Lizards from warm and declining populations are born with extremely short telomeres.

Aging is the price to pay for acquiring and processing energy through cellular activity and life history productivity. Climate warming can exacerbate the inherent pace of aging, as illustrated by a faster erosion of protective telomere DNA sequences. This biomarker integrates individual pace of life and parental effects through the germline, but whether intra- and intergenerational telomere dynamics underlies population trends remains an open question. Here, we investigated the covariation between life history, telomere length (TL), and extinction risk among three age classes in a cold-adapted ectotherm (Zootoca vivipara) facing warming-induced extirpations in its distribution limits. TL followed the same threshold relationships with population extinction risk at birth, maturity, and adulthood, suggesting intergenerational accumulation of accelerated aging rate in declining populations. In dwindling populations, most neonates inherited already short telomeres, suggesting they were born physiologically old and unlikely to reach recruitment. At adulthood, TL further explained females' reproductive performance, switching from an index of individual quality in stable populations to a biomarker of reproductive costs in those close to extirpation. We compiled these results to propose the aging loop hypothesis and conceptualize how climate-driven telomere shortening in ectotherms may accumulate across generations and generate tipping points before local extirpation.

Breeding phenology drives variation in reproductive output, reproductive costs, and offspring fitness in a viviparous ectotherm.

Phenological advances are a widespread response to global warming and can contribute to determine the climate vulnerability of organisms, particularly in ectothermic species, which are highly dependent on ambient temperatures to complete their life cycle. Yet, the relative contribution of breeding dates and temperature conditions during gestation on fitness of females and their offspring is poorly documented in reptiles. Here, we exposed females of the common lizard Zootoca vivipara to contrasting thermal scenarios (cold vs. hot treatment) during gestation and quantified effects of parturition dates and thermal treatment on life-history traits of females and their offspring for 1 year. Overall, our results suggest that parturition date has a greater impact than thermal conditions during gestation on life history strategies. In particular, we found positive effects of an earlier parturition date on juvenile survival, growth, and recruitment suggesting that environmental-dependent selection and/or differences in parental quality between early and late breeders underlie seasonal changes in offspring fitness. Yet, an earlier parturition date compromised the energetic condition of gravid females, which suggests the existence of a mother-offspring conflict regarding the optimization of parturition dates. While numerous studies focused on the direct effects of alterations in incubation temperatures on reptile life-history traits, our results highlight the importance of considering the role of breeding phenology in assessing the short- and long-term effects of thermal developmental plasticity.

Predicting population genetic change in an autocorrelated random environment: Insights from a large automated experiment.

Most natural environments exhibit a substantial component of random variation, with a degree of temporal autocorrelation that defines the color of environmental noise. Such environmental fluctuations cause random fluctuations in natural selection, affecting the predictability of evolution. But despite long-standing theoretical interest in population genetics in stochastic environments, there is a dearth of empirical estimation of underlying parameters of this theory. More importantly, it is still an open question whether evolution in fluctuating environments can be predicted indirectly using simpler measures, which combine environmental time series with population estimates in constant environments. Here we address these questions by using an automated experimental evolution approach. We used a liquid-handling robot to expose over a hundred lines of the micro-alga Dunaliella salina to randomly fluctuating salinity over a continuous range, with controlled mean, variance, and autocorrelation. We then tracked the frequencies of two competing strains through amplicon sequencing of nuclear and choloroplastic barcode sequences. We show that the magnitude of environmental fluctuations (determined by their variance), but also their predictability (determined by their autocorrelation), had large impacts on the average selection coefficient. The variance in frequency change, which quantifies randomness in population genetics, was substantially higher in a fluctuating environment. The reaction norm of selection coefficients against constant salinity yielded accurate predictions for the mean selection coefficient in a fluctuating environment. This selection reaction norm was in turn well predicted by environmental tolerance curves, with population growth rate against salinity. However, both the selection reaction norm and tolerance curves underestimated the variance in selection caused by random environmental fluctuations. Overall, our results provide exceptional insights into the prospects for understanding and predicting genetic evolution in randomly fluctuating environments.

Genomic signatures of inbreeding depression for a threatened Aotearoa New Zealand passerine.

