Parameterizing Pantherinae: De Novo Mutation Rate Estimates from Panthera and Neofelis Pedigrees

Estimates of de novo mutation rates are essential for phylogenetic and demographic analyses, but their inference has previously been impeded by high error rates in sequence data and uncertainty in the fossil record. Here, we directly estimate de novo germline mutation rates for all extant members of Panthera, as well as the closely related outgroup Neofelis nebulosa, using pedigrees. We use a previously validated pipeline (RatesTools) to calculate mutation rates for each species and subsequently explore the impacts of the novel rates on historic effective population size estimates in each of these charismatic felids of conservation concern. Importantly, we find that the choice of reference genome, the data type and coverage, and the individual all impact estimates of the mutation rate, but these can be largely ameliorated through extensive manual curation. Despite these stochastic effects, manual validation of de novo mutation candidates permitted the reliable inference of pantherine mutation rates. We inferred that base pair mutation rates for all species fell between 3.6 × 10-9 and 7.6 × 10-9 per generation per base pair (mean 5.5 × 10-9 ± 1.7 × 10-9 across Pantherinae at a mean parental age of 5.5 years). Similar to other studies, we show a positive trend of mean parental age with mutation rate and our inferred rates are well within the expected range for other mammals.

Population History Across Timescales in an Urban Archipelago

Contemporary patterns of genetic variation reflect the cumulative history of a population. Population splitting, migration, and changes in population size leave genomic signals that enable their characterization. Existing methods aimed at reconstructing these features of demographic history are often restricted in their temporal resolution, leaving gaps about how basic evolutionary parameters change over time. To illustrate the prospects for extracting insights about dynamic population histories, we turn to a system that has undergone dramatic changes on both geological and contemporary timescales-an urbanized, near-shore archipelago. Using whole genome sequences, we employed both common and novel summaries of variation to infer the demographic history of three populations of endemic white-footed mice (Peromyscus leucopus) in Massachusetts' Boston Harbor. We find informative contrasts among the inferences drawn from these distinct patterns of diversity. While demographic models that fit the joint site frequency spectrum (jSFS) coincide with the known geological history of the Boston Harbor, patterns of linkage disequilibrium reveal collapses in population size on contemporary timescales that are not recovered by our jSFS-derived models. Historical migration between populations is also absent from best-fitting models for the jSFS, but rare variants show unusual clustering along the genome within individual mice, a novel pattern that is reproduced by simulations of recent migration. Together, our findings indicate that these urban archipelago populations have been shaped by both ancient geological processes and recent human influence. More broadly, our study demonstrates that the temporal resolution of demographic history can be extended by examining multiple facets of genomic variation.

Long-distance gene flow and recombination shape the evolutionary history of a maize pathogen

The evolutionary history of crop pathogens is shaped by a complex interaction of natural and anthropogenic factors. The fungus Colletotrichumgraminicola causes maize anthracnose which results in significant yield losses worldwide. We conducted a comprehensive investigation into the evolutionary genomics of C.graminicola using a collection of 212 isolates from 17 countries across five continents. Genomic analyses supported the existence of three geographically isolated genetic lineages, with a significant pattern of isolation by distance. We identified two distinct gene flow patterns, driven by short- and long-distance dispersal, likely resulting from the natural spread of the pathogen and the exchange of contaminated seeds. We present evidence of genetic introgression between lineages, suggesting a long history of recombination. We identified significant recombination events coalescing at distinct points in time, with the North American lineage displaying evidence of the most ancient recombination. Demographic modelling has indicated that North America is an intermediate between Brazil, Europe and an ancestral, unsampled source population, which is hypothesised to be Mesoamerican. Our analyses revealed that the global genomic structure of C.graminicola is shaped by geographic differentiation driven by long-distance migration and a long history of recombination and introgression. We show historical relationships amongst these lineages, identifying a potential route for fungal spread, with the North American population emerging ancestrally, followed sequentially by the Brazilian and European populations. Our research indicates that the European lineage is more virulent, which has implications for the potential emergence of new outbreaks of maize anthracnose in Europe.

Inference of the Demographic Histories and Selective Effects of Human Gut Commensal Microbiota Over the Course of Human History

Despite the importance of gut commensal microbiota to human health, there is little knowledge about their evolutionary histories, including their demographic histories and distributions of fitness effects (DFEs) of mutations. Here, we infer the demographic histories and DFEs for amino acid-changing mutations of 39 of the most prevalent and abundant commensal gut microbial species found in Westernized individuals over timescales exceeding human generations. Some species display contractions in population size and others expansions, with several of these events coinciding with several key historical moments in human history. DFEs across species vary from highly to mildly deleterious, with differences between accessory and core gene DFEs largely driven by genetic drift. Within genera, DFEs tend to be more congruent, reflective of underlying phylogenetic relationships. Together, these findings suggest that gut microbes have distinct demographic and selective histories.

Whole-Genome Evaluation of Genetic Rescue: The Case of a Curiously Isolated and Endangered Butterfly

Genetic rescue, or the translocation of individuals among populations to augment gene flow, can help ameliorate inbreeding depression and loss of adaptive potential in small and isolated populations. Genetic rescue is currently under consideration for an endangered butterfly in Canada, the Half-moon Hairstreak (Satyrium semiluna). A small, unique population persists in Waterton Lakes National Park, Alberta, isolated from other populations by more than 400 km. However, whether genetic rescue would actually be helpful has not been evaluated. Here, we generate the first chromosome-level genome assembly and whole-genome resequence data for the species. We find that the Alberta population maintains extremely low genetic diversity and is genetically very divergent from the nearest populations in British Columbia and Montana. Runs of homozygosity suggest this is due to a long history of inbreeding, and coalescent analyses show that the population has been small and isolated, yet stable, for up to 40k years. When a population like this maintains its viability despite inbreeding and low genetic diversity, it has likely undergone purging of deleterious recessive alleles and could be threatened by the reintroduction of such alleles via genetic rescue. Ecological niche modelling indicates that the Alberta population also exhibits environmental associations that are atypical of the species. Together, these evolutionary and ecological divergences suggest that population crosses may result in outbreeding depression. We therefore infer that genetic rescue has a relatively unique potential to be harmful rather than helpful for this population at present. However, because of its reduced adaptive potential, the Alberta population may still benefit from future genetic rescue as climate and habitat conditions change. Proactive experimental population crosses should therefore be completed to assess reproductive compatibility and progeny fitness.

