Competing adaptations maintain nonadaptive variation in a wild cricket population.

How emerging adaptive variants interact is an important factor in the evolution of wild populations, but the opportunity to empirically study this interaction is rare. We recently documented the emergence of an adaptive phenotype "curly-wing" in Hawaiian populations of field crickets (Teleogryllus oceanicus). Curly-wing inhibits males' ability to sing, protecting them from eavesdropping parasitoid flies (Ormia ochracea). Surprisingly, curly-wing co-occurs with similarly protective silent "flatwing" phenotypes in multiple populations, in which neither phenotype has spread to fixation. These two phenotypes are frequently coexpressed, but since either sufficiently reduces song amplitude to evade the fly, their coexpression confers no additional fitness benefit. Numerous "off-target" phenotypic changes are known to accompany flatwing, and we find that curly-wing, too, negatively impacts male courtship ability and affects mass and survival of females under lab conditions. We show through crosses and genomic and mRNA sequencing that curly-wing expression is associated with variation on a single autosome. In parallel analyses of flatwing, our results reinforce previous findings of X-linked single-locus inheritance. By combining insights into the genetic architecture of these alternative phenotypes with simulations and field observations, we show that the co-occurrence of these two adaptations impedes either from fixing, despite extreme fitness benefits, due to fitness epistasis. This co-occurrence of similar adaptive forms in the same populations might be more common than is generally considered and could be an important force inhibiting adaptive evolution in wild populations of sexually reproducing organisms.

Deciphering signatures of natural selection via deep learning.

Identifying genomic regions influenced by natural selection provides fundamental insights into the genetic basis of local adaptation. However, it remains challenging to detect loci under complex spatially varying selection. We propose a deep learning-based framework, DeepGenomeScan, which can detect signatures of spatially varying selection. We demonstrate that DeepGenomeScan outperformed principal component analysis- and redundancy analysis-based genome scans in identifying loci underlying quantitative traits subject to complex spatial patterns of selection. Noticeably, DeepGenomeScan increases statistical power by up to 47.25% under nonlinear environmental selection patterns. We applied DeepGenomeScan to a European human genetic dataset and identified some well-known genes under selection and a substantial number of clinically important genes that were not identified by SPA, iHS, Fst and Bayenv when applied to the same dataset.

KLFDAPC: a supervised machine learning approach for spatial genetic structure analysis.

Geographic patterns of human genetic variation provide important insights into human evolution and disease. A commonly used tool to detect and describe them is principal component analysis (PCA) or the supervised linear discriminant analysis of principal components (DAPC). However, genetic features produced from both approaches could fail to correctly characterize population structure for complex scenarios involving admixture. In this study, we introduce Kernel Local Fisher Discriminant Analysis of Principal Components (KLFDAPC), a supervised non-linear approach for inferring individual geographic genetic structure that could rectify the limitations of these approaches by preserving the multimodal space of samples. We tested the power of KLFDAPC to infer population structure and to predict individual geographic origin using neural networks. Simulation results showed that KLFDAPC has higher discriminatory power than PCA and DAPC. The application of our method to empirical European and East Asian genome-wide genetic datasets indicated that the first two reduced features of KLFDAPC correctly recapitulated the geography of individuals and significantly improved the accuracy of predicting individual geographic origin when compared to PCA and DAPC. Therefore, KLFDAPC can be useful for geographic ancestry inference, design of genome scans and correction for spatial stratification in GWAS that link genes to adaptation or disease susceptibility.

Variation in the genomic basis of parallel phenotypic and ecological divergence in benthic and pelagic morphs of Icelandic Arctic charr (Salvelinus alpinus).

