Utility of computer simulations in landscape genetics

Move it or lose it: Predicted effects of culverts and population density on Mojave desert tortoise (Gopherus agassizii) connectivity

Roadways and railways can reduce wildlife movements across landscapes, negatively impacting population connectivity. Connectivity may be improved by structures that allow safe passage across linear barriers, but connectivity could be adversely influenced by low population densities. The Mojave desert tortoise is threatened by habitat loss, fragmentation, and population declines. The tortoise continues to decline as disturbance increases across the Mojave Desert in the southwestern United States. While underground crossing structures, like hydrological culverts, have begun receiving attention, population density has not been considered in tortoise connectivity. Our work asks a novel question: How do culverts and population density affect connectivity and potentially drive genetic and demographic patterns? To explore the role of culverts and population density, we used agent-based spatially explicit forward-in-time simulations of gene flow. We constructed resistance surfaces with a range of barriers to movement and representative of tortoise habitat with anthropogenic disturbance. We predicted connectivity under variable population densities. Simulations were run for 200 non-overlapping generations (3400 years) with 30 replicates using 20 microsatellite loci. We evaluated population genetic structure and diversity and found that culverts would not entirely negate the effects of linear barriers, but gene flow improved. Our results also indicated that density is important for connectivity. Low densities resulted in declines regardless of the landscape barrier scenario (> 75% population census size, > 97% effective population size). Results from our simulation using current anthropogenic disturbance predicted decreased population connectivity over time. Genetic and demographic effects were detectable within five generations (85 years) following disturbance with estimated losses in effective population size of 69%. The pronounced declines in effective population size indicate this could be a useful monitoring metric. We suggest management strategies that improve connectivity, such as roadside fencing tied to culverts, conservation areas in a connected network, and development restricted to disturbed areas.

Scientometric Analysis for Spatial Autocorrelation-Related Research from 1991 to 2021

Spatial autocorrelation describes the interdependent relationship between the realizations or observations of a variable that is distributed across a geographical landscape, which may be divided into different units/areas according to natural or political boundaries. Researchers of Geographical Information Science (GIS) always consider spatial autocorrelation. However, spatial autocorrelation research covers a wide range of disciplines, not only GIS, but spatial econometrics, ecology, biology, etc. Since spatial autocorrelation relates to multiple disciplines, it is difficult gain a wide breadth of knowledge on all its applications, which is very important for beginners to start their research as well as for experienced scholars to consider new perspectives in their works. Scientometric analyses are conducted in this paper to achieve this end. Specifically, we employ scientometrc indicators and scientometric network mapping techniques to discover influential journals, countries, institutions, and research communities; key topics and papers; and research development and trends. The conclusions are: (1) journals categorized into ecological and biological domains constitute the majority of TOP journals;(2) northern American countries, European countries, Australia, Brazil, and China contribute the most to spatial autocorrelation-related research; (3) eleven research communities consisting of three geographical communities and eight communities of other domains were detected; (4) hot topics include spatial autocorrelation analysis for molecular data, biodiversity, spatial heterogeneity, and variability, and problems that have emerged in the rapid development of China; and (5) spatial statistics-based approaches and more intensive problem-oriented applications are, and still will be, the trend of spatial autocorrelation-related research. We also refine the results from a geographer’s perspective at the end of this paper.

Consequences of the Last Glacial Period on the Genetic Diversity of Southeast Asians

The last glacial period (LGP) promoted a loss of genetic diversity in Paleolithic populations of modern humans from diverse regions of the world by range contractions and habitat fragmentation. However, this period also provided some currently submersed lands, such as the Sunda shelf in Southeast Asia (SEA), that could have favored the expansion of our species. Concerning the latter, still little is known about the influence of the lowering sea level on the genetic diversity of current SEA populations. Here, we applied approximate Bayesian computation, based on extensive spatially explicit computer simulations, to evaluate the fitting of mtDNA data from diverse SEA populations with alternative evolutionary scenarios that consider and ignore the LGP and migration through long-distance dispersal (LDD). We found that both the LGP and migration through LDD should be taken into consideration to explain the currently observed genetic diversity in these populations and supported a rapid expansion of first populations throughout SEA. We also found that temporarily available lands caused by the low sea level of the LGP provided additional resources and migration corridors that favored genetic diversity. We conclude that migration through LDD and temporarily available lands during the LGP should be considered to properly understand and model the first expansions of modern humans.

Geonomics: forward-time, spatially explicit, and arbitrarily complex landscape genomic simulations

Understanding the drivers of spatial patterns of genomic diversity has emerged as a major goal of evolutionary genetics. The flexibility of forward-time simulation makes it especially valuable for these efforts, allowing for the simulation of arbitrarily complex scenarios in a way that mimics how real populations evolve. Here, we present Geonomics, a Python package for performing complex, spatially explicit, landscape genomic simulations with full spatial pedigrees that dramatically reduces user workload yet remains customizable and extensible because it is embedded within a popular, general-purpose language. We show that Geonomics results are consistent with expectations for a variety of validation tests based on classic models in population genetics and then demonstrate its utility and flexibility with a trio of more complex simulation scenarios that feature polygenic selection, selection on multiple traits, simulation on complex landscapes, and nonstationary environmental change. We then discuss runtime, which is primarily sensitive to landscape raster size, memory usage, which is primarily sensitive to maximum population size and recombination rate, and other caveats related to the model’s methods for approximating recombination and movement. Taken together, our tests and demonstrations show that Geonomics provides an efficient and robust platform for population genomic simulations that capture complex spatial and evolutionary dynamics.

