The evolutionary forces shaping life history divergence within species are largely unknown. Turquoise killifish display differences in lifespan among wild populations, representing an ideal natural experiment in evolution and diversification of life history. By combining genome sequencing and population genetics, we investigate the evolutionary forces shaping lifespan among wild turquoise killifish populations. We generate an improved reference genome assembly and identify genes under positive and purifying selection, as well as those evolving neutrally. Short-lived populations from the outer margin of the species range have small population size and accumulate deleterious mutations in genes significantly enriched in the WNT signaling pathway, neurodegeneration, cancer and the mTOR pathway. We propose that limited population size due to habitat fragmentation and repeated population bottlenecks, by increasing the genome-wide mutation load, exacerbates the effects of mutation accumulation and cumulatively contribute to the short adult lifespan. The extent to which drift and selection shape life history trait evolution across species in nature is a fundamental question in evolutionary biology. Variations in population size among natural populations is expected to affect the rate of accumulation of advantageous and slightly deleterious gene variants, hence impacting the relative contribution of selection and drift to genetic polymorphisms . PopulatAmong vertebrates, killifish represent a unique system, as they repeatedly and independently colonized highly fragmented habitats, characterized by cycles of rainfalls and drought . While oNothobranchius furzeri) is the shortest-lived vertebrate with a thoroughly documented post-embryonic life, which, in the shortest-lived strains, amounts to four months , we combined the currently available reference genomes  is 1.003 Gb, accounting for 66% of the entire genome, that is 20% higher than a previous estimate  based on 50kb-non-overlapping sliding windows using PoPoolation . Hence, dry populations have overall smaller genetic diversity than populations from less dry regions. To infer the genetic distance between the populations, we computed the genome-wide pairwise genetic differentiation between populations using FST  and genetic diversity  of the populations, we used the published mutational rate of 2.6321e−9 per base pair per generation for Nothobranchius, computed via dated phylogeny and θWatterson  by resequencing at high-coverage single individuals for each population . The popatterson . In lineTo test whether regions underlying longevity QTL in turquoise killifish  display ST). To find highly differentiated regions that may underlie positive selection in natural turquoise killifish populations, we scanned for regions with elevated genetic differentiation between pairs of populations, that is exceeding the 0.995 quantile of Z-transformed non-overlapping 50 kb sliding windows of FST. To find regions under purifying selection, we scanned for regions with lowered genetic differentiation among populations, that is below the 0.005 quantile of Z-transformed non-overlapping 50 kb sliding windows of FST . The region on chromosome 10 contains four genes: XM_015941868, XM_015941869, lss and hibch. All genes under the major FST peak on chromosome 10 showed significant difference in allele frequencies between the intermediate and wet population  and additionally, hibch had significantly different allele frequencies between the dry and wet population .We then analyzed genomic regions carrying signatures of positive and purifying selection in the natural turquoise killifish populations irrespective of the QTL regions . The FSTulations  and onlyulations . Genes sST peaks between populations that differ in lifespan, are not necessarily causally involved in lifespan differences between populations, as sequence differences could segregate in populations due to population structure and drift. However, to test whether the genes located in genomic regions that are significantly divergent between populations could be functionally involved in age-related phenotypes, we investigated whether gene expression in these genes varied as a function of age. Analyzing available turquoise killifish longitudinal RNA-Seq datasets generated in liver, brain and skin . XM_015956265 encodes a transcriptional modulator with ubiquitous functions, ranging from activation of dopamine transporter to repression of myc, fmr1 and thymidine kinase promoters .Genes under Fand skin , we fountabolism , biosynttabolism , and protabolism , respectromoters . Howevergdf6 , cnot11 and lcp1, which are involved in phagocytosis  using the asymptotic McDonald-Kreitman α  as an outgroup, we inferred the fraction of positive selection by pooling all coding sites , we confirmed the lowest asymptotic McDonald-Kreitman α value in the dry population GNP (Nothobranchius rachovii (NRC) as outgroup species, the asymptotic McDonald-Kreitman α ranged from −0.06 to 0.23 among populations, indicating that more alleles were driven to fixation by positive selection in the ancestral lineage leading to Nothobranchius furzeri and Nothobranchius orthonotus. In particular, the wet population NF303 had the highest asymptotic McDonald-Kreitman α value   of new mN. orthonotus and N. rachovii . Upon exclusion of potential mutations at neighboring sites (CMD: codons with multiple differences), CpG hypermutation and genes containing mutations with highly detrimental effect on protein function based on SnpEFF analysis, the dry population GNP had higher mean Consurf score at the low frequency bin , indicating that purifying selection is relaxed in GNP. We calculated DoS in all populations using independently as outgroup species N. orthonotus and N. rachovii   index, wrachovii .th DoS quantile (i.e. genes under relaxation of selection) in the GNP population for pathways associated with age-related diseases, including gastric cancer, breast cancer, neurodegenerative disease, mTOR signaling and WNT signaling  is the shortest-lived known vertebrate and while its natural populations show similar timing for sexual maturation, exhibit differences in lifespan along a cline of altitude and aridity in south-eastern Africa (Nothobranchius furzeri) and study the evolutionary forces shaping genome evolution among natural populations.The turquoise killifish . Blood was drawn from the heart and high molecular weight DNA was isolated with Qiagen MagAttract kit following manufacturer’s instructions. Gemcode v2 DNA library generation was performed by Novogene . Briefly, a proportion of the sample was run on a pulse field agarose gel to confirm high molecularity >100 kb. Based on a genome size estimate of 1.54 Gb , 0.6 ng of DNA was used to construct 2 Gemcode libraries, sequenced on two HiSeq X lanes to obtain a raw coverage of approximately 60X each. The reported input molecular length by SuperNova  was 118 DNA was extracted from a single GRZ male individual’s muscle tissue by grinding in liquid nitrogen followed by phenol-chloroform extraction (Sigma). The rapid sequencing kit (SQK-RAD004) and the ligation kit (SQK-LSK108) were sued to prepare six libraries and were sequenced on 6 MinION flow cells (R9.4.1). These runs yielded a total of 3.3 Gb of sequences after trimming and correction by HALC . For corNothobranchius furzeri. Allpath-LG (https://github.com/ksahlin/BESST_RNA) was used to further scaffold the assembly with RNASeq libraries, Allmaps was again used to assign the fixed scaffolds back to linkage groups, increasing the assignable bases to 92.2% (879Mb) with 80.3% (765Mb) with determined orientation. The assembly was again broken with longranger and reassigned to LG with Allmaps, and the scaffolds were further partitioned to linkage groups due to linkage of some left-over scaffolds with an assigned scaffold. Each partitioned scaffold groups were subjected to the ARCS + LINKS pipeline again, to constraint the previously unassigned scaffolds onto the same linkage group. Allmaps was run again on the improved scaffolds, resulting in 94.5% (903.4Mb) of bases assigned and 89.1% (852Mb) of bases oriented. Longranger was run again, visually checked and compared with the RADtag markers. Eleven mis-oriented positions were identified and corrected. Gaps were further patched by GMCloser  were downloaded from GenBank , and aliNothobranchius orthonotus and Nothobranchius rachovii were generated from sequencing data and the same method used in http://broadinstitute.github.io/picard/) package. Reads were realigned around INDELs with the IndelRealigner tool in GATK v3.4.46 (The pseudogenomes for  v3.4.46 . Variant v3.4.46  mpileup N. furzeri (NFZ v2.0). Subsequently, the QTL marker sequences were mapped to the database. Only markers with full support for the total length of 95 bp were considered as QTL markers.The quantitative trait loci (QTL) markers published in Xiphophorus maculatus and Oryzias latipes. Result were visualized using Circos (genoPlotR package (Xiphophorus maculatus and Oryzias latipes were generated with Synteny DB (http://syntenydb.uoregon.edu)  .The Koeppen-Geiger classification data was taken from The ethanol preserved fin tissue was washed with 1X PBS before extraction. Fin tissue was digested with 10 µg/mL Proteinase K (Thermo Fisher) in 10 mM TRIS pH 8; 10 mM EDTA; 0.5 SDS at 50°C overnight. DNA was extracted with phenol-chloroform-isoamylalcohol (Sigma) followed by a washing step with chloroform (Sigma). Next, DNA was precipitated by adding 2.5 vol of chilled 100% ethanol and 0.26 vol of 7.5M Ammonium Acetate (Sigma) at −20°C overnight. DNA was collected via centrifugation at 4°C at 12000 rpm for 20 min. After a final washing step with 70% ice-cold ethanol and air drying, DNA was eluted in 30 µl of nuclease-free water. DNA quality was checked on one agarose gels stained with RotiSafe (Roth) and a UV-VIS spectrometer . DNA concentration was measured with Qubit fluorometer . For each population, the DNA of the individuals were pooled at equimolar contribution . DNA pools were given to the Cologne Center of Genomic  for library preparation. The total amount of DNA provided to the sequencing facility was 3.2 μg per pooled population sample. Libraries were sequenced with 150 bp x two paired-ends on the HiSeq4000. Sequencing of pooled samples resulted in a range of 419–517 million paired-end reads for each population .https://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Trimmed