Demographic inference for spatially heterogeneous populations using long shared haplotypes

We introduce a modified spatial Λ-Fleming-Viot process to model the ancestry of individuals in a population occupying a continuous spatial habitat divided into two areas by a sharp discontinuity of the dispersal rate and effective population density. We derive an analytical formula for the expected number of shared haplotype segments between two individuals depending on their sampling locations. This formula involves the transition density of a skew diffusion which appears as a scaling limit of the ancestral lineages of individuals in this model. We then show that this formula can be used to infer the dispersal parameters and the effective population density of both regions, using a composite likelihood approach, and we demonstrate the efficiency of this method on a range of simulated data sets.

Variation in spatial population structure in the Anopheles gambiae species complex

Anopheles gambiae, Anopheles coluzzii, and Anopheles arabiensis are three of the most widespread vectors of malaria parasites, with geographical ranges stretching across wide swaths of Africa. Understanding the population structure of these closely related species, including the extent to which populations are connected by gene flow, is essential for understanding how vector control implemented in one location might indirectly affect vector populations in other locations. Here, we assessed the population structure of each species based on a combined data set of publicly available and newly processed whole-genome sequences. The data set included single nucleotide polymorphisms from whole genomes of 2,410 individual mosquitoes sampled from 128 locations across 19 African countries. We found that A. gambiae sampled from several countries in West and Central Africa showed low genetic differentiation from each other according to principal components analysis (PCA) and ADMIXTURE modeling. Using Estimated Effective Migration Surfaces (EEMS), we showed that this low genetic differentiation indicates high effective migration rates for A. gambiae across this region. Outside of this region, we found eight groups of sampling locations from Central, East, and Southern Africa for which A. gambiae showed higher genetic differentiation, and lower effective migration rates, between each other and the West/Central Africa group. These results indicate that the barriers to and corridors for migration between populations of A. gambiae differ across the geographical range of this malaria vector species. Using the same methods, we found higher genetic differentiation and lower migration rates between populations of A. coluzzii in West and Central Africa than for A. gambiae in the same region. In contrast, we found lower genetic differentiation and higher migration rates between populations of A. arabiensis in Tanzania, compared to A. gambiae in the same region. These differences between A. gambiae, A. coluzzii, and A. arabiensis indicate that migration barriers and corridors may vary, even between very closely related species. Overall, our results demonstrate that migration rates vary both within and between species of Anopheles mosquitoes, presumably based on species-specific responses to the ecological or environmental conditions that may impede or facilitate migration, and the geographical patterns of these conditions across the landscape. Together with previous findings, this study provides robust evidence that migration rates between populations of malaria vectors depend on the ecological context, which should be considered when planning surveillance of vector populations, monitoring for insecticide resistance, and evaluating interventions.

A geographic history of human genetic ancestry

Describing the distribution of genetic variation across individuals is a fundamental goal of population genetics. In humans, traditional approaches for describing population genetic variation often rely on discrete genetic ancestry labels, which, despite their utility, can obscure the complex, multi-faceted nature of human genetic history. These labels risk oversimplifying ancestry by ignoring its temporal depth and geographic continuity, and may therefore conflate notions of race, ethnicity, geography, and genetic ancestry. Here, we present a method that capitalizes on the rich genealogical information encoded in genomic tree sequences to infer the geographic locations of the shared ancestors of a sample of sequenced individuals. We use this method to infer the geographic history of genetic ancestry of a set of human genomes sampled from Europe, Asia, and Africa, accurately recovering major population movements on those continents. Our findings demonstrate the importance of defining the spatial-temporal context of genetic ancestry to describing human genetic variation and caution against the oversimplified interpretations of genetic data prevalent in contemporary discussions of race and ancestry.

A spatial approach to jointly estimate Wright's neighborhood size and long-term effective population size

Spatially continuous patterns of genetic differentiation, which are common in nature, are often poorly described by existing population genetic theory or methods that assume either panmixia or discrete, clearly definable populations. There is therefore a need for statistical approaches in population genetics that can accommodate continuous geographic structure, and that ideally use georeferenced individuals as the unit of analysis, rather than populations or subpopulations. In addition, researchers are often interested in describing the diversity of a population distributed continuously in space; this diversity is intimately linked to both the dispersal potential and the population density of the organism. A statistical model that leverages information from patterns of isolation by distance to jointly infer parameters that control local demography (such as Wright's neighborhood size), and the long-term effective size (Ne) of a population would be useful. Here, we introduce such a model that uses individual-level pairwise genetic and geographic distances to infer Wright's neighborhood size and long-term Ne. We demonstrate the utility of our model by applying it to complex, forward-time demographic simulations as well as an empirical dataset of the two-form bumblebee (Bombus bifarius). The model performed well on simulated data relative to alternative approaches and produced reasonable empirical results given the natural history of bumblebees. The resulting inferences provide important insights into the population genetic dynamics of spatially structured populations.

Isolation by environment and its consequences for range shifts with global change: Landscape genomics of the invasive plant common tansy

Invasive species are a growing global economic and ecological problem. However, it is not well understood how environmental factors mediate invasive range expansion. In this study, we investigated the recent and rapid range expansion of common tansy across environmental gradients in Minnesota, USA. We densely sampled individuals across the expanding range and performed reduced representation sequencing to generate a dataset of 3071 polymorphic loci for 176 individuals. We used non-spatial and spatially explicit analyses to determine the relative influences of geographic distance and environmental variation on patterns of genomic variation. We found no evidence for isolation by distance but strong evidence for isolation by environment, indicating that environmental factors may have modulated patterns of range expansion. Land use classification and soils were particularly important variables related to population structure although they operated on different spatial scales; land use classification was related to broad-scale patterns and soils were related to fine-scale patterns. All analyses indicated a distinctive genetic cluster in the most recently invaded portion of the range. Individuals from the far northwestern range margin were separated from the remainder of the range by reduced migration, which was associated with environmental resistance. This portion of the range was invaded primarily in the last 15 years. Ecological niche models also indicated that this cluster was associated with the expansion of the niche. While invasion is often assumed to be primarily influenced by dispersal limitation, our results suggest that ongoing invasion and range shifts with climate change may be strongly affected by environmental heterogeneity.

Some reef-building corals only disperse metres per generation

Understanding the dispersal potential of different species is essential for predicting recovery trajectories following local disturbances and the potential for adaptive loci to spread to populations facing extreme environmental changes. However, dispersal distances have been notoriously difficult to estimate for scleractinian corals, where sexually (as gametes or larvae) or asexually (as fragments or larvae) derived propagules disperse through vast oceans. Here, we demonstrate that generational dispersal distances for sexually produced propagules can be indirectly inferred for corals using individual-based isolation-by-distance (IbD) analyses by combining reduced-representation genomic sequencing with photogrammetric spatial mapping. Colonies from the genus Agaricia were densely sampled across plots at four locations and three depths in Curaçao. Seven cryptic taxa were found among the three nominal species (Agaricia agaricites, Agaricia humilis and Agaricia lamarcki), with four taxa showing generational dispersal distances within metres (two taxa within A. agaricites and two within A. humilis). However, no signals of IbD were found in A. lamarcki taxa and thus these taxa probably disperse relatively longer distances. The short distances estimated here imply that A. agaricites and A. humilis populations are reliant on highly localized replenishment and demonstrate the need to estimate dispersal distances quantitatively for more coral species.