Evaluating Causes of Current Genetic Gradients of Modern Humans of the Iberian Peninsula

The history of modern humans in the Iberian Peninsula includes a variety of population arrivals sometimes presenting admixture with resident populations. Genetic data from current Iberian populations revealed an overall east–west genetic gradient that some authors interpreted as a direct consequence of the Reconquista, where Catholic Kingdoms expanded their territories toward the south while displacing Muslims. However, this interpretation has not been formally evaluated. Here, we present a qualitative analysis of the causes of the current genetic gradient observed in the Iberian Peninsula using extensive spatially explicit computer simulations based on a variety of evolutionary scenarios. Our results indicate that the Neolithic range expansion clearly produces the orientation of the observed genetic gradient. Concerning the Reconquista (including political borders among Catholic Kingdoms and regions with different languages), if modeled upon a previous Neolithic expansion, it effectively favored the orientation of the observed genetic gradient and shows local isolation of certain regions (i.e., Basques and Galicia). Despite additional evolutionary scenarios could be evaluated to more accurately decipher the causes of the Iberian genetic gradient, here we show that this gradient has a more complex explanation than that previously hypothesized.

Can We Compare Effect Size of Spatial Genetic Structure Between Studies and Species Using Moran Eigenvector Maps?

As the field of landscape genetics is progressing toward comparative empirical studies and meta-analysis, it is important to know how best to compare the strength of spatial genetic structure between studies and species. Moran’s Eigenvector Maps are a promising method that does not make an assumption of isolation-by-distance in a homogeneous environment but can discern cryptic structure that may result from multiple processes operating in heterogeneous landscapes. MEMgene uses spatial filters from Moran’s Eigenvector Maps as predictor variables to explain variation in a genetic distance matrix, and it returns adjusted R2 as a measure of the amount of genetic variation that is spatially structured. However, it is unclear whether, and under which conditions, this value can be used to compare the degree of spatial genetic structure (effect size) between studies. This study addresses the fundamental question of comparability at two levels: between independent studies (meta-analysis mode) and between species sampled at the same locations (comparative mode). We used published datasets containing 9,900 haploid, biallelic, neutral loci simulated on a quasi-continuous, square landscape under four demographic scenarios (island model, isolation-by-distance, expansion from one or two refugia). We varied the genetic resolution (number of individuals and loci) and the number of random sampling locations. We considered two measures of effect size, the MEMgene adjusted R2 and multivariate Moran’s I, which is related to Moran’s Eigenvector Maps. Both metrics were highly sensitive to the number of locations, even when using standardized effect sizes, SES, and the number of individuals sampled per location, but not to the number of loci. In comparative mode, using the same Moran Eigenvector Maps for all species, even those with missing values at some sampling locations, reduced bias due to the number of locations under isolation-by-distance (stationary process) but increased it under expansion from one or two refugia (non-stationary process). More robust measures of effect size need to be developed before the strength of spatial genetic structure can be accurately compared, either in a meta-analysis of independent empirical studies or within a comparative, multispecies landscape genetic study.

Primate landscape genetics: A review and practical guide

Landscape genetics is an emerging field that integrates population genetics, landscape ecology, and spatial statistics to investigate how geographical and environmental features and evolutionary processes such as gene flow, genetic drift, and selection structure genetic variation at both the population and individual levels, with implications for ecology, evolution, and conservation biology. Despite being particularly well suited for primatologists, this method is currently underutilized. Here, we synthesize the current state of research on landscape genetics in primates. We begin by outlining how landscape genetics has been used to disentangle the drivers of diversity, followed by a review of how landscape genetic methods have been applied to primates. This is followed by a section highlighting special considerations when applying the methods to primates, and a practical guide to facilitate further landscape genetics studies using both existing and de novo datasets. We conclude by exploring future avenues of inquiry that could be facilitated by recent developments as well as underdeveloped applications of landscape genetics to primates.