reads were subsequently mapped to the reference genome with BWA-MEM v0.7.12 (http://broadinstitute.github.io/picard/) and GATK . Data f v0.7.12 . The SAM v0.7.12 . Filteriand GATK . Briefly package  and werel script . Coding ectively .Population GNP consists of two sampling sites  with very low genetic differentiation . SequencVariance-sliding.pl script from the PoPoolation program , the ploidy  and the mutational rate μ (θ = 2peNμ). Therefore, eN can be obtained when θ, the ploidy and the mutational rate μ are known. The turquoise killifish is a diploid organism with a mutational rate of 2.6321e−nine per base pair per generation  in a genome-wide approach using non-overlapping sliding windows of 50 kb with a minimum count of four per SNP, a minimum coverage of 20, a maximum coverage of 94 for GNP, 77 for NF414, and 70 for NF303 and the corresponding pool size of each population . Each sliding window had to be at least covered to 30% to be included in the estimation. The same thresholds, except the minimum covered fraction, with different sliding window sizes were used to calculate the gene-wise FST for the complete gene body  and single SNPs within genes . The non-informative positions were excluded from the output. Significance of allele differences per base-pair within the gene-coordinates were calculated with the fisher´s exact test implemented in the fisher-test.pl script of PoPoolation2 . Next, regions potentially under strong selection were identified by applying an outlier approach. Outliers were identified as non-overlapping windows of 50 kb within the 0.5% of lowest and highest genetic differentiation per comparison. To reduce the number of false-positive results, the outlier threshold was chosen at 0.5% highest and lowest percentile of each pairwise genetic differentiation .filter-pileup-by-gtf.pl script of PoPoolation. For coding sequences, the --keep-mode was set to retain all coding sequences. The non-coding sequences were obtained by using the default option and thus discarding the coding sequences from the MPILEUP file. Snape produces a posterior probability of segregation for each position. The posterior probability of segregation was used to filter low-confidence SNPs and indicated in the specific section.SNP calling was performed with Snape . The proNothobranchius orthonotus and Nothobranchius rachovii. For each population the genomic information of the respective pseudogenome was extracted with bedtools getfasta command (Polarization of synonymous sites (four-fold degenerated sites) and non-synonymous sites (zero-fold degenerated sites) was done using the pseudogenomes of outgroups  command  and the The rate of substitutions that were driven to fixation by positive selection was evaluated with an improved method based on the McDonald-Kreitman test . The tesTo further investigate the signature of selection, the direction of selection (DoS) index for every gene was calculated . DoS staNothobranchius orthonotus). PolyDFE2.0 estimates either the full DFE, containing deleterious, neutral and beneficial mutations, or only the deleterious DFE. The best model for each population was obtained using a model testing approach with three different models implemented in PolyDFE2.0 . Due to possible biases from erroneous polarization or unknown demography, runs accounting for polarization errors and demography  were included. Initial parameters were automatically estimated with the –e option, as recommended. To ensure that the parameter space is explored thoroughly, the basin hopping option was applied with a maximum of 500 iterations (-b). The best model for each population was chosen based on the Akaike Information Criterion (AIC). Confidence intervals were generated by running 200 bootstrap datasets with the same parameters used to infer the best model.The distribution of fitness effects (DFE) was inferred using the program polyDFE2.0 . For this, a measure of relative fitness, drawn from the same reflected gamma distribution (mean of −2.5 and shape of 0.35) and a dominance coefficient of 0.1. All models start with an initial burn-in phase of 50000 generations to generate genetic diversity and a stable population. The models vary in either having immediate population size changes followed by constant population size  or having exponential growth . We further distinguished between a split directly after the burn-in phase  or at a later timepoint, following the PSMC’ analysis interpretation . The scripts are available on https://github.com/valenzano-lab/simulation_DFE. We performed 25 replicates per model and used a sample size of 30 diploid individuals to retrieve the fixed and segregating mutations. The synonymous and non-synonymous site-frequency-spectra were built by projecting the retrieved mutations into 10 chromosomes, comparable to the site-frequency-spectra generated for the observed data of the turquoise killifish populations and subsequently used as the input for PolyDFE2.0. Model choice of the negative distribution of fitness effects was performed similar to the initial analysis as described previously, except we did not include the ancestral misidentification error.We performed simulations with SLiM (version 3.3) , using dNothobranchius furzeri (NFZ v2.0) was implemented to the SnpEFF pipeline. Subsequently, a database for variant annotation with the genome NFZ v2.0 FASTA file and the annotation GTF file was generated. For variant annotation the population specific synonymous and non-synonymous sites with a change in respect to the reference genome NFZ v2.0 were used to infer the impact of these sites. The possible annotation impact classes were low, moderate, and high. SNPs with a frequency below 5% or above 95% were excluded for this analysis. To be consistent with the analysis of the distribution of fitness effects, only positions also found to be present in the N. orthonotus pseudogenome were considered. Positions with warnings in the variant annotation were removed.Classification of changes in the coding-sequence (CDS) was done with the variant annotator SnpEFF . The newN. furzeri and synonymous and non-synonymous genomic positions were matched with the derived allele frequency of N. orthonotus and N. rachovii, respectively. The derived frequencies were binned in five bins and we used pairwise Wilcoxon rank sum test to assess significance after correcting for multiple testing (Benjamini and Hochberg adjustment) between each subsequent bin per population and matching bins between populations.The Consurf score was calculated accordingly to the method used in http://cpdb.molgen.mpg.de; version34)  and pathway overrepresentation analysis was performed with the online tool ConsensusPathDB (rsion34)  using ‘Krsion34) , if avaiStatistical analyses were performed using R studio version 1.0.136 (R version 3.3.2 ) on a lohttps://github.com/stschiff/msmc/blob/master/guide.md) was followed; VCF-files and masked files were generated with the bamCaller.py script (MSMC-tools package). This step requires the chromosome coverage information to mask regions with too low or too high coverage. As recommended in the guidelines, the average coverage per chromosome was calculated using SAMTOOLS. In addition, this step was performed using a coverage threshold of 18 as recommended by generate_multihetsep.py script (MSMC-tools package). Subsequently, for each sample PSMC’ was run independently. Bootstrapping was performed for 30 samples per individual and input files were generated with the multihetsep_bootstrap.py script (MSMCtools package).To infer the demographic history, we performed whole genome re-sequencing of single individuals from all populations resulting in mean genome coverage between 13-21x . DemograNothobranchius furzeri As the editors have judged that your manuscript is of interest, but as described below that additional experiments are required before it is published, we would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv  along with this decision letter and a formal designation that the manuscript is 'in revision at In addition to a point by point rebuttal, in particular please respond to the following points:1) The major conclusions are based on too narrow an interpretation of the senescence literature. They have not considered mutation accumulation as a basis for their results. They need to do so and to adjust the rest of the paper accordingly or at least need to explain to me why they did not include it.2) The authors should explain the inferred DFEs and show that the DFE inference method to produce reasonable results on simulated data with a similar demographic history to what they infer from the killifish data.“There is also concern about the DFE study, since, if I understand Figure 4D correctly, the wetter habitat populations have a higher proportion of strongly deleterious SNPs than the GNP (dry) population. Does this go against the argument presented in the paper?”Reviewer #1:In their manuscript, the authors investigate the evolutionary forces shaping lifespan in killifish populations. They produced an improved genome assembly and annotation and generate population genetic data for 3 populations of the species. By contrasting the relative wetness of the habitats and using an array of population genetic analyses, they make statements about their effective population sizes, loci with signatures of positive selection (extreme differentiation), and contrasts in their distributions of fitness effects (DFEs). They conclude that the QTL loci previously linked to differences in lifespan did not show signs of positive selection and that genetic drift has allowed a relatively large proportion of slightly deleterious polymorphisms to persist in the smaller population with the most arid habitat.Generally, I found the study interesting and the choice of system an important one since this model is being used for longevity research. The conclusion that lifespan differences between populations is driven mainly by drift has implications for how results from this model are interpreted in the future. There are a few major concerns which I believe are related to this conclusion. They are detailed below.1) The study ultimately cannot comment on the initial ancestral evolution of shorter time to reproductive maturity or lifespan because it is analyzing differences between populations, all of which already have short lifespans and time to maturity. This is not a critical concern however, and the