australia. We specifically evaluated the importance of four landscape features: forest cover, land cover, water stream cover, and elevation. We found that both species had high genetic diversity (mean percentage of polymorphic loci, 55.1% for A. salicina versus. 64.3% for A. stenophylla) and differentiation among local sampling locations (A. salicina: Φ_PT = 0.301, 30%; A. stenophylla: Φ_PT = 0.235, 23%). Population structure analysis showed that both species had high levels of structure (6 clusters each) and admixture in some sampling locations, particularly A. stenophylla. Although both species have a similar geographic range, the drivers of genetic connectivity for each species were very different. Genetic variation in A. salicina seems to be mainly driven by geographic distance, while for A. stenophylla, land cover appears to be the most important factor. This suggests that for the latter species, gene flow among populations is affected by habitat fragmentation. We conclude that these largely co‐occurring species require different management actions to maintain population connectivity. We recommend active management of A. stenophylla in the MDB to improve gene flow in the adversity of increasing disturbances (e.g., droughts) driven by climate change and anthropogenic factors.     

Urban landscape genetics: canopy cover predicts gene flow between white-footed mouse (Peromyscus leucopus) populations in New York City

In this study, I examine the influence of urban canopy cover on gene flow between 15 white-footed mouse (Peromyscus leucopus) populations in New York City parklands. Parks in the urban core are often highly fragmented, leading to rapid genetic differentiation of relatively nonvagile species. However, a diverse array of 'green' spaces may provide dispersal corridors through 'grey' urban infrastructure. I identify urban landscape features that promote genetic connectivity in an urban environment and compare the success of two different landscape connectivity approaches at explaining gene flow. Gene flow was associated with 'effective distances' between populations that were calculated based on per cent tree canopy cover using two different approaches: (i) isolation by effective distance (IED) that calculates the single best pathway to minimize passage through high-resistance (i.e. low canopy cover) areas, and (ii) isolation by resistance (IBR), an implementation of circuit theory that identifies all low-resistance paths through the landscape. IBR, but not IED, models were significantly associated with three measures of gene flow (Nm from F(ST) , BayesAss+ and Migrate-n) after factoring out the influence of isolation by distance using partial Mantel tests. Predicted corridors for gene flow between city parks were largely narrow, linear parklands or vegetated spaces that are not managed for wildlife, such as cemeteries and roadway medians. These results have implications for understanding the impacts of urbanization trends on native wildlife, as well as for urban reforestation efforts that aim to improve urban ecosystem processes.     

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

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

Nonequilibrium conditions following landscape rearrangement: the relative contribution of past and current hydrological landscapes on the genetic structure of a stream-dwelling fish

Interpreting patterns of population structure in nature is often challenging, especially in dynamic landscapes where population genetic connectivity evolves over time. In this study, we document the absence of migration-drift equilibrium in a stream-dwelling euryhaline fish resulting from past fine-scale drainage rearrangements and evaluate the relative contribution of past and current hydrological landscapes on observed population structure. Based on allelic variation at nine microsatellite loci, genetic relationships among 12 populations of brook charr, Salvelinus fontinalis, from Gros Morne National Park of Canada (GMNP, Newfoundland, Canada) did not reflect current stream hierarchical structure. In addition, we observed no correlation between population differentiation and contemporary landscape features (waterway distance and sums of altitudinal differences). Instead, population relationships were consistent with historical hydrological structure predicted a priori based on geomorphological and biogeographical evidences. Also, population differentiation was strongly correlated with inferred historical landscape features. Contemporary barriers have apparently preserved the signature of past genetic connectivity by constraining gene flow. Based on the relationships between population differentiation and current and past landscape features at various spatial scales, we suggest that brook charr genetic diversity in GMNP is mostly the result of small distance migrations at the time of colonization and subsequent differentiation through drift. This study highlights the potential of approaching landscapes from a combination of contemporary and historical perspectives when interpreting nonequilibrium population structures resulting from landscape rearrangement.     

Landscape characteristics influencing the genetic structure of greater sage‐grouse within the stronghold of their range: a holistic modeling approach

Given the significance of animal dispersal to population dynamics and geographic variability, understanding how dispersal is impacted by landscape patterns has major ecological and conservation importance. Speaking to the importance of dispersal, the use of linear mixed models to compare genetic differentiation with pairwise resistance derived from landscape resistance surfaces has presented new opportunities to disentangle the menagerie of factors behind effective dispersal across a given landscape. Here, we combine these approaches with novel resistance surface parameterization to determine how the distribution of high‐ and low‐quality seasonal habitat and individual landscape components shape patterns of gene flow for the greater sage‐grouse (Centrocercus urophasianus) across Wyoming. We found that pairwise resistance derived from the distribution of low‐quality nesting and winter, but not summer, seasonal habitat had the strongest correlation with genetic differentiation. Although the patterns were not as strong as with habitat distribution, multivariate models with sagebrush cover and landscape ruggedness or forest cover and ruggedness similarly had a much stronger fit with genetic differentiation than an undifferentiated landscape. In most cases, landscape resistance surfaces transformed with 17.33‐km‐diameter moving windows were preferred, suggesting small‐scale differences in habitat were unimportant at this large spatial extent. Despite the emergence of these overall patterns, there were differences in the selection of top models depending on the model selection criteria, suggesting research into the most appropriate criteria for landscape genetics is required. Overall, our results highlight the importance of differences in seasonal habitat preferences to patterns of gene flow and suggest the combination of habitat suitability modeling and linear mixed models with our resistance parameterization is a powerful approach to discerning the effects of landscape on gene flow.     