Past, present, future: tracking and simulating genetic differentiation over time in a closed metapopulation system

Genetic differentiation plays an essential role in the assessment of metapopulation systems of conservation concern. Migration rates affect the degree of genetic differentiation between subpopulations, with increasing genetic differentiation leading to increasing extinction risk. Analyses of genetic differentiation repeated over time together with projections into the future are therefore important to inform conservation. We investigated genetic differentiation in a closed metapopulation system of an obligate forest grouse, the Western capercaillie Tetrao urogallus, by comparing microsatellite population structure between a historic and a recent time period. We found an increase in genetic differentiation over a period of approximately 15 years. Making use of forward simulations accounting for population dynamics and genetics from both time periods, we explored future genetic differentiation by implementing scenarios of differing migration rates. Using migration rates derived from the recent dataset, simulations predicted further increase of genetic differentiation by 2050. We then examined effects of two realistic yet hypothetical migration scenarios on genetic differentiation. While isolation of a subpopulation led to overall increased genetic differentiation, the re-establishment of connectivity between two subpopulations maintained genetic differentiation at recent levels. Our results emphasize the importance of maintaining connectivity between subpopulations in order to prevent further genetic differentiation and loss of genetic variation. The simulation set-up we developed is highly adaptable and will aid researchers and conservationists alike in anticipating consequences of conservation strategies for metapopulation systems.

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.

Spatial dynamics of the Girolando breed in Brazil: analysis of genetic integration and environmental factors

Brazil is one of the world's largest milk producers. Several scientific studies have been developed related to landscape analyses that combine genetic with landscape structure data. In the present study, we aimed to analyze the relationship between genetic, environmental, and socioeconomic aspects of production in Girolando cattle in Brazil, as well as verify the spatial patterns of its genetic diversity. Genetic values and accuracy of 46,289 animals were used as well as information from DNA of 310 Girolando animals. Canonic, discriminant, and cluster analyses were conducted in SAS® and K-means method in ArcGIS 10.3 software. The relationship between genetic and geographic distance was analyzed using different methods in software Alleles in Space®. Clusters with animals with higher genetic values for milk production are located in municipalities with lower gross domestic product, fewer family-based establishments, and lower human development index. These clusters are associated with regions with higher area planted with crops, lower percentage of pastures that were less degraded, higher humidity, lower temperature range, and lower normalized difference vegetation index (NDVI) values. The greater the geographical distance between groups of animals, the greater the genetic distance between them with a significant distinction over 504 km. There is high genetic heterogeneity among animals. From these results, it will be possible to develop methodologies for better evaluation of the animals within the production systems.

On the application, reporting, and sharing of in silico simulations for genetic studies

In silico simulations play an indispensable role in the development and application of statistical models and methods for genetic studies. Simulation tools allow for the evaluation of methods and investigation of models in a controlled manner. With the growing popularity of evolutionary models and simulation‐based statistical methods, genetic simulations have been applied to a wide variety of research disciplines such as population genetics, evolutionary genetics, genetic epidemiology, ecology, and conservation biology. In this review, we surveyed 1409 articles from five journals that publish on major application areas of genetic simulations. We identified 432 papers in which genetic simulations were used and examined the targets and applications of simulation studies and how these simulation methods and simulated data sets are reported and shared. Whereas a large proportion (30%) of the surveyed articles reported the use of genetic simulations, only 28% of these genetic simulation studies used existing simulation software, 2% used existing simulated data sets, and 19% and 12% made source code and simulated data sets publicly available, respectively. Moreover, 15% of articles provided no information on how simulation studies were performed. These findings suggest a need to encourage sharing and reuse of existing simulation software and data sets, as well as providing more information regarding the performance of simulations.

Influence of Paleolithic range contraction, admixture and long-distance dispersal on genetic gradients of modern humans in Asia

Cavalli-Sforza and coauthors originally explored the genetic variation of modern humans throughout the world and observed an overall east-west genetic gradient in Asia. However, the specific environmental and population genetics processes causing this gradient were not formally investigated and promoted discussion in recent studies. Here we studied the influence of diverse environmental and population genetics processes on Asian genetic gradients and identified which could have produced the observed gradient. To do so, we performed extensive spatially-explicit computer simulations of genetic data under the following scenarios: (a) variable levels of admixture between Paleolithic and Neolithic populations, (b) migration through long-distance dispersal (LDD), (c) Paleolithic range contraction induced by the last glacial maximum (LGM), and (d) Neolithic range expansions from one or two geographic origins (the Fertile Crescent and the Yangzi and Yellow River Basins). Next, we estimated genetic gradients from the simulated data and we found that they were sensible to the analysed processes, especially to the range contraction induced by LGM and to the number of Neolithic expansions. Some scenarios were compatible with the observed east-west genetic gradient, such as the Paleolithic expansion with a range contraction induced by the LGM or two Neolithic range expansions from both the east and the west. In general, LDD increased the variance of genetic gradients among simulations. We interpreted the obtained gradients as a consequence of both allele surfing caused by range expansions and isolation by distance along the vast east-west geographic axis of this continent.

Harnessing paleo-environmental modeling and genetic data to predict intraspecific genetic structure

Spatially explicit simulations of gene flow within complex landscapes could help forecast the responses of populations to global and anthropological changes. Simulating how past climate change shaped intraspecific genetic variation can provide a validation of models in anticipation of their use to predict future changes. We review simulation models that provide inferences on population genetic structure. Existing simulation models generally integrate complex demographic and genetic processes but are less focused on the landscape dynamics. In contrast to previous approaches integrating detailed demographic and genetic processes and only secondarily landscape dynamics, we present a model based on parsimonious biological mechanisms combining habitat suitability and cellular processes, applicable to complex landscapes. The simulation model takes as input (a) the species dispersal capacities as the main biological parameter, (b) the species habitat suitability, and (c) the landscape structure, modulating dispersal. Our model emphasizes the role of landscape features and their temporal dynamics in generating genetic differentiation among populations within species. We illustrate our model on caribou/reindeer populations sampled across the entire species distribution range in the Northern Hemisphere. We show that simulations over the past 21 kyr predict a population genetic structure that matches empirical data. This approach looking at the impact of historical landscape dynamics on intraspecific structure can be used to forecast population structure under climate change scenarios and evaluate how species range shifts might induce erosion of genetic variation within species.

