Purifying and balancing selection on embryonic semi-lethal haplotypes in a wild mammal.

Embryonic lethal mutations are arguably the earliest and most severe manifestation of inbreeding depression, but their impact on wild populations is not well understood. Here, we combined genomic, fitness, and life-history data from 5,925 wild Soay sheep sampled over nearly three decades to explore the impact of embryonic lethal mutations and their evolutionary dynamics. We searched for haplotypes that in their homozygous state are unusually rare in the offspring of known carrier parents and found three putatively semi-lethal haplotypes with 27%-46% fewer homozygous offspring than expected. Two of these haplotypes are decreasing in frequency, and gene-dropping simulations through the pedigree suggest that this is partially due to purifying selection. In contrast, the frequency of the third semi-lethal haplotype remains relatively stable over time. We show that the haplotype could be maintained by balancing selection because it is also associated with increased postnatal survival and body weight and because its cumulative frequency change is lower than in most drift-only simulations. Our study highlights embryonic mutations as a largely neglected contributor to inbreeding depression and provides a rare example of how harmful genetic variation can be maintained through balancing selection in a wild mammal population.

Conservation management strategy impacts inbreeding and mutation load in scimitar-horned oryx.

In an age of habitat loss and overexploitation, small populations, both captive and wild, are increasingly facing the effects of isolation and inbreeding. Genetic management has therefore become a vital tool for ensuring population viability. However, little is known about how the type and intensity of intervention shape the genomic landscape of inbreeding and mutation load. We address this using whole-genome sequence data of the scimitar-horned oryx (Oryx dammah), an iconic antelope that has been subject to contrasting management strategies since it was declared extinct in the wild. We show that unmanaged populations are enriched for long runs of homozygosity (ROH) and have significantly higher inbreeding coefficients than managed populations. Additionally, despite the total number of deleterious alleles being similar across management strategies, the burden of homozygous deleterious genotypes was consistently higher in unmanaged groups. These findings emphasize the risks associated with deleterious mutations through multiple generations of inbreeding. As wildlife management strategies continue to diversify, our study reinforces the importance of maintaining genome-wide variation in vulnerable populations and has direct implications for one of the largest reintroduction attempts in the world.

Selection, recombination and population history effects on runs of homozygosity (ROH) in wild red deer (Cervus elaphus).

The distribution of runs of homozygosity (ROH) may be shaped by a number of interacting processes such as selection, recombination and population history, but little is known about the importance of these mechanisms in shaping ROH in wild populations. We combined an empirical dataset of >3000 red deer genotyped at >35,000 genome-wide autosomal SNPs and evolutionary simulations to investigate the influence of each of these factors on ROH. We assessed ROH in a focal and comparison population to investigate the effect of population history. We investigated the role of recombination using both a physical map and a genetic linkage map to search for ROH. We found differences in ROH distribution between both populations and map types indicating that population history and local recombination rate have an effect on ROH. Finally, we ran forward genetic simulations with varying population histories, recombination rates and levels of selection, allowing us to further interpret our empirical data. These simulations showed that population history has a greater effect on ROH distribution than either recombination or selection. We further show that selection can cause genomic regions where ROH is common, only when the effective population size (Ne) is large or selection is particularly strong. In populations having undergone a population bottleneck, genetic drift can outweigh the effect of selection. Overall, we conclude that in this population, genetic drift resulting from a historical population bottleneck is most likely to have resulted in the observed ROH distribution, with selection possibly playing a minor role.

Inbreeding depression and the probability of racing in the Thoroughbred horse.

Small effective population sizes and active inbreeding can lead to inbreeding depression due to deleterious recessive mutations exposed in the homozygous state. The Thoroughbred racehorse has low levels of population genetic diversity, but the effects of genomic inbreeding in the population are unknown. Here, we quantified inbreeding based on runs of homozygosity (ROH) using 297 K SNP genotypes from 6128 horses born in Europe and Australia, of which 13.2% were unraced. We show that a 10% increase in inbreeding (FROH) is associated with a 7% lower probability of ever racing. Moreover, a ROH-based genome-wide association study identified a haplotype on ECA14 which, in its homozygous state, is linked to a 32.1% lower predicted probability of ever racing, independent of FROH. The haplotype overlaps a candidate gene, EFNA5, that is highly expressed in cartilage tissue, which when damaged is one of the most common causes of catastrophic musculoskeletal injury in racehorses. Genomics-informed breeding aiming to reduce inbreeding depression and avoid damaging haplotype carrier matings will improve population health and racehorse welfare.