Sympatric adaptive phenotypic divergence should be underlain by genomic differentiation between subpopulations. When divergence drives similar patterns of phenotypic and ecological variation within species we expect evolution to draw on common allelic variation. We investigated divergence histories and genomic signatures of adaptive divergence between benthic and pelagic morphs of Icelandic Arctic charr. Divergence histories for each of four populations were reconstructed using coalescent modelling and 14,187 single nucleotide polymorphisms. Sympatric divergence with continuous gene flow was supported in two populations while allopatric divergence with secondary contact was supported in one population; we could not differentiate between demographic models in the fourth population. We detected parallel patterns of phenotypic divergence along benthic-pelagic evolutionary trajectories among populations. Patterns of genomic differentiation between benthic and pelagic morphs were characterized by outlier loci in many narrow peaks of differentiation throughout the genome, which may reflect the eroding effects of gene flow on nearby neutral loci. We then used genome-wide association analyses to relate both phenotypic (body shape and size) and ecological (carbon and nitrogen stable isotopes) variation to patterns of genomic differentiation. Many peaks of genomic differentiation were associated with phenotypic and ecological variation in the three highly divergent populations, suggesting a genomic basis for adaptive divergence. We detected little evidence for a parallel genomic basis of differentiation as most regions and outlier loci were not shared among populations. Our results show that adaptive divergence can have varied genomic consequences in populations with relatively recent common origins, similar divergence histories, and parallel phenotypic divergence.

Speciation in the deep: genomics and morphology reveal a new species of beaked whale Mesoplodon eueu.

The deep sea has been described as the last major ecological frontier, as much of its biodiversity is yet to be discovered and described. Beaked whales (ziphiids) are among the most visible inhabitants of the deep sea, due to their large size and worldwide distribution, and their taxonomic diversity and much about their natural history remain poorly understood. We combine genomic and morphometric analyses to reveal a new Southern Hemisphere ziphiid species, Ramari's beaked whale, Mesoplodon eueu, whose name is linked to the Indigenous peoples of the lands from which the species holotype and paratypes were recovered. Mitogenome and ddRAD-derived phylogenies demonstrate reciprocally monophyletic divergence between M. eueu and True's beaked whale (M. mirus) from the North Atlantic, with which it was previously subsumed. Morphometric analyses of skulls also distinguish the two species. A time-calibrated mitogenome phylogeny and analysis of two nuclear genomes indicate divergence began circa 2 million years ago (Ma), with geneflow ceasing 0.35-0.55 Ma. This is an example of how deep sea biodiversity can be unravelled through increasing international collaboration and genome sequencing of archival specimens. Our consultation and involvement with Indigenous peoples offers a model for broadening the cultural scope of the scientific naming process.

Accounting for stochasticity in demographic compensation along the elevational range of an alpine plant.

Demographic compensation arises when vital rates change in opposite directions across populations, buffering the variation in population growth rates, and is a mechanism often invoked to explain the stability of species geographic ranges. However, studies on demographic compensation have disregarded the effects of temporal variation in vital rates and their temporal correlations, despite theoretical evidence that stochastic dynamics can affect population persistence in temporally varying environments. We carried out a seven-year-long demographic study on the perennial plant Arabis alpina (L.) across six populations encompassing most of its elevational range. We discovered demographic compensation in the form of negative correlations between the means of plant vital rates, but also between their temporal coefficients of variation, correlations and elasticities. Even if their contribution to demographic compensation was small, this highlights a previously overlooked, but potentially important, role of stochastic processes in stabilising population dynamics at range margins.

Perturbation drives changing metapopulation dynamics in a top marine predator.

Metapopulation theory assumes a balance between local decays/extinctions and local growth/new colonisations. Here we investigate whether recent population declines across part of the UK harbour seal range represent normal metapopulation dynamics or are indicative of perturbations potentially threatening the metapopulation viability, using 20 years of population trends, location tracking data (n = 380), and UK-wide, multi-generational population genetic data (n = 269). First, we use microsatellite data to show that two genetic groups previously identified are distinct metapopulations: northern and southern. Then, we characterize the northern metapopulation dynamics in two different periods, before and after the start of regional declines (pre-/peri-perturbation). We identify source-sink dynamics across the northern metapopulation, with two putative source populations apparently supporting three likely sink populations, and a recent metapopulation-wide disruption of migration coincident with the perturbation. The northern metapopulation appears to be in decay, highlighting that changes in local populations can lead to radical alterations in the overall metapopulation's persistence and dynamics.

Genetic Diversity and Connectivity of Southern Right Whales (Eubalaena australis) Found in the Brazil and Chile-Peru Wintering Grounds and the South Georgia (Islas Georgias del Sur) Feeding Ground.

