Dealing With the Complexity of Effective Population Size in Conservation Practice

Effective population size (Ne) is one of the most important parameters in evolutionary biology, as it is linked to the long-term survival capability of species. Therefore, Ne greatly interests conservation geneticists, but it is also very relevant to policymakers, managers, and conservation practitioners. Molecular methods to estimate Ne rely on various assumptions, including no immigration, panmixia, random sampling, absence of spatial genetic structure, and/or mutation-drift equilibrium. Species are, however, often characterized by fragmented populations under changing environmental conditions and anthropogenic pressure. Therefore, the estimation methods' assumptions are seldom addressed and rarely met, possibly leading to biased and inaccurate Ne estimates. To address the challenges associated with estimating Ne for conservation purposes, the COST Action 18134, Genomic Biodiversity Knowledge for Resilient Ecosystems (G-BiKE), organized an international workshop that met in August 2022 in Brașov, Romania. The overarching goal was to operationalize the current knowledge of Ne estimation methods for conservation practitioners and decision-makers. We set out to identify datasets to evaluate the sensitivity of Ne estimation methods to violations of underlying assumptions and to develop data analysis strategies that addressed pressing issues in biodiversity monitoring and conservation. Referring to a comprehensive body of scientific work on Ne, this meeting report is not intended to be exhaustive but rather to present approaches, workshop findings, and a collection of papers that serve as fruits of those efforts. We aimed to provide insights and opportunities to help bridge the gap between scientific research and conservation practice.

Resilience of genetic diversity in forest trees over the Quaternary

The effect of past environmental changes on the demography and genetic diversity of natural populations remains a contentious issue and has rarely been investigated across multiple, phylogenetically distant species. Here, we perform comparative population genomic analyses and demographic inferences for seven widely distributed and ecologically contrasting European forest tree species based on concerted sampling of 164 populations across their natural ranges. For all seven species, the effective population size, Ne, increased or remained stable over many glacial cycles and up to 15 million years in the most extreme cases. Surprisingly, the drastic environmental changes associated with the Pleistocene glacial cycles have had little impact on the level of genetic diversity of dominant forest tree species, despite major shifts in their geographic ranges. Based on their trajectories of Ne over time, the seven tree species can be divided into three major groups, highlighting the importance of life history and range size in determining synchronous variation in genetic diversity over time. Altogether, our results indicate that forest trees have been able to retain their evolutionary potential over very long periods of time despite strong environmental changes.

Funk C, Tallmon D, Boucher C. ONeSAMP 3.0: estimation of effective population size via single nucleotide polymorphism data from one population

The genetic effective size (Ne) is arguably one of the most important characteristics of a population as it impacts the rate of loss of genetic diversity. Methods that estimate Ne are important in population and conservation genetic studies as they quantify the risk of a population being inbred or lacking genetic diversity. Yet there are very few methods that can estimate the Ne from data from a single population and without extensive information about the genetics of the population, such as a linkage map, or a reference genome of the species of interest. We present ONeSAMP 3.0, an algorithm for estimating Ne from single nucleotide polymorphism data collected from a single population sample using approximate Bayesian computation and local linear regression. We demonstrate the utility of this approach using simulated Wright-Fisher populations, and empirical data from five endangered Channel Island fox (Urocyon littoralis) populations to evaluate the performance of ONeSAMP 3.0 compared to a commonly used Ne estimator. Our results show that ONeSAMP 3.0 is broadly applicable to natural populations and is flexible enough that future versions could easily include summary statistics appropriate for a suite of biological and sampling conditions. ONeSAMP 3.0 is publicly available under the GNU General Public License at https://github.com/AaronHong1024/ONeSAMP_3.

Estimating the Effective Size of European Wolf Populations

Molecular methods are routinely used to estimate the effective size of populations (N e). However, underlying model assumptions are frequently violated to an unknown extent. Although simulations can detect sources of bias and help to adjust sampling strategies and analyses methods, additional information from empirical data can also be used to calibrate methods and improve molecular N e estimation methods. Here, we take advantage of long-term genetic and ecological monitoring data of the grey wolf (Canis lupus) in Germany, and detailed population genetic studies in Poland, Spain and Portugal to improve N e estimation strategies in this species, and species with similar life history traits. We first calculated N e from average lifetime reproductive success and detailed census data from the German population, which served as a baseline to compare to molecular estimates based on linkage disequilibrium and sibship frequency. This yielded a robust N e/N c estimation that we used to calibrate molecular estimates of German, Polish and Iberian wolf populations. The linkage disequilibrium method was strongly influenced by spatial genetic structure, much more than the sibship frequency method. When N e was estimated in local neighbourhoods, both methods yielded comparable results. Estimates of the metapopulation effective size seemed to correspond generally well with the sum of the estimates of local neighbourhoods. Overall, we found that the number of packs is a good proxy of the effective population size. Using this as a rule of thumb, we evaluated for all European wolf populations the N e 500 indicator and concluded that half of the European wolf populations do not yet fulfil this criterion.