For small and isolated populations, the increased chance of mating between related individuals can result in a substantial reduction in individual and population fitness. Despite the increasing availability of genomic data to measure inbreeding accurately across the genome, inbreeding depression studies for threatened species are still scarce due to the difficulty of measuring fitness in the wild. Here, we investigate inbreeding and inbreeding depression for the extensively monitored Tiritiri Matangi island population of a threatened Aotearoa New Zealand passerine, the hihi (Notiomystis cincta). First, using a custom 45 k single nucleotide polymorphism (SNP) array, we explore genomic inbreeding patterns by inferring homozygous segments across the genome. Although all individuals have similar levels of ancient inbreeding, highly inbred individuals are affected by recent inbreeding, which can probably be explained by bottleneck effects such as habitat loss after European arrival and their translocation to the island in the 1990s. Second, we investigate genomic inbreeding effects on fitness, measured as lifetime reproductive success, and its three components, juvenile survival, adult annual survival and annual reproductive success, in 363 hihi. We find that global inbreeding significantly affects juvenile survival but none of the remaining fitness traits. Finally, we employ a genome-wide association approach to test the locus-specific effects of inbreeding on fitness, and identify 13 SNPs significantly associated with lifetime reproductive success. Our findings suggest that inbreeding depression does impact hihi, but at different genomic scales for different traits, and that purging has therefore failed to remove all variants with deleterious effects from this population of conservation concern.

Fluctuating optimum and temporally variable selection on breeding date in birds and mammals.

Temporal variation in natural selection is predicted to strongly impact the evolution and demography of natural populations, with consequences for the rate of adaptation, evolution of plasticity, and extinction risk. Most of the theory underlying these predictions assumes a moving optimum phenotype, with predictions expressed in terms of the temporal variance and autocorrelation of this optimum. However, empirical studies seldom estimate patterns of fluctuations of an optimum phenotype, precluding further progress in connecting theory with observations. To bridge this gap, we assess the evidence for temporal variation in selection on breeding date by modeling a fitness function with a fluctuating optimum, across 39 populations of 21 wild animals, one of the largest compilations of long-term datasets with individual measurements of trait and fitness components. We find compelling evidence for fluctuations in the fitness function, causing temporal variation in the magnitude, but not the direction of selection. However, fluctuations of the optimum phenotype need not directly translate into variation in selection gradients, because their impact can be buffered by partial tracking of the optimum by the mean phenotype. Analyzing individuals that reproduce in consecutive years, we find that plastic changes track movements of the optimum phenotype across years, especially in bird species, reducing temporal variation in directional selection. This suggests that phenological plasticity has evolved to cope with fluctuations in the optimum, despite their currently modest contribution to variation in selection.

Phenotypic plasticity drives phenological changes in a Mediterranean blue tit population.

Earlier phenology induced by climate change, such as the passerines' breeding time, is observed in many natural populations. Understanding the nature of such changes is key to predict the responses of wild populations to climate change. Genetic changes have been rarely investigated for laying date, though it has been shown to be heritable and under directional selection, suggesting that the trait could evolve. In a Corsican blue tit population, the birds' laying date has significantly advanced over 40 years, and we here determine whether this response is of plastic or evolutionary origin, by comparing the predictions of the breeder's and the Robertson-Price (STS) equations, to the observed genetic changes. We compare the results obtained for two fitness proxies (fledgling and recruitment success), using models accounting for their zero inflation. Because the trait appears heritable and under directional selection, the breeder's equation predicts that genetic changes could drive a significant part of the phenological change observed. We, however, found that fitness proxies and laying date are not genetically correlated. The STS, therefore, predicts no evolution of the breeding time, predicting correctly the absence of trend in breeding values. Our results also emphasize that when investigating selection on a plastic trait under fluctuating selection, part of the fitness-trait phenotypic covariance can be due to within individual covariance. In the case of repeated measurements, splitting within and between individual covariance can shift our perspective on the actual intensity of selection over multiple selection episodes, shedding light on the potential for the trait to evolve.

Variation in shape and consistency of selection between populations of the threatened Hihi (Notiomystis cincta).