Ocean currents and the population genetic signature of fish migrations

Animal migrations are a fascinating and global phenomenon, yet they are often difficult to study and sometimes poorly understood. Here, we build on classic ecological theory by hypothesizing that some enigmatic spawning migrations across coastal marine habitats can be inferred from the population genetic signature of larval dispersal by ocean currents. We test this assumption by integrating spatially realistic simulations of alternative spawning migration routes, associated patterns of larval dispersal, and associated variation in the population genetic structure of eastern Australian sea mullet (Mugil cephalus). We then use simulation results to assess the implications of alternative spawning destinations for larval replenishment, and we contrast simulated against measured population genetic variation. Both analyses suggest that the spawning migrations of M. cephalus in eastern Australia are likely to be localized (approximately 100 km along the shore), and that spawning is likely to occur in inshore waters. Our conclusions are supported by multiple lines of evidence available through independent studies, but they challenge the more traditional assumption of a single, long-distance migration event with subsequent offshore spawning in the East Australian Current. More generally, our study operationalizes classic theory on the relationship between fish migrations, ocean currents, and reproductive success. However, rather than confirming the traditionally assumed adaptation of migratory behavior to dominant ocean current flow, our findings support the concept of a genetically measurable link between fish migrations and local oceanographic conditions, specifically water temperature and coastal retention of larvae. We believe that future studies using similar approaches for high resolution and spatially realistic ecological-genetic scenario testing can help rapidly advance our understanding of key ecological processes in many other marine species.

Selecting among Alternative Scenarios of Human Evolution by Simulated Genetic Gradients

Selecting among alternative scenarios of human evolution is nowadays a common methodology to investigate the history of our species. This strategy is usually based on computer simulations of genetic data under different evolutionary scenarios, followed by a fitting of the simulated data with the real data. A recent trend in the investigation of ancestral evolutionary processes of modern humans is the application of genetic gradients as a measure of fitting, since evolutionary processes such as range expansions, range contractions, and population admixture (among others) can lead to different genetic gradients. In addition, this strategy allows the analysis of the genetic causes of the observed genetic gradients. Here, we review recent findings on the selection among alternative scenarios of human evolution based on simulated genetic gradients, including pros and cons. First, we describe common methodologies to simulate genetic gradients and apply them to select among alternative scenarios of human evolution. Next, we review previous studies on the influence of range expansions, population admixture, last glacial period, and migration with long-distance dispersal on genetic gradients for some regions of the world. Finally, we discuss this analytical approach, including technical limitations, required improvements, and advice. Although here we focus on human evolution, this approach could be extended to study other species.

Simulating impacts of rapid forest loss on population size, connectivity and genetic diversity of Sunda clouded leopards (Neofelis diardi) in Borneo

Habitat loss is the greatest threat to biodiversity in Borneo, and to anticipate and combat its effects it is important to predict the pattern of loss and its consequences. Borneo is a region of extremely high biodiversity from which forest is being lost faster than in any other. The little-known Sunda clouded leopard (Neofelis diardi) is the top predator in Borneo and is likely to depend critically on habitat connectivity that is currently being rapidly lost to deforestation. We modeled the effects of landscape fragmentation on population size, genetic diversity and population connectivity for the Sunda clouded leopard across the entirety of Borneo. We modelled the impacts of land use change between the years 2000, 2010 and projected forwards to 2020. We found substantial reductions across all metrics between 2000 and 2010: the proportion of landscape connected by dispersal fell by approximately 12.5% and the largest patch size declined by around 15.1%, leading to a predicted 11.4% decline in clouded leopard numbers. We also predict that these trends will accelerate greatly towards 2020, with the percentage of the landscape being connected by dispersal falling by about 57.8%, the largest patch size falling by around 62.8% and the predicted clouded leopard population falling by 62.5% between 2010 and 2020. We predicted that these large declines in clouded leopard population size and connectivity will also substantially reduce the genetic diversity of the remaining clouded leopard population.

