An overview of the utility of population simulation software in molecular ecology

Simulating pollen flow and field sampling constraints helps revise seed sampling recommendations for conserving genetic diversity

Premise

In this study, we use simulations to determine how pollen flow and sampling constraints can influence the genetic conservation found in seed collections.

Methods

We simulated genotypes of parental individuals and crossed the parentals based on three different ranges of pollen flow (panmictic, limited, and highly limited) to create new seed sets for sampling. We tested a variety of sampling scenarios modeled on those occurring in nature and calculated the proportion of alleles conserved in each scenario.

Results

We found that pollen flow greatly influences collection outcomes, with panmictic pollen flow resulting in seed sets containing 21.6% more alleles than limited pollen flow and 48.6% more alleles than highly limited pollen flow, although this impact diminishes when large numbers of maternal plants are sampled. Simulations of realistic seed sampling (sampling more seed from some plants and fewer from others) showed a relatively minor impact (<2.5%) on genetic diversity conserved compared to ideal sampling (uniform sampling across all maternal plants).

Discussion

We conclude that future work must consider limited pollen flow, but collectors can be flexible with their sampling in the field as long as many unique maternal plants are sampled. Simulations remain a fruitful method to advance ex situ sampling guidelines.

slimr: An R package for tailor-made integrations of data in population genomic simulations over space and time

Software for realistically simulating complex population genomic processes is revolutionizing our understanding of evolutionary processes, and providing novel opportunities for integrating empirical data with simulations. However, the integration between standalone simulation software and R is currently not well developed. Here, we present slimr, an R package designed to create a seamless link between standalone software SLiM >3.0, one of the most powerful population genomic simulation frameworks, and the R development environment, with its powerful data manipulation and analysis tools. We show how slimr facilitates smooth integration between genetic data, ecological data and simulation in a single environment. The package enables pipelines that begin with data reading, cleaning and manipulation, proceed to constructing empirically based parameters and initial conditions for simulations, then to running numerical simulations and finally to retrieving simulation results in a format suitable for comparisons with empirical data - aided by advanced analysis and visualization tools provided by R. We demonstrate the use of slimr with an example from our own work on the landscape population genomics of desert mammals, highlighting the advantage of having a single integrated tool for both data analysis and simulation. slimr makes the powerful simulation ability of SLiM directly accessible to R users, allowing integrated simulation projects that incorporate empirical data without the need to switch between software environments. This should provide more opportunities for evolutionary biologists and ecologists to use realistic simulations to better understand the interplay between ecological and evolutionary processes.

Genomics and conservation: Guidance from training to analyses and applications

Environmental change is intensifying the biodiversity crisis and threatening species across the tree of life. Conservation genomics can help inform conservation actions and slow biodiversity loss. However, more training, appropriate use of novel genomic methods and communication with managers are needed. Here, we review practical guidance to improve applied conservation genomics. We share insights aimed at ensuring effectiveness of conservation actions around three themes: (1) improving pedagogy and training in conservation genomics including for online global audiences, (2) conducting rigorous population genomic analyses properly considering theory, marker types and data interpretation and (3) facilitating communication and collaboration between managers and researchers. We aim to update students and professionals and expand their conservation toolkit with genomic principles and recent approaches for conserving and managing biodiversity. The biodiversity crisis is a global problem and, as such, requires international involvement, training, collaboration and frequent reviews of the literature and workshops as we do here.

"Bet hedging" against climate change in developing and adult animals: roles for stochastic gene expression, phenotypic plasticity, epigenetic inheritance and adaptation

Animals from embryos to adults experiencing stress from climate change have numerous mechanisms available for enhancing their long-term survival. In this review we consider these options, and how viable they are in a world increasingly experiencing extreme weather associated with climate change. A deeply understood mechanism involves natural selection, leading to evolution of new adaptations that help cope with extreme and stochastic weather events associated with climate change. While potentially effective at staving off environmental challenges, such adaptations typically occur very slowly and incrementally over evolutionary time. Consequently, adaptation through natural selection is in most instances regarded as too slow to aid survival in rapidly changing environments, especially when considering the stochastic nature of extreme weather events associated with climate change. Alternative mechanisms operating in a much shorter time frame than adaptation involve the rapid creation of alternate phenotypes within a life cycle or a few generations. Stochastic gene expression creates multiple phenotypes from the same genotype even in the absence of environmental cues. In contrast, other mechanisms for phenotype change that are externally driven by environmental clues include well-understood developmental phenotypic plasticity (variation, flexibility), which can enable rapid, within-generation changes. Increasingly appreciated are epigenetic influences during development leading to rapid phenotypic changes that can also immediately be very widespread throughout a population, rather than confined to a few individuals as in the case of favorable gene mutations. Such epigenetically-induced phenotypic plasticity can arise rapidly in response to stressors within a generation or across a few generations and just as rapidly be "sunsetted" when the stressor dissipates, providing some capability to withstand environmental stressors emerging from climate change. Importantly, survival mechanisms resulting from adaptations and developmental phenotypic plasticity are not necessarily mutually exclusive, allowing for classic "bet hedging". Thus, the appearance of multiple phenotypes within a single population provides for a phenotype potentially optimal for some future environment. This enhances survival during stochastic extreme weather events associated with climate change. Finally, we end with recommendations for future physiological experiments, recommending in particular that experiments investigating phenotypic flexibility adopt more realistic protocols that reflect the stochastic nature of weather.