Population history across timescales in an urban archipelago

Contemporary patterns of genetic variation reflect the cumulative history of a population. Population splitting, migration, and changes in population size leave genomic signals that enable their characterization. Existing methods aimed at reconstructing these features of demographic history are often restricted in their temporal resolution, leaving gaps about how basic evolutionary parameters change over time. To illustrate the prospects for extracting insights about dynamic population histories, we turn to a system that has undergone dramatic changes on both geological and contemporary timescales - an urbanized, near-shore archipelago. Using whole genome sequences, we employed both common and novel summaries of variation to infer the demographic history of three populations of endemic white-footed mice (Peromyscus leucopus) in Massachusetts' Boston Harbor. We find informative contrasts among the inferences drawn from these distinct patterns of diversity. While demographic models that fit the joint site frequency spectrum (jSFS) coincide with the known geological history of the Boston Harbor, patterns of linkage disequilibrium reveal collapses in population size on contemporary timescales that are not recovered by our candidate models. Historical migration between populations is also absent from best-fitting models for the jSFS, but rare variants show unusual clustering along the genome within individual mice, a pattern that is reproduced by simulations of recent migration. Together, our findings indicate that these urban archipelago populations have been shaped by both ancient geological processes and recent human influence. More broadly, our study demonstrates that the temporal resolution of demographic history can be extended by examining multiple facets of genomic variation.

Inferring the Demographic History of Aye-Ayes (Daubentonia madagascariensis) from High-Quality, Whole-Genome, Population-Level Data

The nocturnal aye-aye, Daubentonia madagascariensis, is one of the most elusive lemurs on the island of Madagascar. The timing of its activity and arboreal lifestyle has generally made it difficult to obtain accurate assessments of population size using traditional census methods. Therefore, alternative estimates provided by population genetic inference are essential for yielding much needed information for conservation measures and for enabling ecological and evolutionary studies of this species. Here, we utilize genomic data from 17 individuals-including 5 newly sequenced, high-coverage genomes-to estimate this history. Essential to this estimation are recently published annotations of the aye-aye genome which allow for variation at putatively neutral genomic regions to be included in the estimation procedures, and regions subject to selective constraints, or in linkage to such sites, to be excluded owing to the biasing effects of selection on demographic inference. By comparing a variety of demographic estimation tools to develop a well-supported model of population history, we find strong support for two demes, separating northern Madagascar from the rest of the island. Additionally, we find that the aye-aye has experienced two severe reductions in population size. The first occurred rapidly, ∼3,000 to 5,000 years ago, and likely corresponded with the arrival of humans to Madagascar. The second occurred over the past few decades and is likely related to substantial habitat loss, suggesting that the species is still undergoing population decline and remains at great risk for extinction.

Photosymbiosis shaped animal genome architecture and gene evolution as revealed in giant clams

Symbioses are major drivers of organismal diversification and phenotypic innovation. However, how long-term symbioses shape whole genome evolution in metazoans is still underexplored. Here, we use a giant clam (Tridacna maxima) genome to demonstrate how symbiosis has left complex signatures in an animal's genome. Giant clams thrive in oligotrophic waters by forming a remarkable association with photosymbiotic dinoflagellate algae. Genome-based demographic inferences uncover a tight correlation between T. maxima global population change and major paleoclimate and habitat shifts, revealing how abiotic and biotic factors may dictate T. maxima microevolution. Comparative analyses reveal genomic features that may be symbiosis-driven, including expansion and contraction of immunity-related gene families and a large proportion of lineage-specific genes. Strikingly, about 70% of the genome is composed of repetitive elements, especially transposable elements, most likely resulting from a symbiosis-adapted immune system. This work greatly enhances our understanding of genomic drivers of symbiosis that underlie metazoan evolution and diversification.

Population Genomics of Adaptive Radiation

Adaptive radiations are rich laboratories for exploring, testing, and understanding key theories in evolution and ecology because they offer spectacular displays of speciation and ecological adaptation. Particular challenges to the study of adaptive radiation include high levels of species richness, rapid speciation, and gene flow between species. Over the last decade, high-throughput sequencing technologies and access to population genomic data have lessened these challenges by enabling the analysis of samples from many individual organisms at whole-genome scales. Here we review how population genomic data have facilitated our knowledge of adaptive radiation in five key areas: (1) phylogenetics, (2) hybridization, (3) timing and rates of diversification, (4) the genomic basis of trait evolution, and (5) the role of genome structure in divergence. We review current knowledge in each area, highlight outstanding questions, and focus on methods that facilitate detection of complex patterns in the divergence and demography of populations through time. It is clear that population genomic data are revolutionising the ability to reconstruct evolutionary history in rapidly diversifying clades. Additionally, studies are increasingly emphasising the central role of gene flow, re-use of standing genetic variation during adaptation, and structural genomic elements as facilitators of the speciation process in adaptive radiations. We highlight hybridization-and the hypothesized processes by which it shapes diversification-and questions seeking to bridge the divide between microevolutionary and macroevolutionary processes as rich areas for future study. Overall, access to population genomic data has facilitated an exciting era in adaptive radiation research, with implications for deeper understanding of fundamental evolutionary processes across the tree of life.

The genetic consequences of historic climate change on the contemporary population structure of a widespread temperate North American songbird

Studies of widely distributed species can inform our understanding of how past demographic events tied to historic glaciation and ongoing population genetic processes interact to shape contemporaneous patterns of biodiversity at a continental scale. In this study, we used whole-genome resequencing to investigate the current population structure and genetic signatures of past demographic events in the widespread migratory American goldfinch (Spinus tristis). Phylogenetic relationships inferred from whole mitochondrial genomes were poorly resolved. In contrast, a genome-wide panel of > 4.5 million single nucleotide polymorphisms (SNPs) strongly supported the existence of eastern and western populations separated by western mountain ranges and additional population structuring within the western clade. Demographic modeling estimated that the eastern and western populations diverged approximately one million years ago, and both populations experienced subsequent population bottlenecks during the last glacial period. Species distribution models showed a severe contraction of suitable habitat for the American goldfinch during this period, with predicted discontinuities that are consistent with multiple, isolated glacial refugia that coincide with present-day population structure. Low overall genetic differentiation between the eastern and western populations (FST ∼ 0.01) suggests ongoing gene flow accompanied divergence, and individuals with admixed genomic signatures were sampled along a potential contact zone. Nevertheless, outlier SNPs were identified near genes associated with feather color, song, and migratory behavior and provide strong candidates for further study of the mechanisms underlying reproductive isolation and speciation in birds.

Evolution of parasitological knowledge: can the past inform the future?

The growth of scientific knowledge is often likened to the evolution and diversification of life: new disciplines branch off older ones, and subsequently prosper or decline in a manner reminiscent of the expansion or extinction of diverse lineages of organisms. Based on a parallel between evolutionary diversification and knowledge growth, I examine the expansion of subdisciplines within 'ecological and evolutionary parasitology'. Bibliometric data are used to map the rise and fall of subdisciplines over time, capturing historical trends over the past several decades. This historical overview is followed by a critical consideration of its practical applications for decision-making, ranging from rational funding allocation among subdisciplines to whether the collective planning of future research directions is a desirable option.

