Galeocerdo cuvier) DNA samples that were collected from eastern Australia over the past century. Using Single Nucleotide Polymorphism (SNP) loci, we documented a significant change in genetic composition of tiger sharks born between ~1939 and 2015. The change was most likely due to a shift over time in the relative contribution of two well-differentiated, but hitherto cryptic populations. Our data strongly indicate a dramatic shift in the relative contribution of these two populations to the overall tiger shark abundance on the east coast of Australia, possibly associated with differences in direct or indirect exploitation rates.Over the last century, many shark populations have declined, primarily due to overexploitation in commercial, artisanal and recreational fisheries. In addition, in some locations the use of shark control programs also has had an impact on shark numbers. Still, there is a general perception that populations of large ocean predators cover wide areas and therefore their diversity is less susceptible to local anthropogenic disturbance. Here we report on temporal genomic analyses of tiger shark ( High genetic diversity may also have positive ecosystem effects by promoting productivity, abundance, and stability of community structure6. Thus, species with many discrete populations with different genetic adaptations, including life history strategies, are assumed better at withstanding exploitation and environmental variation, than a single panmictic population. Intraspecific diversity protection is a specified objective of the Convention on Biological Diversity , but is rarely an integral part of monitoring activities, in particular for species that are not critically endangered8. Indeed, the lack of dedicated sampling programs and the many species requiring monitoring makes it logistically impossible to oversee intra-specific diversity in most instances. In addition, the difficulties associated with the use of high-resolution genetic methods when sample sizes are low or when tissue samples vary in age, composition and provenance9 also hinder our ability to assess intra-specific diversity.Intraspecific genetic diversity is essential for long-term population persistence to avoid the negative effects of inbreeding11. Studies on processes such as intra-population loss of diversity, adaptive change caused by evolutionary drivers in the environment, as well as the movement, decline, or extirpation of populations through time and space can now be undertaken12. In the marine realm, there are numerous examples of local population reductions and extinctions, in particular for large sharks and rays14, but closely coupling these incidences with genetic and genomic data is challenging due to the lack of temporal genetic data for elasmobranch species. Spatiotemporal genetic samples  can be used to test for changes in distribution of elasmobranch populations including potential extirpations and replacements, as well as intra-population changes in levels of genetic variability, and putative adaptive genetic changes in the timeframe of recent environmental changes or exploitation. This is of significant interest with respect to populations of large sharks, where there is a strong need for identification of genetic populations and their historical trajectories, as they represent both the relevant unit for evolution and management16 in order to contribute significantly to short-term sustainable exploitation as well as long-term protection of intra-specific biodiversity. Failing to identify and incorporate biologically-discrete populations in management may cause overharvesting of particular (cryptic) populations or population segments, resulting in extinction or reduction of genetic diversity within populations7.The use of DNA extracted from museum specimens, in combination with modern molecular analytical tools, has revolutionized the ability to assess genetic changes at contemporary time scalesGaleocerdo cuvier) is one of the world’s largest sharks, with a circumglobal distribution in tropical and sub-tropical waters18. Throughout its range, the species is exploited by multiple users, from target and bycatch in commercial, artisanal, and recreational fisheries20, to shark control operations to improve bather safety22, which have been linked to on-going population decline and reduced average size of individuals24. The species is globally listed as Near Threatened on the International Union for the Conservation of Nature’s (IUCN) Red List of Threatened Species due to a suspected decline of ~ 30% over the past ~ 60 years25. This general decline covers variable regional population trends ranging from relatively stable abundances, e.g. North-western Atlantic26, to severe local depletions as observed in the Red Sea, Eritrea, Iranian Gulf, as well India and Pakistan27. In the Arabian Seas on-going local reported declines of 30–50% over three generations led the species to be locally assessed as Vulnerable27. Of greater concern, however, is the reported 74% decline in catch per unit effort (CPUE) for tiger sharks off Queensland  over the past 25 years, which was accompanied by a 21% decline in the average size of individuals24. Thus, despite