A Bocage Landscape Restricts the Gene Flow of Pest Vole Populations

The population dynamics of most animal species inhabiting agro-ecosystems may be determined by landscape characteristics, with agricultural intensification and the reduction of natural habitats influencing dispersal and hence limiting gene flow. Increasing landscape complexity would thus benefit many endangered species by providing different ecological niches, but it could also lead to undesired effects in species that can act as crop pests and disease reservoirs. We tested the hypothesis that a highly variegated landscape influences patterns of genetic structure in agricultural pest voles. Ten populations of fossorial water vole, Arvicola scherman, located in a bocage landscape in Atlantic NW Spain were studied using DNA microsatellite markers and a graph-based model. The results showed a strong isolation-by-distance pattern with a significant genetic correlation at smaller geographic scales, while genetic differentiation at larger geographic scales indicated a hierarchical pattern of up to eight genetic clusters. A metapopulation-type structure was observed, immersed in a landscape with a low proportion of suitable habitats. Matrix scale rather than matrix heterogeneity per se may have an important effect upon gene flow, acting as a demographic sink. The identification of sub-populations, considered to be independent management units, allows the establishment of feasible population control efforts in this area. These insights support the use of agro-ecological tools aimed at recreating enclosed field systems when planning integrated managements for controlling patch-dependent species such as grassland voles.

Global genetic diversity status and trends: towards a suite of Essential Biodiversity Variables (EBVs) for genetic composition

Biodiversity underlies ecosystem resilience, ecosystem function, sustainable economies, and human well‐being. Understanding how biodiversity sustains ecosystems under anthropogenic stressors and global environmental change will require new ways of deriving and applying biodiversity data. A major challenge is that biodiversity data and knowledge are scattered, biased, collected with numerous methods, and stored in inconsistent ways. The Group on Earth Observations Biodiversity Observation Network (GEO BON) has developed the Essential Biodiversity Variables (EBVs) as fundamental metrics to help aggregate, harmonize, and interpret biodiversity observation data from diverse sources. Mapping and analyzing EBVs can help to evaluate how aspects of biodiversity are distributed geographically and how they change over time. EBVs are also intended to serve as inputs and validation to forecast the status and trends of biodiversity, and to support policy and decision making. Here, we assess the feasibility of implementing Genetic Composition EBVs (Genetic EBVs), which are metrics of within‐species genetic variation. We review and bring together numerous areas of the field of genetics and evaluate how each contributes to global and regional genetic biodiversity monitoring with respect to theory, sampling logistics, metadata, archiving, data aggregation, modeling, and technological advances. We propose four Genetic EBVs: (i) Genetic Diversity; (ii) Genetic Differentiation; (iii) Inbreeding; and (iv) Effective Population Size (N_e). We rank Genetic EBVs according to their relevance, sensitivity to change, generalizability, scalability, feasibility and data availability. We outline the workflow for generating genetic data underlying the Genetic EBVs, and review advances and needs in archiving genetic composition data and metadata. We discuss how Genetic EBVs can be operationalized by visualizing EBVs in space and time across species and by forecasting Genetic EBVs beyond current observations using various modeling approaches. Our review then explores challenges of aggregation, standardization, and costs of operationalizing the Genetic EBVs, as well as future directions and opportunities to maximize their uptake globally in research and policy. The collection, annotation, and availability of genetic data has made major advances in the past decade, each of which contributes to the practical and standardized framework for large‐scale genetic observation reporting. Rapid advances in DNA sequencing technology present new opportunities, but also challenges for operationalizing Genetic EBVs for biodiversity monitoring regionally and globally. With these advances, genetic composition monitoring is starting to be integrated into global conservation policy, which can help support the foundation of all biodiversity and species' long‐term persistence in the face of environmental change. We conclude with a summary of concrete steps for researchers and policy makers for advancing operationalization of Genetic EBVs. The technical and analytical foundations of Genetic EBVs are well developed, and conservation practitioners should anticipate their increasing application as efforts emerge to scale up genetic biodiversity monitoring regionally and globally.

AdmixSim 2: a forward-time simulator for modeling complex population admixture

BACKGROUND

Computer simulations have been widely applied in population genetics and evolutionary studies. A great deal of effort has been made over the past two decades in developing simulation tools. However, there are not many simulation tools suitable for studying population admixture.

RESULTS

We here developed a forward-time simulator, AdmixSim 2, an individual-based tool that can flexibly and efficiently simulate population genomics data under complex evolutionary scenarios. Unlike its previous version, AdmixSim 2 is based on the extended Wright-Fisher model, and it implements many common evolutionary parameters to involve gene flow, natural selection, recombination, and mutation, which allow users to freely design and simulate any complex scenario involving population admixture. AdmixSim 2 can be used to simulate data of dioecious or monoecious populations, autosomes, or sex chromosomes. To our best knowledge, there are no similar tools available for the purpose of simulation of complex population admixture. Using empirical or previously simulated genomic data as input, AdmixSim 2 provides phased haplotype data for the convenience of further admixture-related analyses such as local ancestry inference, association studies, and other applications. We here evaluate the performance of AdmixSim 2 based on simulated data and validated functions via comparative analysis of simulated data and empirical data of African American, Mexican, and Uyghur populations.

CONCLUSIONS

AdmixSim 2 is a flexible simulation tool expected to facilitate the study of complex population admixture in various situations.

Global Commitments to Conserving and Monitoring Genetic Diversity Are Now Necessary and Feasible

Global conservation policy and action have largely neglected protecting and monitoring genetic diversity—one of the three main pillars of biodiversity. Genetic diversity (diversity within species) underlies species’ adaptation and survival, ecosystem resilience, and societal innovation. The low priority given to genetic diversity has largely been due to knowledge gaps in key areas, including the importance of genetic diversity and the trends in genetic diversity change; the perceived high expense and low availability and the scattered nature of genetic data; and complicated concepts and information that are inaccessible to policymakers. However, numerous recent advances in knowledge, technology, databases, practice, and capacity have now set the stage for better integration of genetic diversity in policy instruments and conservation efforts. We review these developments and explore how they can support improved consideration of genetic diversity in global conservation policy commitments and enable countries to monitor, report on, and take action to maintain or restore genetic diversity.