authors seems to be aware of this, but it should be stated upfront so readers appreciate the distinction. It remains a possibility that the species-wide trait of short lifespan and time to reproductive maturity arose through selection in response to the extreme seasonality of their habitat.2) It is not entirely clear whether the populations studied here are the same as those in which the lifespan QTL were discovered. What are the genetic and geographic distances between these populations and those studied by Valenzano et al.If the populations are substantially different, then the finding of no evidence of positive selection at the QTL is limited, since lifespan associated QTL in these populations could be entirely different. This is an important concern to address.3) The reader would benefit from explanations about the expectations for the α statistic, since many will not have encountered it before. What are expectations under relevant scenarios? What do values of 0, 1, and negative really mean? How robust are such inferences to demographics and other confounders?Because conclusions of that paper are somewhat reliant on these values and related tests about the DFEs and DoS, it is important to spell this out and to understand their interpretation, strengths and weaknesses.4) Each of the population genetic tests could be affected by demographic changes. The authors should address this possibility head on and explain how the method or its interpretation is or is not affected by the scenarios they inferred in Figure 1A.5) The DFE analysis in Figure 4D seems to suggest a very high proportion of highly deleterious SNPs. ~60% of SNPs with a value of 4Nes < -100. This value seems high. Wouldn't we expect most segregating SNPs to be nearly neutral?Could the authors explain? Is this high proportion of highly deleterious SNPs due to SNPs at very low frequency?6) Given point 5 above, the wet population has a much higher proportion of highly deleterious SNPs than the arid population. Couldn't it be argued that the wet population is suffering poorer genetic health, since it has a higher proportion of highly damaging SNPs?The Consurf analysis seems more convincing to me than the DFE analysis.Reviewer #2:Nothobranchus fuerzi. These fish live in ponds that are dry for large portions of every year. They are able to persist because they lay eggs that are desiccation resistant, but also because the adults have very rapid development, enabling them to mature quickly and reproduce during the brief times that the ponds hold water. The adults also age rapidly and have short life expectancies. The different populations represented in the study come from ponds distributed along a gradient of annual rainfall, so some ponds are destined to retain water for much shorter intervals of time than others. The fish from ponds which are expected to have short durations do not have faster development in the laboratory, which is surprising, yet they do have faster aging and shorter life expectancies. This paper is one of a series in which the authors argue that the real reason these fish have shorter life expectancies is that these ponds of smaller effective population sizes and hence are more susceptible to genetic drift.The authors present a diversity of analyses of patterns of genetic variation among populations of the annual killifish In offering this review, I must also acknowledge my background and limitations. I am familiar with the biology of these fish, with the empirical study of aging and with the literature on the evolution of aging. I also have a good background in evolutionary biology. I am not a practitioner of the sorts of genetic methods employed in this study and am less able to judge them.Life. Second, I feel that there are critical deficiencies in the science.I have two categories of concern about this paper. The first is that I feel it is poorly written and inappropriately written for the general audience targeted by a journal like ePresentation: This work is actually a test of alternative hypotheses, yet the authors never clearly state this. They instead present an argument in favor of one of the two hypotheses while the contrast between the two is implicit. The two hypotheses are: 1) the differences in life expectancy among the various populations are a consequence of how lifespan evolved in each of them, 2) the shorter lifespans of fish from ponds of shorter duration are a by-product of genetic drift.Hypothesis 1: There are diverse hypotheses for the evolution of lifespan and aging, but I will only address three of them here. The two that date to the 1950's are "antagonistic pleiotropy"  and "mutation accumulation" . Medawar's hypothesis is relevant here, since he predicts that species/populations that experience shorter life expectancy will more readily accumulate deleterious mutations that act late in life because natural selection (purifying selection) will weaken with age as a consequence of the small number of individuals that survive to advanced ages.In supporting their argument for the importance of genetic drift, the authors show an accumulation of genetic variation in the population that presumably has reduced life expectancy plus they present evidence that this variation is likely to be deleterious. A problem I see with their argument is that mutation accumulation makes the same prediction, save for the restriction that the deleterious mutations be ones that are age-specific in their effects. The fact is that they do not know whether or not the variation they characterize is age-specific in its expression. I cannot see how they can discriminate between deleterious mutations that accumulate because of genetic drift versus those that accumulate because of mutation accumulation. The only compelling argument I do see is their evidence for effective population size, but the low end of the estimated population size for the more rapidly aging population is in the vicinity of 20,000, which is not that small and not by itself likely to generate severe genetic drift. Furthermore, this population has been declining in size over time. My understanding of the expected trend for long term declines in population size  is that there will be more genetic variation than expected, given the current population size.N. fuerzi. The study is good in terms of the spectrum of life history variables they assess. A noteworthy feature of the results for N. fuerzi is the differences among populations in lifespan in a controlled laboratory environment  without any clear evidence of a tradeoff in other aspects of the life history . In fact, the short-lived population did tend to invest more in eggs early in life, but the difference was relatively small. As an aside, they put some effort into addressing a third hypothesis for the evolution of aging, which is condition-dependent selection . I think that condition dependence is an unlikely mechanism in this system. It can be effective if short life expectancy is due to predation, for example, because adaptation to predation can indirectly affect aging. When a pool dries, there is no option for adaptation in a fish – they all die.A second implicit argument for genetic drift is the results of their 2017 paper in Evolution in which they quantify other aspects of the life histories of multiple populations of this species. This paper is a lab study of the life histories of multiple populations of a number of different species of annual killifish, one of which is We can agree that there is not strong support for any form of antagonistic pleiotropy revealed in the 2017 paper. The best shot is a slightly higher level of reproductive investment in the population from a shorter-lived pond, which would balance against reduced lifespan. I would not feel tempted to make this argument, but I would advise not banking too heavily on this study fully eliminating antagonistic pleiotropy as a source of differences among populations in lifespan. I would be inclined to first take another shot at this approach.In summary, they need to be explicit about the alternative hypotheses and need to address the possible role of mutation accumulation. Note that there is empirical evidence in favor of a contribution of mutation accumulation to aging. In many other regards, their presentation is not one that will readily appeal to a general audience. I found their figures to be particularly difficult to interpret.Science: I have confounded presentation with science a bit since my argument about mutation accumulation applies to the scientific aspects of the presentation, but there are other issues as well. I summarize some of these in the following comments on specific portions of the manuscript.Other comments:Subsection “Population genetics of natural turquoise killifish populations”: If they are working with lab lines, then how can we know whether any genetic drift associated with the different lines represents differences among natural populations vs. drift that occurred during prolonged laboratory culture. The effective size of the laboratory lines and their duration in captivity matter. If the genomes were just from wild-caught fish then this is not an issue, but it could apply to the results reported in the 2017 Evolution paper.Another issue is to better illustrate the climatic differences among the study populations. I can see that they fall at different places in a rainfall gradient, but it would be better to give a more concrete comparison, like the duration of holding water. Are any such comparisons available?ST values? I had thought that various forms of balancing selection can yield the same results that you attribute to purifying selection .Subsection “Genetic differentiation among turquoise killifish populations”: Are positive natural selection and purifying selection the only interpretations for the FBut, there are very good reasons why there could be portions of the genome under selection that were not revealed by your QTL study. Consider adding a power analysis of these results if you really want to make this argument; e.g., how big could an effect have been yet not show up as significant? What if the evolution of aging and lifespan were polygenic, with each gene making a small contribution? Positive results from QTL can be very informative, but I do not thing the reverse is true.This section is a bit deceptive in presentation. These regions are all ones that were not associated with any QTL for lifespan, correct? This means