The shape and intensity of natural selection can vary between years, potentially resulting in a chronic reduction of fitness as individuals need to track a continually changing optimum of fitness (i.e., a "lag load"). In endangered species, often characterized by small population size, the lack of genetic diversity is expected to limit the response to this constant need to adjust to fluctuating selection, increasing the fitness burden and thus the risk of extinction. Here, we use long-term monitoring data to assess whether the type of selection for a key fitness trait (i.e., lay date) differs between two reintroduced populations of a threatened passerine bird, the hihi (Notiomystis cincta). We apply recent statistical developments to test for the presence or absence of fluctuation in selection in both the Tiritiri Matangi Island and the Karori sanctuary populations. Our results support the presence of stabilizing selection in Tiritiri Matangi with a potential moving optimum for lay date. In Karori our results favour a regime of directional selection. Although the shape of selection may differ, for both populations an earlier lay date generally increases fitness in both environments. Further, the moving optimum models of lay date on Tiritiri Matangi, suggesting that selection varies between years, imply a substantial lag load in addition to the fitness burden caused by the population laying too late. Our results highlight the importance of characterizing the form and temporal variation of selection for each population to predict the effects of environmental change and to inform management.

Finding the adaptive needles in a population-structured haystack: A case study in a New Zealand mollusc.

Genetic adaptation to future environmental conditions is crucial to help species persist as the climate changes. Genome scans are powerful tools to understand adaptive landscapes, enabling us to correlate genetic diversity with environmental gradients while disentangling neutral from adaptive variation. However, low gene flow can lead to both local adaptation and highly structured populations, and is a major confounding factor for genome scans, resulting in an inflated number of candidate loci. Here, we compared candidate locus detection in a marine mollusc (Onithochiton neglectus), taking advantage of a natural geographical contrast in the levels of genetic structure between its populations. O. neglectus is endemic to New Zealand and distributed throughout an environmental gradient from the subtropical north to the subantarctic south. Due to a brooding developmental mode, populations tend to be locally isolated. However, adult hitchhiking on rafting kelp increases connectivity among southern populations. We applied two genome scans for outliers (Bayescan and PCAdapt) and two genotype-environment association (GEA) tests (BayeScEnv and RDA). To limit issues with false positives, we combined results using the geometric mean of q-values and performed association tests with random environmental variables. This novel approach is a compromise between stringent and relaxed approaches widely used before, and allowed us to classify candidate loci as low confidence or high confidence. Genome scans for outliers detected a large number of significant outliers in strong and moderately structured populations. No high-confidence GEA loci were detected in the context of strong population structure. However, 86 high-confidence loci were associated predominantly with latitudinally varying abiotic factors in the less structured southern populations. This suggests that the degree of connectivity driven by kelp rafting over the southern scale may be insufficient to counteract local adaptation in this species. Our study supports the expectation that genome scans may be prone to errors in highly structured populations. Nonetheless, it also empirically demonstrates that careful statistical controls enable the identification of candidate loci that invite more detailed investigations. Ultimately, genome scans are valuable tools to help guide further research aiming to determine the potential of non-model species to adapt to future environments.

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.

Polygenic basis for adaptive morphological variation in a threatened Aotearoa | New Zealand bird, the hihi (Notiomystis cincta).

To predict if a threatened species can adapt to changing selective pressures, it is crucial to understand the genetic basis of adaptive traits, especially in species historically affected by severe bottlenecks. We estimated the heritability of three hihi (Notiomystis cincta) morphological traits known to be under selection (nestling tarsus length, body mass and head-bill length) using 523 individuals and 39 699 single nucleotide polymorphisms (SNPs) from a 50 K Affymetrix SNP chip. We then examined the genetic architecture of the traits via chromosome partitioning analyses and genome-wide association scans (GWAS). Heritabilities estimated using pedigree relatedness or genomic relatedness were low. For tarsus length, the proportion of genetic variance explained by each chromosome was positively correlated with its size, and more than one chromosome explained significant variation for body mass and head-bill length. Finally, GWAS analyses suggested many loci of small effect contributing to trait variation for all three traits, although one locus (an SNP within an intron of the transcription factor HEY2) was tentatively associated with tarsus length. Our findings suggest a polygenic nature for the morphological traits, with many small effect size loci contributing to the majority of the variation, similar to results from many other wild populations. However, the small effective population size, polygenic architecture and already low heritabilities suggest that both the total response and rate of response to selection are likely to be limited in hihi.

A General Method for Simultaneously Accounting for Phylogenetic and Species Sampling Uncertainty via Rubin's Rules in Comparative Analysis.