On the connections between the spatial Lambda-Fleming-Viot model and other processes for analysing geo-referenced genetic data

The introduction of the spatial Lambda-Fleming-Viot model (ΛV) in population genetics was mainly driven by the pioneering work of Alison Etheridge, in collaboration with Nick Barton and Amandine Véber about ten years ago (Barton et al., 2010; Barton et al., 2013). The ΛV model provides a sound mathematical framework for describing the evolution of a population of related individuals along a spatial continuum. It alleviates the "pain in the torus" issue with Wright and Malécot's isolation by distance model and is sampling consistent, making it a tool of choice for statistical inference. Yet, little is known about the potential connections between the ΛV and other stochastic processes generating trees and the spatial coordinates along the corresponding lineages. This work focuses on a version of the ΛV whereby lineages move rapidly over small distances. Using simulations, we show that the induced ΛV tree-generating process is well approximated by a birth-death model. Our results also indicate that Brownian motions modelling the movements of lines of descent along birth-death trees do not generally provide a good approximation of the ΛV due to habitat boundaries effects that play an increasingly important role in the long run. Accounting for habitat boundaries through reflected Brownian motions considerably increases the similarity to the ΛV model however. Finally, we describe efficient algorithms for fast simulation of the backward and forward in time versions of the ΛV model.

Estimating scale-specific and localized spatial patterns in allele frequency

Characterizing spatial patterns in allele frequencies is fundamental to evolutionary biology because these patterns contain evidence of underlying processes. However, the spatial scales at which gene flow, changing selection, and drift act are often unknown. Many of these processes can operate inconsistently across space, causing nonstationary patterns. We present a wavelet approach to characterize spatial pattern in allele frequency that helps solve these problems. We show how our approach can characterize spatial patterns in relatedness at multiple spatial scales, i.e. a multilocus wavelet genetic dissimilarity. We also develop wavelet tests of spatial differentiation in allele frequency and quantitative trait loci (QTL). With simulation, we illustrate these methods under different scenarios. We also apply our approach to natural populations of Arabidopsis thaliana to characterize population structure and identify locally adapted loci across scales. We find, for example, that Arabidopsis flowering time QTL show significantly elevated genetic differentiation at 300-1,300 km scales. Wavelet transforms of allele frequencies offer a flexible way to reveal geographic patterns and underlying evolutionary processes.

Adaptation and the Geographic Spread of Crop Species

Crops are plant species that were domesticated starting about 11,000 years ago from several centers of origin, most prominently the Fertile Crescent, East Asia, and Mesoamerica. From their domestication centers, these crops spread across the globe and had to adapt to differing environments as a result of this dispersal. We discuss broad patterns of crop spread, including the early diffusion of crops associated with the rise and spread of agriculture, the later movement via ancient trading networks, and the exchange between the Old and New Worlds over the last ∼550 years after the European colonization of the Americas. We also examine the various genetic mechanisms associated with the evolutionary adaptation of crops to their new environments after dispersal, most prominently seasonal adaptation associated with movement across latitudes, as well as altitudinal, temperature, and other environmental factors.

Aseasonal, undirected migration in insects: 'Invisible' but common

Many insect pests are long-distance migrants, moving from lower latitudes where they overwinter to higher latitudes in spring to exploit superabundant, but seasonally ephemeral, host crops. These seasonal long-distance migration events are relatively easy to recognize, and justifiably garner much research attention. Evidence indicates several pest species that overwinter in diapause, and thus inhabit a year-round range, also engage in migratory flight, which is somewhat "invisible" because displacement is nondirectional and terminates among conspecifics. Support for aseasonal, undirected migration is related to recognizing true migratory flight behavior, which differs fundamentally from most other kinds of flight in that it is nonappetitive. Migrating adults are not searching for resources and migratory flight is not arrested by encounters with potential resources. The population-level consequence of aseasonal, undirected migration is spatial mixing of individuals within the larger metapopulation, which has important implications for population dynamics, gene flow, pest management, and insect resistance management.

Testing for adaptive changes linked to range expansion following a single introduction of the fall webworm

Adaptive evolution following colonization can affect the impact of invasive species. The fall webworm (FWW) invaded China 40 years ago through a single introduction event involving a severe bottleneck and subsequently diverged into two genetic groups. The well-recorded invasion history of FWW, coupled with a clear pattern of genetic divergence, provides an opportunity to investigate whether there is any sign of adaptive evolution following the invasion. Based on genome-wide SNPs, we identified genetically separated western and eastern groups of FWW and correlated spatial variation in SNPs with geographical and climatic factors. Geographical factors explained a similar proportion of the genetic variation across all populations compared with climatic factors. However, when the two population groups were analysed separately, environmental factors explained more variation than geographical factors. SNP outliers in populations of the western group had relatively stronger response to precipitation than temperature-related variables. Functional annotation of SNP outliers identified genes associated with insect cuticle protein potentially related to desiccation adaptation in the western group and genes associated with lipase biosynthesis potentially related to temperature adaptation in the eastern group. Our study suggests that invasive species may maintain the evolutionary potential to adapt to heterogeneous environments despite a single invasion event. The molecular data suggest that quantitative trait comparisons across environments would be worthwhile.

How Can Genomics Help or Hinder Wildlife Conservation?

Genomic data are becoming increasingly affordable and easy to collect, and new tools for their analysis are appearing rapidly. Conservation biologists are interested in using this information to assist in management and planning but are typically limited financially and by the lack of genomic resources available for non-model taxa. It is therefore important to be aware of the pitfalls as well as the benefits of applying genomic approaches. Here, we highlight recent methods aimed at standardizing population assessments of genetic variation, inbreeding, and forms of genetic load and methods that help identify past and ongoing patterns of genetic interchange between populations, including those subjected to recent disturbance. We emphasize challenges in applying some of these methods and the need for adequate bioinformatic support. We also consider the promises and challenges of applying genomic approaches to understand adaptive changes in natural populations to predict their future adaptive capacity.