Developments from analysis of variance through to generalized linear models and beyond

The collaborations between statisticians and biologists during the 100 years since AAB was founded have led to a very impressive list of statistical techniques, whose use now goes well beyond agriculture and biology. One example is the maximum likelihood methodology for probit analysis, arising from the collaboration between Sir Ronald Fisher and Chester Bliss. Others include analysis of variance, design of experiments, generalized linear models and the residual, or restricted, maximum likelihood (REML) algorithm for fitting unbalanced linear mixed models.     

Syntopic frogs reveal different patterns of interaction with the landscape: A comparative landscape genetic study of Pelophylax nigromaculatus and Fejervarya limnocharis from central China

Amphibians are often considered excellent environmental indicator species. Natural and man‐made landscape features are known to form effective genetic barriers to amphibian populations; however, amphibians with different characteristics may have different species–landscape interaction patterns. We conducted a comparative landscape genetic analysis of two closely related syntopic frog species from central China, Pelophylax nigromaculatus (PN) and Fejervarya limnocharis (FL). These two species differ in several key life history traits; PN has a larger body size and larger clutch size, and reaches sexual maturity later than FL. Microsatellite DNA data were collected and analyzed using conventional (F_ST, isolation by distance (IBD), AMOVA) and recently developed (Bayesian assignment test, isolation by resistance) landscape genetic methods. As predicted, a higher level of population structure in FL (F_ST′ = 0.401) than in PN (F_ST′ = 0.354) was detected, in addition to FL displaying strong IBD patterns (r = .861) unlike PN (r = .073). A general north–south break in FL populations was detected, consistent with the IBD pattern, while PN exhibited clustering of northern‐ and southern‐most populations, suggestive of altered dispersal patterns. Species‐specific resistant landscape features were also identified, with roads and land cover the main cause of resistance to FL, and elevation the main influence on PN. These different species–landscape interactions can be explained mostly by their life history traits, revealing that closely related and ecologically similar species have different responses to the same landscape features. Comparative landscape genetic studies are important in detecting such differences and refining generalizations about amphibians in monitoring environmental changes.     

Exposing the confounding in experimental designs to understand and evaluate them,and formulating linear mixed models for analyzing the data from a designed experiment

Comparative experiments involve the allocation of treatments to units, ideally by randomization. This necessarily confounds treatment information with unit information, which we distinguish from the other forms of information blending, in particular aliasing and marginality. We outline a factor-allocation paradigm for describing experimental designs with the aim of (i) exhibiting the confounding in a design, using analysis-of-variance-like tables, so as to understand and evaluate the design and (ii) formulating a linear mixed model based on the factor allocation that the design involves. The approach exhibits the dispersal of treatments information between units sources, allows designers a choice in the strategy that they adopt for including block-treatment interactions, clarifies differences between experiments, accommodates systematic allocation of factors, and provides a consolidated analysis of nonorthogonal designs. It provides insights into the process of designing experiments and issues that commonly arise with designs. The paradigm has pedagogical advantages and is implemented using the R package dae .     

Conserving threatened riparian ecosystems in the American West: Precipitation gradients and river networks drive genetic connectivity and diversity in a foundation riparian tree (Populus angustifolia)

Gene flow is an evolutionary process that supports genetic connectivity and contributes to the capacity of species to adapt to environmental change. Yet, for most species, little is known about the specific environmental factors that influence genetic connectivity, or their effects on genetic diversity and differentiation. We used a landscape genetic approach to understand how geography and climate influence genetic connectivity in a foundation riparian tree (Populus angustifolia), and their relationships with specieswide patterns of genetic diversity and differentiation. Using multivariate restricted optimization in a reciprocal causal modelling framework, we quantified the relative contributions of riparian network connectivity, terrestrial upland resistance and climate gradients on genetic connectivity. We found that (i) all riparian corridors, regardless of river order, equally facilitated connectivity, while terrestrial uplands provided 2.5× more resistance to gene flow than riparian corridors. (ii) Cumulative differences in precipitation seasonality and precipitation of the warmest quarter were the primary climatic factors driving genetic differentiation; furthermore, maximum climate resistance was 45× greater than riparian resistance. (iii) Genetic diversity was positively correlated with connectivity (R² = 0.3744, p = .0019), illustrating the utility of resistance models for identifying landscape conditions that can support a species' ability to adapt to environmental change. From these results, we present a map highlighting key genetic connectivity corridors across P. angustifolia's range that if disrupted could have long‐term ecological and evolutionary consequences. Our findings provide recommendations for conservation and restoration management of threatened riparian ecosystems throughout the western USA and the high biodiversity they support.