its cosmopolitan nature and dispersal potential, local depletions appear to be relatively common in the species, suggesting some degree of population isolation could occur. However, there is still a lack of adequate spatially resolved data to understand regional population trends of tiger sharks as they are commonly caught in unreported fisheries. Current assessments of the tiger shark population structure have been based on microsatellite and mitochondrial genetic markers, and have demonstrated a clear inter-basin genetic split between the Atlantic and Indo-Pacific Oceans, but a general lack of intra-basin population structuring30. Although this may be a true biological characteristic of the species, it could also reflect analytical challenges related to obtaining sufficient samples with good spatial coverage and the application of low-resolution genetic markers. Thus, in a recent review of research priorities, the development of high-resolution SNPs (Single Nucleotide Polymorphisms) to further resolve tiger shark meta-population structure was strongly recommended31. Moreover, temporal genetic information to assess the anthropogenic effects of exploitation is virtually absent. Such temporal genetic data are necessary to determine both intra-population changes in diversity by genetic drift, migration and selection, but also extent of population displacement or extirpation. Due to its Near Threatened status, potential for over-exploitation and risk to human lives, tiger shark management and conservation actions are high on the public agenda32. As large sharks are highly mobile, management has been primarily focused on international or regional policies, with limited value of enforcing strong local “domestic” regulations16. However, the detection of fine-scale population structure and the effects of local depletion or extirpation on intraspecific genetic diversity could lead to a general paradigm shift in large shark management from a global or regional perspective to a much stronger local focus.The tiger shark  can lead to a rapid decline in heterozygosity and allele diversity36. Such relationship has also been reported in empirical studies (e.g.38). We chose east coast Australia because of documented reductions in tiger shark CPUE in that region and because samples from this region are relatively abundant across both spatial and temporal scales; i.e. tiger sharks samples were available along the eastern Australian coastline and from as early as ~1939 to 2015. This provided an unparalleled opportunity to assess how the alleged population decline may have affected the genetic composition of the species over the past century. In order to collect genetic data over the widest spatial and temporal range, we extracted DNA from tissue samples taken from shark jaws archived in museums and private collections, mostly retained as trophies from fishing competitions, and performed retrospective genomic analyses39. As these trophies are listed with local game-fishing clubs, their size, catch date and location are well documented.Here, we investigated a possible link between the reported decline in tiger shark numbers and local genetic diversity off the east coast of Australia. While mating system can affect effective population size28, and satellite-tag tracking studies have revealed extensive movements up to several thousand kilometres in the region41 with some individuals seen moving as far as New Caledonia (maximum reported distances: of 1141 and 1800 km)43. In this context, we hypothesised that tiger sharks in eastern Australia, form a single panmictic population, from tropical Queensland to temperate Victoria44. Therefore, by employing a local spatiotemporal approach, our study aimed to assess if (i) tiger sharks in eastern Australia are indeed panmictic, (ii) reported declines are related to possible population structure in the species and if so, (iii) whether population distributions and relative abundances have remained stable over the last 100 years. This is the first study of its kind, not only for the region and tiger sharks, but also for elasmobranchs in general.Tiger sharks in Australia are thought to be part of a large Indo-Pacific population45 covering 4137 scaffolds. Of these, 4143 had captured reads with a depth of coverage higher than non-target regions. Scaffolds with a bait had an average coverage of 68.5×, while scaffolds from off-target regions had an overall average depth of 40.7× . Coverage was higher and less variable in contemporary than in historical samples. Single nucleotide polymorphisms (SNPs) were called from all transcriptomic sequences and we identified 35,061 raw SNP variants for 122 samples in 2978 reference scaffolds. After filtering, 4580 SNPs remained. SNPs with significant departures from Hardy–Weinberg Equilibrium (HWE) were removed to produce a final dataset consisting of 1840 SNP loci genotyped in 115 samples from G. cuvier specimens caught between ~1939 and 2015. In addition, we removed samples that had higher levels of missing data , had lack of length/weight information that did not allow to calculate the age, or had erroneous species labelling (identified as a different species), that