Harnessing deep learning for population genetic inference

In population genetics, the emergence of large-scale genomic data for various species and populations has provided new opportunities to understand the evolutionary forces that drive genetic diversity using statistical inference. However, the era of population genomics presents new challenges in analysing the massive amounts of genomes and variants. Deep learning has demonstrated state-of-the-art performance for numerous applications involving large-scale data. Recently, deep learning approaches have gained popularity in population genetics; facilitated by the advent of massive genomic data sets, powerful computational hardware and complex deep learning architectures, they have been used to identify population structure, infer demographic history and investigate natural selection. Here, we introduce common deep learning architectures and provide comprehensive guidelines for implementing deep learning models for population genetic inference. We also discuss current challenges and future directions for applying deep learning in population genetics, focusing on efficiency, robustness and interpretability.

The era of the ARG: An introduction to ancestral recombination graphs and their significance in empirical evolutionary genomics

In the presence of recombination, the evolutionary relationships between a set of sampled genomes cannot be described by a single genealogical tree. Instead, the genomes are related by a complex, interwoven collection of genealogies formalized in a structure called an ancestral recombination graph (ARG). An ARG extensively encodes the ancestry of the genome(s) and thus is replete with valuable information for addressing diverse questions in evolutionary biology. Despite its potential utility, technological and methodological limitations, along with a lack of approachable literature, have severely restricted awareness and application of ARGs in evolution research. Excitingly, recent progress in ARG reconstruction and simulation have made ARG-based approaches feasible for many questions and systems. In this review, we provide an accessible introduction and exploration of ARGs, survey recent methodological breakthroughs, and describe the potential for ARGs to further existing goals and open avenues of inquiry that were previously inaccessible in evolutionary genomics. Through this discussion, we aim to more widely disseminate the promise of ARGs in evolutionary genomics and encourage the broader development and adoption of ARG-based inference.

Discerning structure versus speciation in phylogeographic analysis of Seepage Salamanders (Desmognathus aeneus) using demography, environment, geography, and phenotype

Numerous mechanisms can drive speciation, including isolation by adaptation, distance, and environment. These forces can promote genetic and phenotypic differentiation of local populations, the formation of phylogeographic lineages, and ultimately, completed speciation. However, conceptually similar mechanisms may also result in stabilizing rather than diversifying selection, leading to lineage integration and the long-term persistence of population structure within genetically cohesive species. Processes that drive the formation and maintenance of geographic genetic diversity while facilitating high rates of migration and limiting phenotypic differentiation may thereby result in population genetic structure that is not accompanied by reproductive isolation. We suggest that this framework can be applied more broadly to address the classic dilemma of "structure" versus "species" when evaluating phylogeographic diversity, unifying population genetics, species delimitation, and the underlying study of speciation. We demonstrate one such instance in the Seepage Salamander (Desmognathus aeneus) from the southeastern United States. Recent studies estimated up to 6.3% mitochondrial divergence and four phylogenomic lineages with broad admixture across geographic hybrid zones, which could potentially represent distinct species supported by our species-delimitation analyses. However, while limited dispersal promotes substantial isolation by distance, microhabitat specificity appears to yield stabilizing selection on a single, uniform, ecologically mediated phenotype. As a result, climatic cycles promote recurrent contact between lineages and repeated instances of high migration through time. Subsequent hybridization is apparently not counteracted by adaptive differentiation limiting introgression, leaving a single unified species with deeply divergent phylogeographic lineages that nonetheless do not appear to represent incipient species.

slendr: a framework for spatio-temporal population genomic simulations on geographic landscapes

One of the goals of population genetics is to understand how evolutionary forces shape patterns of genetic variation over time. However, because populations evolve across both time and space, most evolutionary processes also have an important spatial component, acting through phenomena such as isolation by distance, local mate choice, or uneven distribution of resources. This spatial dimension is often neglected, partly due to the lack of tools specifically designed for building and evaluating complex spatio-temporal population genetic models. To address this methodological gap, we present a new framework for simulating spatially-explicit genomic data, implemented in a new R package called slendr (www.slendr.net), which leverages a SLiM simulation back-end script bundled with the package. With this framework, the users can programmatically and visually encode spatial population ranges and their temporal dynamics (i.e., population displacements, expansions, and contractions) either on real Earth landscapes or on abstract custom maps, and schedule splits and gene-flow events between populations using a straightforward declarative language. Additionally, slendr can simulate data from traditional, non-spatial models, either with SLiM or using an alternative built-in coalescent msprime back end. Together with its R-idiomatic interface to the tskit library for tree-sequence processing and analysis, slendr opens up the possibility of performing efficient, reproducible simulations of spatio-temporal genomic data entirely within the R environment, leveraging its wealth of libraries for geospatial data analysis, statistics, and visualization. Here, we present the design of the slendr R package and demonstrate its features on several practical example workflows.

Movement Ecology of Adult Western Corn Rootworm: Implications for Management

Movement of adult western corn rootworm, Diabrotica virgifera virgifera LeConte, is of fundamental importance to this species' population dynamics, ecology, evolution, and interactions with its environment, including cultivated cornfields. Realistic parameterization of dispersal components of models is needed to predict rates of range expansion, development, and spread of resistance to control measures and improve pest and resistance management strategies. However, a coherent understanding of western corn rootworm movement ecology has remained elusive because of conflicting evidence for both short- and long-distance lifetime dispersal, a type of dilemma observed in many species called Reid's paradox. Attempts to resolve this paradox using population genetic strategies to estimate rates of gene flow over space likewise imply greater dispersal distances than direct observations of short-range movement suggest, a dilemma called Slatkin's paradox. Based on the wide-array of available evidence, we present a conceptual model of adult western corn rootworm movement ecology under the premise it is a partially migratory species. We propose that rootworm populations consist of two behavioral phenotypes, resident and migrant. Both engage in local, appetitive flights, but only the migrant phenotype also makes non-appetitive migratory flights, resulting in observed patterns of bimodal dispersal distances and resolution of Reid's and Slatkin's paradoxes.

Evaluating evidence for co-geography in the Anopheles-Plasmodium host-parasite system

The often tight association between parasites and their hosts means that under certain scenarios, the evolutionary histories of the two species can become closely coupled both through time and across space. Using spatial genetic inference, we identify a potential signal of common dispersal patterns in the Anopheles gambiae and Plasmodium falciparum host-parasite system as seen through a between-species correlation of the differences between geographic sampling location and geographic location predicted from the genome. This correlation may be due to coupled dispersal dynamics between host and parasite, but may also reflect statistical artifacts due to uneven spatial distribution of sampling locations. Using continuous-space population genetics simulations, we investigate the degree to which uneven distribution of sampling locations leads to bias in prediction of spatial location from genetic data and implement methods to counter this effect. We demonstrate that while algorithmic bias presents a problem in inference from spatio-genetic data, the correlation structure between A. gambiae and P. falciparum predictions cannot be attributed to spatial bias alone, and is thus likely a genetic signal of co-dispersal in a host-parasite system.