Deep genetic substructure within bonobos

Establishing the genetic and geographic structure of populations is fundamental, both to understand their evolutionary past and preserve their future. Nevertheless, the patterns of genetic population structure are unknown for most endangered species. This is the case for bonobos (Pan paniscus), which, together with chimpanzees (Pan troglodytes), are humans' closest living relatives. Chimpanzees live across equatorial Africa and are classified into four subspecies,1 with some genetic population substructure even within subspecies. Conversely, bonobos live exclusively in the Democratic Republic of Congo and are considered a homogeneous group with low genetic diversity,2 despite some population structure inferred from mtDNA. Nevertheless, mtDNA aside, their genetic structure remains unknown, hampering our understanding of the species and conservation efforts. Mapping bonobo genetic diversity in space is, however, challenging because, being endangered, only non-invasive sampling is possible for wild individuals. Here, we jointly analyze the exomes and mtDNA from 20 wild-born bonobos, the whole genomes of 10 captive bonobos, and the mtDNA of 136 wild individuals. We identify three genetically distinct bonobo groups of inferred Central, Western, and Far-Western geographic origin within the bonobo range. We estimate the split time between the central and western populations to be ∼145,000 years ago and genetic differentiation to be in the order of that of the closest chimpanzee subspecies. Furthermore, our estimated long-term Ne for Far-West (∼3,000) is among the lowest estimated for any great ape lineage. Our results highlight the need to attend to the bonobo substructure, both in terms of research and conservation.

Inferring the demographic history of aye-ayes (Daubentonia madagascariensis) from high-quality, whole-genome, population-level data

The nocturnal aye-aye, Daubentonia madagascariensis, is one of the most elusive lemurs on the island of Madagascar. The timing of its activity and arboreal lifestyle has generally made it difficult to obtain accurate assessments of population size using traditional census methods. Therefore, alternative estimates provided by population genetic inference are essential for yielding much needed information for conservation measures and for enabling ecological and evolutionary studies of this species. Here, we utilize genomic data from 17 unrelated individuals - including 5 newly sequenced, high-coverage genomes - to estimate this history. Essential to this estimation are recently published annotations of the aye-aye genome which allow for variation at putatively neutral genomic regions to be included in the estimation procedures, and regions subject to selective constraints, or in linkage to such sites, to be excluded owing to the biasing effects of selection on demographic inference. By comparing a variety of demographic estimation tools to develop a well-supported model of population history, we find strong support for the species to consist of two demes, separating northern Madagascar from the rest of the island. Additionally, we find that the aye-aye has experienced two severe reductions in population size. The first occurred rapidly, approximately 3,000 to 5,000 years ago, and likely corresponded with the arrival of humans to Madagascar. The second occurred over the past few decades and is likely related to substantial habitat loss, suggesting that the species is still undergoing population decline and remains at great risk for extinction.

Beyond genes-for-behaviour: The potential for genomics to resolve long-standing questions in avian brood parasitism

Behavioural ecology by definition of its founding 'Tinbergian framework' is an integrative field, however, it lags behind in incorporating genomic methods. 'Finding the gene/s for a behaviour' is still rarely feasible or cost-effective in the wild but as we show here, genomic data can be used to address broader questions. Here we use avian brood parasitism, a model system in behavioural ecology as a case study to highlight how behavioural ecologists could use the full potential of state-of-the-art genomic tools. Brood parasite-host interactions are one of the most easily observable and amenable natural laboratories of antagonistic coevolution, and as such have intrigued evolutionary biologists for decades. Using worked examples, we demonstrate how genomic data can be used to study the causes and mechanisms of (co)evolutionary adaptation and answer three key questions for the field: (i) Where and when should brood parasitism evolve?, (ii) When and how should hosts defend?, and (iii) Will coevolution persist with ecological change? In doing so, we discuss how behavioural and molecular ecologists can collaborate to integrate Tinbergen's questions and achieve the coherent science that he promoted to solve the mysteries of nature.

Integrating evolutionary genomics of forest trees to inform future tree breeding amid rapid climate change

Global climate change is leading to rapid and drastic shifts in environmental conditions, posing threats to biodiversity and nearly all life forms worldwide. Forest trees serve as foundational components of terrestrial ecosystems and play a crucial and leading role in combating and mitigating the adverse effects of extreme climate events, despite their own vulnerability to these threats. Therefore, understanding and monitoring how natural forests respond to rapid climate change is a key priority for biodiversity conservation. Recent progress in evolutionary genomics, driven primarily by cutting-edge multi-omics technologies, offers powerful new tools to address several key issues. These include precise delineation of species and evolutionary units, inference of past evolutionary histories and demographic fluctuations, identification of environmentally adaptive variants, and measurement of genetic load levels. As the urgency to deal with more extreme environmental stresses grows, understanding the genomics of evolutionary history, local adaptation, future responses to climate change, and conservation and restoration of natural forest trees will be critical for research at the nexus of global change, population genomics, and conservation biology. In this review, we explore the application of evolutionary genomics to assess the effects of global climate change using multi-omics approaches and discuss the outlook for breeding of climate-adapted trees.

An integrative genomic toolkit for studying the genetic, evolutionary, and molecular underpinnings of eusociality in insects

While genomic resources for social insects have vastly increased over the past two decades, we are still far from understanding the genetic and molecular basis of eusociality. Here, we briefly review three scientific advancements that, when integrated, can be highly synergistic for advancing our knowledge of the genetics and evolution of eusocial traits. Population genomics provides a natural way to quantify the strength of natural selection on coding and regulatory sequences, highlighting genes that have undergone adaptive evolution during the evolution or maintenance of eusociality. Genome-wide association studies (GWAS) can be used to characterize the complex genetic architecture underlying eusocial traits and identify candidate causal variants. Concurrently, CRISPR/Cas9 enables the precise manipulation of gene function to both validate genotype-phenotype associations and study the molecular biology underlying interesting traits. While each approach has its own advantages and disadvantages, which we discuss herein, we argue that their combination will ultimately help us better understand the genetics and evolution of eusocial behavior. Specifically, by triangulating across these three different approaches, researchers can directly identify and study loci that have a causal association with key phenotypes and have evidence of positive selection over the relevant timescales associated with the evolution and maintenance of eusociality in insects.

Whole-genome analysis reveals phylogenetic and demographic history of Eurasian perch

The contemporary diversity and distribution of species are shaped by their evolutionary and ecological history. This can be deciphered with the help of phylogenetic and demographic analysis methods, ideally combining and supplementing information from mitochondrial and nuclear genomes. In this study, we investigated the demographic history of Eurasian perch (Perca fluviatilis), a highly adaptable teleost with a distribution range across Eurasia. We combined whole-genome resequencing data with available genomic resources to analyse the phylogeny, phylogeography, and demographic history of P. fluviatilis populations from Europe and Siberia. We identified five highly diverged evolutionary mtDNA lineages, three of which show a strong signal of admixture in the Baltic Sea region. The estimated mean divergence time between these lineages ranged from 0.24 to 1.42 million years. Based on nuclear genomes, two distinct demographic trajectories were observed in European and Siberian samples reflecting contrasting demographic histories ca. 30,000-100,000 years before the present. A comparison of mtDNA and nuclear DNA evolutionary trees and AMOVA revealed concordances, as well as incongruences, between the two types of data, most likely reflecting recent postglacial colonization and hybridization events. Overall, our findings demonstrate the power and usefulness of genome-wide information for delineating historical processes that have shaped the genome of P. fluviatilis. We also highlight the added value of data-mining existing transcriptomic resources to complement novel sequence data, helping to shed light on putative glacial refugia and postglacial recolonization routes.