Leaf shape and size variation in bur oaks: an empirical study and simulation of sampling strategies

PREMISE

Leaf shape and size figure strongly in plants' adaptation to their environments. Among trees, oaks are notoriously variable in leaf morphology. Our study examines the degree to which within-tree, among-tree, and among-site variation contribute to latitudinal variation in leaf shape and size of bur oak (Quercus macrocarpa: Fagaceae), one of North America's most geographically widespread oak species.

METHODS

Samples were collected from four sites each at northern, central, and southern latitudes of the bur oak range. Ten leaf size traits were measured, and variance in these traits and eight ratios based on these traits was partitioned into tree and population components. Population means were regressed on latitude. We then parameterized a series of leaf collection simulations using empirical covariance among leaves on trees and trees at sites. We used the simulations to assess the efficiency of different collecting strategies for estimating among-population differences in leaf shape and size.

RESULTS

Leaf size was highly responsive to latitude. Site contributed more than tree to total variation in leaf shape and size. Simulations suggest that power to detect among-site variance in leaf shape and size increases with either more leaves per tree (10-11 leaves from each of 5 trees) or more trees per site (5 leaves from each of 10+ trees).

CONCLUSIONS

Our study demonstrates the utility of simulating sampling and controlling for variance in sampling for leaf morphology, whether the questions being addressed are ecological, evolutionary, or taxonomic. Simulation code is provided as an R package (traitsPopSim) to help researchers plan morphological sampling strategies.

Big Data in Conservation Genomics: Boosting Skills, Hedging Bets, and Staying Current in the Field

A current challenge in the fields of evolutionary, ecological, and conservation genomics is balancing production of large-scale datasets with additional training often required to handle such datasets. Thus, there is an increasing need for conservation geneticists to continually learn and train to stay up-to-date through avenues such as symposia, meetings, and workshops. The ConGen meeting is a near-annual workshop that strives to guide participants in understanding population genetics principles, study design, data processing, analysis, interpretation, and applications to real-world conservation issues. Each year of ConGen gathers a diverse set of instructors, students, and resulting lectures, hands-on sessions, and discussions. Here, we summarize key lessons learned from the 2019 meeting and more recent updates to the field with a focus on big data in conservation genomics. First, we highlight classical and contemporary issues in study design that are especially relevant to working with big datasets, including the intricacies of data filtering. We next emphasize the importance of building analytical skills and simulating data, and how these skills have applications within and outside of conservation genetics careers. We also highlight recent technological advances and novel applications to conservation of wild populations. Finally, we provide data and recommendations to support ongoing efforts by ConGen organizers and instructors-and beyond-to increase participation of underrepresented minorities in conservation and eco-evolutionary sciences. The future success of conservation genetics requires both continual training in handling big data and a diverse group of people and approaches to tackle key issues, including the global biodiversity-loss crisis.

SimBit: A high performance, flexible and easy-to-use population genetic simulator

SimBit is a general purpose, high performance forward-in-time population genetics simulator. SimBit can simulate a wide variety of selection scenarios (any selection and dominance coefficients variation, any epistatic interaction, any spatial and temporal changes of selection scenario, etc.), demographic scenarios (any changes in patch sizes, migration rates, realistic demography dependent on fecundity, hard vs. soft selection, exponential vs. logistic growth, gametic or zygotic dispersion, etc.) and mating systems (cloning and selfing rates, hermaphrodites or males and females). SimBit can also track QTLs (with hyperdimensional phenotypes, explicit fitness landscape, plasticity, developmental noise, etc.). Finally, SimBit can simulate multiple species with their ecological relationships. SimBit comes with a R wrapper that simplifies the management of an entire research project from the creation of a grid of parameters and corresponding inputs, running simulations and gathering outputs for analysis. SimBit's performance was extensively benchmarked in comparison to SLiM, Nemo and SFS_CODE, varying population size, recombination rate, mutation rate, and the number of loci. I also reproduced simulations from previous studies, benchmarked QTLs and coalescent tree recording techniques. SimBit was most often the highest performing program with the only notable exception of SLiM outperforming SimBit in scenarios with few loci and low genetic diversity.

The Combined Effect of Haplodiplonty and Partial Clonality on Genotypic and Genetic Diversity in a Finite Mutating Population

Partial clonality is known to affect the genetic composition and evolutionary trajectory of diplontic (single, free-living diploid stage) populations. However, many partially clonal eukaryotes exhibit life cycles in which somatic development occurs in both haploid and diploid individuals (haplodiplontic life cycles). Here, we studied how haplodiplontic life cycles and partial clonality structurally constrain, as immutable parameters, the reshuffling of genetic diversity and its dynamics in populations over generations. We assessed the distribution of common population genetic indices at different proportions of haploids, rates of clonality, mutation rates, and sampling efforts. Our results showed that haplodiplontic life cycles alone in finite populations affect effective population sizes and the ranges of distributions of population genetic indices. With nonoverlapping generations, haplodiplonty allowed the evolution of 2 temporal genetic pools that may diverge in sympatry due to genetic drift under full sexuality and clonality. Partial clonality in these life cycles acted as a homogenizing force between those 2 pools. Moreover, the combined effects of proportion of haploids, rate of clonality, and the relative strength of mutation versus genetic drift impacts the distributions of population genetics indices, rendering it difficult to transpose and use knowledge accumulated from diplontic or haplontic species. Finally, we conclude by providing recommendations for sampling and analyzing the population genetics of partially clonal haplodiplontic taxa.