Mutation load decreases with haplotype age in wild Soay sheep.

Runs of homozygosity (ROH) are pervasive in diploid genomes and expose the effects of deleterious recessive mutations, but how exactly these regions contribute to variation in fitness remains unclear. Here, we combined empirical analyses and simulations to explore the deleterious effects of ROH with varying genetic map lengths in wild Soay sheep. Using a long-term dataset of 4879 individuals genotyped at 417K SNPs, we found that inbreeding depression increases with ROH length. A 1% genomic increase in long ROH (>12.5 cM) reduced the odds of first-year survival by 12.4% compared to only 7.7% for medium ROH (1.56-12.5 cM), whereas short ROH (<1.56 cM) had no effect on survival. We show by forward genetic simulations that this is predicted: compared to shorter ROH, long ROH will have higher densities of deleterious alleles, with larger average effects on fitness and lower population frequencies. Taken together, our results are consistent with the idea that the mutation load decreases in older haplotypes underlying shorter ROH, where purifying selection has had more time to purge deleterious mutations. Finally, our study demonstrates that strong inbreeding depression can persist despite ongoing purging in a historically small population.

partR2: partitioning R2 in generalized linear mixed models.

The coefficient of determination R 2 quantifies the amount of variance explained by regression coefficients in a linear model. It can be seen as the fixed-effects complement to the repeatability R (intra-class correlation) for the variance explained by random effects and thus as a tool for variance decomposition. The R 2 of a model can be further partitioned into the variance explained by a particular predictor or a combination of predictors using semi-partial (part) R 2 and structure coefficients, but this is rarely done due to a lack of software implementing these statistics. Here, we introduce partR2, an R package that quantifies part R 2 for fixed effect predictors based on (generalized) linear mixed-effect model fits. The package iteratively removes predictors of interest from the model and monitors the change in the variance of the linear predictor. The difference to the full model gives a measure of the amount of variance explained uniquely by a particular predictor or a set of predictors. partR2 also estimates structure coefficients as the correlation between a predictor and fitted values, which provide an estimate of the total contribution of a fixed effect to the overall prediction, independent of other predictors. Structure coefficients can be converted to the total variance explained by a predictor, here called 'inclusive' R 2, as the square of the structure coefficients times total R 2. Furthermore, the package reports beta weights (standardized regression coefficients). Finally, partR2 implements parametric bootstrapping to quantify confidence intervals for each estimate. We illustrate the use of partR2 with real example datasets for Gaussian and binomial GLMMs and discuss interactions, which pose a specific challenge for partitioning the explained variance among predictors.

Publisher Correction: The genetic legacy of extreme exploitation in a polar vertebrate.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

The genetic legacy of extreme exploitation in a polar vertebrate.

Understanding the effects of human exploitation on the genetic composition of wild populations is important for predicting species persistence and adaptive potential. We therefore investigated the genetic legacy of large-scale commercial harvesting by reconstructing, on a global scale, the recent demographic history of the Antarctic fur seal (Arctocephalus gazella), a species that was hunted to the brink of extinction by 18th and 19th century sealers. Molecular genetic data from over 2,000 individuals sampled from all eight major breeding locations across the species' circumpolar geographic distribution, show that at least four relict populations around Antarctica survived commercial hunting. Coalescent simulations suggest that all of these populations experienced severe bottlenecks down to effective population sizes of around 150-200. Nevertheless, comparably high levels of neutral genetic variability were retained as these declines are unlikely to have been strong enough to deplete allelic richness by more than around 15%. These findings suggest that even dramatic short-term declines need not necessarily result in major losses of diversity, and explain the apparent contradiction between the high genetic diversity of this species and its extreme exploitation history.

Early sexual dimorphism in the developing gut microbiome of northern elephant seals.