Demographic History and Inbreeding in Two Declining Sea Duck Species Inferred From Whole-Genome Sequence Data

Anthropogenic impact has transitioned from threatening already rare species to causing significant declines in once numerous organisms. Long-tailed duck (Clangula hyemalis) and velvet scoter (Melanitta fusca) were once important quarry sea duck species in NW Europe, but recent declines resulted in their reclassification as vulnerable on the IUCN Red List. We sequenced and assembled genomes for both species and resequenced 15 individuals of each. Using analyses based on site frequency spectra and sequential Markovian coalescence, we found C. hyemalis to show more historical demographic stability, whereas M. fusca was affected particularly by the Last (Weichselian) Glaciation. This likely reflects C. hyemalis breeding continuously across the Arctic, with cycles of glaciation primarily shifting breeding areas south or north without major population declines, whereas the more restricted southern range of M. fusca would lead to significant range contraction during glaciations. Both species showed evidence of declines over the past thousands of years, potentially reflecting anthropogenic pressures with the recent decline indicating an accelerated process. Analysis of runs of homozygosity (ROH) showed low but nontrivial inbreeding, with F ROH from 0.012 to 0.063 in C. hyemalis and ranging from 0 to 0.047 in M. fusca. Lengths of ROH suggested that this was due to ongoing background inbreeding rather than recent declines. Overall, despite demographically important declines, this has not yet led to strong inbreeding and genetic erosion, and the most pressing conservation concern may be the risk of density-dependent (Allee) effects. We recommend monitoring of inbreeding using ROH analysis as a cost-efficient method to track future developments to support effective conservation of these species.

Population genomics unravels a lag phase during the global fall armyworm invasion

The time that elapsed between the initial introduction and the proliferation of an invasive species is referred to as the lag phase. The identification of the lag phase is critical for generating plans for pest management and for the prevention of biosecurity failure. However, lag phases have been identified mostly through retrospective searches of historical records. The agricultural pest fall armyworm (FAW; Spodoptera frugiperda) is native to the New World. FAW invasion was first reported from West Africa in 2016, then it spread quickly through Africa, Asia, and Oceania. Here, using population genomics approaches, we demonstrate that the FAW invasion involved an undocumented lag phase. Invasive FAW populations have negative signs of genomic Tajima's D, and invasive population-specific genetic variations have particularly decreased Tajima's D, supporting a substantial amount of time for the generation of new mutations in introduced FAW populations. Model-based diffusion approximations support the existence of a period with a cessation of gene flow between native and invasive FAW populations. Taken together, these results provide strong support for the presence of a lag phase during the FAW invasion. These results show the usefulness of using population genomics analyses to identify lag phases in biological invasions.

Rolling down that mountain: microgeographical adaptive divergence during a fast population expansion along a steep environmental gradient in European beech

Forest tree populations harbour high genetic diversity thanks to large effective population sizes and strong gene flow, allowing them to diversify through adaptation to local environmental pressures within dispersal distance. Many tree populations also experienced historical demographic fluctuations, including spatial population contraction or expansions at various temporal scales, which may constrain their ability to adapt to environmental variations. Our aim is to investigate how recent contraction and expansion events interfere with local adaptation, by studying patterns of adaptive divergence between closely related stands undergoing environmentally contrasted conditions, and having or not recently expanded. To investigate genome-wide signatures of local adaptation while accounting for demography, we analysed divergence in a European beech population by testing pairwise differentiation among four tree stands at ~35k Single Nucleotide Polymorphisms from ~9k genomic regions. We applied three divergence outlier search methods resting on different assumptions and targeting either single SNPs or contiguous genomic regions, while accounting for the effect of population size variations on genetic divergence. We found 27 signals of selective signatures in 19 target regions. Putatively adaptive divergence involved all stand pairs. We retrieved signals both when comparing old-growth stands and recently colonised areas and when comparing stands within the old-growth area. Therefore, adaptive divergence processes have taken place both over short time spans, under strong environmental contrasts, and over short ecological gradients, in populations that have been stable in the long term. This suggests that standing genetic variation supports local, microgeographic divergence processes, which can maintain genetic diversity at the landscape level.

Alpine Extremophytes in Evolutionary Turmoil: Complex Diversification Patterns and Demographic Responses of a Halophilic Grass in a Central Asian Biodiversity Hotspot

Diversification and demographic responses are key processes shaping species evolutionary history. Yet we still lack a full understanding of ecological mechanisms that shape genetic diversity at different spatial scales upon rapid environmental changes. In this study, we examined genetic differentiation in an extremophilic grass Puccinellia pamirica and factors affecting its population dynamics among the occupied hypersaline alpine wetlands on the arid Pamir Plateau in Central Asia. Using genomic data, we found evidence of fine-scale population structure and gene flow among the localities established across the high-elevation plateau as well as fingerprints of historical demographic expansion. We showed that an increase in the effective population size could coincide with the Last Glacial Period, which was followed by the species demographic decline during the Holocene. Geographic distance plays a vital role in shaping the spatial genetic structure of P. pamirica alongside with isolation-by-environment and habitat fragmentation. Our results highlight a complex history of divergence and gene flow in this species-poor alpine region during the Late Quaternary. We demonstrate that regional climate specificity and a shortage of nonclimate data largely impede predictions of future range changes of the alpine extremophile using ecological niche modeling. This study emphasizes the importance of fine-scale environmental heterogeneity for population dynamics and species distribution shifts.

Biases in ARG-Based Inference of Historical Population Size in Populations Experiencing Selection

Inferring the demographic history of populations provides fundamental insights into species dynamics and is essential for developing a null model to accurately study selective processes. However, background selection and selective sweeps can produce genomic signatures at linked sites that mimic or mask signals associated with historical population size change. While the theoretical biases introduced by the linked effects of selection have been well established, it is unclear whether ancestral recombination graph (ARG)-based approaches to demographic inference in typical empirical analyses are susceptible to misinference due to these effects. To address this, we developed highly realistic forward simulations of human and Drosophila melanogaster populations, including empirically estimated variability of gene density, mutation rates, recombination rates, purifying, and positive selection, across different historical demographic scenarios, to broadly assess the impact of selection on demographic inference using a genealogy-based approach. Our results indicate that the linked effects of selection minimally impact demographic inference for human populations, although it could cause misinference in populations with similar genome architecture and population parameters experiencing more frequent recurrent sweeps. We found that accurate demographic inference of D. melanogaster populations by ARG-based methods is compromised by the presence of pervasive background selection alone, leading to spurious inferences of recent population expansion, which may be further worsened by recurrent sweeps, depending on the proportion and strength of beneficial mutations. Caution and additional testing with species-specific simulations are needed when inferring population history with non-human populations using ARG-based approaches to avoid misinference due to the linked effects of selection.