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.

MetaPopGen 2.0: A multilocus genetic simulator to model populations of large size

Multilocus genetic processes in subdivided populations can be complex and difficult to interpret using theoretical population genetics models. Genetic simulators offer a valid alternative to study multilocus genetic processes in arbitrarily complex scenarios. However, the use of forward-in-time simulators in realistic scenarios involving high numbers of individuals distributed in multiple local populations is limited by computation time and memory requirements. These limitations increase with the number of simulated individuals. We developed a genetic simulator, MetaPopGen 2.0, to model multilocus population genetic processes in subdivided populations of arbitrarily large size. It allows for spatial and temporal variation in demographic parameters, age structure, adult and propagule dispersal, variable mutation rates and selection on survival and fecundity. We developed MetaPopGen 2.0 in the R environment to facilitate its use by non-modeler ecologists and evolutionary biologists. We illustrate the capabilities of MetaPopGen 2.0 for studying adaptation to water salinity in the striped red mullet Mullus surmuletus.

SLiM 3: Forward Genetic Simulations Beyond the Wright-Fisher Model

With the desire to model population genetic processes under increasingly realistic scenarios, forward genetic simulations have become a critical part of the toolbox of modern evolutionary biology. The SLiM forward genetic simulation framework is one of the most powerful and widely used tools in this area. However, its foundation in the Wright-Fisher model has been found to pose an obstacle to implementing many types of models; it is difficult to adapt the Wright-Fisher model, with its many assumptions, to modeling ecologically realistic scenarios such as explicit space, overlapping generations, individual variation in reproduction, density-dependent population regulation, individual variation in dispersal or migration, local extinction and recolonization, mating between subpopulations, age structure, fitness-based survival and hard selection, emergent sex ratios, and so forth. In response to this need, we here introduce SLiM 3, which contains two key advancements aimed at abolishing these limitations. First, the new non-Wright-Fisher or "nonWF" model type provides a much more flexible foundation that allows the easy implementation of all of the above scenarios and many more. Second, SLiM 3 adds support for continuous space, including spatial interactions and spatial maps of environmental variables. We provide a conceptual overview of these new features, and present several example models to illustrate their use.

Combining population genomics and forward simulations to investigate stocking impacts: A case study of Muskellunge (Esox masquinongy) from the St. Lawrence River basin

Understanding the genetic and evolutionary impacts of stocking on wild fish populations has long been of interest as negative consequences such as reduced fitness and loss of genetic diversity are commonly reported outcomes. In an attempt to sustain a fishery, managers implemented nearly five decades of extensive stocking of over a million Muskellunge (Esox masquinongy), a native species in the Lower St. Lawrence River (Québec, Canada). We investigated the effect of this stocking on population genetic structure and allelic diversity in the St. Lawrence River in addition to tributaries and several stocked inland lakes. Using genotype by sequencing, we genotyped 643 individuals representing 22 locations and combined this information with forward simulations to investigate the genetic consequences of long-term stocking. Individuals native to the St. Lawrence watershed were genetically differentiated from stocking sources and tributaries, and inland lakes were naturally differentiated from the main river. Empirical data and simulations within the St. Lawrence River revealed weak stocking effects on admixture patterns. Our data suggest that the genetic structure associated with stocked fish was diluted into its relatively large effective population size. This interpretation is also consistent with a hypothesis that selection against introgression was in operation and relatively efficient within the large St. Lawrence River system. In contrast, smaller populations from adjacent tributaries and lakes displayed greater stocking-related admixture that resulted in comparatively higher heterozygosity than the St. Lawrence. Finally, individuals from inland lakes that were established by stocking maintained a close affinity with their source populations. This study illustrated a benefit of combining extensive genomic data with forward simulations for improved inference regarding population-level genetic effects of long-term stocking, and its relevance for fishery management decision making.

Evolutionary Modeling in SLiM 3 for Beginners

The SLiM forward genetic simulation framework has proved to be a powerful and flexible tool for population genetic modeling. However, as a complex piece of software with many features that allow simulating a diverse assortment of evolutionary models, its initial learning curve can be difficult. Here we provide a step-by-step demonstration of how to build a simple evolutionary model in SLiM 3, to help new users get started. We will begin with a panmictic neutral model, and build up to a model of the evolution of a polygenic quantitative trait under selection for an environmental phenotypic optimum.

The G-matrix Simulator Family: Software for Research and Teaching

Genetic variation plays a fundamental role in all models of evolution. For phenotypes composed of multiple quantitative traits, genetic variation is best quantified as additive genetic variances and covariances, as these values determine the rate and trajectory of evolution. Additive genetic variances and covariances are often summarized conveniently in the G-matrix, which has additive genetic variances for each trait on the diagonal and additive genetic covariances as its off-diagonal elements. The evolution of the G-matrix is an interesting topic in its own right, because the processes that affect trait means also affect the distribution of standing genetic variation, which, in turn, feeds back to affect the rate of change of trait means. Theoretical studies of the G-matrix have profitably employed simulation-based models because the topic is often too complex to yield meaningful analytical results. Here, we present a series of G-matrix simulation software packages, which have emerged from about 15 years of research on this topic. These simulation models are useful for research and for building intuition regarding the evolution of the G-matrix under a wide variety of circumstances. A tutorial and source code also provide a foundation upon which future models can be built. These tools will be useful to students as well as researchers.