that you are looking for age-specific differences in expression in the absence of any affiliation with lifespan, save one exception. I understand the bottom line, which is that you will suggest that the absence of any positive evidence for a genetic basis in lifespan the alternative of genetic drift becomes more plausible. You can do a much better job of making these alternatives more explicit. Delete subsection “Evolutionary origin of the sex chromosome”. It has nothing to do with the central theme of the paper, so it is an unnecessary digression. It also impressed me as a topic that could be expanded on and published on its own.Subsection “Relaxed selection in turquoise killifish populations”: I understand the goal of this section of the paper, which is to make a compelling case for genetic drift as the cause of differences among populations in lifespan. This is the part of the paper that I am not well qualified to judge so I hope other reviewers can do better. But, I can at least question whether or not mutation accumulation could be a plausible alternative, since I believe it is consistent with the patterns reported here. If it is not, then you need to take it on then explain why not.Subsection “Relaxation of selection in age-related disease pathways”: My reaction to this section is the same as the last section. I am limited in my ability to evaluate this, but I also question whether mutation accumulation represents a viable alternative explanation.Discussion paragraph two: Eliminate these lines and save them for a later paper on the sex chromosome.Discussion: I think that the Discussion must be redone in concert with the Introduction. The Introduction should formally present the alternative explanations for differences among populations in aging – antagonistic pleiotropy, mutation accumulation, drift. I think it is okay to just refer to the 2017 paper to dismiss other alternatives, like condition dependent selection. I do not see how you can discriminate between drift and mutation accumulation. I am particularly concerned by your reporting a reasonably large effective population size for the population from the driest environment, in spite of its being the smallest population. If you really want to go this route, then I think it is incumbent on you to do some simulations, or at least something concrete, to argue that the population size is small enough for drift to have been an issue. These issued would be subjects for the Discussion.Subsection “Koeppen-Geiger index and bioclimatic variables”: This is an obscure way of reporting differences in local climate and pond longevity. It would make a big difference if you could give me something more concrete than this since the values you report look small and subtle but the implied effects on biology are large. The two do not fit well together.Reviewer #3:This paper presents a new killifish genome assembly as well as population sequencing data from several killifish populations living in wet and dry habitats. Using this dataset, the authors show that killifish lifespan is inversely correlated with several measures of population diversity. They find no evidence for positive selection favoring genetic variation that appears to shorten lifespan based on a prior QTL analysis, but instead find support for the idea that increased genetic drift could have caused lifespan-shortening variation to reach high frequency in small, outlying killifish populations. Overall the study is very nice and provides some compelling evidence that killifish lifespan has been shortened by weakly deleterious mutation accumulation.1) My one comprehensive issue with the manuscript is that it implies too strongly that the dryness of the outlying killifish habitats directly caused those populations to lose diversity and accumulate deleterious mutations. Even if all killifish habitats had identical rainfall and resources, we would still expect the populations at the origin of the range expansion to have higher diversity and less genetic load than smaller, more recently founded, outlying groups. In the absence of any data from control pairs of killifish populations that are geographically distant from each other but have similar levels of rainfall, the paper should clearly state up front that the differences in diversity between populations might not be driven primarily by wetness vs dryness but would be expected to exist whenever one population is more recently founded than another. In addition to addressing this broad issue, it would be helpful for the authors to address the following more specific issues:ST outlier genes are differentially expressed between fish of different ages but does not comment on how often random control genes likewise vary in expression as a function of age.2) The manuscript notes that FST that is located on the sex chromosome, is its outlier status appropriately calibrated with respect to the overall divergence and diversity levels of the sex chromosomes, which can differ substantially from that of the autosomes?3) For the gene with unusually low F4) In this discussion of the origin of the killifish sex chromosome, it wasn't clear to me whether the sex determining locus was inherited ancestrally or seems to have acquired a de novo sex determining function.5) How good is the evidence that the DFE inference method being used is robust to non-equilibrium demographic history?eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Jessica Tyler as the Senior Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "Intra-Species differences in population size shape life history and genome evolution" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.eLife". Please let us know if you would like to pursue this option. As the editors have judged that your manuscript is of interest, but as described below that additional experiments are required before it is published, we would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv  along with this decision letter and a formal designation that the manuscript is "in revision at In this study the authors use turquoise killifish to determine the evolutionary forces shaping life history divergence within species. Their results support that mutation accumulation plays an important role in shaping lifespan, but the reviewers have also raised concerns that must be addressed before acceptance of this manuscript.Essential revisions:1) The authors still do not state how close these populations are to those studied in the QTL study . Couldn't they just provide coordinates, a map, physical distances, or something to help the reader understand how close they are? As I previously stated, if they are not close, then it raises issues of whether these populations would even have the same loci affecting differences in lifespan. The authors provide the new statements below, but it is not clear how to interpret them objectively. Were these fish collected meters from the previous ones? Kilometers? 100 km?"The populations used in this study are from localities in proximity to those used in the previous QTL study.""we collected wild populations from natural localities as close as possible to the locations of origin of the strains used for the QTL study."The revision also adds:"…or that the populations used in this study and those used for the QTL analysis had a different genetic architecture of lifespan"This statement could be unnecessary if exact locations show them to be quite close.α is still inadequate. Some PopGen aficionados will know, but general eLife readers won't know what these values mean. What are high values, low values… etc. What has changed in the sequences to cause these values to change? The authors must really present these statistics in a didactic way.2) The explanation of MK 3) From the new explanations of the inferred DFE of new mutations, it seems this analysis only reflects inferred population demographics, rather than the actual deleteriousness of polymorphisms in the population. Is it correct to say that it uses inferred demographics to just rescale a statistical distribution chosen to represent the DFE?If so, why not then just present the inferred demographic history? If the DFE analysis isn't analyzing anything about the actual polymorphisms present in the populations, why make that extra inferential jump? Just report the demographics. If this analysis reflects some other quality of the data that translates into actual fitness effects, then it should be stated. If it reflects demographics alone as they would affect a theoretical DFE, then that should be clearly stated.4) The authors have done a thorough job with revisions and substantially improved their manuscript. The one point from my review that I'd like to revisit is my point that more recently founded populations tend to have smaller effective size and less diversity than older populations regardless of founding order. I don't agree with the reasoning that this only applies to temporally separate populations-there is extensive literature in humans on a "serial bottleneck model" that explains why population diversity is proportional to the distance of a population from the origin of the human range expansion in Africa . This work was based on genetic sampling of human populations at the same time, as was done with the killifish populations here, but founding order is important because more recently founded populations have had less time to recover from the associated bottlenecking founder effect. I don't think it's necessary for the authors to do more work to address this point, but I do think they should point out in the text that some proportion of the population size difference between the wet and dry populations is probably due to the fact that killifish have existed at large population size in the wet habitats for a long time, whereas the dry population probably expanded from a small founder population more recently. In addition to a point by point rebuttal, in particular please respond to the following points:1) The major conclusions are based on too narrow an interpretation of the senescence literature. They have not considered mutation accumulation as a basis for their results. They need to do so and to adjust the rest of the paper accordingly or at least need to explain to me why they did not include it.We thank the reviewer to raise this important point. Our findings indeed lend full support to mutation accumulation being the main driver of senescence evolution in our empirical example of natural turquoise killifish populations. Indeed, we believe that our work provides a direct link between the classical theories of ageing to empirical findings in ecology and evolution, connecting the mutation accumulation theory with ecological and demographic constraints  in wild species. Our main result is that genetic drift due to small effective population size exacerbates the effects of mutation accumulation. We agree that this was not clearly stated in the text and have now explicitly spelled out in Abstract, Introduction and Discussion.2) The authors should explain the inferred DFEs and show that the DFE inference method to produce reasonable results on simulated data with a similar demographic history to what they infer from the killifish data.