Isolation by distance versus landscape resistance: Understanding dominant patterns of genetic structure in Northern Spotted Owls (Strix occidentalis caurina)

Landscape genetics investigations examine how the availability and configuration of habitat influence genetic structure of plants and animals. We used landscape genetics to evaluate the role that forest connectivity plays in determining genetic structure of the federally-threatened Northern Spotted Owl (Strix occidentalis caurina) using genotypes of 339 Northern Spotted Owls obtained for 10 microsatellite loci. Spatial clustering analyses identified a distinct genetic cluster at the southern extent of the region examined. This cluster could not be linked to landscape connectivity patterns and suggested that post-Pleistocene processes were involved with its development rather than contemporary landscape configuration. We also compared matrices of pairwise inter-individual genetic distances with resistance distances derived from a circuit-theory based framework. Resistance distances were obtained for an idealized raster map that reflected continuous unimpeded dispersal habitat across the landscape along with five empirically-derived raster maps reflecting the 1870’s, 1940’s, 1986, 1994, and 2012. Resistance distances from the idealized map served as surrogates for linear geographic distances. Relative to idealized conditions, resistance distances were ~250% higher in the 1940’s and ~200% higher from 1986 onward. Resistance distances from the 1870’s were ~40% higher than idealized conditions. Inter-individual genetic distances were most highly correlated with resistance distances from the idealized map rather than any of the empirical maps. Two hypotheses explain our results. First, our results may reflect temporal lags between the onset of large-scale habitat alterations and their novel effects on genetic structure in long-lived species such as Northern Spotted Owls. Second, because Northern Spotted Owls disperse over long distances, our results may indicate that forest habitat has never been sufficiently fragmented to the point where connectivity was disrupted. The second hypothesis could indicate that forest management practices mandated by the Northwest Forest Plan succeeded with one of its primary goals. However, our results do not represent a complete portrayal of the status of Northern Spotted Owls given detection of significant population declines and bottlenecks in other studies. Future investigations based on computer simulations may help distinguish between hypotheses.

Persistence of butterfly populations in fragmented habitats along urban density gradients: motility helps

In a simulation study of genotypes conducted over 100 generations for more than 1600 butterfly's individuals, we evaluate how the increase of anthropogenic fragmentation and reduction of habitat size along urbanisation gradients (from 7 to 59% of impervious land cover) influences genetic diversity and population persistence in butterfly species. We show that in areas characterised by a high urbanisation rate (>56% impervious land cover), a large decrease of both genetic diversity (loss of 60-80% of initial observed heterozygosity) and population size (loss of 70-90% of individuals) is observed over time. This is confirmed by empirical data available for the mobile butterfly species Pieris rapae in a subpart of the study area. Comparing simulated data for P. rapae with its normal dispersal ability and with a reduced dispersal ability, we also show that a higher dispersal ability can be an advantage to survive in an urban or highly fragmented environment. The results obtained here suggest that it is of high importance to account for population persistence, and confirm that it is crucial to maintain habitat size and connectivity in the context of land-use planning.

Consequences of population topology for studying gene flow using link-based landscape genetic methods

Many landscape genetic studies aim to determine the effect of landscape on gene flow between populations. These studies frequently employ link‐based methods that relate pairwise measures of historical gene flow to measures of the landscape and the geographical distance between populations. However, apart from landscape and distance, there is a third important factor that can influence historical gene flow, that is, population topology (i.e., the arrangement of populations throughout a landscape). As the population topology is determined in part by the landscape configuration, I argue that it should play a more prominent role in landscape genetics. Making use of existing literature and theoretical examples, I discuss how population topology can influence results in landscape genetic studies and how it can be taken into account to improve the accuracy of these results. In support of my arguments, I have performed a literature review of landscape genetic studies published during the first half of 2015 as well as several computer simulations of gene flow between populations. First, I argue why one should carefully consider which population pairs should be included in link‐based analyses. Second, I discuss several ways in which the population topology can be incorporated in response and explanatory variables. Third, I outline why it is important to sample populations in such a way that a good representation of the population topology is obtained. Fourth, I discuss how statistical testing for link‐based approaches could be influenced by the population topology. I conclude the article with six recommendations geared toward better incorporating population topology in link‐based landscape genetic studies.

Understanding and monitoring the consequences of human impacts on intraspecific variation

Intraspecific variation is a major component of biodiversity, yet it has received relatively little attention from governmental and nongovernmental organizations, especially with regard to conservation plans and the management of wild species. This omission is ill-advised because phenotypic and genetic variations within and among populations can have dramatic effects on ecological and evolutionary processes, including responses to environmental change, the maintenance of species diversity, and ecological stability and resilience. At the same time, environmental changes associated with many human activities, such as land use and climate change, have dramatic and often negative impacts on intraspecific variation. We argue for the need for local, regional, and global programs to monitor intraspecific genetic variation. We suggest that such monitoring should include two main strategies: (i) intensive monitoring of multiple types of genetic variation in selected species and (ii) broad-brush modeling for representative species for predicting changes in variation as a function of changes in population size and range extent. Overall, we call for collaborative efforts to initiate the urgently needed monitoring of intraspecific variation.