could indicate a possible contamination with reads from other shark species. The final dataset thus contained 106 samples  compared to true ancient DNA (aDNA) studies. Thus, it is highly unlikely that the final SNPs represent artefacts due to deamination or other DNA damage in the historical jaw samples.Sequencing yielded an average of 2,833,138 reads per individual, with slightly lower numbers for historical jaw samples  than contemporary tissue samples . After running the bioinformatics pipeline, 78% of the original sequences went into transcriptome mapping. All samples showed a very low percentage of contaminants, confirming the validity of the capture strategy for sequencing enrichment with shark DNA. An average of 0.12% of the cleaned reads was of mitochondrial origin and excluded from further analysis, except for the few cases when it was used for species confirmation. Out of the 20,000 catshark derived baits, 4544 (22.72%) were post-hoc mapped back to the tiger shark transcriptomeles Fig. . Very loST estimates increased with time. Temporal differentiation was also apparent for the Tasman Sea samples alone, where the majority of the oldest historical samples originated  that also identified K = 2 as the most likely number of clusters .To examine the stability of patterns over time, we used a temporal genetic analysis based on the back-calculated decade of birth of tiger shark individuals, which showed clear evidence of temporal genetic differentiation. This was apparent for the temporal dataset as a whole Table , where ped Table . In contAS Table . A Princues Fig.  showed aues Fig. a supportues Fig. b did notues Fig. . Furtherers Fig. . The DAPers Fig.  showed toci Fig.  contribuoci Fig. , with 0.46 that reported a major decline in abundance of tiger sharks over three generations off the Queensland coast, with greater declines detected at the southern sites. However, we recognise that a number of factors could have partially contributed to our observations and uncertainties around this apparent dramatic shift in abundance, which we discuss below.This is the first study to demonstrate that archived samples in combination with modern genomic tools can reveal temporal changes in biodiversity in large sharks, which otherwise would have remained unnoticed. We identified genomic differences, consistent with two hitherto unidentified populations, at a relatively local scale  for a large migratory shark, the tiger shark. However, this pattern was only evident when historical samples were included, suggesting the displacement or extirpation of a local population. This conclusion is based on the findings that the genetic clustering of our spatiotemporal genotypes provided best statistical support for two population groups, with downstream analysis confirming that these units are genetically distinct and contain different levels of genetic diversity. One of the identified populations was most abundant in the oldest and southern-most samples (i.e. Tasman Sea), and almost completely absent from contemporary (including southern samples) and northern-most samples. We propose that the most parsimonious explanation for this absence is a shift in the relative population abundance likely associated with human fishing activities. Our results mirror the findings of Brown and Roff47, both of which could affect downstream population genomic inferences50. Although our historical samples presented fewer reads than the contemporary samples, the level of missing data was generally low, even for the oldest samples . For example, in ‘cluster 2’ all but two individuals had less than 5% missing data , while all but two individuals in ‘cluster 1’ had less than 1% missing data . ‘Cluster 2’ individuals generally showed a higher level of heterozygosity than individuals from ‘cluster 1’, which is the opposite of the expected pattern of reduced individual heterozygosity due to low coverage caused by allelic drop-out42. The opposite pattern of increased diversity due to a high occurrence of singletons caused by DNA degradation was also implausible due to the filtering, only including SNPs with a minimum allele count of three in the dataset. In addition, the genetic differentiation that we observed between clusters over time was not caused by a few spurious high-differentiated loci , but was found to be spread across the transcriptome. Moreover, if ‘cluster 2’ genotypes were artefacts created by poor quality DNA, this would still fail to explain the complete absence of those genotypes in the historical northern samples with equal DNA quality (GCA). The observed pattern of differentiation is consistent with a scenario of genetic drift accumulated across the genome, at an evolutionary time scale in two semi-independent populations of tiger sharks, as clearly indicated by the genetic cluster analysis and the transcriptome-wide patterns of differentiation (i.e. differentiation was driven my multiple loci spread throughout the transcriptome). Further strength to this hypothesis come from the results obtained by the two independent clustering methods (DAPC and snmf function in LEA), representing different algorithm and population genetic models/assumptions. Both