The gut microbiome is an integral part of a species' ecology, but we know little about how host characteristics impact its development in wild populations. Here, we explored the role of such intrinsic factors in shaping the gut microbiome of northern elephant seals (Mirounga angustirostris) during a critical developmental window of 6 weeks after weaning, when the pups stay ashore without feeding. We found substantial sex differences in the early-life gut microbiome, even though males and females could not yet be distinguished morphologically. Sex and age both explained around 15% of the variation in gut microbial beta diversity, while microbial communities sampled from the same individual showed high levels of similarity across time, explaining another 40% of the variation. Only a small proportion of the variation in beta diversity was explained by health status, assessed by full blood counts, but clinically healthy individuals had a greater microbial alpha diversity than their clinically abnormal peers. Across the post-weaning period, the northern elephant seal gut microbiome was highly dynamic. We found evidence for several colonization and extinction events as well as a decline in Bacteroides and an increase in Prevotella, a pattern that has previously been associated with the transition from nursing to solid food. Lastly, we show that genetic relatedness was correlated with gut microbiome similarity in males but not females, again reflecting early sex differences. Our study represents a naturally diet-controlled and longitudinal investigation of how intrinsic factors shape the early gut microbiome in a species with extreme sex differences in morphology and life history.

GCalignR: An R package for aligning gas-chromatography data for ecological and evolutionary studies.

Chemical cues are arguably the most fundamental means of animal communication and play an important role in mate choice and kin recognition. Consequently, there is growing interest in the use of gas chromatography (GC) to investigate the chemical basis of eco-evolutionary interactions. Both GC-MS (mass spectrometry) and FID (flame ionization detection) are commonly used to characterise the chemical composition of biological samples such as skin swabs. The resulting chromatograms comprise peaks that are separated according to their retention times and which represent different substances. Across chromatograms of different samples, homologous substances are expected to elute at similar retention times. However, random and often unavoidable experimental variation introduces noise, making the alignment of homologous peaks challenging, particularly with GC-FID data where mass spectral data are lacking. Here we present GCalignR, a user-friendly R package for aligning GC-FID data based on retention times. The package was developed specifically for ecological and evolutionary studies that seek to investigate similarity patterns across multiple and often highly variable biological samples, for example representing different sexes, age classes or reproductive stages. The package also implements dynamic visualisations to facilitate inspection and fine-tuning of the resulting alignments and can be integrated within a broader workflow in R to facilitate downstream multivariate analyses. We demonstrate an example workflow using empirical data from Antarctic fur seals and explore the impact of user-defined parameter values by calculating alignment error rates for multiple datasets. The resulting alignments had low error rates for most of the explored parameter space and we could also show that GCalignR performed equally well or better than other available software. We hope that GCalignR will help to simplify the processing of chemical datasets and improve the standardization and reproducibility of chemical analyses in studies of animal chemical communication and related fields.

Sex-specific early survival drives adult sex ratio bias in snowy plovers and impacts mating system and population growth.

Adult sex ratio (ASR) is a central concept in population biology and a key factor in sexual selection, but why do most demographic models ignore sex biases? Vital rates often vary between the sexes and across life history, but their relative contributions to ASR variation remain poorly understood-an essential step to evaluate sex ratio theories in the wild and inform conservation. Here, we combine structured two-sex population models with individual-based mark-recapture data from an intensively monitored polygamous population of snowy plovers. We show that a strongly male-biased ASR (0.63) is primarily driven by sex-specific survival of juveniles rather than adults or dependent offspring. This finding provides empirical support for theories of unbiased sex allocation when sex differences in survival arise after the period of parental investment. Importantly, a conventional model ignoring sex biases significantly overestimated population viability. We suggest that sex-specific population models are essential to understand the population dynamics of sexual organisms: reproduction and population growth are most sensitive to perturbations in survival of the limiting sex. Overall, our study suggests that sex-biased early survival may contribute toward mating system evolution and population persistence, with implications for both sexual selection theory and biodiversity conservation.

Chemical fingerprints encode mother-offspring similarity, colony membership, relatedness, and genetic quality in fur seals.

Chemical communication underpins virtually all aspects of vertebrate social life, yet remains poorly understood because of its highly complex mechanistic basis. We therefore used chemical fingerprinting of skin swabs and genetic analysis to explore the chemical cues that may underlie mother-offspring recognition in colonially breeding Antarctic fur seals. By sampling mother-offspring pairs from two different colonies, using a variety of statistical approaches and genotyping a large panel of microsatellite loci, we show that colony membership, mother-offspring similarity, heterozygosity, and genetic relatedness are all chemically encoded. Moreover, chemical similarity between mothers and offspring reflects a combination of genetic and environmental influences, the former partly encoded by substances resembling known pheromones. Our findings reveal the diversity of information contained within chemical fingerprints and have implications for understanding mother-offspring communication, kin recognition, and mate choice.