Guidelines for planning genomic assessment and monitoring of locally adaptive variation to inform species conservation

Identifying and monitoring locally adaptive genetic variation can have direct utility for conserving species at risk, especially when management may include actions such as translocations for restoration, genetic rescue, or assisted gene flow. However, genomic studies of local adaptation require careful planning to be successful, and in some cases may not be a worthwhile use of resources. Here, we offer an adaptive management framework to help conservation biologists and managers decide when genomics is likely to be effective in detecting local adaptation, and how to plan assessment and monitoring of adaptive variation to address conservation objectives. Studies of adaptive variation using genomic tools will inform conservation actions in many cases, including applications such as assisted gene flow and identifying conservation units. In others, assessing genetic diversity, inbreeding, and demographics using selectively neutral genetic markers may be most useful. And in some cases, local adaptation may be assessed more efficiently using alternative approaches such as common garden experiments. Here, we identify key considerations of genomics studies of locally adaptive variation, provide a road map for successful collaborations with genomics experts including key issues for study design and data analysis, and offer guidelines for interpreting and using results from genomic assessments to inform monitoring programs and conservation actions.

Next-generation conservation genetics and biodiversity monitoring

This special issue of Evolutionary Applications consists of 10 publications investigating the use of next-generation tools and techniques in population genetic analyses and biodiversity assessment. The special issue stems from a 2016 Next Generation Genetic Monitoring Workshop, hosted by the National Institute for Mathematical and Biological Synthesis (NIMBioS) in Tennessee, USA. The improved accessibility of next-generation sequencing platforms has allowed molecular ecologists to rapidly produce large amounts of data. However, with the increased availability of new genomic markers and mathematical techniques, care is needed in selecting appropriate study designs, interpreting results in light of conservation concerns, and determining appropriate management actions. This special issue identifies key attributes of successful genetic data analyses in biodiversity evaluation and suggests ways to improve analyses and their application in current population and conservation genetics research.

Evaluating sample size to estimate genetic management metrics in the genomics era

Inbreeding and relationship metrics among and within populations are useful measures for genetic management of wild populations, but accuracy and precision of estimates can be influenced by the number of individual genotypes analysed. Biologists are confronted with varied advice regarding the sample size necessary for reliable estimates when using genomic tools. We developed a simulation framework to identify the optimal sample size for three widely used metrics to enable quantification of expected variance and relative bias of estimates and a comparison of results among populations. We applied this approach to analyse empirical genomic data for 30 individuals from each of four different free-ranging Rocky Mountain bighorn sheep (Ovis canadensis canadensis) populations in Montana and Wyoming, USA, through cross-species application of an Ovine array and analysis of approximately 14,000 single nucleotide polymorphisms (SNPs) after filtering. We examined intra- and interpopulation relationships using kinship and identity by state metrics, as well as FST between populations. By evaluating our simulation results, we concluded that a sample size of 25 was adequate for assessing these metrics using the Ovine array to genotype Rocky Mountain bighorn sheep herds. However, we conclude that a universal sample size rule may not be able to sufficiently address the complexities that impact genomic kinship and inbreeding estimates. Thus, we recommend that a pilot study and sample size simulation using R code we developed that includes empirical genotypes from a subset of populations of interest would be an effective approach to ensure rigour in estimating genomic kinship and population differentiation.

Insights into population genetics and evolution of polyploids and their ancestors

We have developed the first comprehensive simulator for polyploid genomes (PolySim) and demonstrated its value by performing large-scale simulations to examine the effect of different population parameters on the evolution of polyploids. PolySim is unlimited in terms of ploidy, population size or number of simulated loci. Our process considered the evolution of polyploids from diploid ancestors, polysomic inheritance, inbreeding, recombination rate change in polyploids and gene flow from lower to higher ploidies. We compared the number of segregating single nucleotide polymorphisms, minor allele frequency, heterozygosity, R2 and average kinship relatedness between different simulated scenarios, and to real data from polyploid species. As expected, allotetraploid populations showed no difference from their ancestral diploids when population size remained constant and there was no gene flow or multivalent (MV) pairing between subgenomes. Autotetraploid populations showed significant differences from their ancestors for most parameters and diverged from their ancestral populations faster than allotetraploids. Autotetraploids can have significantly higher heterozygosity, relatedness and extended linkage disequilibrium compared with allotetraploids. Interestingly, autotetraploids were more sensitive to increasing selfing rate and decreasing population size. MV formation can homogenize allotetraploid subgenomes, but this homogenization requires a higher MV rate than previously proposed. Our results can be considered as the first building block to understand polyploid population evolutionary dynamics. PolySim can be used to simulate a wide variety of polyploid organisms that mimic empirical populations, which, in combination with quantitative genetics tools, can be used to investigate the power of genomewide association, genomic selection or breeding programme designs in these species.

Quantitative tools for implementing the new definition of significant portion of the range in the U.S. Endangered Species Act

In 2014, the Fish and Wildlife Service (FWS) and National Marine Fisheries Service announced a new policy interpretation for the U.S. Endangered Species Act (ESA). According to the act, a species must be listed as threatened or endangered if it is determined to be threatened or endangered in a significant portion of its range (SPR). The 2014 policy seeks to provide consistency by establishing that a portion of the range should be considered significant if the associated individuals' "removal would cause the entire species to become endangered or threatened." We reviewed 20 quantitative techniques used to assess whether a portion of a species' range is significant according to the new guidance. Our assessments are based on the 3R criteria-redundancy (i.e., buffering from catastrophe), resiliency (i.e., ability to withstand stochasticity), and representation (i.e., ability to evolve)-that the FWS uses to determine if a species merits listing. We identified data needs for each quantitative technique and considered which methods could be implemented given the data limitations typical of rare species. We also identified proxies for the 3Rs that may be used with limited data. To assess potential data availability, we evaluated 7 example species by accessing data in their species status assessments, which document all the information used during a listing decision. In all species, an SPR could be evaluated with at least one metric for each of the 3Rs robustly or with substantial assumptions. Resiliency assessments appeared most constrained by limited data, and many species lacked information on connectivity between subpopulations, genetic variation, and spatial variability in vital rates. These data gaps will likely make SPR assessments for species with complex life histories or that cross national boundaries difficult. Although we reviewed techniques for the ESA, other countries require identification of significant areas and could benefit from this research.