approaches consistently identified the most likely presence of a distinct cluster in the older southern samples, which was not detectable in the most recent/northern collections. Thus, the relatively large differentiation between the two putative population clusters, the differences in their levels of heterozygosity, and the finding that not all historical Tasman Sea samples clustered together, renders the alternative hypothesis of very strong short-term genetic drift in a single panmictic population distributed across the central Indo-Pacific less plausible. In summary, the reported temporal genetic differentiation is unlikely to be caused by artefacts in sequencing and in the bioinformatics pipeline, by historical genetic drift in a single panmictic population, or by strong temporal genetic signatures of intra-population environmental selection (which would imply differentiation to be driven by a few loci).Our findings are unlikely to be the result of technical issues associated with the use of historical DNA (hDNA). In general, capture sequencing of hDNA has a lower sequencing depth and coverage leading to fewer mapped reads and thus more missing data with increasing sample ageThe use of material from historical collections together with genomic-scale analyses provided a relatively large sample size and sufficient statistical power for individual-based cluster analyses, presenting a unique window to explore population composition of tiger sharks in the past. The collection of historical and contemporary samples from the east coast of Australia comprised a huge logistic effort and, at least for the oldest specimens, represents the majority of high quality samples available and practically possible to sample in the community to date. Our samples thus provide relatively solid evidence of changes in population abundance, but still are unlikely to provide the full picture of genetic variation across space and time in tiger sharks off eastern Australia, in particular with respect to population mixing. Although location of catch was accurate, the sampling effort was mainly opportunistic, and thus variation in composition of samples with respect to age and sex, as well as season of sampling and method, may have influenced our results. Unfortunately, there is little information on sex for the historical samples and thus this assumption cannot be directly proven.58. In eastern Australia, the movements of tiger shark are closely related to water temperature58. Prey densities associated with these gradients may also drive adult shark movements to regularly return to specific areas to feed, i.e. established turtle nesting sites58. As a species that undergoes ontogenetic diet shift to larger-bodied45 prey items as they mature59, this could also partly explain a higher occurrence of the local (‘cluster 2’) population in the historical Tasman Sea samples, which were predominately large trophy specimens. However, little is still known about the use of space by tiger sharks, particularly those that are sexually mature, and there is some suggestion that reproductive cues associated with changes in thermal gradient may also drive movements of large females toward the insular regions of the Pacific Ocean58.Previous tagging studies investigating tiger shark dispersal have shown complex movement patterns including large-scale migrations, seasonal residency, and sex- and size-based dispersal or partial migrations60. However, these were not an issue in our study as, firstly, samples were obtained from dead individuals, and secondly, our kinship analysis did not show related individuals in our samples. Despite possible sampling-associated uncertainties, the observed patterns of temporal genetic divergence are clear. Firstly, our results based on contemporary samples are consistent with two recent studies that detected a single genetic population off the Australian east coast29 and are also compatible with the present understanding of population structure in the Indo-Pacific30. Secondly, the presence of the two identified cryptic groups was not random in space and time. Individuals from ‘cluster 2’ were almost exclusively detected in the southern part of the species’ distribution, most abundant in the oldest samples, and almost absent from contemporary samples. Consequently, we hypothesize tiger sharks in east Australian waters consisted of at least two populations in the past, but likely comprise a single population now. This may sound counterintuitive in the light of satellite tag-tracking studies that have shown evidence of individuals migrating over 1000 km61. However, large migrations and local populations at fine geographical scales are not mutually exclusive since dispersal events may be related to foraging and not reproductive purpose hence contributing differently to a population genetic make-up. Thus, evidence of fine-scale structure could be linked to basic “triangle migrations62 of fishes between parturition sites and juvenile and adult habitats63. Partial migration, ubiquitous among animal populations, and ontogenetic variation in dispersive behaviour, documented in tiger sharks64), might have also affected estimate of genetic