Rapid genetic adaptation to a novel ecosystem despite a large founder event

Introduced and invasive species make excellent natural experiments for investigating rapid evolution. Here, we describe the effects of genetic drift and rapid genetic adaptation in pink salmon (Oncorhynchus gorbuscha) that were accidentally introduced to the Great Lakes via a single introduction event 31 generations ago. Using whole-genome resequencing for 134 fish spanning five sample groups across the native and introduced range, we estimate that the source population's effective population size was 146,886 at the time of introduction, whereas the founding population's effective population size was just 72-a 2040-fold decrease. As expected with a severe founder event, we show reductions in genome-wide measures of genetic diversity, specifically a 37.7% reduction in the number of SNPs and an 8.2% reduction in observed heterozygosity. Despite this decline in genetic diversity, we provide evidence for putative selection at 47 loci across multiple chromosomes in the introduced populations, including missense variants in genes associated with circadian rhythm, immunological response and maturation, which match expected or known phenotypic changes in the Great Lakes. For one of these genes, we use a species-specific agent-based model to rule out genetic drift and conclude our results support a strong response to selection occurring in a period gene (per2) that plays a predominant role in determining an organism's daily clock, matching large day length differences experienced by introduced salmon during important phenological periods. Together, these results inform how populations might evolve rapidly to new environments, even with a small pool of standing genetic variation.

Unraveling the Genetic Legacy of Commercial Whaling and Population Dynamics in Arctic Bowhead Whales and Narwhals

Assessing genetic structure and diversity in wildlife is particularly important in the context of climate change. The Arctic is rapidly warming, and endemic species must adapt quickly or face significant threats to persistence. Bowhead whales (Balaena mysticetus) and narwhals (Monodon monoceros) are two long-lived Arctic species with similar habitat requirements and are often seen together in the Canadian Arctic. Although their ranges overlap extensively, bowhead whales experienced significantly greater commercial whaling mortality than narwhals over several centuries. The similar habitat requirements but different harvest histories of these two species provide an opportunity to examine present-day genetic diversity and the demographic and genetic consequences of commercial whaling. We whole-genome resequenced contemporary Canadian Arctic bowhead whales and narwhals to delineate population structure and reconstruct demographic history. We found higher genetic diversity in bowhead whales compared to narwhals. However, bowhead whale effective population size sharply declined contemporaneously with the intense commercial whaling period. Narwhals, in contrast, exhibited recent growth in effective population size, likely reflecting exposure to limited opportunistic commercial harvest. Bowhead whales will likely continue to experience significant genetic drift in the future, leading to the erosion of genetic diversity. In contrast, narwhals do not seem to be at imminent risk of losing their current levels of genetic variation due to their long-term low effective population size and lack of evidence for a recent decline. This work highlights the importance of considering population trajectories in addition to genetic diversity when assessing the genetics of populations for conservation and management purposes.

Population Size in Evolutionary Biology Is More Than the Effective Size

In population genetics idealized Wright-Fisher (WF) populations are generally considered equivalent to real populations with regard to the major evolutionary processes that influence genotype and allele frequencies. As a result we often model the response of populations by focusing on the effective size N e . The Diversity Partitioning Theorem (DPT) shows that you cannot model the behavior of a system solely on the basis of a diversity (accounting for unevenness among items) without taking richness into account. I show that the census population size (the number of adults, N c ) is equivalent to a richness, and that the effective size N e is equivalent to a true diversity. It follows logically from the DPT that we require both N e and N c to understand how drift, selection, mutation, and gene flow interact to shape the course of evolution of populations. Here I review evidence that both N c and N e affect evolutionary trajectories of populations for neutral and adaptive processes. This also influences how we should consider evolutionary potential and genetic criteria for conservation of populations. The effective size of a population is of huge importance in evolutionary biology, but it should not be the sole focus when population size is concerned. Applied evolutionary studies need to integrate N c in the equation more consistently when modeling the response to selection, mutation, migration, and drift.

Characterising the Genomic Landscape of Differentiation Between Annual and Perennial Rye

Annuality and perenniality represent two different life-history strategies in plants, and an analysis of genomic differentiation between closely related species of different life histories bears the potential to identify the underlying targets of selection. Additionally, understanding the interactions between patterns of recombination and signatures of natural selection is a central aim in evolutionary biology, because patterns of recombination shape the evolution of genomes by affecting the efficacy of selection. Here, our aim was to characterise the landscape of genomic differentiation between weedy annual rye (Secale cereale L.) and wild perennial rye (Secale strictum C. Presl), and explore the extent to which signatures of selection are influenced by recombination rate variation. We used population-level sequence data of annual and perennial rye to analyse population structure and their demographic history. Based on our analyses, annual and perennial rye diverged approximately 26,500 years ago (ya) from an ancestral population size of ~85,000 individuals. We analysed patterns of genetic diversity and genetic differentiation, and found highly differentiated regions located in low-recombination regions, indicative of linked selection. Although all highly differentiated regions, as revealed by F ST-outlier scans, were located in low-recombining regions, not all chromosomes showed this tendency. We therefore performed a gene ontology enrichment analysis, which showed that highly differentiated regions comprise genes involved in photosynthesis. This enrichment was confirmed when F ST outlier scans were performed separately in low- and intermediate-recombining regions, but not in high-recombining regions, suggesting that local recombination rate variation in rye affects outlier scans. Cultivated rye is an annual crop, but the introduction of perenniality may be advantageous in regions with poor soil quality or under low-input farming. Although the resolution of our analysis is limited to a broad-scale, knowledge about the evolutionary divergence between annual and perennial rye might support breeding efforts towards perennial rye cultivation.