Phylogenetic comparative methods (PCMs), especially ones based on linear models, have played a central role in understanding species' trait evolution. These methods, however, usually assume that phylogenetic trees are known without error or uncertainty, but this assumption is most likely incorrect. So far, Markov chain Monte Carlo (MCMC)-based Bayesian methods have mainly been deployed to account for such "phylogenetic uncertainty" in PCMs. Herein, we propose an approach with which phylogenetic uncertainty is incorporated in a simple, readily implementable and reliable manner. Our approach uses Rubin's rules, which are an integral part of a standard multiple imputation procedure, often employed to recover missing data. We see true phylogenetic trees as missing data under this approach. Further, unmeasured species in comparative data (i.e., missing trait data) can be seen as another source of uncertainty in PCMs because arbitrary sampling of species in a given taxon or "species sampling uncertainty" can affect estimation in PCMs. Using two simulation studies, we show our method can account for phylogenetic uncertainty under many different scenarios (e.g., uncertainty in topology and branch lengths) and, at the same time, it can handle missing trait data (i.e., species sampling uncertainty). A unique property of the multiple imputation procedure is that an index, named "relative efficiency," could be used to quantify the number of trees required for incorporating phylogenetic uncertainty. Thus, by using the relative efficiency, we show the required tree number is surprisingly small ($\sim$50 trees). However, the most notable advantage of our method is that it could be combined seamlessly with PCMs that utilize multiple imputation to handle simultaneously phylogenetic uncertainty (i.e., missing true trees) and species sampling uncertainty (i.e., missing trait data) in PCMs.

Dispersal evolution and resource matching in a spatially and temporally variable environment.

Metapopulations may consist of patches of different quality, and are often disturbed by extrinsic processes causing variation of patch quality. The persistence of such metapopulations then depends on the species' dispersal strategy. In a temporally constant environment, the evolution of dispersal rates follows the resource matching rule, i.e. at the evolutionarily stable dispersal strategy the number of competitors in each patch matches the resource availability in each patch. Here, we investigate how the distribution of individuals resulting from convergence stable dispersal strategies would match the distribution of resources in an environment which is temporally variable due to extrinsic disturbance. We develop an analytically tractable asexual model with two qualities of patches. We show that convergence stable dispersal rates are such that resource matching is predicted in expectation before habitat quality variation, and that the distribution of individuals undermatches resources after habitat quality variation. The overall flow of individuals between patches matches the overall flow of resources between patches resulting from environmental variation. We show that these conclusions can be generalized to organisms with sexual reproduction, and to a metapopulation with three qualities of patches when there is no mutational correlation between dispersal rates.

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.

Local adaptation in shell shape traits of a brooding chiton with strong population genomic differentiation.

Comparing divergence in quantitative traits and neutral molecular markers, such as QST-FST comparisons, provides a means to distinguish between natural selection and genetic drift as causes of population differentiation in complex polygenic traits. Onithochiton neglectus (Rochebrune, 1881) is a morphologically variable chiton endemic to New Zealand, with populations distributed over a broad latitudinal environmental gradient. In this species, the morphological variants cluster into 2 geographically separated shell shape groups, and the phenotypic variation in shell shape has been hypothesized to be adaptive. Here, we assessed this hypothesis by comparing neutral genomic differentiation between populations (FST) with an index of phenotypic differentiation (PST). We used 7,562 putatively neutral single-nucleotide polymorphisms (SNPs) across 15 populations and 3 clades of O. neglectus throughout New Zealand to infer FST. PST was calculated from 18 shell shape traits and gave highly variable estimates across populations, clades, and shape groups. By systematically comparing PST with FST, we identified evidence of local adaptation in a number of the O. neglectus shell shape traits. This supports the hypothesis that shell shape could be an adaptive trait, potentially correlated with the ability to live and raft in kelp holdfasts.

Shared Heritability of Blood Pressure and Pulse Wave Velocity: Insights From the STANISLAS Cohort.

BACKGROUND: Pulse wave velocity (PWV) is a marker of arterial stiffness, which is intrinsically highly correlated with blood pressure (BP). However, the interplay of PWV and BP heritability has not been extensively studied. This study aimed to estimate the heritability of PWV and BP and determine the genetic correlation between PWV and BP. METHODS: The heritability of PWV and BP was estimated in 1080 subjects from the STANISLAS (Suivi Temporaire Annuel Non-Invasif de la Santé des Lorrains Assurés Sociaux) cohort with at least one relative using a linear mixed model within one frequentist and one Bayesian framework implemented, respectively, in the Gaston and MCMCglmm R packages. Then their genetic correlations were also estimated. RESULTS: The heritability estimations for PWV were within the same range of the heritability of systolic BP and diastolic BP (23%, 19%, and 27%, respectively). Daytime heritability of BP was higher than nighttime BP. In addition, phenotypic correlations between PWV and systolic BP/diastolic BP were, respectively, 0.34 and 0.23, whereas nonsignificant genetic correlations were 0.08 and 0.22 respectively, indicating that PWV and diastolic BP shared more polygenic codeterminants than PWV and systolic BP. CONCLUSIONS: Our results suggest that the heritability of PWV is >20% and within the same range as BP heritability. It also suggests that the link between PWV and BP goes beyond phenotypic association: PWV and BP (in particular diastolic BP) share common genetic determinants. This genetic interdependence of PWV and BP appears largely polygenic.