Individual-based landscape genomics for conservation: An analysis pipeline

Landscape genomics can harness environmental and genetic data to inform conservation decisions by providing essential insights into how landscapes shape biodiversity. The massive increase in genetic data afforded by the genomic era provides exceptional resolution for answering critical conservation genetics questions. The accessibility of genomic data for non-model systems has also enabled a shift away from population-based sampling to individual-based sampling, which now provides accurate and robust estimates of genetic variation that can be used to examine the spatial structure of genomic diversity, population connectivity and the nature of environmental adaptation. Nevertheless, the adoption of individual-based sampling in conservation genetics has been slowed due, in large part, to concerns over how to apply methods developed for population-based sampling to individual-based sampling schemes. Here, we discuss the benefits of individual-based sampling for conservation and describe how landscape genomic methods, paired with individual-based sampling, can answer fundamental conservation questions. We have curated key landscape genomic methods into a user-friendly, open-source workflow, which we provide as a new R package, A Landscape Genomics Analysis Toolkit in R (algatr). The algatr package includes novel added functionality for all of the included methods and extensive vignettes designed with the primary goal of making landscape genomic approaches more accessible and explicitly applicable to conservation biology.

Evolutionary history of Castanea sativa in the Caucasus driven by Middle and Late Pleistocene paleoenvironmental changes

Due to global climate cooling and aridification since the Paleogene, members of the Neogene flora were extirpated from the Northern Hemisphere or were confined to a few refugial areas. For some species, the final reduction/extinction came in the Pleistocene, but some others have survived climatic transformations up to the present. This has occurred in Castanea sativa, a species of high commercial value in Europe and a significant component of the Caucasian forests' biodiversity. In contrast to the European range, neither the historical biogeography nor the population genetic structure of the species in its isolated Caucasian range has been clarified. Here, based on a survey of 21 natural populations from the Caucasus and a single one from Europe, we provide a likely biogeographic reconstruction and genetic diversity details. By applying Bayesian inference, species distribution modelling and fossil pollen data, we estimated (i) the time of the Caucasian-European divergence during the Middle Pleistocene, (ii) the time of divergence among Caucasian lineages and (iii) outlined the glacial refugia for species. The climate changes related to the Early-Middle Pleistocene Transition are proposed as the major drivers of the intraspecific divergence and European-Caucasian disjunction for the species, while the impact of the last glacial cycle was of marginal importance.

Demographic inference for spatially heterogeneous populations using long shared haplotypes

We introduce a modified spatial Λ-Fleming-Viot process to model the ancestry of individuals in a population occupying a continuous spatial habitat divided into two areas by a sharp discontinuity of the dispersal rate and effective population density. We derive an analytical formula for the expected number of shared haplotype segments between two individuals depending on their sampling locations. This formula involves the transition density of a skew diffusion which appears as a scaling limit of the ancestral lineages of individuals in this model. We then show that this formula can be used to infer the dispersal parameters and the effective population density of both regions, using a composite likelihood approach, and we demonstrate the efficiency of this method on a range of simulated data sets.

On the genes, genealogies, and geographies of Quebec

Population genetic models only provide coarse representations of real-world ancestry. We used a pedigree compiled from 4 million parish records and genotype data from 2276 French and 20,451 French Canadian individuals to finely model and trace French Canadian ancestry through space and time. The loss of ancestral French population structure and the appearance of spatial and regional structure highlights a wide range of population expansion models. Geographic features shaped migrations, and we find enrichments for migration, genetic, and genealogical relatedness patterns within river networks across regions of Quebec. Finally, we provide a freely accessible simulated whole-genome sequence dataset with spatiotemporal metadata for 1,426,749 individuals reflecting intricate French Canadian population structure. Such realistic population-scale simulations provide opportunities to investigate population genetics at an unprecedented resolution.

Dispersal inference from population genetic variation using a convolutional neural network

The geographic nature of biological dispersal shapes patterns of genetic variation over landscapes, making it possible to infer properties of dispersal from genetic variation data. Here, we present an inference tool that uses geographically distributed genotype data in combination with a convolutional neural network to estimate a critical population parameter: the mean per-generation dispersal distance. Using extensive simulation, we show that our deep learning approach is competitive with or outperforms state-of-the-art methods, particularly at small sample sizes. In addition, we evaluate varying nuisance parameters during training-including population density, demographic history, habitat size, and sampling area-and show that this strategy is effective for estimating dispersal distance when other model parameters are unknown. Whereas competing methods depend on information about local population density or accurate inference of identity-by-descent tracts, our method uses only single-nucleotide-polymorphism data and the spatial scale of sampling as input. Strikingly, and unlike other methods, our method does not use the geographic coordinates of the genotyped individuals. These features make our method, which we call "disperseNN," a potentially valuable new tool for estimating dispersal distance in nonmodel systems with whole genome data or reduced representation data. We apply disperseNN to 12 different species with publicly available data, yielding reasonable estimates for most species. Importantly, our method estimated consistently larger dispersal distances than mark-recapture calculations in the same species, which may be due to the limited geographic sampling area covered by some mark-recapture studies. Thus genetic tools like ours complement direct methods for improving our understanding of dispersal.

Isolation by distance in populations with power-law dispersal

Limited dispersal of individuals between generations results in isolation by distance, in which individuals further apart in space tend to be less related. Classic models of isolation by distance assume that dispersal distances are drawn from a thin-tailed distribution and predict that the proportion of the genome that is identical by descent between a pair of individuals should decrease exponentially with the spatial separation between them. However, in many natural populations, individuals occasionally disperse over very long distances. In this work, we use mathematical analysis and coalescent simulations to study the effect of long-range (power-law) dispersal on patterns of isolation by distance. We find that it leads to power-law decay of identity-by-descent at large distances with the same exponent as dispersal. We also find that broad power-law dispersal produces another, shallow power-law decay of identity-by-descent at short distances. These results suggest that the distribution of long-range dispersal events could be estimated from sequencing large population samples taken from a wide range of spatial scales.

Genetic and demographic consequences of range contraction patterns during biological annihilation

Species range contractions both contribute to, and result from, biological annihilation, yet do not receive the same attention as extinctions. Range contractions can lead to marked impacts on populations but are usually characterized only by reduction in extent of range. For effective conservation, it is critical to recognize that not all range contractions are the same. We propose three distinct patterns of range contraction: shrinkage, amputation, and fragmentation. We tested the impact of these patterns on populations of a generalist species using forward-time simulations. All three patterns caused 86-88% reduction in population abundance and significantly increased average relatedness, with differing patterns in declines of nucleotide diversity relative to the contraction pattern. The fragmentation pattern resulted in the strongest effects on post-contraction genetic diversity and structure. Defining and quantifying range contraction patterns and their consequences for Earth's biodiversity would provide useful and necessary information to combat biological annihilation.