diversity. Indeed, pooling of life stages, i.e. across sizes, during data analysis can obscure estimates of stock structure and dispersal65. This is, however, unlikely to be an issue in our study because we were able to identify clear stock structure regardless of the majority of our samples being from large tiger sharks known to be the most dispersive64.Sampling the same individual twice or kin sampling might skew the genetic diversity estimatedGadus morhua)66 and European anchovy (Engraulis encrasicolus)67 and in marine mammals like the bottlenose dolphin (Tursiops truncatus)68. Intraspecific differences in movement and residency patterns is also increasingly being reported in sharks71, including in large predatory species as tiger sharks70. Importantly, our findings suggest that the abundance of the putative southerly population has declined significantly compared to pre-1990s levels. The apparent local depletion of tiger sharks at the south eastern distribution of the species in Australia supports previous studies showing a reduction in the abundance and mean size of tiger sharks caught in this region46. Although the species is not commonly commercially targeted off eastern Australia, it is caught as bycatch, in Queensland and New South Wales’ shark control programs and in recreational fisheries off New South Wales. Off Queensland, tiger shark catch-per-unit-effort has dropped by 74% over the past 25 years, while the average size declined by 21%24, with most of the decline occurring in the southern part of the state23. Catch-per-unit-effort also declined in the NSW beach meshing program22. This reported strong reduction in tiger shark abundance, which coincides with the increase in lethal shark mitigation measures, is of concern and may imply that ongoing lethal mitigation measures as the possible drivers of the observed population declines in the region. Recent estimates of tiger shark bycatch obtained from commercial logbook records indicate between 5 and 10 t routinely caught off Queensland, and approximately 3 t per year off New South Wales. However, New South Wales also accounts for approximately 10 t of tiger shark recreational catches73. Further, since 1936 the game fishing in New South Wales has targeted large tiger sharks for capture point scores and continues to do so over several competitions annually71. Illegal foreign fishing vessels that target large sharks for their fins have been apprehended in the Tasman Sea region and north into the Coral Sea, with tiger shark found to comprise about 20% of the total biomass of sharks caught74. Overall, these activities may have selectively removed the southern population (‘cluster 2’), most likely through asserting a higher level of exploitation than on the more widespread northern population. Ongoing climate change can also contribute to shifting distributions and abundance of marine fish species and populations63, particularly in Australia76, since the region has been identified as one of the global hotspots for ocean warming77. Thus, it is possible that increasing sea temperatures could have negatively affected the putative southern population or contributed to the increase of the northern population in southern waters. This ‘tropicalization’ of southern Australian waters is well studied with several ‘northern’ species shifting their distribution ranges further south, and increasing in abundance in the Tasman Sea (e.g.78). Nevertheless, in the light of the recent dramatic demographic changes in tiger sharks and other large shark populations in the region, it is likely that fishing has played a significant role in the reported changes in abundance of these two genetically differentiated populations. Still, genetic analysis of more contemporary samples from along the east coast of Australia and the Pacific Ocean28 using e.g. a targeted genotyping approach focusing on the most informative SNPs79, could help to further elucidate the apparent change of abundance of the two populations of tiger sharks in eastern Australian waters.The observed pattern of population structure suggests a southern Australian distribution of one of the populations (‘cluster 2’), with the apparently more abundant population (‘cluster 1’) currently found throughout the entire distribution, with a previous limited intrusion to southern New South Wales . It is possible that the southerly population (‘cluster 2’) was a coastal and more resident ecotype and the other population is currently more widespread, more offshore and more migratory, as found in bony fishes such as Atlantic cod . By genotyping many individuals for a high number of SNPs, it may be possible to identify putative populations in species with general low levels of genetic differentiation such as sharks. More importantly, our work also points to significant challenges regarding the scale of current management and biodiversity protection schemes for large sharks. Sustainable management of local populations, through matching the scale of governance with population structure, is important for the protection of the evolutionary legacy of the species and the potential for adapting to future environmental changes83. It is also important for the