As species recover from exploitation, continued assessments of connectivity and population structure are warranted to provide information for conservation and management. This is particularly true in species with high dispersal capacity, such as migratory whales, where patterns of connectivity could change rapidly. Here we build on a previous long-term, large-scale collaboration on southern right whales (Eubalaena australis) to combine new (nnew) and published (npub) mitochondrial (mtDNA) and microsatellite genetic data from all major wintering grounds and, uniquely, the South Georgia (Islas Georgias del Sur: SG) feeding grounds. Specifically, we include data from Argentina (npub mtDNA/microsatellite = 208/46), Brazil (nnew mtDNA/microsatellite = 50/50), South Africa (nnew mtDNA/microsatellite = 66/77, npub mtDNA/microsatellite = 350/47), Chile-Peru (nnew mtDNA/microsatellite = 1/1), the Indo-Pacific (npub mtDNA/microsatellite = 769/126), and SG (npub mtDNA/microsatellite = 8/0, nnew mtDNA/microsatellite = 3/11) to investigate the position of previously unstudied habitats in the migratory network: Brazil, SG, and Chile-Peru. These new genetic data show connectivity between Brazil and Argentina, exemplified by weak genetic differentiation and the movement of 1 genetically identified individual between the South American grounds. The single sample from Chile-Peru had an mtDNA haplotype previously only observed in the Indo-Pacific and had a nuclear genotype that appeared admixed between the Indo-Pacific and South Atlantic, based on genetic clustering and assignment algorithms. The SG samples were clearly South Atlantic and were more similar to the South American than the South African wintering grounds. This study highlights how international collaborations are critical to provide context for emerging or recovering regions, like the SG feeding ground, as well as those that remain critically endangered, such as Chile-Peru.

Computer simulations: tools for population and evolutionary genetics.

Computer simulations are excellent tools for understanding the evolutionary and genetic consequences of complex processes whose interactions cannot be analytically predicted. Simulations have traditionally been used in population genetics by a fairly small community with programming expertise, but the recent availability of dozens of sophisticated, customizable software packages for simulation now makes simulation an accessible option for researchers in many fields. The in silico genetic data produced by simulations, along with greater availability of population-genomics data, are transforming genetic epidemiology, anthropology, evolutionary and population genetics and conservation. In this Review of the state-of-the-art of simulation software, we identify applications of simulations, evaluate simulator capabilities, provide a guide for their use and summarize future directions.

Widespread signals of convergent adaptation to high altitude in Asia and america.

Living at high altitude is one of the most difficult challenges that humans had to cope with during their evolution. Whereas several genomic studies have revealed some of the genetic bases of adaptations in Tibetan, Andean, and Ethiopian populations, relatively little evidence of convergent evolution to altitude in different continents has accumulated. This lack of evidence can be due to truly different evolutionary responses, but it can also be due to the low power of former studies that have mainly focused on populations from a single geographical region or performed separate analyses on multiple pairs of populations to avoid problems linked to shared histories between some populations. We introduce here a hierarchical Bayesian method to detect local adaptation that can deal with complex demographic histories. Our method can identify selection occurring at different scales, as well as convergent adaptation in different regions. We apply our approach to the analysis of a large SNP data set from low- and high-altitude human populations from America and Asia. The simultaneous analysis of these two geographic areas allows us to identify several candidate genome regions for altitudinal selection, and we show that convergent evolution among continents has been quite common. In addition to identifying several genes and biological processes involved in high-altitude adaptation, we identify two specific biological pathways that could have evolved in both continents to counter toxic effects induced by hypoxia.

Changes in selective pressures associated with human population expansion may explain metabolic and immune related pathways enriched for signatures of positive selection.