The evolutionary and functional significance of germline immunoglobulin gene variation

The recombination between immunoglobulin (IG) gene segments determines an individual's naïve antibody repertoire and, consequently, (auto)antigen recognition. Emerging evidence suggests that mammalian IG germline variation impacts humoral immune responses associated with vaccination, infection, and autoimmunity - from the molecular level of epitope specificity, up to profound changes in the architecture of antibody repertoires. These links between IG germline variants and immunophenotype raise the question on the evolutionary causes and consequences of diversity within IG loci. We discuss why the extreme diversity in IG loci remains a mystery, why resolving this is important for the design of more effective vaccines and therapeutics, and how recent evidence from multiple lines of inquiry may help us do so.

Close Kin Dyads Indicate Intergenerational Dispersal and Barriers

AbstractThe movement of individuals through continuous space is typically constrained by dispersal ability and dispersal barriers. A range of approaches have been developed to investigate these. Kindisperse is a new approach that infers recent intergenerational dispersal (σ) from close kin dyads and appears particularly useful for investigating taxa that are difficult to observe individually. This study, focusing on the mosquito Aedes aegypti, shows how the same close kin data can also be used for barrier detection. We empirically demonstrate this new extension of the method using genome-wide sequence data from 266 Ae. aegypti. First, we use the spatial distribution of full-sib dyads collected within one generation to infer past movements of ovipositing female mosquitoes. These dyads indicated the relative barrier strengths of two roads and performed favorably against alternative genetic methods for detecting barriers. We then use Kindisperse to quantify recent intergenerational dispersal (σ=81.5-197.1 m generation^-1/2) from the difference in variance between the sib and the first cousin spatial distributions and, from this, estimate effective population density (ρ=833-4,864 km^-2). Dispersal estimates showed general agreement with those from mark-release-recapture studies. Barriers, σ, ρ, and neighborhood size (331-526) can inform forthcoming releases of dengue-suppressing Wolbachia bacteria into this mosquito population.

Adding pattern and process to eco-evo theory and applications

Eco-evolutionary dynamics result when interacting biological forces simultaneously produce demographic and genetic population responses. Eco-evolutionary simulators traditionally manage complexity by minimizing the influence of spatial pattern on process. However, such simplifications can limit their utility in real-world applications. We present a novel simulation modeling approach for investigating eco-evolutionary dynamics, centered on the driving role of landscape pattern. Our spatially-explicit, individual-based mechanistic simulation approach overcomes existing methodological challenges, generates new insights, and paves the way for future investigations in four focal disciplines: Landscape Genetics, Population Genetics, Conservation Biology, and Evolutionary Ecology. We developed a simple individual-based model to illustrate how spatial structure drives eco-evo dynamics. By making minor changes to our landscape's structure, we simulated continuous, isolated, and semi-connected landscapes, and simultaneously tested several classical assumptions of the focal disciplines. Our results exhibit expected patterns of isolation, drift, and extinction. By imposing landscape change on otherwise functionally-static eco-evolutionary models, we altered key emergent properties such as gene-flow and adaptive selection. We observed demo-genetic responses to these landscape manipulations, including changes in population size, probability of extinction, and allele frequencies. Our model also demonstrated how demo-genetic traits, including generation time and migration rate, can arise from a mechanistic model, rather than being specified a priori. We identify simplifying assumptions common to four focal disciplines, and illustrate how new insights might be developed in eco-evolutionary theory and applications by better linking biological processes to landscape patterns that we know influence them, but that have understandably been left out of many past modeling studies.

Modeling the spatiotemporal spread of beneficial alleles using ancient genomes

Ancient genome sequencing technologies now provide the opportunity to study natural selection in unprecedented detail. Rather than making inferences from indirect footprints left by selection in present-day genomes, we can directly observe whether a given allele was present or absent in a particular region of the world at almost any period of human history within the last 10,000 years. Methods for studying selection using ancient genomes often rely on partitioning individuals into discrete time periods or regions of the world. However, a complete understanding of natural selection requires more nuanced statistical methods which can explicitly model allele frequency changes in a continuum across space and time. Here we introduce a method for inferring the spread of a beneficial allele across a landscape using two-dimensional partial differential equations. Unlike previous approaches, our framework can handle time-stamped ancient samples, as well as genotype likelihoods and pseudohaploid sequences from low-coverage genomes. We apply the method to a panel of published ancient West Eurasian genomes to produce dynamic maps showcasing the inferred spread of candidate beneficial alleles over time and space. We also provide estimates for the strength of selection and diffusion rate for each of these alleles. Finally, we highlight possible avenues of improvement for accurately tracing the spread of beneficial alleles in more complex scenarios.

Using gridCoal to assess whether standard population genetic theory holds in the presence of spatio-temporal heterogeneity in population size

Spatially explicit population genetic models have long been developed, yet have rarely been used to test hypotheses about the spatial distribution of genetic diversity or the genetic divergence between populations. Here, we use spatially explicit coalescence simulations to explore the properties of the island and the two-dimensional stepping stone models under a wide range of scenarios with spatio-temporal variation in deme size. We avoid the simulation of genetic data, using the fact that under the studied models, summary statistics of genetic diversity and divergence can be approximated from coalescence times. We perform the simulations using gridCoal, a flexible spatial wrapper for the software msprime (Kelleher et al., 2016, Theoretical Population Biology, 95, 13) developed herein. In gridCoal, deme sizes can change arbitrarily across space and time, as well as migration rates between individual demes. We identify different factors that can cause a deviation from theoretical expectations, such as the simulation time in comparison to the effective deme size and the spatio-temporal autocorrelation across the grid. Our results highlight that F_ST , a measure of the strength of population structure, principally depends on recent demography, which makes it robust to temporal variation in deme size. In contrast, the amount of genetic diversity is dependent on the distant past when N_e is large, therefore longer run times are needed to estimate N_e than F_ST . Finally, we illustrate the use of gridCoal on a real-world example, the range expansion of silver fir (Abies alba Mill.) since the last glacial maximum, using different degrees of spatio-temporal variation in deme size.

Population genetic models for the spatial spread of adaptive variants: A review in light of SARS-CoV-2 evolution

Theoretical population genetics has long studied the arrival and geographic spread of adaptive variants through the analysis of mathematical models of dispersal and natural selection. These models take on a renewed interest in the context of the COVID-19 pandemic, especially given the consequences that novel adaptive variants have had on the course of the pandemic as they have spread through global populations. Here, we review theoretical models for the spatial spread of adaptive variants and identify areas to be improved in future work, toward a better understanding of variants of concern in Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) evolution and other contemporary applications. As we describe, characteristics of pandemics such as COVID-19-such as the impact of long-distance travel patterns and the overdispersion of lineages due to superspreading events-suggest new directions for improving upon existing population genetic models.

