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.

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.

Ancient dolphin genomes reveal rapid repeated adaptation to coastal waters.

Parallel evolution provides strong evidence of adaptation by natural selection due to local environmental variation. Yet, the chronology, and mode of the process of parallel evolution remains debated. Here, we harness the temporal resolution of paleogenomics to address these long-standing questions, by comparing genomes originating from the mid-Holocene (8610-5626 years before present, BP) to contemporary pairs of coastal-pelagic ecotypes of bottlenose dolphin. We find that the affinity of ancient samples to coastal populations increases as the age of the samples decreases. We assess the youngest genome (5626 years BP) at sites previously inferred to be under parallel selection to coastal habitats and find it contained coastal-associated genotypes. Thus, coastal-associated variants rose to detectable frequencies close to the emergence of coastal habitat. Admixture graph analyses reveal a reticulate evolutionary history between pelagic and coastal populations, sharing standing genetic variation that facilitated rapid adaptation to newly emerged coastal habitats.

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.

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.

Information-based summary statistics for spatial genetic structure inference.

The measurement of biodiversity at all levels of organization is an essential first step to understand the ecological and evolutionary processes that drive spatial patterns of biodiversity. Ecologists have explored the use of a large range of different summary statistics and have come to the view that information-based summary statistics, and in particular so-called Hill numbers, are a useful tool to measure biodiversity. Population geneticists, on the other hand, have focused largely on summary statistics based on heterozygosity and measures of allelic richness. However, recent studies proposed the adoption of information-based summary statistics in population genetics studies. Here, we performed a comprehensive assessment of the power of this family of summary statistics to inform regarding spatial patterns of genetic diversity and we compared it with that of traditional population genetics approaches, namely measures based on allelic richness and heterozygosity. To give an unbiased evaluation, we used three machine learning methods to test the performance of different sets of summary statistics to discriminate between spatial scenarios. We defined three distinct sets, (i) one based on allelic richness measures which included the Jaccard index, (ii) a set based on heterozygosity that included FST and (iii) a set based on Hill numbers derived from Shannon entropy, which included the recently proposed Shannon differentiation, ΔD. The results showed that the last of these performed as well or, under some specific spatial scenarios, even better than the traditional population genetics measures. Interestingly, we found that a rarely or never used genetic differentiation measure based on allelic richness, Jaccard dissimilarity (J), showed the highest discriminatory power to discriminate among spatial scenarios, followed by Shannon differentiation ΔD. We concluded, therefore, that information-based measures as well as Jaccard dissimilarity represent excellent additions to the population genetics toolkit.

Selection on ancestral genetic variation fuels repeated ecotype formation in bottlenose dolphins.

Studying repeated adaptation can provide insights into the mechanisms allowing species to adapt to novel environments. Here, we investigate repeated evolution driven by habitat specialization in the common bottlenose dolphin. Parapatric pelagic and coastal ecotypes of common bottlenose dolphins have repeatedly formed across the oceans. Analyzing whole genomes of 57 individuals, we find that ecotype evolution involved a complex reticulated evolutionary history. We find parallel linked selection acted upon ancient alleles in geographically distant coastal populations, which were present as standing genetic variation in the pelagic populations. Candidate loci evolving under parallel linked selection were found in ancient tracts, suggesting recurrent bouts of selection through time. Therefore, despite the constraints of small effective population size and long generation time on the efficacy of selection, repeated adaptation in long-lived social species can be driven by a combination of ecological opportunities and selection acting on ancestral standing genetic variation.

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.

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.