Genomic islands of divergence or opportunities for introgression?

In animals, introgression between species is often perceived as the breakdown of reproductive isolating mechanisms, but gene flow between incipient species can also represent a source for potentially beneficial alleles. Recently, genome-wide datasets have revealed clusters of differentiated loci ('genomic islands of divergence') that are thought to play a role in reproductive isolation and therefore have reduced gene flow. We use simulations to further examine the evolutionary forces that shape and maintain genomic islands of divergence between two subspecies of the migratory songbird, Swainson's thrush (Catharus ustulatus), which have come into secondary contact since the last glacial maximum. We find that, contrary to expectation, gene flow is high within islands and is highly asymmetric. In addition, patterns of nucleotide diversity at highly differentiated loci suggest selection was more frequent in a single ecotype. We propose a mechanism whereby beneficial alleles spread via selective sweeps following a post-glacial demographic expansion in one subspecies and move preferentially across the hybrid zone. We find no evidence that genomic islands are the result of divergent selection or reproductive isolation, rather our results suggest that differentiated loci both within and outside islands could provide opportunities for adaptive introgression across porous species boundaries.

Genomic models predict successful coral adaptation if future ocean warming rates are reduced

Population genomic surveys suggest that climate-associated genetic variation occurs widely across species, but whether it is sufficient to allow population persistence via evolutionary adaptation has seldom been quantified. To ask whether rapid adaptation in reef-building corals can keep pace with future ocean warming, we measured genetic variation at predicted warm-adapted loci and simulated future evolution and persistence in a high-latitude population of corals from Rarotonga, Cook Islands. Alleles associated with thermal tolerance were present but at low frequencies in this cooler, southerly locality. Simulations based on predicted ocean warming in Rarotonga showed rapid evolution of heat tolerance resulting in population persistence under mild warming scenarios consistent with low CO₂ emission plans, RCP2.6 and RCP4.5. Under more severe scenarios, RCP6.0 and RCP8.5, adaptation was not rapid enough to prevent extinction. Population adaptation was faster for models based on smaller numbers of additive loci that determine thermal tolerance and for higher population growth rates. Finally, accelerated migration via transplantation of thermally tolerant individuals (1 to 5%/year) sped adaptation. These results show that cool-water corals can adapt to warmer oceans but only under mild scenarios resulting from international emissions controls. Incorporation of genomic data into models of species response to climate change offers a promising method for estimating future adaptive processes.

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.

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).

Quantitative evaluation of hybridization and the impact on biodiversity conservation

Anthropogenic hybridization is an increasing conservation threat worldwide. In South Africa, recent hybridization is threatening numerous ungulate taxa. For example, the genetic integrity of the near‐threatened bontebok (Damaliscus pygargus pygargus) is threatened by hybridization with the more common blesbok (D. p. phillipsi). Identifying nonadmixed parental and admixed individuals is challenging based on the morphological traits alone; however, molecular analyses may allow for accurate detection. Once hybrids are identified, population simulation software may assist in determining the optimal conservation management strategy, although quantitative evaluation of hybrid management is rarely performed. In this study, our objectives were to describe species‐wide and localized rates of hybridization in nearly 3,000 individuals based on 12 microsatellite loci, quantify the accuracy of hybrid assignment software (STRUCTURE and NEWHYBRIDS), and determine an optimal threshold of bontebok ancestry for management purposes. According to multiple methods, we identified 2,051 bontebok, 657 hybrids, and 29 blesbok. More than two‐thirds of locations contained at least some hybrid individuals, with populations varying in the degree of introgression. HYBRIDLAB was used to simulate four generations of coexistence between bontebok and blesbok, and to optimize a threshold of ancestry, where most hybrids will be detected and removed, and the fewest nonadmixed bontebok individuals misclassified as hybrids. Overall, a threshold Q‐value (admixture coefficient) of 0.90 would remove 94% of hybrid animals, while a threshold of 0.95 would remove 98% of hybrid animals but also 8% of nonadmixed bontebok. To this end, a threshold of 0.90 was identified as optimal and has since been implemented in formal policy by a provincial nature conservation agency. Due to widespread hybridization, effective conservation plans should be established and enforced to conserve native populations that are genetically unique.

SLiM 2: Flexible, Interactive Forward Genetic Simulations

Modern population genomic datasets hold immense promise for revealing the evolutionary processes operating in natural populations, but a crucial prerequisite for this goal is the ability to model realistic evolutionary scenarios and predict their expected patterns in genomic data. To that end, we present SLiM 2: an evolutionary simulation framework that combines a powerful, fast engine for forward population genetic simulations with the capability of modeling a wide variety of complex evolutionary scenarios. SLiM achieves this flexibility through scriptability, which provides control over most aspects of the simulated evolutionary scenarios with a simple R-like scripting language called Eidos. An example SLiM simulation is presented to illustrate the power of this approach. SLiM 2 also includes a graphical user interface for simulation construction, interactive runtime control, and dynamic visualization of simulation output, facilitating easy and fast model development with quick prototyping and visual debugging. We conclude with a performance comparison between SLiM and two other popular forward genetic simulation packages.