Effects of fine-scale population structure on the distribution of heterozygosity in a long-term study of Antirrhinum majus

Many studies have quantified the distribution of heterozygosity and relatedness in natural populations, but few have examined the demographic processes driving these patterns. In this study, we take a novel approach by studying how population structure affects both pairwise identity and the distribution of heterozygosity in a natural population of the self-incompatible plant Antirrhinum majus. Excess variance in heterozygosity between individuals is due to identity disequilibrium, which reflects the variance in inbreeding between individuals; it is measured by the statistic g2. We calculated g2 together with FST and pairwise relatedness (Fij) using 91 SNPs in 22,353 individuals collected over 11 years. We find that pairwise Fij declines rapidly over short spatial scales, and the excess variance in heterozygosity between individuals reflects significant variation in inbreeding. Additionally, we detect an excess of individuals with around half the average heterozygosity, indicating either selfing or matings between close relatives. We use 2 types of simulation to ask whether variation in heterozygosity is consistent with fine-scale spatial population structure. First, by simulating offspring using parents drawn from a range of spatial scales, we show that the known pollen dispersal kernel explains g2. Second, we simulate a 1,000-generation pedigree using the known dispersal and spatial distribution and find that the resulting g2 is consistent with that observed from the field data. In contrast, a simulated population with uniform density underestimates g2, indicating that heterogeneous density promotes identity disequilibrium. Our study shows that heterogeneous density and leptokurtic dispersal can together explain the distribution of heterozygosity.

Whole-genome analysis of multiple wood ant population pairs supports similar speciation histories, but different degrees of gene flow, across their European ranges

The application of demographic history modelling and inference to the study of divergence between species has become a cornerstone of speciation genomics. Speciation histories are usually reconstructed by analysing single populations from each species, assuming that the inferred population history represents the actual speciation history. However, this assumption may not be met when species diverge with gene flow, for example, when secondary contact may be confined to specific geographic regions. Here, we tested whether divergence histories inferred from heterospecific populations may vary depending on their geographic locations, using the two wood ant species Formica polyctena and F. aquilonia. We performed whole-genome resequencing of 20 individuals sampled in multiple locations across the European ranges of both species. Then, we reconstructed the histories of distinct heterospecific population pairs using a coalescent-based approach. Our analyses always supported a scenario of divergence with gene flow, suggesting that divergence started in the Pleistocene (c. 500 kya) and occurred with continuous asymmetrical gene flow from F. aquilonia to F. polyctena until a recent time, when migration became negligible (2-19 kya). However, we found support for contemporary gene flow in a sympatric pair from Finland, where the species hybridise, but no signature of recent bidirectional gene flow elsewhere. Overall, our results suggest that divergence histories reconstructed from a few individuals may be applicable at the species level. Nonetheless, the geographical context of populations chosen to represent their species should be taken into account, as it may affect estimates of migration rates between species when gene flow is spatially heterogeneous.

Phylogenetics in Space: How Continuous Spatial Structure Impacts Tree Inference

The tendency to discretize biology permeates taxonomy and systematics, leading to models that simplify the often continuous nature of populations. Even when the assumption of panmixia is relaxed, most models still assume some degree of discrete structure. The multispecies coalescent has emerged as a powerful model in phylogenetics, but in its common implementation is entirely space-independent - what we call the "missing z-axis". In this article, we review the many lines of evidence for how continuous spatial structure can impact phylogenetic inference. We illustrate and expand on these by using complex continuous-space demographic models that include distinct modes of speciation. We find that the impact of spatial structure permeates all aspects of phylogenetic inference, including gene tree stoichiometry, topological and branch-length variance, network estimation, and species delimitation. We conclude by utilizing our results to suggest how researchers can identify spatial structure in phylogenetic datasets.

Fine-scale genome-wide signature of Pleistocene glaciation in Thitarodes moths (Lepidoptera: Hepialidae), host of Ophiocordyceps fungus in the Hengduan Mountains

The Hengduan Mountains region is a biodiversity hotspot known for its topologically complex, deep valleys and high mountains. While landscape and glacial refugia have been evoked to explain patterns of inter-species divergence, the accumulation of intra-species (i.e. population level) genetic divergence across the mountain-valley landscape in this region has received less attention. We used genome-wide restriction site-associated DNA sequencing (RADseq) to reveal signatures of Pleistocene glaciation in populations of Thitarodes shambalaensis (Lepidoptera: Hepialidae), the host moth of parasitic Ophiocordyceps sinensis (Hypocreales: Ophiocordycipitaceae) or "caterpillar fungus" endemic to the glacier of eastern Mt. Gongga. We used moraine history along the glacier valleys to model the distribution and environmental barriers to gene flow across populations of T. shambalaensis. We found that moth populations separated by less than 10 km exhibited valley-based population genetic clustering and isolation-by-distance (IBD), while gene flow among populations was best explained by models using information about their distributions at the local last glacial maximum (LGM_L , 58 kya), not their contemporary distribution. Maximum likelihood lineage history among populations, and among subpopulations as little as 500 meters apart, recapitulated glaciation history across the landscape. We also found signals of isolated population expansion following the retreat of LGM_L glaciers. These results reveal the fine-scale, long-term historical influence of landscape and glaciation on the genetic structuring of populations of an endangered and economically important insect species. Similar mechanisms, given enough time and continued isolation, could explain the contribution of glacier refugia to the generation of species diversity among the Hengduan Mountains.

Population genetics in microchannels

Many microbial populations proliferate in small channels. In such environments, reproducing cells organize in parallel lanes. Reproducing cells shift these lanes, potentially expelling other cells from the channel. In this paper, we combine theory and experiments to understand how these dynamics affects the diversity of a microbial population. We theoretically predict that genetic diversity is quickly lost along lanes of cells. Our experiments confirm that a population of proliferating Escherichia coli in a microchannel organizes into lanes of genetically identical cells within a few generations. Our findings elucidate the effect of lane formation on populations evolution, with potential applications ranging from microbial ecology in soil to dynamics of epithelial tissues in higher organisms.

Spatial constraints, such as rigid barriers, affect the dynamics of cell populations, potentially altering the course of natural evolution. In this paper, we investigate the population genetics of Escherichia coli proliferating in microchannels with open ends. Our analysis is based on a population model, in which reproducing cells shift entire lanes of cells toward the open ends of the channel. The model predicts that diversity is lost very rapidly within lanes but at a much slower pace among lanes. As a consequence, two mixed, neutral E. coli strains competing in a microchannel must organize into an ordered regular stripe pattern in the course of a few generations. These predictions are in quantitative agreement with our experiments. We also demonstrate that random mutations appearing in the middle of the channel are much more likely to reach fixation than those occurring elsewhere. Our results illustrate fundamental mechanisms of microbial evolution in spatially confined space.