Conserving Evolutionary Potential: Combining Landscape Genomics with Established Methods to Inform Plant Conservation

Biodiversity conservation requires conserving evolutionary potential-the capacity for wild populations to adapt. Understanding genetic diversity and evolutionary dynamics is critical for informing conservation decisions that enhance adaptability and persistence under environmental change. We review how emerging landscape genomic methods provide plant conservation programs with insights into evolutionary dynamics, including local adaptation and its environmental drivers. Landscape genomic approaches that explore relationships between genomic variation and environments complement rather than replace established population genomic and common garden approaches for assessing adaptive phenotypic variation, population structure, gene flow, and demography. Collectively, these approaches inform conservation actions, including genetic rescue, maladaptation prediction, and assisted gene flow. The greatest on-the-ground impacts from such studies will be realized when conservation practitioners are actively engaged in research and monitoring. Understanding the evolutionary dynamics shaping the genetic diversity of wild plant populations will inform plant conservation decisions that enhance the adaptability and persistence of species in an uncertain future.

Conservation genomics provides insights into genetic resilience and adaptation of the endangered Chinese hazelnut, Corylus chinensis

Global climate change has increased concerns regarding biodiversity loss. However, many key conservation issues still required further research, including demographic history, deleterious mutation load, adaptive evolution, and putative introgression. Here we generated the first chromosome-level genome of the endangered Chinese hazelnut, Corylus chinensis, and compared the genomic signatures with its sympatric widespread C. kwechowensis-C. yunnanensis complex. We found large genome rearrangements across all Corylus species and identified species-specific expanded gene families that may be involved in adaptation. Population genomics revealed that both C. chinensis and the C. kwechowensis-C. yunnanensis complex had diverged into two genetic lineages, forming a consistent pattern of southwestern-northern differentiation. Population size of the narrow southwestern lineages of both species have decreased continuously since the late Miocene, whereas the widespread northern lineages have remained stable (C. chinensis) or have even recovered from population bottlenecks (C. kwechowensis-C. yunnanensis complex) during the Quaternary. Compared with C. kwechowensis-C. yunnanensis complex, C. chinensis showed significantly lower genomic diversity and higher inbreeding level. However, C. chinensis carried significantly fewer deleterious mutations than C. kwechowensis-C. yunnanensis complex, as more effective purging selection reduced the accumulation of homozygous variants. We also detected signals of positive selection and adaptive introgression in different lineages, which facilitated the accumulation of favorable variants and formation of local adaptation. Hence, both types of selection and exogenous introgression could have mitigated inbreeding and facilitated survival and persistence of C. chinensis. Overall, our study provides critical insights into lineage differentiation, local adaptation, and the potential for future recovery of endangered trees.

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.

Rapid diversification of grey mangroves (Avicennia marina) driven by geographic isolation and extreme environmental conditions in the Arabian Peninsula

Biological systems occurring in ecologically heterogeneous and spatially discontinuous habitats provide an ideal opportunity to investigate the relative roles of neutral and selective factors in driving lineage diversification. The grey mangroves (Avicennia marina) of Arabia occur at the northern edge of the species' range and are subject to variable, often extreme, environmental conditions, as well as historic large fluctuations in habitat availability and connectivity resulting from Quaternary glacial cycles. Here, we analyse fully sequenced genomes sampled from 19 locations across the Red Sea, the Arabian Sea and the Persian/Arabian Gulf (PAG) to reconstruct the evolutionary history of the species in the region and to identify adaptive mechanisms of lineage diversification. Population structure and phylogenetic analyses revealed marked genetic structure correlating with geographic distance and highly supported clades among and within the seas surrounding the Arabian Peninsula. Demographic modelling showed times of divergence consistent with recent periods of geographic isolation and low marine connectivity during glaciations, suggesting the presence of (cryptic) glacial refugia in the Red Sea and the PAG. Significant migration was detected within the Red Sea and the PAG, and across the Strait of Hormuz to the Arabian Sea, suggesting gene flow upon secondary contact among populations. Genetic-environment association analyses revealed high levels of adaptive divergence and detected signs of multi-loci local adaptation driven by temperature extremes and hypersalinity. These results support a process of rapid diversification resulting from the combined effects of historical factors and ecological selection and reveal mangrove peripheral environments as relevant drivers of lineage diversity.

Chromosome-level genome assembly and demographic history of Euryodendron excelsum in monotypic genus endemic to China

Euryodendron excelsum is in a monotypic genus Euryodendron, endemic to China. It has intermediate morphisms in the Pentaphylacaceae or Theaceae families, which make it distinct. Due to anthropogenic disturbance, E. excelsum is currently found in very restricted and fragmented areas with extremely small populations. Although much research and effort has been applied towards its conservation, its long-term survival mechanisms and evolutionary history remain elusive, especially from a genomic aspect. Therefore, using a combination of long/short whole genome sequencing, RNA sequencing reads, and Hi-C data, we assembled and annotated a high-quality genome for E. excelsum. The genome assembly of E. excelsum comprised 1,059,895,887 bp with 99.66% anchored into 23 pseudo-chromosomes and a 99.0% BUSCO completeness. Comparative genomic analysis revealed the expansion of terpenoid and flavonoid secondary metabolite genes, and displayed a tandem and/or proximal duplication framework of these genes. E. excelsum also displayed genes associated with growth, development, and defence adaptation from whole genome duplication. Demographic analysis indicated that its fluctuations in population size and its recent population decline were related to cold climate changes. The E. excelsum genome assembly provides a highly valuable resource for evolutionary and ecological research in the future, aiding its conservation, management, and restoration.

Past volcanic activity predisposes an endemic threatened seabird to negative anthropogenic impacts

Humans are regularly cited as the main driver of current biodiversity extinction, but the impact of historic volcanic activity is often overlooked. Pre-human evidence of wildlife abundance and diversity are essential for disentangling anthropogenic impacts from natural events. Réunion Island, with its intense and well-documented volcanic activity, endemic biodiversity, long history of isolation and recent human colonization, provides an opportunity to disentangle these processes. We track past demographic changes of a critically endangered seabird, the Mascarene petrel Pseudobulweria aterrima, using genome-wide SNPs. Coalescent modeling suggested that a large ancestral population underwent a substantial population decline in two distinct phases, ca. 125,000 and 37,000 years ago, coinciding with periods of major eruptions of Piton des Neiges. Subsequently, the ancestral population was fragmented into the two known colonies, ca. 1500 years ago, following eruptions of Piton de la Fournaise. In the last century, both colonies declined significantly due to anthropogenic activities, and although the species was initially considered extinct, it was rediscovered in the 1970s. Our findings suggest that the current conservation status of wildlife on volcanic islands should be firstly assessed as a legacy of historic volcanic activity, and thereafter by the increasing anthropogenic impacts, which may ultimately drive species towards extinction.

Special Issue "Primate Phylogeny and Genetics"

New phylogenetic tools and population genetics methods have been developed and vastly advanced over the last decade [...].