maintenance of healthy marine ecosystems that could provide services to human society84. Accordingly, management focus will need to include localized protection measures, such as local seasonal closures or marine reserves to properly match the geographic scale of the population85. Specifically, for the east coast of Australia, we recommend to further investigate our findings, to elucidate the current abundance and distribution of the two populations and establish measures to protect the putative southern population component, which appears to have faced a significant historical decline, primarily driven by either direct and indirect exploitation. Overall, our work highlights the need to include historical samples in studies of population structure of large migratory marine species, as these harbour a treasure trove of information and can shed light on complex demographic patterns and aid in the development of accurate conservation actions at relevant geographical scales.The apparent occurrence (and loss) of localized cryptic populations of tiger sharks, at a finer geographical level than previously believed, raises a number of concerns regarding identification and monitoring of intraspecific biodiversity in large sharks. Our study suggests that localized populations may be more common than anticipated from recent genetic studies using markers with lower resolution86. Historical samples (~1939–1999) comprised dried tiger shark jaws and vertebrae obtained from museum collections, fishers, and other private or public collections. The initial dataset consisted of 115 unique sharks. As tiger sharks are long-lived and the sampled individuals were highly variable in age, we estimated the year of birth for each sample to allow for a more accurate temporal genetic comparison  and age. Growth rate estimation (t) was based on vertebral aging for both females and males using the Von Bertalanffy growth function (VBGF) , and a combined set where information on sex was not available . For individuals with only fork length (LF) available, total length was calculated using the relationship LT = 22.607 + 1.096 LF87. For the 20 sharks without length information, total weight (WT) was used to first obtain LF using a regression equation parameterized for tiger sharks in the north-western Atlantic88, the closest population to our target species from which there is available data (LF = ((WT/2.5281) × 10–6)(1/3.2603)).Tiger shark specimens were caught over a time-span of close to 80 years ~1939–2015). Samples originated from north-eastern and eastern Australia, extending from the Gulf of Carpentaria (GCA), through the Coral Sea (CRS) to the Tasman Sea (TAS)  of tissue per extraction. For genomic library preparation, DNA from contemporary samples was sheared to an average fragment size of 200 bp with a M220 focused ultrasonicator . DNA from historical material was fragmented due to degradation over time and therefore used directly. Genomic-capture libraries were prepared using the KAPA Hyper Prep Kit  according to manufacturer’s instructions. A total of 50 ng input DNA per sample was used with a one in five dilution of the TruSeq DNA HT dual-index adaptors . Ten PCR cycles for library amplification were used for the contemporary samples and twelve for the historical. Selected regions of genomic DNA were captured with a MyBaits (MYcroarray) target enrichment kit, consisting of 20.000 biotinylated RNA baits (120 bp each) developed from pancreas, liver and brain derived transcriptome sequences89 of the small-spotted catshark (Scyliorhinus canicula). At the time of bait development this was the most taxonomically similar species from which a large genomic resource was available for bait design that would likely capture tiger shark transcribed regions. For more details about DNA extraction, library preparation, and bait design see Nielsen et al.39. All DNA samples were captured individually, using 135 ng of DNA library as input, which had previously been treated with a 1× AMPure XP beads clean-up. Hybridization capture of tiger shark DNA was conducted for 24 h at 60 °C in solution for subsequent paired-end (2 × 125 bp) sequencing on an Illumina HiSeq2000 v4. Prior to sequencing, the captured libraries were amplified using thirteen PCR cycles and purified using 0.8× AMPure XP beads. Quality Control (QC) steps were performed using a Bioanalyzer , thus the final sequencing libraries could be pooled in equal nM concentrations. Samples were sequenced in two lanes. One “historical” lane consisting of 38 jaw samples and 5 fin samples, and a “contemporary” lane with 75 fin samples and 2 jaw samples. The difference in sample number per lane accounted for more variable template numbers among historical samples and thus secured a higher minimum number of sequences per individual. The lanes reciprocally included the same jaw samples that were sequenced in both lanes, similar to one of the contemporary tissue samples in the historical lane, allowing for estimation of “lane effects” and to evaluate reproducibility of multilocus genotypes through the molecular, bioinformatics and population genomics pipelines.Historical tissue material was collected following the protocol