Unraveling the Complexity of the N e/N c Ratio for Conservation of Large and Widespread Pelagic Fish Species: Current Status and Challenges

Estimating and understanding the ratio between effective population size (N e) and census population size (N c) are pivotal in the conservation of large marine pelagic fish species, including bony fish such as tunas and cartilaginous fish such as sharks, given the challenges associated with obtaining accurate estimates of their abundance. The difficulties inherent in capturing and monitoring these species in vast and dynamic marine environments often make direct estimation of their population size challenging. By focusing on N e, it is conceivable in certain cases to approximate census size once the N e/N c ratio is known, although this ratio can vary and does not always increase linearly, as it is influenced by various ecological and evolutionary factors. Thus, this ratio presents challenges and complexities in the context of pelagic species conservation. To delve deeper into these challenges, firstly, we recall the diverse types of effective population sizes, including contemporary and historical sizes, and their implications in conservation biology. Secondly, we outline current knowledge about the influence of life history traits on the N e/N c ratio in the light of examples drawn from large and abundant pelagic fish species. Despite efforts to document an increasing number of marine species using recent technologies and statistical methods, establishing general rules to predict N e/N c remains elusive, necessitating further research and investment. Finally, we recall statistical challenges in relating N e and N c emphasizing the necessity of aligning temporal and spatial scales. This last part discusses the roles of generation and reproductive cycle effective population sizes to predict genetic erosion and guiding management strategies. Collectively, these sections underscore the multifaceted nature of effective population size estimation, crucial for preserving genetic diversity and ensuring the long-term viability of populations. By navigating statistical and theoretical complexities, and addressing methodological challenges, scientists should be able to advance our understanding of the N e/N c ratio.

Comparing Genetic Ne Reconstructions Over Time With Long-Time Wolf Monitoring Data in Two Populations

Many methods are now available to calculate N e , but their performance varies depending on assumptions. Although simulated data are useful to discover certain types of bias, real empirical data supported by detailed known population histories allow us to discern how well methods perform with actual messy and complex data. Here, we focus on two genomic data sets of grey wolf populations for which population size changes of the past 40-120 years are well documented. We use this background to explore in what detail we can retrieve the known population history from these populations, in the light of pitfalls relating to population history, sampling design and the change in the spatial scale at which N e is estimated as we go further back in time. The Scandinavian wolf population was founded in the early 1980s from a few individuals and has gradually expanded up to 510 wolves. Although the founder event of the Scandinavian population was detected by GONE, the founding effective population size was strongly overestimated when the most recent samples were used, but less so when older samples were considered. Nevertheless, the present-day N e corresponds to theoretical expectations. The western Great Lakes wolf population of Minnesota is the only population in the contiguous United States that persisted throughout the 20th century, surviving intense persecution. We found a good concordance between the estimated N e and trends in census size data, but the reconstruction of N e clearly highlights the difficulty of interpreting results in spatially structured populations that underwent demographic fluctuations.

Dealing With Assumptions and Sampling Bias in the Estimation of Effective Population Size: A Case Study in an Amphibian Population

Accurately estimating effective population size (N e) is essential for understanding evolutionary processes and guiding conservation efforts. This study investigates N e estimation methods in spatially structured populations using a population of moor frog (Rana arvalis) as a case study. We assessed the behaviour of N e estimates derived from the linkage disequilibrium (LD) method as we changed the spatial configuration of samples. Moor frog eggs were sampled from 25 breeding patches (i.e., separate vernal ponds, ditches or parts of larger fens) within a single population, revealing an isolation-by-distance pattern and a local spatial genetic structure. Varying buffer sizes around each patch were used to examine the impact of sampling window size on the estimation of effective number of breeders (N b). Our results indicate a downward bias in LD N b estimates with increasing buffer size, suggesting an underestimation of N b. The observed bias is attributed to LD resulting from including genetically divergent individuals (mixture-LD) confounding LD due to drift. This emphasises the significance of considering even subtle spatial genetic patterns. The implications of these findings are discussed, emphasising the need to account for spatial genetic structure to accurately assess population viability and inform conservation efforts. This study contributes to our understanding of the challenges associated with N e estimation in spatially structured populations and underscores the importance of refining methodologies to address population-specific spatial dynamics for effective conservation planning and management.

Pitfalls and windfalls of detecting demographic declines using population genetics in long-lived species

Detecting recent demographic changes is a crucial component of species conservation and management, as many natural populations face declines due to anthropogenic habitat alteration and climate change. Genetic methods allow researchers to detect changes in effective population size (Ne) from sampling at a single timepoint. However, in species with long lifespans, there is a lag between the start of a decline in a population and the resulting decrease in genetic diversity. This lag slows the rate at which diversity is lost, and therefore makes it difficult to detect recent declines using genetic data. However, the genomes of old individuals can provide a window into the past, and can be compared to those of younger individuals, a contrast that may help reveal recent demographic declines. To test whether comparing the genomes of young and old individuals can help infer recent demographic bottlenecks, we use forward-time, individual-based simulations with varying mean individual lifespans and extents of generational overlap. We find that age information can be used to aid in the detection of demographic declines when the decline has been severe. When average lifespan is long, comparing young and old individuals from a single timepoint has greater power to detect a recent (within the last 50 years) bottleneck event than comparing individuals sampled at different points in time. Our results demonstrate how longevity and generational overlap can be both a hindrance and a boon to detecting recent demographic declines from population genomic data.