Using Computational Simulations to Model Deleterious Variation and Genetic Load in Natural Populations

AbstractDeleterious genetic variation is abundant in wild populations, and understanding the ecological and conservation implications of such variation is an area of active research. Genomic methods are increasingly used to quantify the impacts of deleterious variation in natural populations; however, these approaches remain limited by an inability to accurately predict the selective and dominance effects of mutations. Computational simulations of deleterious variation offer a complementary tool that can help overcome these limitations, although such approaches have yet to be widely employed. In this perspective article, we aim to encourage ecological and conservation genomics researchers to adopt greater use of computational simulations to aid in deepening our understanding of deleterious variation in natural populations. We first provide an overview of the components of a simulation of deleterious variation, describing the key parameters involved in such models. Next, we discuss several approaches for validating simulation models. Finally, we compare and validate several recently proposed deleterious mutation models, demonstrating that models based on estimates of selection parameters from experimental systems are biased toward highly deleterious mutations. We describe a new model that is supported by multiple orthogonal lines of evidence and provide example scripts for implementing this model (https://github.com/ckyriazis/simulations_review).

Inference of the demographic histories and selective effects of human gut commensal microbiota over the course of human history

Despite the importance of gut commensal microbiota to human health, there is little knowledge about their evolutionary histories, including their population demographic histories and their distributions of fitness effects (DFE) of new mutations. Here, we infer the demographic histories and DFEs of 27 of the most highly prevalent and abundant commensal gut microbial species in North Americans over timescales exceeding human generations using a collection of lineages inferred from a panel of healthy hosts. We find overall reductions in genetic variation among commensal gut microbes sampled from a Western population relative to an African rural population. Additionally, some species in North American microbiomes display contractions in population size and others expansions, potentially occurring at several key historical moments in human history. DFEs across species vary from highly to mildly deleterious, with accessory genes experiencing more drift compared to core genes. Within genera, DFEs tend to be more congruent, reflective of underlying phylogenetic relationships. Taken together, these findings suggest that human commensal gut microbes have distinct evolutionary histories, possibly reflecting the unique roles of individual members of the microbiome.

Tensor Decomposition-based Feature Extraction and Classification to Detect Natural Selection from Genomic Data

Inferences of adaptive events are important for learning about traits, such as human digestion of lactose after infancy and the rapid spread of viral variants. Early efforts toward identifying footprints of natural selection from genomic data involved development of summary statistic and likelihood methods. However, such techniques are grounded in simple patterns or theoretical models that limit the complexity of settings they can explore. Due to the renaissance in artificial intelligence, machine learning methods have taken center stage in recent efforts to detect natural selection, with strategies such as convolutional neural networks applied to images of haplotypes. Yet, limitations of such techniques include estimation of large numbers of model parameters under nonconvex settings and feature identification without regard to location within an image. An alternative approach is to use tensor decomposition to extract features from multidimensional data although preserving the latent structure of the data, and to feed these features to machine learning models. Here, we adopt this framework and present a novel approach termed T-REx, which extracts features from images of haplotypes across sampled individuals using tensor decomposition, and then makes predictions from these features using classical machine learning methods. As a proof of concept, we explore the performance of T-REx on simulated neutral and selective sweep scenarios and find that it has high power and accuracy to discriminate sweeps from neutrality, robustness to common technical hurdles, and easy visualization of feature importance. Therefore, T-REx is a powerful addition to the toolkit for detecting adaptive processes from genomic data.

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.

Contrasting whole-genome and reduced representation sequencing for population demographic and adaptive inference: an alpine mammal case study

Genomes capture the adaptive and demographic history of a species, but the choice of sequencing strategy and sample size can impact such inferences. We compared whole genome and reduced representation sequencing approaches to study the population demographic and adaptive signals of the North American mountain goat (Oreamnos americanus). We applied the restriction site-associated DNA sequencing (RADseq) approach to 254 individuals and whole genome resequencing (WGS) approach to 35 individuals across the species range at mid-level coverage (9X) and to 5 individuals at high coverage (30X). We used ANGSD to estimate the genotype likelihoods and estimated the effective population size (Ne), population structure, and explicitly modelled the demographic history with δaδi and MSMC2. The data sets were overall concordant in supporting a glacial induced vicariance and extremely low Ne in mountain goats. We evaluated a set of climatic variables and geographic location as predictors of genetic diversity using redundancy analysis. A moderate proportion of total variance (36% for WGS and 21% for RADseq data sets) was explained by geography and climate variables; both data sets support a large impact of drift and some degree of local adaptation. The empirical similarities of WGS and RADseq presented herein reassuringly suggest that both approaches will recover large demographic and adaptive signals in a population; however, WGS offers several advantages over RADseq, such as inferring adaptive processes and calculating runs-of-homozygosity estimates. Considering the predicted climate-induced changes in alpine environments and the genetically depauperate mountain goat, the long-term adaptive capabilities of this enigmatic species are questionable.

The genomic footprint of whaling and isolation in fin whale populations

Twentieth century industrial whaling pushed several species to the brink of extinction, with fin whales being the most impacted. However, a small, resident population in the Gulf of California was not targeted by whaling. Here, we analyzed 50 whole-genomes from the Eastern North Pacific (ENP) and Gulf of California (GOC) fin whale populations to investigate their demographic history and the genomic effects of natural and human-induced bottlenecks. We show that the two populations diverged ~16,000 years ago, after which the ENP population expanded and then suffered a 99% reduction in effective size during the whaling period. In contrast, the GOC population remained small and isolated, receiving less than one migrant per generation. However, this low level of migration has been crucial for maintaining its viability. Our study exposes the severity of whaling, emphasizes the importance of migration, and demonstrates the use of genome-based analyses and simulations to inform conservation strategies.

Expanding the stdpopsim species catalog, and lessons learned for realistic genome simulations

Simulation is a key tool in population genetics for both methods development and empirical research, but producing simulations that recapitulate the main features of genomic datasets remains a major obstacle. Today, more realistic simulations are possible thanks to large increases in the quantity and quality of available genetic data, and the sophistication of inference and simulation software. However, implementing these simulations still requires substantial time and specialized knowledge. These challenges are especially pronounced for simulating genomes for species that are not well-studied, since it is not always clear what information is required to produce simulations with a level of realism sufficient to confidently answer a given question. The community-developed framework stdpopsim seeks to lower this barrier by facilitating the simulation of complex population genetic models using up-to-date information. The initial version of stdpopsim focused on establishing this framework using six well-characterized model species (Adrion et al., 2020). Here, we report on major improvements made in the new release of stdpopsim (version 0.2), which includes a significant expansion of the species catalog and substantial additions to simulation capabilities. Features added to improve the realism of the simulated genomes include non-crossover recombination and provision of species-specific genomic annotations. Through community-driven efforts, we expanded the number of species in the catalog more than threefold and broadened coverage across the tree of life. During the process of expanding the catalog, we have identified common sticking points and developed the best practices for setting up genome-scale simulations. We describe the input data required for generating a realistic simulation, suggest good practices for obtaining the relevant information from the literature, and discuss common pitfalls and major considerations. These improvements to stdpopsim aim to further promote the use of realistic whole-genome population genetic simulations, especially in non-model organisms, making them available, transparent, and accessible to everyone.