BACKGROUND: The study of local adaptation processes is a very important research topic in the field of population genomics. There is a particular interest in the study of human populations because they underwent a process of rapid spatial expansion and faced important environmental changes that translated into changes in selective pressures. New mutations may have been selected for in the new environment and previously existing genetic variants may have become detrimental. Immune related genes may have been released from the selective pressure exerted by pathogens in the ancestral environment and new variants may have been positively selected due to pathogens present in the newly colonized habitat. Also, variants that had a selective advantage in past environments may have become deleterious in the modern world due to external stimuli including climatic, dietary and behavioral changes, which could explain the high prevalence of some polygenic diseases such as diabetes and obesity. RESULTS: We performed an enrichment analysis to identify gene sets enriched for signals of positive selection in humans. We used two genome scan methods, XPCLR and iHS to detect selection using a dense coverage of SNP markers combined with two gene set enrichment approaches. We identified immune related gene sets that could be involved in the protection against pathogens especially in the African population. We also identified the glycolysis & gluconeogenesis gene set, related to metabolism, which supports the thrifty genotype hypothesis invoked to explain the current high prevalence of diseases such as diabetes and obesity. Extending our analysis to the gene level, we found signals for 23 candidate genes linked to metabolic syndrome, 13 of which are new candidates for positive selection. CONCLUSIONS: Our study provides a list of genes and gene sets associated with immunity and metabolic syndrome that are enriched for signals of positive selection in three human populations (Europeans, Africans and Asians). Our results highlight differences in the relative importance of pathogens as drivers of local adaptation in different continents and provide new insights into the evolution and high incidence of metabolic syndrome in modern human populations.

The DNA of coral reef biodiversity: predicting and protecting genetic diversity of reef assemblages.

Conservation of ecological communities requires deepening our understanding of genetic diversity patterns and drivers at community-wide scales. Here, we use seascape genetic analysis of a diversity metric, allelic richness (AR), for 47 reef species sampled across 13 Hawaiian Islands to empirically demonstrate that large reefs high in coral cover harbour the greatest genetic diversity on average. We found that a species's life history (e.g. depth range and herbivory) mediates response of genetic diversity to seascape drivers in logical ways. Furthermore, a metric of combined multi-species AR showed strong coupling to species richness and habitat area, quality and stability that few species showed individually. We hypothesize that macro-ecological forces and species interactions, by mediating species turnover and occupancy (and thus a site's mean effective population size), influence the aggregate genetic diversity of a site, potentially allowing it to behave as an apparent emergent trait that is shaped by the dominant seascape drivers. The results highlight inherent feedbacks between ecology and genetics, raise concern that genetic resilience of entire reef communities is compromised by factors that reduce coral cover or available habitat, including thermal stress, and provide a foundation for new strategies for monitoring and preserving biodiversity of entire reef ecosystems.

Detection of selective sweeps in structured populations: a comparison of recent methods.

Identifying genomic regions targeted by positive selection has been a long-standing interest of evolutionary biologists. This objective was difficult to achieve until the recent emergence of next-generation sequencing, which is fostering the development of large-scale catalogues of genetic variation for increasing number of species. Several statistical methods have been recently developed to analyse these rich data sets, but there is still a poor understanding of the conditions under which these methods produce reliable results. This study aims at filling this gap by assessing the performance of genome-scan methods that consider explicitly the physical linkage among SNPs surrounding a selected variant. Our study compares the performance of seven recent methods for the detection of selective sweeps (iHS, nSL, EHHST, xp-EHH, XP-EHHST, XPCLR and hapFLK). We use an individual-based simulation approach to investigate the power and accuracy of these methods under a wide range of population models under both hard and soft sweeps. Our results indicate that XPCLR and hapFLK perform best and can detect soft sweeps under simple population structure scenarios if migration rate is low. All methods perform poorly with moderate-to-high migration rates, or with weak selection and very poorly under a hierarchical population structure. Finally, no single method is able to detect both starting and nearly completed selective sweeps. However, combining several methods (XPCLR or hapFLK with iHS or nSL) can greatly increase the power to pinpoint the selected region.

Influence of environmental heterogeneity on the distribution and persistence of a subterranean rodent in a highly unstable landscape.

In this study we combine information from landscape characteristics, demographic inference and species distribution modelling to identify environmental factors that shape the genetic distribution of the fossorial rodent Ctenomys. We sequenced the mtDNA control region and amplified 12 microsatellites from 27 populations distributed across the Iberá wetland ecosystem. Hierarchical Bayesian modelling was used to construct phylogenies and estimate divergence times. We developed species distribution models to determine what climatic variables and soil parameters predicted species presence by comparing the current to the historic and predicted future distribution of the species. Finally, we explore the impact of environmental variables on the genetic structure of Ctenomys based on current and past species distributions. The variables that consistently correlated with the predicted distribution of the species and explained the observed genetic differentiation among populations included the distribution of well-drained sandy soils and temperature seasonality. A core region of stable suitable habitat was identified from the Last Interglacial, which is projected to remain stable into the future. This region is also the most genetically diverse and is currently under strong anthropogenic pressure. Results reveal complex demographic dynamics, which have been in constant change in both time and space, and are likely linked to the evolution of the Paraná River. We suggest that any alteration of soil properties (climatic or anthropic) may significantly impact the availability of suitable habitat and consequently the ability of individuals to disperse. The protection of this core stable habitat is of prime importance given the increasing levels of human disturbance across this wetland system and the threat of climate change.