“There is also concern about the DFE study, since, if I understand Figure 4D correctly, the wetter habitat populations have a higher proportion of strongly deleterious SNPs than the GNP (dry) population. Does this go against the argument presented in the paper?”We fully agree with the editor and with the reviewers that the DFE analysis demands clearer explanation.newly arising mutations. When purifying selection is strong, more new mutations will be strongly deleterious and thus be removed effectively from the population. That is, the strongly deleterious mutations are not observed in the data. However, when purifying selection is weaker, some of the deleterious mutations “move” to the weakly deleterious category and segregate in the population. This is why the population with the smaller effective population size (dry population) has less strongly deleterious new mutations, but relatively more slightly deleterious mutations. Another way to see this point is that mutations with same negative selection coefficient s would be more deleterious (more to the left in the DFE plot) in larger populations than in smaller ones, as 4*Ne*s scales linearly with population size.We would like to point out that the difference between the DFE spectra between dry and wet populations is along the expected direction. DFE refers to the fitness effects of s of the deleterious mutations was drawn from the same reflected gamma distribution (mean of -2.5 and shape of 0.35). All models start with an initial burn-in phase of 50000 generations to generate genetic diversity and a stable ancestral population. We re-scaled the population genetic parameters to an initial population size of 10k to increase the speed of the simulation. This should not affect the results and is a common method. The models vary in either having immediate population size changes  or having exponential growth . We further distinguished between a split directly after the burn-in phase  or at a later timepoint, following the PSMC’ analysis interpretation .To address this point numerically, we performed a new simulation with SLiM , using similar demographic parameters to those from the PSMC’ analysis. We simulated four models with two populations that diverged from the same ancestral population and are identical in all population genetic parameters. Importantly, the selection coefficient The output of the simulation was used to build the site-frequency-spectra that were used as the input for PolyDFE. We found that the PolyDFE results recovered the same conclusions as those we empirically observed between the large and the small population. In fact, although the selection coefficient for the simulations was drawn from the same gamma distribution for large and small populations, the small population evolved fewer strongly deleterious novel mutations, but more slightly deleterious mutations, as expected under weaker purifying selection. The results of this simulation are displayed in Figure 4—figure supplement 1.Reviewer #1:[…]1) The study ultimately cannot comment on the initial ancestral evolution of shorter time to reproductive maturity or lifespan because it is analyzing differences between populations, all of which already have short lifespans and time to maturity. This is not a critical concern however, and the authors seems to be aware of this, but it should be stated upfront so readers appreciate the distinction. It remains a possibility that the species-wide trait of short lifespan and time to reproductive maturity arose through selection in response to the extreme seasonality of their habitat.Nothobranchius furzeri. Certainly, since all Nothobranchius share common adaptations, including embryonic diapause and rapid developmental time, we cannot use the current dataset to search for genes important for genus-wide adaptations that emerged in the common ancestor of Nothobranchius. To note, different species in the genus Nothobranchius differ in lifespan – with species living up to 2 years – even though they have embryonic diapause and reach sexual maturity roughly at the same time. Similarly, different wild populations of the species Nothobranchius furzeri reach sexual maturation at the same time, even though have different lifespan in nature and in captivity. Hence, early life history adaptations  appear somehow disjointed in the genus Nothobranchius and in the species Nothobranchius furzeri. We believe that African killifish are an example of how early life history traits may be under different selective constraints and evolutionary trajectories than late life history traits. This very aspect was addressed in a previous work conducted by our group and published in 2019 . The present work is intended to give insights to the mechanisms that resulted in lifespan differences between populations of the turquoise killifish. We now clarify in the text that our study does not address the evolution of rapid sexual maturation and diapause in killifish, but rather provide an evidence of how nearly neutral evolution adds genome-wide deleterious gene variants that predominantly affect late life history traits.As correctly noted by the reviewer, the current study specifically focuses on population differences within the model species 2) It is not entirely clear whether the populations studied here are the same as those in which the lifespan QTL were discovered. What are the genetic and geographic distances between these populations and those studied by Valenzano et al.If the populations are substantially different, then the finding of no evidence of positive selection at the QTL is limited, since lifespan associated QTL in these populations could be entirely different. This is an important concern to address.This point is well taken. Although the strains used in Valenzano et al. are derived from wild populations that differ in their lifespan, we cannot assume that the lifespan QTL architecture is shared across any pair of long- vs. short-lived turquoise killifish populations. This very question is what we wanted to explore when we first started this study, and therefore we collected wild populations from natural localities as close as possible to the locations of origin of the strains used for the QTL study. We clarify this point now in results.3) The reader would benefit from explanations about the expectations for the α statistic, since many will not have encountered it before. What are expectations under relevant scenarios? What do values of 0, 1, and negative really mean? How robust are such inferences to demographics and other confounders?Because conclusions of that paper are somewhat reliant on these values and related tests about the DFEs and DoS, it is important to spell this out and to understand their interpretation, strengths and weaknesses.We fully agree with the reviewer and have now added explanations of what each of these statistics means, what are the expectations, and how our results fit such expectations.4) Each of the population genetic tests could be affected by demographic changes. The authors should address this possibility head on and explain how the method or its interpretation is or is not affected by the scenarios they inferred in Figure 1A.α, a desirable feature of these statistics. Indeed, the main argument of our work is that demographic changes, affecting drift, lead to differences in genetic load. Rather than a confounder, we believe that demographic changes are a direct cause of the observed differences among turquoise killifish populations.PolyDFE fully accounts for demographic changes (population size is in the x axis of each plot). Fluctuations in demography are cancelled out when computing DoS and MK 5) The DFE analysis in Figure 4D seems to suggest a very high proportion of highly deleterious SNPs. ~60% of SNPs with a value of 4Nes < -100. This value seems high. Wouldn't we expect most segregating SNPs to be nearly neutral?Could the authors explain? Is this high proportion of highly deleterious SNPs due to SNPs at very low frequency?s would have a more deleterious effect in large populations than in small populations, since 4*s*Ne is bigger as Ne gets larger. Hence, DFE interprets how purifying selection purges mutations given their selection coefficient and the population size. We have now explained this part extensively and performed a novel simulation with SLiM3 to model how DFE differs in a small and a large population evolved from the same ancestral population. The results of this simulation support our findings and interpretations and are now presented as Figure 4—figure supplement 1. We thank again the reviewer for this note, as we feel that our finding was strengthened by the simulation.We realize the DFE analysis needed more explanation and we are thankful to the reviewer for requesting more clarity on this part. DFE computes the fitness effect of novel mutations. Mutations with similar (e.g. negative) selection coefficient 6) Given point 5 above, the wet population has a much higher proportion of highly deleterious SNPs than the arid population. Couldn't it be argued that the wet population is suffering poorer genetic health, since it has a higher proportion of highly damaging SNPs?See response to point 5). More novel mutations are strongly deleterious in the wet population, and thus they are removed very effectively by purifying selection.The Consurf analysis seems more convincing to me than the DFE analysis.We appreciate that the reviewer judges that the Consurf analysis is convincing, and we hope that now also the expanded explanation of the DFE analysis strengthens our findings.Reviewer #2:1) My one comprehensive issue with the manuscript is that it implies too strongly