Biobank-scale inference of ancestral recombination graphs enables genealogical analysis of complex traits

Genome-wide genealogies compactly represent the evolutionary history of a set of genomes and inferring them from genetic data has the potential to facilitate a wide range of analyses. We introduce a method, ARG-Needle, for accurately inferring biobank-scale genealogies from sequencing or genotyping array data, as well as strategies to utilize genealogies to perform association and other complex trait analyses. We use these methods to build genome-wide genealogies using genotyping data for 337,464 UK Biobank individuals and test for association across seven complex traits. Genealogy-based association detects more rare and ultra-rare signals (N = 134, frequency range 0.0007-0.1%) than genotype imputation using ~65,000 sequenced haplotypes (N = 64). In a subset of 138,039 exome sequencing samples, these associations strongly tag (average r = 0.72) underlying sequencing variants enriched (4.8×) for loss-of-function variation. These results demonstrate that inferred genome-wide genealogies may be leveraged in the analysis of complex traits, complementing approaches that require the availability of large, population-specific sequencing panels.

Developing an appropriate evolutionary baseline model for the study of SARS-CoV-2 patient samples

Over the past 3 years, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has spread through human populations in several waves, resulting in a global health crisis. In response, genomic surveillance efforts have proliferated in the hopes of tracking and anticipating the evolution of this virus, resulting in millions of patient isolates now being available in public databases. Yet, while there is a tremendous focus on identifying newly emerging adaptive viral variants, this quantification is far from trivial. Specifically, multiple co-occurring and interacting evolutionary processes are constantly in operation and must be jointly considered and modeled in order to perform accurate inference. We here outline critical individual components of such an evolutionary baseline model-mutation rates, recombination rates, the distribution of fitness effects, infection dynamics, and compartmentalization-and describe the current state of knowledge pertaining to the related parameters of each in SARS-CoV-2. We close with a series of recommendations for future clinical sampling, model construction, and statistical analysis.

Tensor decomposition based feature extraction and classification to detect natural selection from genomic data

Inferences of adaptive events are important for learning about traits, such as human digestion of lactose after infancy and the rapid spread of viral variants. Early efforts toward identifying footprints of natural selection from genomic data involved development of summary statistic and likelihood methods. However, such techniques are grounded in simple patterns or theoretical models that limit the complexity of settings they can explore. Due to the renaissance in artificial intelligence, machine learning methods have taken center stage in recent efforts to detect natural selection, with strategies such as convolutional neural networks applied to images of haplotypes. Yet, limitations of such techniques include estimation of large numbers of model parameters under non-convex settings and feature identification without regard to location within an image. An alternative approach is to use tensor decomposition to extract features from multidimensional data while preserving the latent structure of the data, and to feed these features to machine learning models. Here, we adopt this framework and present a novel approach termed T-REx , which extracts features from images of haplotypes across sampled individuals using tensor decomposition, and then makes predictions from these features using classical machine learning methods. As a proof of concept, we explore the performance of T-REx on simulated neutral and selective sweep scenarios and find that it has high power and accuracy to discriminate sweeps from neutrality, robustness to common technical hurdles, and easy visualization of feature importance. Therefore, T-REx is a powerful addition to the toolkit for detecting adaptive processes from genomic data.

Complex cycles of divergence and migration shape lineage structure in the common kingsnake species complex

Aim

The Nearctic is a complex patchwork of habitats and geologic features that form barriers to gene flow resulting in phylogeographic structure and speciation in many lineages. Habitats are rarely stable over geologic time, and the Nearctic has undergone major climatic changes in the past few million years. We use the common kingsnake species complex to study how climate, geography, and history influence lineage formation over a large, complex landscape.

Location

Nearctic/North America.

Taxon

Common kingsnake, Lampropeltis getula, species complex.

Methods

We analyzed genome-wide sequence data from 51 snakes spanning the majority of the species complex's range. We used population clustering, generalized dissimilarity modeling, and coalescent methods to identify the number of genetic clusters within the L. getula complex, infer the environmental correlates of genetic differentiation, and estimate models of divergence and gene flow among lineages.

Results

We identified three major lineages within the L. getula complex and further continuous spatial structure within lineages. The most important ecological correlates of genetic distance in the complex are related to aridity and precipitation, consistent with lineage breaks at the Great Plains/Desert ecotone and the Cochise Filter Barrier. Lineages are estimated to have undergone multiple rounds of isolation and secondary contact, with highly asymmetric migration occurring at present.

Main conclusions

Changing climates combined with a large and geologically complex landscape have resulted in a mosaic of discrete and spatially continuous genetic structure. Multiple rounds of isolation and secondary contact as climate fluctuated over the past ~4.4 My have likely driven the evolution of discrete lineages that maintain high levels of gene flow. Continuous structure is strongly shaped by aridity and precipitation, suggesting roles for major precipitation gradients in helping to maintain lineage identity in the face of gene flow when lineages are in geographic contact.

Genetic evidence for widespread population size expansion in North American boreal birds prior to the Last Glacial Maximum

Pleistocene climate cycles are well documented to have shaped contemporary species distributions and genetic diversity. Northward range expansions in response to deglaciation following the Last Glacial Maximum (LGM; approximately 21 000 years ago) are surmised to have led to population size expansions in terrestrial taxa and changes in seasonal migratory behaviour. Recent findings, however, suggest that some northern temperate populations may have been more stable than expected through the LGM. We modelled the demographic history of 19 co-distributed boreal-breeding North American bird species from full mitochondrial gene sets and species-specific molecular rates. We used these demographic reconstructions to test how species with different migratory strategies were affected by glacial cycles. Our results suggest that effective population sizes increased in response to Pleistocene deglaciation earlier than the LGM, whereas genetic diversity was maintained throughout the LGM despite shifts in geographical range. We conclude that glacial cycles prior to the LGM have most strongly shaped contemporary genetic diversity in these species. We did not find a relationship between historic population dynamics and migratory strategy, contributing to growing evidence that major switches in migratory strategy during the LGM are unnecessary to explain contemporary migratory patterns.

Long-term Small Population Size, Deleterious Variation, and Altitude Adaptation in the Ethiopian Wolf, a Severely Endangered Canid

Ethiopian wolves, a canid species endemic to the Ethiopian Highlands, have been steadily declining in numbers for decades. Currently, out of 35 extant species, it is now one of the world's most endangered canids. Most conservation efforts have focused on preventing disease, monitoring movements and behavior, and assessing the geographic ranges of sub-populations. Here, we add an essential layer by determining the Ethiopian wolf's demographic and evolutionary history using high-coverage (∼40×) whole-genome sequencing from 10 Ethiopian wolves from the Bale Mountains. We observe exceptionally low diversity and enrichment of weakly deleterious variants in the Ethiopian wolves in comparison with two North American gray wolf populations and four dog breeds. These patterns are consequences of long-term small population size, rather than recent inbreeding. We infer the demographic history of the Ethiopian wolf and find it to be concordant with historic records and previous genetic analyses, suggesting Ethiopian wolves experienced a series of both ancient and recent bottlenecks, resulting in a census population size of fewer than 500 individuals and an estimated effective population size of approximately 100 individuals. Additionally, long-term small population size may have limited the accumulation of strongly deleterious recessive mutations. Finally, as the Ethiopian wolves have inhabited high-altitude areas for thousands of years, we searched for evidence of high-altitude adaptation, finding evidence of positive selection at a transcription factor in a hypoxia-response pathway [CREB-binding protein (CREBBP)]. Our findings are pertinent to continuing conservation efforts and understanding how demography influences the persistence of deleterious variation in small populations.