wisepair: a computer program for individual matching in genetic tracking studies

Individual-based data sets tracking organisms over space and time are fundamental to answering broad questions in ecology and evolution. A 'permanent' genetic tag circumvents a need to invasively mark or tag animals, especially if there are little phenotypic differences among individuals. However, genetic tracking of individuals does not come without its limits; correctly matching genotypes and error rates associated with laboratory work can make it difficult to parse out matched individuals. In addition, defining a sampling design that effectively matches individuals in the wild can be a challenge for researchers. Here, we combine the two objectives of defining sampling design and reducing genotyping error through an efficient Python-based computer-modelling program, wisepair. We describe the methods used to develop the computer program and assess its effectiveness through three empirical data sets, with and without reference genotypes. Our results show that wisepair outperformed similar genotype matching programs using previously published from reference genotype data of diurnal poison frogs (Allobates femoralis) and without-reference (faecal) genotype sample data sets of harbour seals (Phoca vitulina) and Eurasian otters (Lutra lutra). In addition, due to limited sampling effort in the harbour seal data, we present optimal sampling designs for future projects. wisepair allows for minimal sacrifice in the available methods as it incorporates sample rerun error data, allelic pairwise comparisons and probabilistic simulations to determine matching thresholds. Our program is the lone tool available to researchers to define parameters a priori for genetic tracking studies.

When homoplasy mimics hybridization: a case study of Cape hakes (Merluccius capensis and M. paradoxus)

In the marine environment, an increasing number of studies have documented introgression and hybridization using genetic markers. Hybridization appears to occur preferentially between sister-species, with the probability of introgression decreasing with an increase in evolutionary divergence. Exceptions to this pattern were reported for the Cape hakes (Merluccius capensis and M. paradoxus), two distantly related Merluciidae species that diverged 3-4.2 million years ago. Yet, it is expected that contemporary hybridization between such divergent species would result in reduced hybrid fitness. We analysed 1,137 hake individuals using nine microsatellite markers and control region mtDNA data to assess the validity of the described hybridization event. To distinguish between interbreeding, ancestral polymorphism and homplasy we sequenced the flanking region of the most divergent microsatellite marker. Simulation and empirical analyses showed that hybrid identification significantly varied with the number of markers, model and approach used. Phylogenetic analyses based on the sequences of the flanking region of Mmerhk-3b, combined with the absence of mito-nuclear discordance, suggest that previously reported hybridization between M. paradoxus and M. capensis cannot be substantiated. Our findings highlight the need to conduct a priori simulation studies to establish the suitability of a particular set of microsatellite loci for detecting multiple hybridization events. In our example, the identification of hybrids was severely influenced by the number of loci and their variability, as well as the different models employed. More importantly, we provide quantifiable evidence showing that homoplasy mimics the effects of heterospecific crossings which can lead to the incorrect identification of hybridization.

The importance of effective sampling for exploring the population dynamics of haploid-diploid seaweeds

The mating system partitions genetic diversity within and among populations and the links between life history traits and mating systems have been extensively studied in diploid organisms. As such most evolutionary theory is focused on species for which sexual reproduction occurs between diploid male and diploid female individuals. However, there are many multicellular organisms with biphasic life cycles in which the haploid stage is prolonged and undergoes substantial somatic development. In particular, biphasic life cycles are found across green, brown and red macroalgae. Yet, few studies have addressed the population structure and genetic diversity in both the haploid and diploid stages in these life cycles. We have developed some broad guidelines with which to develop population genetic studies of haploid-diploid macroalgae and to quantify the relationship between power and sampling strategy. We address three common goals for studying macroalgal population dynamics, including haploid-diploid ratios, genetic structure and paternity analyses.

From Wolves to Dogs, and Back: Genetic Composition of the Czechoslovakian Wolfdog

The Czechoslovakian Wolfdog is a unique dog breed that originated from hybridization between German Shepherds and wild Carpathian wolves in the 1950s as a military experiment. This breed was used for guarding the Czechoslovakian borders during the cold war and is currently kept by civilian breeders all round the world. The aim of our study was to characterize, for the first time, the genetic composition of this breed in relation to its known source populations. We sequenced the hypervariable part of the mtDNA control region and genotyped the Amelogenin gene, four sex-linked microsatellites and 39 autosomal microsatellites in 79 Czechoslovakian Wolfdogs, 20 German Shepherds and 28 Carpathian wolves. We performed a range of population genetic analyses based on both empirical and simulated data. Only two mtDNA and two Y-linked haplotypes were found in Czechoslovakian Wolfdogs. Both mtDNA haplotypes were of domestic origin, while only one of the Y-haplotypes was shared with German Shepherds and the other was unique to Czechoslovakian Wolfdogs. The observed inbreeding coefficient was low despite the small effective population size of the breed, possibly due to heterozygote advantages determined by introgression of wolf alleles. Moreover, Czechoslovakian Wolfdog genotypes were distinct from both parental populations, indicating the role of founder effect, drift and/or genetic hitchhiking. The results revealed the peculiar genetic composition of the Czechoslovakian Wolfdog, showing a limited introgression of wolf alleles within a higher proportion of the dog genome, consistent with the reiterated backcrossing used in the pedigree. Artificial selection aiming to keep wolf-like phenotypes but dog-like behavior resulted in a distinctive genetic composition of Czechoslovakian Wolfdogs, which provides a unique example to study the interactions between dog and wolf genomes.