What does effective population size tell us about loss of allelic variation?

There are two primary measures of the amount of genetic variation in a population at a locus: heterozygosity and the number of alleles. Effective population size (N e) provides both an expectation of the amount of heterozygosity in a population at drift-mutation equilibrium and the rate of loss of heterozygosity because of genetic drift. In contrast, the number of alleles in a population at drift-mutation equilibrium is a function of both N e and census size (N C). In addition, populations with the same N e can lose allelic variation at very different rates. Allelic variation is generally much more sensitive to bottlenecks than heterozygosity. Expressions used to adjust for the effects of violations of the ideal population on N e do not provide good predictions of the loss of allelic variation. These effects are much greater for loci with many alleles, which are often important for adaptation. We show that there is a linear relationship between the reduction of N C and the corresponding reduction of the expected number of alleles at drift-mutation equilibrium. This makes it possible to predict the expected effect of a bottleneck on allelic variation. Heterozygosity provides good estimates of the rate of adaptive change in the short-term, but allelic variation provides important information about long-term adaptive change. The guideline of long-term N e being greater than 500 is often used as a primary genetic metric for evaluating conservation status. We recommend that this guideline be expanded to take into account allelic variation as well as heterozygosity.

Climate change introduces threatened killer whale populations and conservation challenges to the Arctic

The Arctic is the fastest-warming region on the planet, and the lengthening ice-free season is opening Arctic waters to sub-Arctic species such as the killer whale (Orcinus orca). As apex predators, killer whales can cause significant ecosystem-scale changes. Setting conservation priorities for killer whales and their Arctic prey species requires knowledge of their evolutionary history and demographic trajectory. Using whole-genome resequencing of 24 killer whales sampled in the northwest Atlantic, we first explored the population structure and demographic history of Arctic killer whales. To better understand the broader geographic relationship of these Arctic killer whales to other populations, we compared them to a globally sampled dataset. Finally, we assessed threats to Arctic killer whales due to anthropogenic harvest by reviewing the peer-reviewed and gray literature. We found that there are two highly genetically distinct, non-interbreeding populations of killer whales using the eastern Canadian Arctic. These populations appear to be as genetically different from each other as are ecotypes described elsewhere in the killer whale range; however, our data cannot speak to ecological differences between these populations. One population is newly identified as globally genetically distinct, and the second is genetically similar to individuals sampled from Greenland. The effective sizes of both populations recently declined, and both appear vulnerable to inbreeding and reduced adaptive potential. Our survey of human-caused mortalities suggests that harvest poses an ongoing threat to both populations. The dynamic Arctic environment complicates conservation and management efforts, with killer whales adding top-down pressure on Arctic food webs crucial to northern communities' social and economic well-being. While killer whales represent a conservation priority, they also complicate decisions surrounding wildlife conservation and resource management in the Arctic amid the effects of climate change.

Estimation of contemporary effective population size in plant populations: Limitations of genomic datasets

Effective population size (N e) is a pivotal evolutionary parameter with crucial implications in conservation practice and policy. Genetic methods to estimate N e have been preferred over demographic methods because they rely on genetic data rather than time-consuming ecological monitoring. Methods based on linkage disequilibrium (LD), in particular, have become popular in conservation as they require a single sampling and provide estimates that refer to recent generations. A software program based on the LD method, GONE, looks particularly promising to estimate contemporary and recent-historical N e (up to 200 generations in the past). Genomic datasets from non-model species, especially plants, may present some constraints to the use of GONE, as linkage maps and reference genomes are seldom available, and SNP genotyping is usually based on reduced-representation methods. In this study, we use empirical datasets from four plant species to explore the limitations of plant genomic datasets when estimating N e using the algorithm implemented in GONE, in addition to exploring some typical biological limitations that may affect N e estimation using the LD method, such as the occurrence of population structure. We show how accuracy and precision of N e estimates potentially change with the following factors: occurrence of missing data, limited number of SNPs/individuals sampled, and lack of information about the location of SNPs on chromosomes, with the latter producing a significant bias, previously unexplored with empirical data. We finally compare the N e estimates obtained with GONE for the last generations with the contemporary N e estimates obtained with the programs currentNe and NeEstimator.

Pitfalls and windfalls of detecting demographic declines using population genetics in long-lived species

Detecting recent demographic changes is a crucial component of species conservation and management, as many natural populations face declines due to anthropogenic habitat alteration and climate change. Genetic methods allow researchers to detect changes in effective population size (N e ) from sampling at a single timepoint. However, in species with long lifespans, there is a lag between the start of a decline in a population and the resulting decrease in genetic diversity. This lag slows the rate at which diversity is lost, and therefore makes it difficult to detect recent declines using genetic data. However, the genomes of old individuals can provide a window into the past, and can be compared to those of younger individuals, a contrast that may help reveal recent demographic declines. To test whether comparing the genomes of young and old individuals can help infer recent demographic bottlenecks, we use forward-time, individual-based simulations with varying mean individual lifespans and extents of generational overlap. We find that age information can be used to aid in the detection of demographic declines when the decline has been severe. When average lifespan is long, comparing young and old individuals from a single timepoint has greater power to detect a recent (within the last 50 years) bottleneck event than comparing individuals sampled at different points in time. Our results demonstrate how longevity and generational overlap can be both a hindrance and a boon to detecting recent demographic declines from population genomic data.