that the dryness of the outlying killifish habitats directly caused those populations to lose diversity and accumulate deleterious mutations. Even if all killifish habitats had identical rainfall and resources, we would still expect the populations at the origin of the range expansion to have higher diversity and less genetic load than smaller, more recently founded, outlying groups. In the absence of any data from control pairs of killifish populations that are geographically distant from each other but have similar levels of rainfall, the paper should clearly state up front that the differences in diversity between populations might not be driven primarily by wetness vs dryness but would be expected to exist whenever one population is more recently founded than another. In addition to addressing this broad issue, it would be helpful for the authors to address the following more specific issues:We thank the reviewer for requiring clarification on these important aspects.Nothobranchius species (N. orthonotus and N. rachovii) that populations from dry habitats have higher genetic load. We hypothesize that dry habitats lead to population fragmentation , hence creating a constellation of small, isolated populations. We now emphasize this point further in Discussion.In a previous study, we have indeed shown a robust phylogenetic correlation between climatic parameters and annual life cycle across different killifish species . Furthermore, our previous study showed in two independent dry-wet pairs of We understand the point about founding order, but we believe that this reasoning better applies to populations temporally separated, i.e. with more recently founded populations recently diverging from the ancestral population. However, in our case we sampled populations from dry and wet habitats at the same time. We consider these as divergent populations, i.e. derived from the same ancestral population. Sampling two populations simultaneously prevents us from treating any of the sampled populations as the ancestor, as their divergence time from the ancestral population is identical. Thus, according to this model, genetic differences can occur on either branch. Because in the analysis we were making a relative comparison between two contemporary populations, we believe that founding order won’t be applicable. However, as discussed above, smaller populations in dry regions at the margin of the habitat distribution of the species may experience extreme genetic drift due to population fragmentation, contributing to weakening of purifying selection and accumulation of deleterious gene variants. To note, populations from dry regions are also at higher altitude than populations from wet regions. Hence, gene flow is expected to happen from dry to wet, and not vice versa. This, in turn, may further contribute to genetic diversity depletion in dry areas and maintenance of higher diversity in wet regions.ST outlier genes are differentially expressed between fish of different ages but does not comment on how often random control genes likewise vary in expression as a function of age.2) The manuscript notes that FST outliers. We reported the statistics in the text and still report which genes are significant.We fully agree with this point. We now performed this additional analysis and we couldn’t find an enrichment of significant differentially expressed genes in the FST that is located on the sex chromosome, is its outlier status appropriately calibrated with respect to the overall divergence and diversity levels of the sex chromosomes, which can differ substantially from that of the autosomes?3) For the gene with unusually low FST values on all chromosomes. We found that the FST values on the sex chromosome (chromosome 3) were neither the lowest nor the highest values of all chromosomes in all comparisons. Here we attach a boxplot of Z-transformed FST values for every chromosome and every comparison we did.We thank the reviewer for this important point. We now checked the range of the F4) In this discussion of the origin of the killifish sex chromosome, it wasn't clear to me whether the sex determining locus was inherited ancestrally or seems to have acquired a de novo sex determining function.With our analysis we want to show that the current sex chromosome of the turquoise killifish emerged from two autosomes. Unfortunately, we cannot say whether the sex determining locus was inherited ancestrally. However, since the sex-determining gene occurs in a region corresponding to an ancestral chromosomal translocation, it is likely that it may have acquired its current function simultaneously with this chromosomal rearrangement. However, we cannot yet date the timing of this event.5) How good is the evidence that the DFE inference method being used is robust to non-equilibrium demographic history?The program polyDFE can account for demography and polarization error . We included runs with these options enabled in the analysis of polyDFE. We also simulated SFS (site frequency spectra) using the demography extracted from the PSMC’ results to validate that demography is accounted for and that polyDFE is precise. The simulated data recovered the reflected gamma distribution and confirmed that polyDFE worked for our model. We now present these results as Figure 4—figure supplement 1.Essential revisions:1) The authors still do not state how close these populations are to those studied in the QTL study . Couldn't they just provide coordinates, a map, physical distances, or something to help the reader understand how close they are? As I previously stated, if they are not close, then it raises issues of whether these populations would even have the same loci affecting differences in lifespan. The authors provide the new statements below, but it is not clear how to interpret them objectively. Were these fish collected meters from the previous ones? Kilometers? 100 km?"The populations used in this study are from localities in proximity to those used in the previous QTL study.""we collected wild populations from natural localities as close as possible to the locations of origin of the strains used for the QTL study."The revision also adds:"…or that the populations used in this study and those used for the QTL analysis had a different genetic architecture of lifespan"This statement could be unnecessary if exact locations show them to be quite close.We have now provided the exact locations of the populations used in the present and in the QTL work as supplementary Figure 1—figure supplement 1. Additionally, we have specified that these populations belong to the same drainage system. The map shows the physical distance among localities. We have referred to this map in the text and spelled out the relative distance in Km in the figure caption.2) The explanation of MK α is still inadequate. Some PopGen aficionados will know, but general eLife readers won't know what these values mean. What are high values, low values… etc. What has changed in the sequences to cause these values to change? The authors must really present these statistics in a didactic way.We have now expanded the section relative to the McDonald-Kreitman statistics to clarify this metrics to a broader audience. We now explain the implications of this metrics and how the plots should be interpreted. I hope this expanded part clarifies better the utility of using this powerful approach.3) From the new explanations of the inferred DFE of new mutations, it seems this analysis only reflects inferred population demographics, rather than the actual deleteriousness of polymorphisms in the population. Is it correct to say that it uses inferred demographics to just rescale a statistical distribution chosen to represent the DFE?If so, why not then just present the inferred demographic history? If the DFE analysis isn't analyzing anything about the actual polymorphisms present in the populations, why make that extra inferential jump? Just report the demographics. If this analysis reflects some other quality of the data that translates into actual fitness effects, then it should be stated. If it reflects demographics alone as they would affect a theoretical DFE, then that should be clearly stated.42 of new mutations”, hence we are probably unable to better address this point raised by the reviewer.DFE indeed accounts for both demography and deleteriousness of the variants. Hence, it is not redundant to the inferred demographic history. When inferring the DFE, demography  needs to be statistically accounted for, because the pattern of genetic variation observed in the data changes due to past demographic history. However, the inferred DFE itself reflects the deleterious or beneficial newly arising functional mutations (nonsynonymous sites). Hence, DFE takes into consideration the actual polymorphisms present in the populations at neutral and functionally relevant sites, from which it estimates actual fitness effects. By showing how different populations with different demographic history have a different distribution of fitness effects of newly emerging mutations, we do feel that the DFE analysis adds a significant novelty to our results, connecting demography with fitness. We clearly stated in the text that DFE is measured based on polymorphisms “To directly estimate the fitness effect of gene variants associated with each population, we analyzed population-specific genetic polymorphisms to assign mutations as beneficial, neutral or detrimental, and determine the distribution of fitness effect (DFE)4) The authors have done a thorough job with revisions and substantially improved their manuscript. The one point from my review that I'd like to revisit is my point that more recently founded populations tend to have smaller effective size and less diversity than older populations regardless of founding order. I don't agree with the reasoning that this only applies to temporally separate populations-there is extensive literature in humans on a "serial bottleneck model" that explains why population diversity is proportional to the distance of a population from the origin of the human range expansion in Africa . This work was based on genetic sampling of human populations at the same time, as was done with the killifish populations here, but founding order is important because more recently founded populations have had less time to recover from the associated bottlenecking founder effect. I don't think it's necessary for the authors to do more work to address this point, but I do think they should point out in the text that some proportion of the population size difference between the wet and dry populations is probably due to the fact that killifish have existed at large population size in the wet habitats for a long time, whereas the dry population probably expanded from a small founder population more recently.We thank the reviewer for the feedback on our revised manuscript. We also agree with the statement that founding order and population isolation may play a key role in explaining the differences in population size that we observe among populations. Indeed, in the Introduction we mention the importance of population fragmentation in dry populations and we now have added in Discussion a mention to the possible isolation of populations from drier habitats. However, we still know too little about the temporal sequence of population separation. Our favorite hypothesis though is that indeed populations from more coastal areas may be more ancestral. We have now added in Discussion a part referring to how limited gene flow in isolated populations may be also caused by recent founder effect.