Habitat-linked genetic variation supports microgeographic adaptive divergence in an island-endemic bird species

We investigated the potential mechanisms driving habitat-linked genetic divergence within a bird species endemic to a single 250 km² island. The island scrub-jay (Aphelocoma insularis) exhibits microgeographic divergence in bill morphology across pine-oak ecotones on Santa Cruz Island, California (USA) similar to adaptive differences described in mainland congeners over much larger geographic scales. To test whether individuals exhibit genetic differentiation related to habitat type and divergence in bill length, we genotyped over 3,000 single nucleotide polymorphisms (SNPs) in 123 adult island scrub-jay males from across Santa Cruz Island using restriction site-associated DNA sequencing (RADseq). Neutral landscape genomic analyses revealed that genome-wide genetic differentiation was primarily related to geographic distance and differences in habitat composition. We also found 168 putatively adaptive loci associated with habitat type using multivariate redundancy analysis (RDA) while controlling for spatial effects. Finally, two genome-wide association analyses revealed a polygenic basis to variation in bill length with multiple loci detected in or near genes known to affect bill morphology in other birds. Our findings support the hypothesis that divergent selection at microgeographic scales can cause adaptive divergence in the presence of ongoing gene flow.

Population genomic signatures of the oriental fruit moth related to the Pleistocene climates

The Quaternary climatic oscillations are expected to have had strong impacts on the evolution of species. Although legacies of the Quaternary climates on population processes have been widely identified in diverse groups of species, adaptive genetic changes shaped during the Quaternary have been harder to decipher. Here, we assembled a chromosome-level genome of the oriental fruit moth and compared genomic variation among refugial and colonized populations of this species that diverged in the Pleistocene. High genomic diversity was maintained in refugial populations. Demographic analysis showed that the effective population size of refugial populations declined during the penultimate glacial maximum (PGM) but remained stable during the last glacial maximum (LGM), indicating a strong impact of the PGM rather than the LGM on this pest species. Genome scans identified one chromosomal inversion and a mutation of the circadian gene Clk on the neo-Z chromosome potentially related to the endemicity of a refugial population. In the colonized populations, genes in pathways of energy metabolism and wing development showed signatures of selection. These different genomic signatures of refugial and colonized populations point to multiple impacts of Quaternary climates on adaptation in an extant species.

Comparative Population Genetics in the Human Gut Microbiome

Genetic variation in the human gut microbiome is responsible for conferring a number of crucial phenotypes like the ability to digest food and metabolize drugs. Yet, our understanding of how this variation arises and is maintained remains relatively poor. Thus, the microbiome remains a largely untapped resource, as the large number of coexisting species in the microbiome presents a unique opportunity to compare and contrast evolutionary processes across species to identify universal trends and deviations. Here we outline features of the human gut microbiome that, while not unique in isolation, as an assemblage make it a system with unparalleled potential for comparative population genomics studies. We consciously take a broad view of comparative population genetics, emphasizing how sampling a large number of species allows researchers to identify universal evolutionary dynamics in addition to new genes, which can then be leveraged to identify exceptional species that deviate from general patterns. To highlight the potential power of comparative population genetics in the microbiome, we reanalyze patterns of purifying selection across ∼40 prevalent species in the human gut microbiome to identify intriguing trends which highlight functional categories in the microbiome that may be under more or less constraint.

Populations, Traits, and Their Spatial Structure in Humans

The spatial distribution of genetic variants is jointly determined by geography, past demographic processes, natural selection, and its interplay with environmental variation. A fraction of these genetic variants are "causal alleles" that affect the manifestation of a complex trait. The effect exerted by these causal alleles on complex traits can be independent or dependent on the environment. Understanding the evolutionary processes that shape the spatial structure of causal alleles is key to comprehend the spatial distribution of complex traits. Natural selection, past population size changes, range expansions, consanguinity, assortative mating, archaic introgression, admixture, and the environment can alter the frequencies, effect sizes, and heterozygosities of causal alleles. This provides a genetic axis along which complex traits can vary. However, complex traits also vary along biogeographical and sociocultural axes which are often correlated with genetic axes in complex ways. The purpose of this review is to consider these genetic and environmental axes in concert and examine the ways they can help us decipher the variation in complex traits that is visible in humans today. This initiative necessarily implies a discussion of populations, traits, the ability to infer and interpret "genetic" components of complex traits, and how these have been impacted by adaptive events. In this review, we provide a history-aware discussion on these topics using both the recent and more distant past of our academic discipline and its relevant contexts.

Adaptive divergence in bill morphology and other thermoregulatory traits is facilitated by restricted gene flow in song sparrows on the California Channel Islands

Disentangling the effects of neutral and adaptive processes in maintaining phenotypic variation across environmental gradients is challenging in natural populations. Song sparrows (Melospiza melodia) on the California Channel Islands occupy a pronounced east-west climate gradient within a small spatial scale, providing a unique opportunity to examine the interaction of genetic isolation (reduced gene flow) and the environment (selection) in driving variation. We used reduced representation genomic libraries to infer the role of neutral processes (drift and restricted gene flow) and divergent selection in driving variation in thermoregulatory traits with an emphasis on the mechanisms that maintain bill divergence among islands. Analyses of 22,029 neutral SNPs confirm distinct population structure by island with restricted gene flow and relatively large effective population sizes, suggesting bill differences are probably not a product of genetic drift. Instead, we found strong support for local adaptation using 3294 SNPs in differentiation-based and environmental association analyses coupled with genome-wide association tests. Specifically, we identified several putatively adaptive and candidate loci in or near genes involved in bill development pathways (e.g., BMP, CaM, Wnt), confirming the highly complex and polygenic architecture underlying bill morphology. Furthermore, we found divergence in genes associated with other thermoregulatory traits (i.e., feather structure, plumage colour, and physiology). Collectively, these results suggest strong divergent selection across an island archipelago results in genomic changes in a suite of traits associated with climate adaptation over small spatial scales. Future research should move beyond studying univariate traits to better understand multidimensional responses to complex environmental conditions.

Broad Concordance in the Spatial Distribution of Adaptive and Neutral Genetic Variation across an Elevational Gradient in Deer Mice

When species are continuously distributed across environmental gradients, the relative strength of selection and gene flow shape spatial patterns of genetic variation, potentially leading to variable levels of differentiation across loci. Determining whether adaptive genetic variation tends to be structured differently than neutral variation along environmental gradients is an open and important question in evolutionary genetics. We performed exome-wide population genomic analysis on deer mice sampled along an elevational gradient of nearly 4,000 m of vertical relief. Using a combination of selection scans, genotype-environment associations, and geographic cline analyses, we found that a large proportion of the exome has experienced a history of altitude-related selection. Elevational clines for nearly 30% of these putatively adaptive loci were shifted significantly up- or downslope of clines for loci that did not bear similar signatures of selection. Many of these selection targets can be plausibly linked to known phenotypic differences between highland and lowland deer mice, although the vast majority of these candidates have not been reported in other studies of highland taxa. Together, these results suggest new hypotheses about the genetic basis of physiological adaptation to high altitude, and the spatial distribution of adaptive genetic variation along environmental gradients.