Simulations of multiple breeding strategy scenarios in common bean for assessing genomic selection accuracy and model updating

The aim of this study was to evaluate the accuracy of the ridge regression best linear unbiased prediction model across different traits, parent population sizes, and breeding strategies when estimating breeding values in common bean (Phaseolus vulgaris). Genomic selection was implemented to make selections within a breeding cycle and compared across five different breeding strategies (single seed descent, mass selection, pedigree method, modified pedigree method, and bulk breeding) following 10 breeding cycles. The model was trained on a simulated population of recombinant inbreds genotyped for 1010 single nucleotide polymorphism markers including 38 known quantitative trait loci identified in the literature. These QTL included 11 for seed yield, eight for white mold disease incidence, and 19 for days to flowering. Simulation results revealed that realized accuracies fluctuate depending on the factors investigated: trait genetic architecture, breeding strategy, and the number of initial parents used to begin the first breeding cycle. Trait architecture and breeding strategy appeared to have a larger impact on accuracy than the initial number of parents. Generally, maximum accuracies (in terms of the correlation between true and estimated breeding value) were consistently achieved under a mass selection strategy, pedigree method, and single seed descent method depending on the simulation parameters being tested. This study also investigated model updating, which involves retraining the prediction model with a new set of genotypes and phenotypes that have a closer relation to the population being tested. While it has been repeatedly shown that model updating generally improves prediction accuracy, it benefited some breeding strategies more than others. For low heritability traits (e.g., yield), conventional phenotype-based selection methods showed consistent rates of genetic gain, but genetic gain under genomic selection reached a plateau after fewer cycles. This plateauing is likely a cause of faster fixation of alleles and a diminishing of genetic variance when selections are made based on estimated breeding value as opposed to phenotype.

Genetic erosion in an endangered desert fish during a megadrought despite long-term supportive breeding

Human water use combined with a recent megadrought have reduced river and stream flow through the southwest United States and led to periodic drying of formerly perennial river segments. Reductions in snowmelt runoff and increased extent of drying collectively threaten short-lived, obligate aquatic species, including the endangered Rio Grande silvery minnow (Hybognathus amarus). This species is subject to boom-and-bust population dynamics, under which large fluctuations in abundance are expected to lower estimates of effective population size and erode genetic diversity over time. Rates of diversity loss are also affected by additions of hatchery-origin fish used to supplement the wild population. We used demographic and genetic data from wild and hatchery individuals to examine the relationship of genetic diversity and effective population size to abundance over the last two decades. Genetic diversity was low during the early 2000s, but diversity and demographic metrics stabilized after the hatchery program was initiated and environmental conditions improved. Yet, from 2017 onward, allelic diversity declined (Cohen's d = 1.34) and remained low despite hatchery stocking and brief wild population recovery. Across the time series, single-sample estimates of effective population size based on linkage disequilibrium (LD Ne ) were positively associated (r = 0.53) with wild abundance and total abundance, but as the proportion of hatchery-origin spawners increased, LD Ne declined (r = -0.55). Megadrought limited wild spawner abundance and precluded refreshment of hatchery brood stocks with wild fish; hence, we predict a riverine population increasingly dominated by hatchery-origin individuals and accelerated loss of genetic diversity despite supplementation. We recommend an adaptive and accelerated management plan that integrates river flow management and hatchery operations to slow the pace of genetic diversity loss exacerbated by megadrought.

A lack of genetic diversity and minimal adaptive evolutionary divergence in introduced Mysis shrimp after 50 years

The successes of introduced populations in novel habitats often provide powerful examples of evolution and adaptation. In the 1950s, opossum shrimp (Mysis diluviana) individuals from Clearwater Lake in Minnesota, USA were transported and introduced to Twin Lakes in Colorado, USA by fisheries managers to supplement food sources for trout. Mysis were subsequently introduced from Twin Lakes into numerous lakes throughout Colorado. Because managers kept detailed records of the timing of the introductions, we had the opportunity to test for evolutionary divergence within a known time interval. Here, we used reduced representation genomic data to investigate patterns of genetic diversity, test for genetic divergence between populations, and for evidence of adaptive evolution within the introduced populations in Colorado. We found very low levels of genetic diversity across all populations, with evidence for some genetic divergence between the Minnesota source population and the introduced populations in Colorado. There was little differentiation among the Colorado populations, consistent with the known provenance of a single founding population, with the exception of the population from Gross Reservoir, Colorado. Demographic modeling suggests that at least one undocumented introduction from an unknown source population hybridized with the population in Gross Reservoir. Despite the overall low genetic diversity we observed, F ST outlier and environmental association analyses identified multiple loci exhibiting signatures of selection and adaptive variation related to elevation and lake depth. The success of introduced species is thought to be limited by genetic variation, but our results imply that populations with limited genetic variation can become established in a wide range of novel environments. From an applied perspective, the observed patterns of divergence between populations suggest that genetic analysis can be a useful forensic tool to determine likely sources of invasive species.