Emergent patterns of population genetic structure for a coral reef community.

What shapes variation in genetic structure within a community of codistributed species is a central but difficult question for the field of population genetics. With a focus on the isolated coral reef ecosystem of the Hawaiian Archipelago, we assessed how life history traits influence population genetic structure for 35 reef animals. Despite the archipelago's stepping stone configuration, isolation by distance was the least common type of genetic structure, detected in four species. Regional structuring (i.e. division of sites into genetically and spatially distinct regions) was most common, detected in 20 species and nearly in all endemics and habitat specialists. Seven species displayed chaotic (spatially unordered) structuring, and all were nonendemic generalist species. Chaotic structure also associated with relatively high global FST. Pelagic larval duration (PLD) was not a strong predictor of variation in population structure (R2=0.22), but accounting for higher FST values of chaotic and invertebrate species, compared to regionally structured and fish species, doubled the power of PLD to explain variation in global FST (adjusted R2=0.50). Multivariate correlation of eight species traits to six genetic traits highlighted dispersal ability, taxonomy (i.e. fish vs. invertebrate) and habitat specialization as strongest influences on genetics, but otherwise left much variation in genetic traits unexplained. Considering that the study design controlled for many sampling and geographical factors, the extreme interspecific variation in spatial genetic patterns observed for Hawaìi marine species may be generated by demographic variability due to species-specific abundance and migration patterns and/or seascape and historical factors.

Genome scan methods against more complex models: when and how much should we trust them?

The recent availability of next-generation sequencing (NGS) has made possible the use of dense genetic markers to identify regions of the genome that may be under the influence of selection. Several statistical methods have been developed recently for this purpose. Here, we present the results of an individual-based simulation study investigating the power and error rate of popular or recent genome scan methods: linear regression, Bayescan, BayEnv and LFMM. Contrary to previous studies, we focus on complex, hierarchical population structure and on polygenic selection. Additionally, we use a false discovery rate (FDR)-based framework, which provides an unified testing framework across frequentist and Bayesian methods. Finally, we investigate the influence of population allele frequencies versus individual genotype data specification for LFMM and the linear regression. The relative ranking between the methods is impacted by the consideration of polygenic selection, compared to a monogenic scenario. For strongly hierarchical scenarios with confounding effects between demography and environmental variables, the power of the methods can be very low. Except for one scenario, Bayescan exhibited moderate power and error rate. BayEnv performance was good under nonhierarchical scenarios, while LFMM provided the best compromise between power and error rate across scenarios. We found that it is possible to greatly reduce error rates by considering the results of all three methods when identifying outlier loci.

Collecting baleen whale blow samples by drone: A minimally intrusive tool for conservation genetics.

In coastal British Columbia, Canada, marine megafauna such as humpback whales (Megaptera novaeangliae) and fin whales (Balaenoptera physalus velifera) have been subject to a history of exploitation and near extirpation. While their populations have been in recovery, significant threats are posed to these vulnerable species by proposed natural resource ventures in this region, in addition to the compounding effects of anthropogenic climate change. Genetic tools play a vital role in informing conservation efforts, but the associated collection of tissue biopsy samples can be challenging for the investigators and disruptive to the ongoing behaviour of the targeted whales. Here, we evaluate a minimally intrusive approach based on collecting exhaled breath condensate, or respiratory 'blow' samples, from baleen whales using an unoccupied aerial system (UAS), within Gitga'at First Nation territory for conservation genetics. Minimal behavioural responses to the sampling technique were observed, with no response detected 87% of the time (of 112 UAS deployments). DNA from whale blow (n = 88 samples) was extracted, and DNA profiles consisting of 10 nuclear microsatellite loci, sex identification and mitochondrial (mt) DNA haplotypes were constructed. An average of 7.5 microsatellite loci per individual were successfully genotyped. The success rates for mtDNA and sex assignment were 80% and 89% respectively. Thus, this minimally intrusive sampling method can be used to describe genetic diversity and generate genetic profiles for individual identification. The results of this research demonstrate the potential of UAS-collected whale blow for conservation genetics from a remote location.