The Tempo and Mode of Adaptation in a Complex Natural Population: the Microbiome

Adaptation is a fundamental process by which populations evolve to grow more fit in their environments. Recent studies are starting to show us that commensal microbes can evolve on short timescales of days and months, suggesting that ecological changes are not the only means by which microbes in complex natural populations respond to selection pressures. However, we still lack a complete understanding of the tempo and mode of adaptation in microbiomes given the many complex forces that natural populations experience, which include ecological pressures, changes in population size, spatial structure, and fluctuations in selection pressures. Advances in modeling complex populations and scenarios will allow us to understand adaptation not only in microbiomes but also more generically in other natural populations that experience similar complexities.

Fine-scale spatial genetic structure across the species range reflects recent colonization of high elevation habitats in silver fir (Abies alba Mill.)

Variation in genetic diversity across species ranges has long been recognized as highly informative for assessing populations’ resilience and adaptive potential. The spatial distribution of genetic diversity within populations, referred to as fine‐scale spatial genetic structure (FSGS), also carries information about recent demographic changes, yet it has rarely been connected to range scale processes. We studied eight silver fir (Abies alba Mill.) population pairs (sites), growing at high and low elevations, representative of the main genetic lineages of the species. A total of 1,368 adult trees and 540 seedlings were genotyped using 137 and 116 single nucleotide polymorphisms (SNPs), respectively. Sites revealed a clear east‐west isolation‐by‐distance pattern consistent with the post‐glacial colonization history of the species. Genetic differentiation among sites (F_CT = 0.148) was an order of magnitude greater than between elevations within sites (F_SC = 0.031), nevertheless high elevation populations consistently exhibited a stronger FSGS. Structural equation modelling revealed that elevation and, to a lesser extent, post‐glacial colonization history, but not climatic and habitat variables, were the best predictors of FSGS across populations. These results suggest that high elevation habitats have been colonized more recently across the species range. Additionally, paternity analysis revealed a high reproductive skew among adults and a stronger FSGS in seedlings than in adults, suggesting that FSGS may conserve the signature of demographic changes for several generations. Our results emphasize that spatial patterns of genetic diversity within populations provide information about demographic history complementary to non‐spatial statistics, and could be used for genetic diversity monitoring, especially in forest trees.

The evolution of comparative phylogeography: putting the geography (and more) into comparative population genomics

Comparative population genomics is an ascendant field using genomic comparisons between species to draw inferences about forces regulating genetic variation. Comparative phylogeography, by contrast, focuses on the shared lineage histories of species codistributed geographically and is decidedly organismal in perspective. Comparative phylogeography is approximately 35 years old, and, by some metrics, is showing signs of reduced growth. Here, we contrast the goals and methods of comparative population genomics and comparative phylogeography and argue that comparative phylogeography offers an important perspective on evolutionary history that succeeds in integrating genomics with landscape evolution in ways that complement the suprageographic perspective of comparative population genomics. Focusing primarily on terrestrial vertebrates, we review the history of comparative phylogeography, its milestones and ongoing conceptual innovations, its increasingly global focus, and its status as a bridge between landscape genomics and the process of speciation. We also argue that, as a science with a strong “sense of place,” comparative phylogeography offers abundant “place-based” educational opportunities with its focus on geography and natural history, as well as opportunities for collaboration with local communities and indigenous peoples. Although comparative phylogeography does not yet require whole-genome sequencing for many of its goals, we conclude that it nonetheless plays an important role in grounding our interpretation of genetic variation in the fundamentals of geography and Earth history.

Ecological Interactions, Environmental Gradients, and Gene Flow in Local Adaptation

Despite long-standing interest in local adaptation of plants to their biotic and abiotic environment, existing theory, and many case studies, little work to date has addressed within-species evolution of concerted strategies and how these might contrast with patterns across species. Here we consider the interactions between pollinators, herbivores, and resource availability in shaping plant local adaptation, how these interactions impact plant phenotypes and gene flow, and the conditions where multiple traits align along major environmental gradients such as latitude and elevation. Continued work in emerging model systems will benefit from the melding of classic experimental approaches with novel population genetic analyses to reveal patterns and processes in plant local adaptation.

Fast and flexible estimation of effective migration surfaces

Spatial population genetic data often exhibits ‘isolation-by-distance,’ where genetic similarity tends to decrease as individuals become more geographically distant. The rate at which genetic similarity decays with distance is often spatially heterogeneous due to variable population processes like genetic drift, gene flow, and natural selection. Petkova et al., 2016 developed a statistical method called Estimating Effective Migration Surfaces (EEMS) for visualizing spatially heterogeneous isolation-by-distance on a geographic map. While EEMS is a powerful tool for depicting spatial population structure, it can suffer from slow runtimes. Here, we develop a related method called Fast Estimation of Effective Migration Surfaces (FEEMS). FEEMS uses a Gaussian Markov Random Field model in a penalized likelihood framework that allows for efficient optimization and output of effective migration surfaces. Further, the efficient optimization facilitates the inference of migration parameters per edge in the graph, rather than per node (as in EEMS). With simulations, we show conditions under which FEEMS can accurately recover effective migration surfaces with complex gene-flow histories, including those with anisotropy. We apply FEEMS to population genetic data from North American gray wolves and show it performs favorably in comparison to EEMS, with solutions obtained orders of magnitude faster. Overall, FEEMS expands the ability of users to quickly visualize and interpret spatial structure in their data.

Local climate adaptation and gene flow in the native range of two co-occurring fruit moths with contrasting invasiveness

Invasive species pose increasing threats to global biodiversity and ecosystems. While previous studies have characterized successful invaders based on ecological traits, characteristics related to evolutionary processes have rarely been investigated. Here we compared gene flow and local adaptation using demographic analyses and outlier tests in two co-occurring moth pests across their common native range of China, one of which (the peach fruit moth, Carposina sasakii) has maintained its native distribution, while the other (the oriental fruit moth, Grapholita molesta) has expanded its range globally during the past century. We found that both species showed a pattern of genetic differentiation and an evolutionary history consistent with a common southwestern origin and northward expansion in their native range. However, for the noninvasive species, genetic differentiation was closely aligned with the environment, and there was a relatively low level of gene flow, whereas in the invasive species, genetic differentiation was associated with geography. Genome scans indicated stronger patterns of climate-associated loci in the noninvasive species. While strong local adaptation and reduced gene flow across its native range may have decreased the invasiveness of C. sasakii, this requires further validation with additional comparisons of invasive and noninvasive species across their native range.

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.