Disentangling the genetic consequences of demographic change

Quantifying the impact of human activity on the capacity of populations to persist is paramount to conservation biology, as numerous species and populations have already been driven to or beyond the brink of extinction. Those populations that persist are often a sobering example of the evolutionary power of human-disturbance, such as the loss of tusks in African elephants resulting from ivory harvesting (Campbell-Staton et al., 2021) and rapid life-history evolution in northern Atlantic cod in response to fisheries (Olsen et al., 2004). These evolutionary responses reflect a delicate interplay between demographic and selective processes (e.g., evolutionary rescue: Bell & Gonzalez, 2009; Gomulkiewicz & Holt, 1995), both of which can modify genetic variation for fitness. While quantifying fitness remains a difficult challenge, generalizable insights into the evolutionary consequences of population collapse can be provided in systems with independent demographic shifts in response to human activity. Unfortunately, such was the case for sea otter populations across its range in the 18th and 19th centuries, where the fur-trade had catastrophic, range-wide effects on sea otter (Enhydra lutris) populations. In a From the Cover article in this issue of Molecular Ecology, Beichman et al. (2022) combine a population genomic spatiotemporal data set and theoretical simulations not only to quantify past demographic change in response to sea otter exploitation, but also to understand the consequences of population collapse on species persistence.

How ancient forest fragmentation and riparian connectivity generate high levels of genetic diversity in a microendemic Malagasy tree

Understanding landscape changes is central to predicting evolutionary trajectories and defining conservation practices. While human-driven deforestation is intense throughout Madagascar, exceptions in areas such as the Loky-Manambato region (north) raise questions regarding the causes and age of forest fragmentation. The Loky-Manambato region also harbours a rich and endemic flora, whose evolutionary origin remains poorly understood. We assessed the genetic diversity of an endangered microendemic Malagasy olive species (Noronhia spinifolia Hong-Wa) to better understand the vegetation dynamics in the Loky-Manambato region and its influence on past evolutionary processes. We characterized 72 individuals sampled across eight forests through nuclear and mitochondrial restriction-associated DNA sequencing data and chloroplast microsatellites. Combined population and landscape genetics analyses indicate that N. spinifolia diversity is largely explained by the current forest cover, highlighting a long-standing habitat mosaic in the region. This sustains a major and long-term role of riparian corridors in maintaining connectivity across these antique mosaic habitats, calling for the study of organismal interactions that promote gene flow.

eSMC: a statistical model to infer admixture events from individual genomics data

Background

Inferring historical population admixture events yield essential insights in understanding a species demographic history. Methods are available to infer admixture events in demographic history with extant genetic data from multiple sources. Due to the deficiency in ancient population genetic data, there lacks a method for admixture inference from a single source. Pairwise Sequentially Markovian Coalescent (PSMC) estimates the historical effective population size from lineage genomes of a single individual, based on the distribution of the most recent common ancestor between the diploid’s alleles. However, PSMC does not infer the admixture event.

Results

Here, we proposed eSMC, an extended PSMC model for admixture inference from a single source. We evaluated our model’s performance on both in silico data and real data. We simulated population admixture events at an admixture time range from 5 kya to 100 kya (5 years/generation) with population admix ratio at 1:1, 2:1, 3:1, and 4:1, respectively. The root means the square error is $$\pm 7.61$$ ± 7.61 kya for all experiments. Then we implemented our method to infer the historical admixture events in human, donkey and goat populations. The estimated admixture time for both Han and Tibetan individuals range from 60 kya to 80 kya (25 years/generation), while the estimated admixture time for the domesticated donkeys and the goats ranged from 40 kya to 60 kya (8 years/generation) and 40 kya to 100 kya (6 years/generation), respectively. The estimated admixture times were concordance to the time that domestication occurred in human history.

Conclusion

Our eSMC effectively infers the time of the most recent admixture event in history from a single individual’s genomics data. The source code of eSMC is hosted at https://github.com/zachary-zzc/eSMC.

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.

Some complexities in interpreting apparent effects of hitchhiking: A commentary on Gompert et al. (2022)

We write to address recent claims by regarding the potentially important and underappreciated phenomena of "indirect selection," the observation that neutral regions may be affected by natural selection. We argue both that this phenomenon-generally known as genetic hitchhiking-is neither new nor poorly studied, and that the patterns described by the authors have multiple alternative explanations.

SNP4OrphanSpecies: A bioinformatics pipeline to isolate molecular markers for studying genetic diversity of orphan species

Background

For several decades, an increase in disease or pest emergences due to anthropogenic introduction or environmental changes has been recorded. This increase leads to serious threats to the genetic and species diversity of numerous ecosystems. Many of these events involve species with poor or no genomic resources (called here "orphan species"). This lack of resources is a serious limitation to our understanding of the origin of emergent populations, their ability to adapt to new environments and to predict future consequences to biodiversity. Analyses of genetic diversity are an efficient method to obtain this information rapidly, but require available polymorphic genetic markers.

New information

We developed a generic bioinformatics pipeline to rapidly isolate such markers with the goal for the pipeline to be applied in studies of invasive taxa from different taxonomic groups, with a special focus on forest fungal pathogens and insect pests. This pipeline is based on: 1) an automated de novo genome assembly obtained from shotgun whole genome sequencing using paired-end Illumina technology; 2) the isolation of single-copy genes conserved in species related to the studied emergent organisms; 3) primer development for multiplexed short sequences obtained from these conserved genes. Previous studies have shown that intronic regions of these conserved genes generally contain several single nucleotide polymorphisms within species. The pipeline's functionality was evaluated with sequenced genomes of five invasive or expanding pathogen and pest species in Europe (Armillariaostoyae (Romagn.) Herink 1973, Bursaphelenchusxylophilus Steiner & Buhrer 1934, Sphaeropsissapinea (fr.) Dicko & B. Sutton 1980, Erysiphealphitoides (Griffon & Maubl.) U. Braun & S. Takam. 2000, Thaumetopoeapityocampa Denis & Schiffermüller, 1775). We successfully isolated several pools of one hundred short gene regions for each assembled genome, which can be amplified in multiplex. The bioinformatics pipeline is user-friendly and requires little computational resources. This easy-to-set-up and run method for genetic marker identification will be useful for numerous laboratories studying biological invasions, but with limited resources and expertise in bioinformatics.