Unidirectional trans-Atlantic gene flow and a mixed spawning area shape the genetic connectivity of Atlantic bluefin tuna

The commercially important Atlantic bluefin tuna (Thunnus thynnus), a large migratory fish, has experienced notable recovery aided by accurate resource assessment and effective fisheries management efforts. Traditionally, this species has been perceived as consisting of eastern and western populations, spawning respectively in the Mediterranean Sea and the Gulf of Mexico, with mixing occurring throughout the Atlantic. However, recent studies have challenged this assumption by revealing weak genetic differentiation and identifying a previously unknown spawning ground in the Slope Sea used by Atlantic bluefin tuna of uncertain origin. To further understand the current and past population structure and connectivity of Atlantic bluefin tuna, we have assembled a unique dataset including thousands of genome-wide single-nucleotide polymorphisms (SNPs) from 500 larvae, young of the year and spawning adult samples covering the three spawning grounds and including individuals of other Thunnus species. Our analyses support two weakly differentiated but demographically connected ancestral populations that interbreed in the Slope Sea. Moreover, we also identified signatures of introgression from albacore (Thunnus alalunga) into the Atlantic bluefin tuna genome, exhibiting varied frequencies across spawning areas, indicating strong gene flow from the Mediterranean Sea towards the Slope Sea. We hypothesize that the observed genetic differentiation may be attributed to increased gene flow caused by a recent intensification of westward migration by the eastern population, which could have implications for the genetic diversity and conservation of western populations. Future conservation efforts should consider these findings to address potential genetic homogenization in the species.

Riverscape community genomics: A comparative analytical approach to identify common drivers of spatial structure

Genetic differentiation among local groups of individuals, that is, genetic β-diversity, is a key component of population persistence related to connectivity and isolation. However, most genetic investigations of natural populations focus on a single species, overlooking opportunities for multispecies conservation plans to benefit entire communities in an ecosystem. We present an approach to evaluate genetic β-diversity within and among many species and demonstrate how this riverscape community genomics approach can be applied to identify common drivers of genetic structure. Our study evaluated genetic β-diversity in 31 co-distributed native stream fishes sampled from 75 sites across the White River Basin (Ozarks, USA) using SNP genotyping (ddRAD). Despite variance among species in the degree of genetic divergence, general spatial patterns were identified corresponding to river network architecture. Most species (N = 24) were partitioned into discrete subpopulations (K = 2-7). We used partial redundancy analysis to compare species-specific genetic β-diversity across four models of genetic structure: Isolation by distance (IBD), isolation by barrier (IBB), isolation by stream hierarchy (IBH), and isolation by environment (IBE). A significant proportion of intraspecific genetic variation was explained by IBH (x̄ = 62%), with the remaining models generally redundant. We found evidence for consistent spatial modularity in that gene flow is higher within rather than between hierarchical units (i.e., catchments, watersheds, basins), supporting the generalization of the stream hierarchy model. We discuss our conclusions regarding conservation and management and identify the 8-digit hydrologic unit (HUC) as the most relevant spatial scale for managing genetic diversity across riverine networks.

Demographically explicit scans for barriers to gene flow using gIMble

Identifying regions of the genome that act as barriers to gene flow between recently diverged taxa has remained challenging given the many evolutionary forces that generate variation in genetic diversity and divergence along the genome, and the stochastic nature of this variation. Progress has been impeded by a conceptual and methodological divide between analyses that infer the demographic history of speciation and genome scans aimed at identifying locally maladaptive alleles i.e. genomic barriers to gene flow. Here we implement genomewide IM blockwise likelihood estimation (gIMble), a composite likelihood approach for the quantification of barriers, that bridges this divide. This analytic framework captures background selection and selection against barriers in a model of isolation with migration (IM) as heterogeneity in effective population size (Ne) and effective migration rate (me), respectively. Variation in both effective demographic parameters is estimated in sliding windows via pre-computed likelihood grids. gIMble includes modules for pre-processing/filtering of genomic data and performing parametric bootstraps using coalescent simulations. To demonstrate the new approach, we analyse data from a well-studied pair of sister species of tropical butterflies with a known history of post-divergence gene flow: Heliconius melpomene and H. cydno. Our analyses uncover both large-effect barrier loci (including well-known wing-pattern genes) and a genome-wide signal of a polygenic barrier architecture.

Robustness of Hill's overlapping-generation method for calculating Ne to extreme patterns of reproductive success