described in Nielsen et al.90 before proceeding with the downstream analysis. Briefly, the de-multiplexed reads were controlled for quality using FastQC91. All adaptors were removed using AdapterRemoval92 and reads were filtered by length and quality, with a minimum length of 30 bp and base quality of 28. Filtered reads were merged using FLASH93 with default parameters, and checked for contaminants using Kraken294. Both unpaired and concordantly merged reads were mapped against possible sources of contaminants  using the Bowtie295 “sensitive” option. The cleaned reads were also mapped against the mitochondrial genome of tiger shark (NCBI Reference Sequence: NC_022193.196). Previous studies have shown that “off-target capture” is common, i.e. capture of genomic regions of both nuclear and mitochondrial origin not matching the baits. For example, in highly degraded samples target template DNA may not bind and amplify as well as in good quality samples and templates, resulting in more amplification of non-targeted regions of the genome11. Moreover, mtDNA sequences are commonly captured, or directly sequenced, due to the high copy number of mitochondrial DNA compared to nuclear DNA97. This phenomenon may even be desirable, as it allows assessment of mtDNA diversity. After removal of mitochondrial sequences, the reads were mapped against the transcriptome of tiger shark45, using the BWA-mem algorithm. This transcriptome includes 179,867 unique contigs greater than200 bp length . After mapping, PCR duplicates were removed using Picard-tools (http://broadinstitute.github.io/picard/). We checked the patterns of DNA damage of the remaining reads, using Mapdamage2.098. Coverage and depth of the target regions were estimated using Samtools99, and finally we called SNPs using Freebayes100 with default parameters. The raw SNPs obtained were further filtered to keep only biallelic SNPs with quality above 30 and minimum allele count of three. Only SNPs with a maximum level of missing data of 20% were maintained. Additionally, we filtered for excess depth to reduce the possible presence of paralogs and multi-copy loci. Linkage disequilibrium (LD) between SNPs within bins of 800 bp (maximum length of the merged reads plus 150 bp each side) was estimated using the prune function in bcftools (SAMtools package), by calculating the square correlation between alleles of each pair of loci, r2101 and keeping only SNPs with an r2 < 0.25. To test the reliability of our final SNPs, we compared genotypes for duplicate control samples and only maintained SNPs that matched more than 80% of the pairwise comparisons . Finally, we filtered for significant departure from HWE (p < 0.05) to remove systematic genotyping errors. All filtering steps but the LD pruning were done using VCFtools102.We customized a bioinformatics pipeline to ensure removal of potential contaminants, artefacts and low quality reads24, especially in the southern part of Queensland103. For the temporal analysis we estimated temporal genetic differentiation using all samples (n = 106) including individuals belonging to different spatiotemporal sample groups, namely GCA_1970-1990 (n = 12), GCA_2000 (n = 14), CRS_1970-1990 (n = 7), CRS_2000 (n = 15), TAS_1910-1960 (n = 19), TAS_1970-1990 (n = 14) and TAS_2000 (n = 26). All estimations of pairwise FST104 between spatial and temporal samples were performed using the R package StAMPP105 and their significance was assessed with 1,000 permutations over loci. For all FST comparisons the p-values were adjusted using a False Discovery Rate (FDR) method. A Principal Coordinates Analysis (PCoA) was applied to the pairwise FST matrix to summarize and plot the differences reported in the table using the pcoa function in the ape 5.0 package106 in R. We performed a Principal Component Analysis (PCA) to explore the spatial and temporal structure of individual genotypes using the R package adegenet107. An initial PCA of all samples revealed two identical genotypes  and one of them was subsequently removed. A PCA of the contemporary samples revealed two extreme outliers of which one of them was identified as another species  based on mtDNA sequences. Both samples were removed from further analysis. For the temporal estimates the dataset (n = 106) was divided into the three periods: 1910–1960 (n = 19), 1970–1990 (n = 33) and 2000 (n = 55). In order to assess current spatial genetic differentiation a subset of 74 contemporary individuals (based on year of catch) from the three different areas: GCA (n = 26), CRS (n = 21) and TAS (n = 27) were selected. In order to test for possible differences in individual heterozygosity among population groups, we used an ad-hoc R script, to calculate the proportion of heterozygous loci out of all genotyped loci for an individual, thereby accounting for differences in total number of genotyped loci (i.e. missing data) across individuals. The boxplot was realized using the ggplot2 package in R108 to highlight the median of each cluster. The distribution of FST across loci was plotted as a function of heterozygosity using ggplot2. Estimates of Weir and Cockerham104 FST and heterozygosity were obtained using VCFtools. A test for