Spondyliosoma cantharus, in the East Atlantic and Mediterranean Sea, was investigated with nuclear (S7) and mitochondrial (cytochrome b) DNA markers. Significant divergence of four regional genetic groups was observed: North Eastern Atlantic, Mediterranean Sea, Western African Transition (Cape Verde) and Gulf of Guinea (Angola). The two southern populations (Cape Verde and Angola) each comprised reciprocally monophyletic mtDNA lineages, revealed low levels of diversity in Cape Verde and high diversity for Angola despite being represented by only 14 individuals. A complete divergence between North Atlantic and Mediterranean populations was depicted by the mitochondrial marker, but a highly shared nuclear haplotype revealed an incomplete lineage sorting between these regions. Bayesian skyline plots and associated statistics revealed different dynamics among the four regions. Cape Verde showed no expansion and the expansion time estimated for Angola was much older than for the other regions. Mediterranean region seems to have experienced an early population growth but has remained with a stable population size for the last 30000 years while the North Atlantic population has been steadily growing. The lack of genetic structuring within these regions should not be taken as evidence of demographic panmixia in light of potential resolution thresholds and previous evidence of intra-regional phenotypic heterogeneity.Resolution of population structure represents an effective way to define biological stocks and inform efficient fisheries management. In the present study, the phylogeography of the protogynous sparid  Determining the spatial patterns of demographic independence and mixing among post-juvenile fish populations is critical for sustainability of fisheries3. Molecular genetic methods have been used for a long time to infer stock structure in fishes, as they proved to be efficient tools at identifying demographically independent units for fisheries management4. Overfishing such units can lead to a collapse of the fishery since a recovery of population sizes from migration is unlikely to occur and with the loss of a genetic stock, a species also loses the individuals that are adapted to a particular habitat through evolution, i.e. local adaptation5.An essential part of sustainable management of fisheries is the identification of biological fish stocks, as it is the unit at which population assessments should be undertaken and management measures are appliedSpondyliosoma cantharus, that occurs over a wide range of the eastern Atlantic, from Scandinavia to Namibia, around Madeira, Cape Verde and the Canary Islands. It is also common in the Mediterranean Sea and the western Black Sea6. Landings have reached almost 10 000 t per year in the last decade, with a major input from the north and central Eastern Atlantic, showing an increasing trend since 19807. Life history characteristics of S. cantharus make it particularly vulnerable to local over-exploitation since it is a protogynous hermaphrodite, slow-growing, long-lived8, showing habitat specificity during the spawning season, with spawning aggregations and male nest guarding behaviours10.Sparids are highly important fisheries resources, with numerous species being commercially exploited. One of these species is the black seabream, S. cantharus, little information on stock structure is available for the species. Only two studies on body morphology11 and otolith shape and isotopes ratios12 have analysed the population structure of the species, revealing clear distinct morphotypes for the Canary Islands and Angola and evident phenotypic differences among several of the European areas analysed. The use of genetic approaches along with phenotypic ones is advised in order to create an interdisciplinary perspective and increase the probability of correct stock identification13. In this study the phylogeography and population structure of S. cantharus was explored across the East Atlantic and Mediterranean Sea using both mitochondrial (cytochrome b) and nuclear (S7) DNA, in order to identify putative stocks and compare the results to those found for the phenotypic traits.Despite the wide geographical distribution of b sequences were obtained, aligned, and trimmed to 663 bp, with 63 distinct haplotypes identified (GenBank accession nos. MH545766 – MH545828) showing 99 polymorphic sites yielding 81 transitions and 26 transversions. For S7, 366 sequences  with 483 bp were analysed and 85 haplotypes were identified (GenBank accession nos. MH545829 – MH545911), with 90 polymorphic sites representing 44 transitions, 34 transversions and 16 sites showed insertion/deletion variation in a total of 7 indel events with an average indel length event of 2.857.A total of 263 cytb of S. cantharus revealed four distinct groups  and the S7  was defined. Pairwise FST and ΦST values for the populations within each region  but not for S7 . However, when the mantel test was carried out for populations within each region, isolation by distance was not found for either cytb  or S7 .Mantel test showed significant correlations between genetic and geographical distances for cytb (73%) and S7 (62%). The WAFT region showed the lower values for all the diversity indices in both DNA markers  and GLGN (represented by Angola). All the analyses clearly disclosed genetic structure among these regions, with each of the southern populations (GLGN and WAFT) comprising reciprocally monophyletic endemic clades and evidence of limited migration between NEAT and MEDS.In the present study the genetic structure of 15. The Strait of Gibraltar with 12.9 km wide and 286 m deep can represent a phylogeographical break, but no obvious relationship has been established between species dispersal ability or life history, and the observed patterns of genetic isolation between Atlantic and Mediterranean populations15. In the Sparidae family, divergent patterns have been observed for species with similar biology. A clear differentiation from Atlantic and Mediterranean populations have been found for species such as Lithognathus mormyrus, Dentex dentex16, Diplodus puntazzo17 but not for others like Pagrus pagrus18, Pagellus bogaraveo16, P. erythrinus19, Diplodus sargus20. For S. cantharus16, found different patterns when using mitochondrial DNA, where clear differentiation between Atlantic and Mediterranean populations was detected, while no differentiation was found for allozymes data. In the present study the cytb marker evidenced a clear separation from NEAT and MEDS regions, with only two shared haplotypes that occurred in all NEAT populations and the two nearest MEDS populations, MU and VL. This points for a secondary contact with unidirectional gene flow from the Atlantic to the Mediterranean. The nuclear allele sharing could reflect a combination of nuclear introgression and incomplete lineage sorting. In fact, even if mutation rates were similar, mitochondrial DNA is expected to have lineage sorting four times faster than the nuclear loci, since the former have a single copy per individual and is inherited uniparentally and thus has a smaller genetically effective population size21. For protogynous fishes such as S. cantharus, this sorting difference may be somewhat smaller since all breeders contribute to the mitochondrial gene pool of the species. The mtDNA genomic compartment in this species can be ½ and not ¼ as for most species, because the same individual will mature as female and change sex along its life and consequently, “all” individuals will function as females first, transmitting a copy of mtDNA into the population.The Atlantic Ocean and the Mediterranean Sea are connected by the Strait of Gibraltar since the end of the Messinian Salinity Crisis (some 5.33 million years ago)S. cantharus populations within each region showed no population structure, with very low pairwise FST and ΦST values for both DNA markers, and lack of IBD found among samples collected within NEAT and MEDS regions. Within each region shared mitochondrial and nuclear haplotypes were present in all populations and nucleotide diversity showed no defined geographical trend with similar values for most populations. The high number of singletons found is also common among marine fishes, where the usual haplotype distribution for large fish populations is a small number in medium to high frequencies, with most occurring in low frequencies or in single copy22.b results showed haplotypic diversity values similar to the NEAT and MEDS regions and higher nucleotide diversity despite the low number of individuals analysed. Expansion time for GLGN region dates from late Pleistocene, and these populations are likely to have maintained large number of individuals during glacial cycles since this area can function as tropical refugia where the mild conditions23 allowed to maintain a high number of breeders, justifying the high diversity found in the small number of individuals analysed. No data for the S7 could be obtained, since this DNA marker could not be amplified for the samples in this population. Nuclear DNA experience lower mutation rates than mitochondrial DNA24 and thus slower sorting among population is expected to occur, as discussed above for NEAT and MEDS regions. Unfortunately, the