High variance in reproductive success generates a false signature of a genetic bottleneck in populations of constant size: a simulation study.

BACKGROUND: Demographic bottlenecks can severely reduce the genetic variation of a population or a species. Establishing whether low genetic variation is caused by a bottleneck or a constantly low effective number of individuals is important to understand a species' ecology and evolution, and it has implications for conservation management. Recent studies have evaluated the power of several statistical methods developed to identify bottlenecks. However, the false positive rate, i.e. the rate with which a bottleneck signal is misidentified in demographically stable populations, has received little attention. We analyse this type of error (type I) in forward computer simulations of stable populations having greater than Poisson variance in reproductive success (i.e., variance in family sizes). The assumption of Poisson variance underlies bottleneck tests, yet it is commonly violated in species with high fecundity. RESULTS: With large variance in reproductive success (Vk ≥ 40, corresponding to a ratio between effective and census size smaller than 0.1), tests based on allele frequencies, allelic sizes, and DNA sequence polymorphisms (heterozygosity excess, M-ratio, and Tajima's D test) tend to show erroneous signals of a bottleneck. Similarly, strong evidence of population decline is erroneously detected when ancestral and current population sizes are estimated with the model based method MSVAR. CONCLUSIONS: Our results suggest caution when interpreting the results of bottleneck tests in species showing high variance in reproductive success. Particularly in species with high fecundity, computer simulations are recommended to confirm the occurrence of a population bottleneck.

The number of markers and samples needed for detecting bottlenecks under realistic scenarios, with and without recovery: a simulation-based study.

Detecting bottlenecks is a common task in molecular ecology. While several bottleneck detection method sexist, evaluations of their power have focused only on severe bottlenecks (e.g. to Ne ~10). As a component of a recent review, Peery et al. (2012) analysed the power of two approaches, the M-ratio and heterozygote excess tests, to detect moderate bottlenecks (e.g. to Ne ~100),which is realistic for many conservation situations. In this Comment, we address three important points relevant to but not considered in Peery et al. Under moderate bottleneck scenarios, we test the (i) relative advantage of sampling more markers vs. more individuals, (ii) potential power to detect the bottleneck when utilizing dozens of microsatellites (a realistic possibility for contemporary studies) and (iii) reduction in power when post bottle neck recovery has occurred. For the realistic situations examined,we show that (i) doubling the number of loci shows equal or better power than tripling the number of individuals,(ii) increasing the number of markers (up to 100) results in continued additive gains in power, and (iii)recovery after a moderate amount of time or gradual change in size reduces power, by up to one-half. Our results provide a practical supplement to Peery et al. and encourage the continued use of bottleneck detection methods in the genomic age, but also emphasize that the power under different sampling schemes should be estimated,using simulation modelling, as a routine component of molecular ecology studies.

Deep Learning in Population Genetics.

Population genetics is transitioning into a data-driven discipline thanks to the availability of large-scale genomic data and the need to study increasingly complex evolutionary scenarios. With likelihood and Bayesian approaches becoming either intractable or computationally unfeasible, machine learning, and in particular deep learning, algorithms are emerging as popular techniques for population genetic inferences. These approaches rely on algorithms that learn non-linear relationships between the input data and the model parameters being estimated through representation learning from training data sets. Deep learning algorithms currently employed in the field comprise discriminative and generative models with fully connected, convolutional, or recurrent layers. Additionally, a wide range of powerful simulators to generate training data under complex scenarios are now available. The application of deep learning to empirical data sets mostly replicates previous findings of demography reconstruction and signals of natural selection in model organisms. To showcase the feasibility of deep learning to tackle new challenges, we designed a branched architecture to detect signals of recent balancing selection from temporal haplotypic data, which exhibited good predictive performance on simulated data. Investigations on the interpretability of neural networks, their robustness to uncertain training data, and creative representation of population genetic data, will provide further opportunities for technological advancements in the field.