Landscape Population Genomics of Forsythia (Forsythia suspensa) Reveal That Ecological Habitats Determine the Adaptive Evolution of Species

Understanding the genetic mechanisms of adaptation to environmental variables is a key concern in molecular ecology and evolutionary biology. Determining the adaptive evolutionary direction and evaluating the adaptation status of species can improve our understanding of these mechanisms. In this study, we sampled 20 populations of Forsythia suspensa to infer the relationship between environmental variables and adaptive genetic variations. Population structure analysis revealed that four genetic groups of F. suspensa exist resulting from divergent selection driven by seven environmental variables. A total of 26 outlier loci were identified by both BayeScan and FDIST2, 23 of which were environment-associated loci (EAL). Environmental association analysis revealed that the environmental variables related to the ecological habitats of F. suspensa are associated with high numbers of EAL. Results of EAL characterization in F. suspensa are consistent with the hypothesis that ecological habitats determine the adaptive evolution of this species. Moreover, a model of species adaptation to environmental variables was proposed in this study. The adaptation model was used to further evaluate the adaptation status of F. suspensa to environmental variables. This study will be useful to help us in understanding the adaptive evolution of species in regions lacking strong selection pressure.

skelesim: an extensible, general framework for population genetic simulation in R

Simulations are a key tool in molecular ecology for inference and forecasting, as well as for evaluating new methods. Due to growing computational power and a diversity of software with different capabilities, simulations are becoming increasingly powerful and useful. However, the widespread use of simulations by geneticists and ecologists is hindered by difficulties in understanding these softwares' complex capabilities, composing code and input files, a daunting bioinformatics barrier and a steep conceptual learning curve. skelesim (an R package) guides users in choosing appropriate simulations, setting parameters, calculating genetic summary statistics and organizing data output, in a reproducible pipeline within the R environment. skelesim is designed to be an extensible framework that can 'wrap' around any simulation software (inside or outside the R environment) and be extended to calculate and graph any genetic summary statistics. Currently, skelesim implements coalescent and forward-time models available in the fastsimcoal2 and rmetasim simulation engines to produce null distributions for multiple population genetic statistics and marker types, under a variety of demographic conditions. skelesim is intended to make simulations easier while still allowing full model complexity to ensure that simulations play a fundamental role in molecular ecology investigations. skelesim can also serve as a teaching tool: demonstrating the outcomes of stochastic population genetic processes; teaching general concepts of simulations; and providing an introduction to the R environment with a user-friendly graphical user interface (using shiny).

Barriers to gene flow and ring species formation

Ring species are groups of organisms that dispersed along a ring-shaped region in such a way that the two ends of the population that meet after many generations are reproductively isolated. They provide a rare opportunity to understand the role of spatial structuring in speciation. Here, we simulate the evolution of ring species assuming that individuals become sexually isolated if the genetic distance between them is above a certain threshold. The model incorporates two forms of dispersal limitation: exogenous geographic barriers that limit the population range and endogenous barriers that result in genetic structuring within the population range. As expected, species' properties that reduce gene flow within the population range facilitate the evolution of reproductive isolation and ring species formation. However, if populations are confined to narrow ranges by geographic barriers, ring species formation increases when local mating is less spatially restricted. Ring species are most likely to form if a population expands while confined to a quasi-unidimensional range but preserving high mobility in the direction of the range expansion. These conditions are unlikely to be met or persist in real populations and may explain why ring species are rare.

Navigating the pitfalls and promise of landscape genetics

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

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

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

Comparing RADseq and microsatellites to infer complex phylogeographic patterns, an empirical perspective in the Crucian carp, Carassius carassius, L

The conservation of threatened species must be underpinned by phylogeographic knowledge. This need is epitomized by the freshwater fish Carassius carassius, which is in decline across much of its European range. Restriction site-associated DNA sequencing (RADseq) is increasingly used for such applications; however, RADseq is expensive, and limitations on sample number must be weighed against the benefit of large numbers of markers. This trade-off has previously been examined using simulation studies; however, empirical comparisons between these markers, especially in a phylogeographic context, are lacking. Here, we compare the results from microsatellites and RADseq for the phylogeography of C. carassius to test whether it is more advantageous to genotype fewer markers (microsatellites) in many samples, or many markers (SNPs) in fewer samples. These data sets, along with data from the mitochondrial cytochrome b gene, agree on broad phylogeographic patterns, showing the existence of two previously unidentified C. carassius lineages in Europe: one found throughout northern and central-eastern European drainages and a second almost exclusively confined to the Danubian catchment. These lineages have been isolated for approximately 2.15 m years and should be considered separate conservation units. RADseq recovered finer population structure and stronger patterns of IBD than microsatellites, despite including only 17.6% of samples (38% of populations and 52% of samples per population). RADseq was also used along with approximate Bayesian computation to show that the postglacial colonization routes of C. carassius differ from the general patterns of freshwater fish in Europe, likely as a result of their distinctive ecology.