absence of nuclear DNA information for the GLGN region preclude to explore the occurrence of shared haplotypes with the northern regions, as described for other species such as Pomatomus saltatrix25.Despite efforts to gather samples from multiple areas within the Gulf of Guinea only samples from one population, Angola, could be collected. Cyt26. This region is strongly influenced by large-scale oceanic circulation and dominant currents which are likely to promote a physical barrier to larval drifting across the archipelago27 serving to restrict gene flow. The fact that fishing has been increasing in this region since 200528 can also contribute to the decline of the genetic pool available in the archipelago, which can eventually lead to genetic drift in small populations29 reducing the potential of the population to adapt to rapid environmental changes.The diversity indices found for both DNA markers in WAFT population were much lower than those estimated for the other areas. The much recent time of expansion estimated for this region can justify these low values since the population might have still had no time to highly increase diversity, persisting a single strong founder recolonization effect32 holding a more recent time since expansion than that estimated from BSP and mismatch distribution. Nevertheless, it is likely that during the climatic oscillations that occurred over the LGP, the species had refugia in two different areas, one in the Mediterranean and one in the southern North Eastern Atlantic and that these populations have been evolving separately since then. Refugia in North Eastern Atlantic is probable to have occurred near south Iberian Peninsula. Although no latitudinal cline in allelic richness is present, populations from Portuguese coast showed slightly higher values.The population expansion time in the NEAT and MEDS regions, as estimated by BSP, dates from the Last Glacial Period (LGP). These values must be looked with caution, since no molecular clock calibration is available for the species and the substitution rates inferred at population levels are much higher than those for species levelS. cantharus33 could also influence the low degree of genetic differentiation found along the European coasts, as the correlation between the PLD and FST estimates has been found to be very strong in recent modelling approaches34. However, phenotypic information available on the species points for segregation among NEAT populations, with clear morphological features of body and otoliths shape varying across the sampled areas12. The lack of within region genetic structure, with the low values of FST and ΦST, can be due to just enough gene flow existing to homogenize neutral markers despite the limited exchange of individuals on average among sites. In fact, despite the proved value of neutral genetic markers in elucidating patterns of genetic connectivity among wild populations, they might not be appropriate or sensitive enough to detect shallow genetic structure or to provide information about adaptive genetic variation35. The values of divergence for quantitative trait loci between populations are expected to be much higher than those obtained with neutral markers35. Markers with high mutational rates (STRs) or a genome wide analysis (SNPs) or even candidate genes for environmental responses/morphological traits could provide a deeper insight of species stock structure (see36 and references therein). Several authors have already detected, for other species, no population structure with molecular markers but differentiation with phenotypical traits (eg38.), pointing to the importance from a fisheries management point of view that several methodologies should be gathered to establishing an effective knowledge of the species population structure.The relatively long pelagic larval phase duration (PLD) reported for S. cantharus populations. The Cape Verde and Angola regions revealed two distinct clades and the Northeast Atlantic and the Mediterranean populations showed a clear separation but with incomplete lineage sorting from a common ancestor revealed by the nuclear marker. Similar diversity was estimated for all populations analysed with exception of Cape Verde. The shallow diversity, low Ne, and seemingly isolated nature may compromise the resilience of the Cape Verde population to increasing fishing pressure and environmental changes and this should be taken in account and considered for future studies in the area.The obtained results reveal a large scale structure for Despite the evidence from the available phenotypic traits data of significant differences within NEAT populations, genetic data showed no differentiation among populations for both NEAT and MEDS regions. This indicates that gene flow exists among locations although with some restriction as suggested by some differentiation in derived, more recent mutations that account for morphological differentiation. The markers used in this study are good for analysing event at an evolutionary timescale but less powerful for detecting events occurring at an ecological timescale. The establishment of unequivocal stock structure of the species will benefit with the use of markers with high mutation rates. The knowledge on stock structure of exploited species is essential in a world where the rising demand for fish protein and consequent increase in fishing pressure combined with global warming boosts the challenges faced by these species.A total of 263 black seabream samples were collected from 9 locations along the Eastern Atlantic and 5 locations in the Mediterranean Sea Fig. . Samplesb (cytb) and nuclear DNA first intron of the S7 ribosomal protein gene (S7) were amplified by polymerase chain reaction (PCR). For cytb, amplification of the region of interest was obtained with pair of primers purposely designed by the authors, based on 9 aligned cytb complete sequences of Spondyliosoma cantharus obtained from GenBank (http://www.ncbi.nlm.nih.gov/genbank) : BRBcytbF (forward) 5′-GCTGACTCATCCGAAATCTT-3′ and BRBcytbR (reverse) 5′-ATGTAGGGGTCTTCAACTGG-3′. S7 amplifications were performed with the following pair of primers: S7RPEX1F (forward) 5′-TGGCCTCTTCCTTGGCCGTC-3′ and S7RPEX2R (reverse) 5′-AACTCGTCTGGCTTTTCGCC-3′39. PCR amplification reactions were performed in a 20 μl total-reaction volume with 10 μl of REDExtract-N-ampl PCR reaction mix (Sigma–Aldrich), 0.8 μl of each primer (10 mM), 4.4 μl of bidistilled water and 4 μl of template DNA. An initial denaturation at 94 °C for 5/3 min was followed by 30 cycles  and a final extension at 72 °C for 10 minutes on a BioRad Mycycler thermal cycler . PCR products were purified with the SureClean kit (Bioline) following the manufacturer’s protocol, and the same primers were used for the sequencing reaction provided by Macrogen  and STABVIDA .Genomic DNA was extracted from fin samples with the REDExtract-N-Amp kit (Sigma-Aldrich) following the manufacturer’s instructions. Mitochondrial DNA cytochrome 41 in BIOEDIT42. S7 haplotypes of length-variant heterozygotes were determined using Mixed Sequence Reader43, and manual adjustments were made whenever necessary. Allelic states of S7 sequences were estimated using the Bayesian programme PHASE 2.146. Five runs with different seeds for the random number generator and 500 iterations as burn-in, 500 main iterations and a thinning interval = 1 were performed to check for consistency across results. All runs returned consistent allele identities.Sequences were aligned using Clustal W47 using a TCS network48. For cytb network some additional GenBank sequences from Madeira (MD) and Greece (GR) were added to extend the sampling area .The relationship between haplotypes was analysed by haplotype networks built with the software PopART49 was used to estimate genetic structure among and between regions. Estimates of genetic divergence among regions and among populations within region were calculated with the fixation index FST and ΦST49 as implemented in ARLEQUIN V3.550, with p-values being corrected for multi comparisons by Benjamini-Hochberg (BH) false discovery rate method51.Hierarchical analysis of molecular variance (AMOVA)ST was computed with the Mantel test53, also in ARLEQUIN with 1 × 105 permutations.The correlation between geographical distance, measured along the coastline, and F55 were estimated for all populations using ARLEQUIN V3.550. Graphical presentation of diversity indices was obtained using ggplot2 package56 for R57.Diversity indices b by the haplotype networks and AMOVA analyses was investigated in ARLEQUIN V3.5 with neutrality tests, Tajima’s D58 and Fu’s FS59, the R214 was also estimated using package pegas60 for R57. Mismatch distribution analysis was also performed for each region and compared to the distribution expected in populations affected by sudden expansion (1 × 105 replicates), under the assumption of selective neutrality, in which a unimodal distribution is expected61. The sum of squared deviation (SSD) and raggedness index (Hri) were used to detect departure between observed and expected distributions. If evidence of expansion was found  the τ parameter of demographic and spatial expansions was used to estimate the time since the expansion , using the equation t = (τ × n)/(2 × μ × k), where μ is the nucleotide mutation rate, k is the sequenced number of nucleotides and n is the generation time 62. Since there is no information on cytb mutation rate for S. cantharus, the values used in the present study were 2% per nucleotide per Myr based on16. The generation time used was 3.8 years, according to63.Demographic history for the region structure depicted for cytS. cantharus was also inferred using Bayesian skyline plot (BSP)64, employing the Bayesian MCMC coalescent method and a strict clock as implemented in BEAST 1.8.465. Best-fit model of nucleotide substitution for cytb was estimated with ModelFinder in IQ-TREE66. The TN + G4 was the model selected and an average mutation rate of 2% per nucleotide per Myr was used. The Bayesian distribution was generated with 1 × 108 Markov Chain Monte Carlo (MCMC) steps, and the convergence of parameters was visually checked with effective samples sizes (ESS) of estimates over 200 with TRACER 1.7.167. The time to most recent common ancestor (tMRCA) and the median and corresponding credibility intervals of the BSP were assessed for each population.Past population demography of Supplementary information.