Evolutionary processes driving spatial patterns of intraspecific genetic diversity in river ecosystems

Describing, understanding and predicting the spatial distribution of genetic diversity is a central issue in biological sciences. In river landscapes, it is generally predicted that neutral genetic diversity should increase downstream, but there have been few attempts to test and validate this assumption across taxonomic groups. Moreover, it is still unclear what are the evolutionary processes that may generate this apparent spatial pattern of diversity. Here, we quantitatively synthesized published results from diverse taxa living in river ecosystems, and we performed a meta-analysis to show that a downstream increase in intraspecific genetic diversity (DIGD) actually constitutes a general spatial pattern of biodiversity that is repeatable across taxa. We further demonstrated that DIGD was stronger for strictly waterborne dispersing than for overland dispersing species. However, for a restricted data set focusing on fishes, there was no evidence that DIGD was related to particular species traits. We then searched for general processes underlying DIGD by simulating genetic data in dendritic-like river systems. Simulations revealed that the three processes we considered (downstream-biased dispersal, increase in habitat availability downstream and upstream-directed colonization) might generate DIGD. Using random forest models, we identified from simulations a set of highly informative summary statistics allowing discriminating among the processes causing DIGD. Finally, combining these discriminant statistics and approximate Bayesian computations on a set of twelve empirical case studies, we hypothesized that DIGD were most likely due to the interaction of two of these three processes and that contrary to expectation, they were not solely caused by downstream-biased dispersal.

How Well Do Molecular and Pedigree Relatedness Correspond, in Populations with Diverse Mating Systems, and Various Types and Quantities of Molecular and Demographic Data?

Kinship analyses are important pillars of ecological and conservation genetic studies with potentially far-reaching implications. There is a need for power analyses that address a range of possible relationships. Nevertheless, such analyses are rarely applied, and studies that use genetic-data-based-kinship inference often ignore the influence of intrinsic population characteristics. We investigated 11 questions regarding the correct classification rate of dyads to relatedness categories (relatedness category assignments; RCA) using an individual-based model with realistic life history parameters. We investigated the effects of the number of genetic markers; marker type (microsatellite, single nucleotide polymorphism SNP, or both); minor allele frequency; typing error; mating system; and the number of overlapping generations under different demographic conditions. We found that (i) an increasing number of genetic markers increased the correct classification rate of the RCA so that up to >80% first cousins can be correctly assigned; (ii) the minimum number of genetic markers required for assignments with 80 and 95% correct classifications differed between relatedness categories, mating systems, and the number of overlapping generations; (iii) the correct classification rate was improved by adding additional relatedness categories and age and mitochondrial DNA data; and (iv) a combination of microsatellite and single-nucleotide polymorphism data increased the correct classification rate if <800 SNP loci were available. This study shows how intrinsic population characteristics, such as mating system and the number of overlapping generations, life history traits, and genetic marker characteristics, can influence the correct classification rate of an RCA study. Therefore, species-specific power analyses are essential for empirical studies.

Ecological speciation in the tropics: insights from comparative genetic studies in Amazonia

Evolution creates and sustains biodiversity via adaptive changes in ecologically relevant traits. Ecologically mediated selection contributes to genetic divergence both in the presence or absence of geographic isolation between populations, and is considered an important driver of speciation. Indeed, the genetics of ecological speciation is becoming increasingly studied across a variety of taxa and environments. In this paper we review the literature of ecological speciation in the tropics. We report on low research productivity in tropical ecosystems and discuss reasons accounting for the rarity of studies. We argue for research programs that simultaneously address biogeographical and taxonomic questions in the tropics, while effectively assessing relationships between reproductive isolation and ecological divergence. To contribute toward this goal, we propose a new framework for ecological speciation that integrates information from phylogenetics, phylogeography, population genomics, and simulations in evolutionary landscape genetics (ELG). We introduce components of the framework, describe ELG simulations (a largely unexplored approach in ecological speciation), and discuss design and experimental feasibility within the context of tropical research. We then use published genetic datasets from populations of five codistributed Amazonian fish species to assess the performance of the framework in studies of tropical speciation. We suggest that these approaches can assist in distinguishing the relative contribution of natural selection from biogeographic history in the origin of biodiversity, even in complex ecosystems such as Amazonia. We also discuss on how to assess ecological speciation using ELG simulations that include selection. These integrative frameworks have considerable potential to enhance conservation management in biodiversity rich ecosystems and to complement historical biogeographic and evolutionary studies of tropical biotas.

Comparative evaluation of potential indicators and temporal sampling protocols for monitoring genetic erosion

Genetic biodiversity contributes to individual fitness, species' evolutionary potential, and ecosystem stability. Temporal monitoring of the genetic status and trends of wild populations' genetic diversity can provide vital data to inform policy decisions and management actions. However, there is a lack of knowledge regarding which genetic metrics, temporal sampling protocols, and genetic markers are sufficiently sensitive and robust, on conservation-relevant timescales. Here, we tested six genetic metrics and various sampling protocols (number and arrangement of temporal samples) for monitoring genetic erosion following demographic decline. To do so, we utilized individual-based simulations featuring an array of different initial population sizes, types and severity of demographic decline, and DNA markers [single nucleotide polymorphisms (SNPs) and microsatellites] as well as decline followed by recovery. Number of alleles markedly outperformed other indicators across all situations. The type and severity of demographic decline strongly affected power, while the number and arrangement of temporal samples had small effect. Sampling 50 individuals at as few as two time points with 20 microsatellites performed well (good power), and could detect genetic erosion while 80-90% of diversity remained. This sampling and genotyping effort should often be affordable. Power increased substantially with more samples or markers, and we observe that power of 2500 SNPs was nearly equivalent to 250 microsatellites, a result of theoretical and practical interest. Our results suggest high potential for using historic collections in monitoring programs, and demonstrate the need to monitor genetic as well as other levels of biodiversity.