Bayesian models for comparative analysis integrating phylogenetic uncertainty.

BACKGROUND: Uncertainty in comparative analyses can come from at least two sources: a) phylogenetic uncertainty in the tree topology or branch lengths, and b) uncertainty due to intraspecific variation in trait values, either due to measurement error or natural individual variation. Most phylogenetic comparative methods do not account for such uncertainties. Not accounting for these sources of uncertainty leads to false perceptions of precision (confidence intervals will be too narrow) and inflated significance in hypothesis testing (e.g. p-values will be too small). Although there is some application-specific software for fitting Bayesian models accounting for phylogenetic error, more general and flexible software is desirable. METHODS: We developed models to directly incorporate phylogenetic uncertainty into a range of analyses that biologists commonly perform, using a Bayesian framework and Markov Chain Monte Carlo analyses. RESULTS: We demonstrate applications in linear regression, quantification of phylogenetic signal, and measurement error models. Phylogenetic uncertainty was incorporated by applying a prior distribution for the phylogeny, where this distribution consisted of the posterior tree sets from Bayesian phylogenetic tree estimation programs. The models were analysed using simulated data sets, and applied to a real data set on plant traits, from rainforest plant species in Northern Australia. Analyses were performed using the free and open source software OpenBUGS and JAGS. CONCLUSIONS: Incorporating phylogenetic uncertainty through an empirical prior distribution of trees leads to more precise estimation of regression model parameters than using a single consensus tree and enables a more realistic estimation of confidence intervals. In addition, models incorporating measurement errors and/or individual variation, in one or both variables, are easily formulated in the Bayesian framework. We show that BUGS is a useful, flexible general purpose tool for phylogenetic comparative analyses, particularly for modelling in the face of phylogenetic uncertainty and accounting for measurement error or individual variation in explanatory variables. Code for all models is provided in the BUGS model description language.

Genetic variance in fitness indicates rapid contemporary adaptive evolution in wild animals.

The rate of adaptive evolution, the contribution of selection to genetic changes that increase mean fitness, is determined by the additive genetic variance in individual relative fitness. To date, there are few robust estimates of this parameter for natural populations, and it is therefore unclear whether adaptive evolution can play a meaningful role in short-term population dynamics. We developed and applied quantitative genetic methods to long-term datasets from 19 wild bird and mammal populations and found that, while estimates vary between populations, additive genetic variance in relative fitness is often substantial and, on average, twice that of previous estimates. We show that these rates of contemporary adaptive evolution can affect population dynamics and hence that natural selection has the potential to partly mitigate effects of current environmental change.

Genetic Variations and Differential DNA Methylation to Face Contrasted Climates in Small Ruminants: An Analysis on Traditionally-Managed Sheep and Goats.

The way in which living organisms mobilize a combination of long-term adaptive mechanisms and short-term phenotypic plasticity to face environmental variations is still largely unknown. In the context of climate change, understanding the genetic and epigenetic bases for adaptation and plasticity is a major stake for preserving genomic resources and the resilience capacity of livestock populations. We characterized both epigenetic and genetic variations by contrasting 22 sheep and 21 goats from both sides of a climate gradient, focusing on free-ranging populations from Morocco. We produced for each individual Whole-Genome Sequence at 12X coverage and MeDIP-Seq data, to identify regions under selection and those differentially methylated. For both species, the analysis of genetic differences (FST) along the genome between animals from localities with high vs. low temperature annual variations detected candidate genes under selection in relation to environmental perception (5 genes), immunity (4 genes), reproduction (8 genes) and production (11 genes). Moreover, we found for each species one differentially methylated gene, namely AGPTA4 in goat and SLIT3 in sheep, which were both related, among other functions, to milk production and muscle development. In both sheep and goats, the comparison between genomic regions impacted by genetic and epigenetic variations suggests that climatic variations impacted similar biological pathways but different genes.