For species with overlapping generations, the most widely used method to calculate effective population size (Ne) is Hill's, the key parameter for which is lifetime variance in offspring number ([Formula: see text]). Hill's model assumes a stable age structure and constant abundance, and sensitivity to those assumptions has been evaluated previously. Here I evaluate the robustness of Hill's model to extreme patterns of reproductive success, whose effects have not been previously examined: (1) very strong reproductive skew; (2) strong temporal autocorrelations in individual reproductive success; and (3) strong covariance of individual reproduction and survival. Genetic drift (loss of heterozygosity and increase in allele frequency variance) was simulated in age-structured populations using methods that generated no autocorrelations or covariances (Model NoCor); or created strong positive (Model Positive) or strong negative (Model Negative) temporal autocorrelations in reproduction and covariances between reproduction and survival. Compared to Model NoCor, the other models led to greatly elevated or reduced [Formula: see text], and hence greatly reduced or elevated Ne, respectively. A new index is introduced (ρα,α+), which is the correlation between (1) the number of offspring produced by each individual at the age at maturity (α), and (2) the total number of offspring produced during the rest of their lifetimes. Mean ρα,α+ was ≈0 under Model NoCor, strongly positive under Model Positive, and strongly negative under Model Negative. Even under the most extreme reproductive scenarios in Models Positive and Negative, when [Formula: see text] was calculated from the realized population pedigree and used to calculate Ne in Hill's model, the result accurately predicted the rate of genetic drift in simulated populations. These results held for scenarios where age-specific reproductive skew was random (variance ≈ mean) and highly overdispersed (variance up to 20 times the mean). Collectively, these results are good news for researchers as they demonstrate the robustness of Hill's model even in extreme reproductive scenarios.

Assessment of long-term trends in genetic mean and variance after the introduction of genomic selection in layers: a simulation study

Nucleus-based breeding programs are characterized by intense selection that results in high genetic gain, which inevitably means reduction of genetic variation in the breeding population. Therefore, genetic variation in such breeding systems is typically managed systematically, for example, by avoiding mating the closest relatives to limit progeny inbreeding. However, intense selection requires maximum effort to make such breeding programs sustainable in the long-term. The objective of this study was to use simulation to evaluate the long-term impact of genomic selection on genetic mean and variance in an intense layer chicken breeding program. We developed a large-scale stochastic simulation of an intense layer chicken breeding program to compare conventional truncation selection to genomic truncation selection optimized with either minimization of progeny inbreeding or full-scale optimal contribution selection. We compared the programs in terms of genetic mean, genic variance, conversion efficiency, rate of inbreeding, effective population size, and accuracy of selection. Our results confirmed that genomic truncation selection has immediate benefits compared to conventional truncation selection in all specified metrics. A simple minimization of progeny inbreeding after genomic truncation selection did not provide any significant improvements. Optimal contribution selection was successful in having better conversion efficiency and effective population size compared to genomic truncation selection, but it must be fine-tuned for balance between loss of genetic variance and genetic gain. In our simulation, we measured this balance using trigonometric penalty degrees between truncation selection and a balanced solution and concluded that the best results were between 45° and 65°. This balance is specific to the breeding program and depends on how much immediate genetic gain a breeding program may risk vs. save for the future. Furthermore, our results show that the persistence of accuracy is better with optimal contribution selection compared to truncation selection. In general, our results show that optimal contribution selection can ensure long-term success in intensive breeding programs using genomic selection.

Effective population size in a partially clonal plant is not predicted by the number of genetic individuals

Estimating effective population size (N _e) is important for theoretical and practical applications in evolutionary biology and conservation. Nevertheless, estimates of N _e in organisms with complex life-history traits remain scarce because of the challenges associated with estimation methods. Partially clonal plants capable of both vegetative (clonal) growth and sexual reproduction are a common group of organisms for which the discrepancy between the apparent number of individuals (ramets) and the number of genetic individuals (genets) can be striking, and it is unclear how this discrepancy relates to N _e. In this study, we analysed two populations of the orchid Cypripedium calceolus to understand how the rate of clonal versus sexual reproduction affected N _e. We genotyped >1000 ramets at microsatellite and SNP loci, and estimated contemporary N _e with the linkage disequilibrium method, starting from the theoretical expectation that variance in reproductive success among individuals caused by clonal reproduction and by constraints on sexual reproduction would lower N _e. We considered factors potentially affecting our estimates, including different marker types and sampling strategies, and the influence of pseudoreplication in genomic data sets on N _e confidence intervals. The magnitude of N _e/N _ramets and N _e/N _genets ratios we provide may be used as reference points for other species with similar life-history traits. Our findings demonstrate that N _e in partially clonal plants cannot be predicted based on the number of genets generated by sexual reproduction, because demographic changes over time can strongly influence N _e. This is especially relevant in species of conservation concern in which population declines may not be detected by only ascertaining the number of genets.

Population dynamics of Baltic herring since the Viking Age revealed by ancient DNA and genomics

The world's oceans are currently facing major stressors in the form of overexploitation and anthropogenic climate change. The Baltic Sea was home to the first "industrial" fishery ∼800 y ago targeting the Baltic herring, a species that is still economically and culturally important today. Yet, the early origins of marine industries and the long-term ecological consequences of historical and contemporary fisheries remain debated. Here, we study long-term population dynamics of Baltic herring to evaluate the past impacts of humans on the marine environment. We combine modern whole-genome data with ancient DNA (aDNA) to identify the earliest-known long-distance herring trade in the region, illustrating that extensive fish trade began during the Viking Age. We further resolve population structure within the Baltic and observe demographic independence for four local herring stocks over at least 200 generations. It has been suggested that overfishing at Øresund in the 16th century resulted in a demographic shift from autumn-spawning to spring-spawning herring dominance in the Baltic. We show that while the Øresund fishery had a negative impact on the western Baltic herring stock, the demographic shift to spring-spawning dominance did not occur until the 20th century. Instead, demographic reconstructions reveal population trajectories consistent with expected impacts of environmental change and historical reports on shifting fishing targets over time. This study illustrates the joint impact of climate change and human exploitation on marine species as well as the role historical ecology can play in conservation and management policies.