The effect of the dispersal kernel on isolation-by-distance in a continuous population

Under models of isolation-by-distance, population structure is determined by the probability of identity-by-descent between pairs of genes according to the geographic distance between them. Well established analytical results indicate that the relationship between geographical and genetic distance depends mostly on the neighborhood size of the population which represents a standardized measure of gene flow. To test this prediction, we model local dispersal of haploid individuals on a two-dimensional landscape using seven dispersal kernels: Rayleigh, exponential, half-normal, triangular, gamma, Lomax and Pareto. When neighborhood size is held constant, the distributions produce similar patterns of isolation-by-distance, confirming predictions. Considering this, we propose that the triangular distribution is the appropriate null distribution for isolation-by-distance studies. Under the triangular distribution, dispersal is uniform over the neighborhood area which suggests that the common description of neighborhood size as a measure of an effective, local panmictic population is valid for popular families of dispersal distributions. We further show how to draw random variables from the triangular distribution efficiently and argue that it should be utilized in other studies in which computational efficiency is important.

Detecting and quantifying introgression in hybridized populations: simplifying assumptions yield overconfidence and uncertainty

A growing threat to the conservation of many native species worldwide is genetic introgression from non-native species. Although improved molecular genetic techniques are increasing the availability of species-diagnostic markers for many species, efficient field sampling design and reliable data interpretation require accurate estimates of uncertainty associated with the detection of non-native alleles and the quantification of introgression in native populations. Using fish populations as examples, we developed a simulation model of an age-structured population that tracks the introduction and inheritance of non-native alleles across generations by simulating stochastic mating and survival of individual fish and the resulting transmission of diagnostic markers. To simulate detection and quantification of introgression, we sampled varying combinations of n fish and m diagnostic markers to detect and quantify introgression from thousands of virtual, independent fish populations for a wide range of hybridization scenarios. Using the results of simulated sampling, we quantified the extent to which common simplifying assumptions regarding population structure and inheritance mechanisms can lead to the following: (i) overconfidence in our ability to detect non-native alleles and (ii) unrealistically narrow confidence intervals for estimates of the proportion of non-native alleles present. Under many circumstances, commonly used simplifying assumptions underestimate the probability of failing to detect ongoing introgression and the uncertainty associated with estimates of introgression by orders of magnitude. Such overconfidence in our ability to detect and quantify introgression can affect critical conservation and management decisions regarding native species undergoing or at risk of introgression from non-native species.

Multi-taxa integrated landscape genetics for zoonotic infectious diseases: deciphering variables influencing disease emergence

Zoonotic disease transmission systems involve sets of species interacting with each other and their environment. This complexity impedes development of disease monitoring and control programs that require reliable identification of spatial and biotic variables and mechanisms facilitating disease emergence. To overcome this difficulty, we propose a framework that simultaneously examines all species involved in disease emergence by integrating concepts and methods from population genetics, landscape ecology, and spatial statistics. Multi-taxa integrated landscape genetics (MTILG) can reveal how interspecific interactions and landscape variables influence disease emergence patterns. We test the potential of our MTILG-based framework by modelling the emergence of a disease system across multiple species dispersal, interspecific interaction, and landscape scenarios. Our simulations showed that both interspecific-dependent dispersal patterns and landscape characteristics significantly influenced disease spread. Using our framework, we were able to detect statistically similar inter-population genetic differences and highly correlated spatial genetic patterns that imply species-dependent dispersal. Additionally, species that were assigned coupled-dispersal patterns were affected to the same degree by similar landscape variables. This study underlines the importance of an integrated approach to investigating emergence of disease systems. MTILG is a robust approach for such studies and can identify potential avenues for targeted disease management strategies.

Assessing Genetic Structure in Common but Ecologically Distinct Carnivores: The Stone Marten and Red Fox

The identification of populations and spatial genetic patterns is important for ecological and conservation research, and spatially explicit individual-based methods have been recognised as powerful tools in this context. Mammalian carnivores are intrinsically vulnerable to habitat fragmentation but not much is known about the genetic consequences of fragmentation in common species. Stone martens (Martes foina) and red foxes (Vulpes vulpes) share a widespread Palearctic distribution and are considered habitat generalists, but in the Iberian Peninsula stone martens tend to occur in higher quality habitats. We compared their genetic structure in Portugal to see if they are consistent with their differences in ecological plasticity, and also to illustrate an approach to explicitly delineate the spatial boundaries of consistently identified genetic units. We analysed microsatellite data using spatial Bayesian clustering methods (implemented in the software BAPS, GENELAND and TESS), a progressive partitioning approach and a multivariate technique (Spatial Principal Components Analysis-sPCA). Three consensus Bayesian clusters were identified for the stone marten. No consensus was achieved for the red fox, but one cluster was the most probable clustering solution. Progressive partitioning and sPCA suggested additional clusters in the stone marten but they were not consistent among methods and were geographically incoherent. The contrasting results between the two species are consistent with the literature reporting stricter ecological requirements of the stone marten in the Iberian Peninsula. The observed genetic structure in the stone marten may have been influenced by landscape features, particularly rivers, and fragmentation. We suggest that an approach based on a consensus clustering solution of multiple different algorithms may provide an objective and effective means to delineate potential boundaries of inferred subpopulations. sPCA and progressive partitioning offer further verification of possible population structure and may be useful for revealing cryptic spatial genetic patterns worth further investigation.