Challenges in quantifying genome erosion for conservation

Massive defaunation and high extinction rates have become characteristic of the Anthropocene. Genetic effects of population decline can lead populations into an extinction vortex, where declining populations show lower genetic fitness, in turn leading to lower populations still. The lower genetic fitness in a declining population due to a shrinking gene pool is known as genetic erosion. Three different types of genetic erosion are highlighted in this review: overall homozygosity, genetic load and runs of homozygosity (ROH), which are indicative of inbreeding. The ability to quantify genetic erosion could be a very helpful tool for conservationists, as it can provide them with an objective, quantifiable measure to use in the assessment of species at risk of extinction. The link between conservation status and genetic erosion should become more apparent. Currently, no clear correlation can be observed between the current conservation status and genetic erosion. However, the high quantities of genetic erosion in wild populations, especially in those species dealing with habitat fragmentation and habitat decline, may be early signs of deteriorating populations. Whole genome sequencing data is the way forward to quantify genetic erosion. Extra screening steps for genetic load and hybridization can be included, since they could potentially have great impact on population fitness. This way, the information yielded from genetic sequence data can provide conservationists with an objective genetic method in the assessment of species at risk of extinction. However, the great complexity of genome erosion quantification asks for consensus and bridging science and its applications, which remains challenging.

Simulation-Based Evaluation of Methods, Data Types, and Temporal Sampling Schemes for Detecting Recent Population Declines

Understanding recent population trends is critical to quantifying species vulnerability and implementing effective management strategies. To evaluate the accuracy of genomic methods for quantifying recent declines (beginning <120 generations ago), we simulated genomic data using forward-time methods (SLiM) coupled with coalescent simulations (msprime) under a number of demographic scenarios. We evaluated both site frequency spectrum (SFS)-based methods (momi2, Stairway Plot) and methods that employ linkage disequilibrium information (NeEstimator, GONE) with a range of sampling schemes (contemporary-only samples, sampling two time points, and serial sampling) and data types (RAD-like data and whole-genome sequencing). GONE and momi2 performed best overall, with >80% power to detect severe declines with large sample sizes. Two-sample and serial sampling schemes could accurately reconstruct changes in population size, and serial sampling was particularly valuable for making accurate inferences when genotyping errors or minor allele frequency cutoffs distort the SFS or under model mis-specification. However, sampling only contemporary individuals provided reliable inferences about contemporary size and size change using either site frequency or linkage-based methods, especially when large sample sizes or whole genomes from contemporary populations were available. These findings provide a guide for researchers designing genomics studies to evaluate recent demographic declines.

Population genomic monitoring provides insight into conservation status but no correlation with demographic estimates of extinction risk in a threatened trout

The current extinction crisis requires effective assessment and monitoring tools. Genetic approaches are appealing given the relative ease of field sampling required to estimate genetic diversity characteristics assumed related to population size, evolutionary potential, and extinction risk, and to evaluate hybridization with non-native species simultaneously. However, linkages between population genetic metrics of diversity from survey-style field collections and demographic estimates of population size and extinction risk are still in need of empirical examples, especially for remotely distributed species of conservation concern where the approach might be most beneficial. We capitalized on an exceptional opportunity to evaluate congruence between genetic diversity metrics and demographic-based estimates of abundance and extinction risk from a comprehensive Multiple Population Viability Analysis (MPVA) in a threatened fish, the Lahontan cutthroat trout (LCT). We sequenced non-native trout reference samples and recently collected and archived tissue samples of most remaining populations of LCT (N = 60) and estimated common genetic assessment metrics, predicting minimal hybridization with non-native trout, low diversity, and declining diversity over time. We further hypothesized genetic metrics would correlate positively with MPVA-estimated abundance and negatively with extinction probability. We uncovered several instances of hybridization that pointed to immediate management needs. After removing hybridized individuals, cautious interpretation of low effective population sizes (2-63) suggested reduced evolutionary potential for many LCT populations. Other genetic metrics did not decline over time nor correlate with MPVA-based estimates of harmonic mean abundance or 30-year extinction probability. Our results demonstrate benefits of genetic monitoring for efficiently detecting hybridization and, though genetic results were disconnected from demographic assessment of conservation status, they suggest reduced evolutionary potential and likely a higher conservation risk than currently recognized for this threatened fish. We emphasize that genetic information provides essential complementary insight, in addition to demographic information, for evaluating species status.

How Can We Resolve Lewontin's Paradox?

We discuss the genetic, demographic, and selective forces that are likely to be at play in restricting observed levels of DNA sequence variation in natural populations to a much smaller range of values than would be expected from the distribution of census population sizes alone-Lewontin's Paradox. While several processes that have previously been strongly emphasized must be involved, including the effects of direct selection and genetic hitchhiking, it seems unlikely that they are sufficient to explain this observation without contributions from other factors. We highlight a potentially important role for the less-appreciated contribution of population size change; specifically, the likelihood that many species and populations may be quite far from reaching the relatively high equilibrium diversity values that would be expected given their current census sizes.