Fine-scale landscape genetics of the American badger (Taxidea taxus): disentangling landscape effects and sampling artifacts in a poorly understood species

Landscape genetics is a powerful tool for conservation because it identifies landscape features that are important for maintaining genetic connectivity between populations within heterogeneous landscapes. However, using landscape genetics in poorly understood species presents a number of challenges, namely, limited life history information for the focal population and spatially biased sampling. Both obstacles can reduce power in statistics, particularly in individual-based studies. In this study, we genotyped 233 American badgers in Wisconsin at 12 microsatellite loci to identify alternative statistical approaches that can be applied to poorly understood species in an individual-based framework. Badgers are protected in Wisconsin owing to an overall lack in life history information, so our study utilized partial redundancy analysis (RDA) and spatially lagged regressions to quantify how three landscape factors (Wisconsin River, Ecoregions and land cover) impacted gene flow. We also performed simulations to quantify errors created by spatially biased sampling. Statistical analyses first found that geographic distance was an important influence on gene flow, mainly driven by fine-scale positive spatial autocorrelations. After controlling for geographic distance, both RDA and regressions found that Wisconsin River and Agriculture were correlated with genetic differentiation. However, only Agriculture had an acceptable type I error rate (3-5%) to be considered biologically relevant. Collectively, this study highlights the benefits of combining robust statistics and error assessment via simulations and provides a method for hypothesis testing in individual-based landscape genetics.

Fine-Grained Distribution of a Non-Native Resource Can Alter the Population Dynamics of a Native Consumer

New interactions with non-native species can alter selection pressures on native species. Here, we examined the effect of the spatial distribution of a non-native species, a factor that determines ecological and evolutionary outcomes but that is poorly understood, particularly on a fine scale. Specifically, we explored a native butterfly population and a non-native plant on which the butterfly oviposits despite the plant’s toxicity to larvae. We developed an individual-based model to describe movement and oviposition behaviors of each butterfly, which were determined by plant distribution and the butterfly's host preference genotype. We estimated the parameter values of the model from rich field data. We simulated various patterns of plant distributions and compared the rates of butterfly population growth and changes in the allele frequency of oviposition preference. Neither the number nor mean area of patches of non-native species affected the butterfly population, whereas plant abundance, patch shape, and distance to the nearest native and non-native patches altered both the population dynamics and genetics. Furthermore, we found a dramatic decrease in population growth rates when we reduced the distance to the nearest native patch from 147 m to 136 m. Thus changes in the non-native resource distribution that are critical to the fate of the native herbivore could only be detected at a fine-grained scale that matched the scale of a female butterfly’s movement. In addition, we found that the native butterfly population was unlikely to be rescued by the exclusion of the allele for acceptance of the non-native plant as a host. This study thus highlights the importance of including both ecological and evolutionary dynamics in analyses of the outcome of species interactions and provides insights into habitat management for non-native species.

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

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

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

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

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

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

Genetic data simulators and their applications: an overview

Computer simulations have played an indispensable role in the development and applications of statistical models and methods for genetic studies across multiple disciplines. The need to simulate complex evolutionary scenarios and pseudo-datasets for various studies has fueled the development of dozens of computer programs with varying reliability, performance, and application areas. To help researchers compare and choose the most appropriate simulators for their studies, we have created the genetic simulation resources (GSR) website, which allows authors of simulation software to register their applications and describe them with more than 160 defined attributes. This article summarizes the properties of 93 simulators currently registered at GSR and provides an overview of the development and applications of genetic simulators. Unlike other review articles that address technical issues or compare simulators for particular application areas, we focus on software development, maintenance, and features of simulators, often from a historical perspective. Publications that cite these simulators are used to summarize both the applications of genetic simulations and the utilization of simulators.

Challenges in analysis and interpretation of microsatellite data for population genetic studies

Advancing technologies have facilitated the ever-widening application of genetic markers such as microsatellites into new systems and research questions in biology. In light of the data and experience accumulated from several years of using microsatellites, we present here a literature review that synthesizes the limitations of microsatellites in population genetic studies. With a focus on population structure, we review the widely used fixation (F ST) statistics and Bayesian clustering algorithms and find that the former can be confusing and problematic for microsatellites and that the latter may be confounded by complex population models and lack power in certain cases. Clustering, multivariate analyses, and diversity-based statistics are increasingly being applied to infer population structure, but in some instances these methods lack formalization with microsatellites. Migration-specific methods perform well only under narrow constraints. We also examine the use of microsatellites for inferring effective population size, changes in population size, and deeper demographic history, and find that these methods are untested and/or highly context-dependent. Overall, each method possesses important weaknesses for use with microsatellites, and there are significant constraints on inferences commonly made using microsatellite markers in the areas of population structure, admixture, and effective population size. To ameliorate and better understand these constraints, researchers are encouraged to analyze simulated datasets both prior to and following data collection and analysis, the latter of which is formalized within the approximate Bayesian computation framework. We also examine trends in the literature and show that microsatellites continue to be widely used, especially in non-human subject areas. This review assists with study design and molecular marker selection, facilitates sound interpretation of microsatellite data while fostering respect for their practical limitations, and identifies lessons that could be applied toward emerging markers and high-throughput technologies in population genetics.