selection was performed using pcadapt109, with a qvalue of 0.1 as cut-off to identify putative selective outliers and bayescan99 with parameters ‐n 5000 ‐thin 10 ‐nbp 20 ‐pilot 5000 ‐burn 50000 ‐pr_odds 100. To detect the presence of possible genetic clusters identified by the pairwise FST analysis, a Discriminant Analysis of Principal Components (DAPC) was applied as it better detects variability and division among populations compared to other software for population structure analysis (e.g. STRUCTURE110) as it is free of assumptions about any underlying population genetics model. For the DAPC we used the adegenet package in R. Since the results of a DAPC analysis can be highly affected by the number of discriminant functions used, we ran the xvalDapc function first to select the best number of discriminant functions to represent the data. The model was corrected for overfitting by using the a-score estimate, with 26 PCs retained representing the optimal trade-off between power of discrimination and over-fitting. The recommended number of PCs obtained using this approach (26) was used to run a Bayesian information criterion (BIC) analysis and the k-means algorithm to identify the most likely number of clusters that could explain the variability within our dataset. For comparison, the snmf function implemented in the LEA package111 was also tested, since it represents a different model-based approach for clusters identification in large datasets. The value of K that best explain the data was selected using the cross-entropy criterion, with 100 repetitions for K value. A Fishers exact test was applied to pairwise comparisons between different temporal collections to determine whether any possible change in relative proportion among groups was statistically significant. Estimates of relatedness were performed using VCFtools to exclude kin sampling that could skew the estimates of genetic diversity. Finally, Site Frequency Spectrum per temporal and spatial collections were generated in ANGSD112 to further investigate whether our results could be affected by technical biases (e.g. excess of singletons). Due to the lack of knowledge on the ancestral state, the folded version of the “realSFS” function was used to polarize the allele frequencies on the reference used to produce the vcf file (-anc -fold 1). In addition, Pi and Watterson theta were estimated for each temporal and spatial group using the “sfsr” function (https://github.com/andrewparkermorgan/sfsr).Back-calculated year of birth ranged between 1917 (the oldest) and 2012 (the youngest). For all downstream analysis, we grouped samples into four time periods based on their estimated decade of birth: 1910–1960, 1970–1990 and 2000, the latter comprising all contemporary samples (2000–2015). These date ranges were used as named time periods throughout the manuscript. The three periods were also associated with different catch rates in the study area, with the highest catch rate between 1960 and 1980, which significantly decreased after 2000Supplementary Information.


Musa balbisiana, one of the two main progenitor species of many banana cultivars. We investigated the genetic structure of M. balbisiana across its distribution range using microsatellite markers. Accessions stored at the International Musa Germplasm Transit Centre (ITC) ex situ collection were compared with plant material collected from multiple countries and home gardens from Vietnam. Genetic structure analyses revealed that accessions could be divided into three main clusters. Vietnamese and Chinese populations were assigned to a first and second cluster respectively. A third cluster consisted of ITC and home garden accessions. Samples from Papua New Guinea were allocated to the cluster with Chinese populations but were assigned to a separate fourth cluster if the number of allowed clusters was set higher. Only one ITC accession grouped with native M. balbisiana populations and one group of ITC accessions was nearly genetically identical to home garden samples. This questioned their wild status, including accessions used as reference for wild M. balbisiana. Moreover, most ITC accessions and home garden samples were genetically distinct from wild populations. Our results highlight that additional germplasm should be collected from the native distribution range, especially from Northeast India, Myanmar, China, and the Philippines and stored for ex situ conservation at the ITC. The lack of passport data for many M. balbisiana accessions also complicates the interpretation of genetic information in relation to cultivation and historical dispersal routes.Collection and storage of crop wild relative (CWR) germplasm is crucial for preserving species genetic diversity and crop improvement. Nevertheless, much of the genetic variation of CWRs is absent in ex situ collections and detailed passport data are often lacking. Here, we focussed on The online version contains supplementary material available at 10.1007/s10722-022-01389-4. Crop wild relatives (CWRs) are wild species that are closely related to their associated crop and are a source of genetic diversity for crop improvement. They potentially contain new key alleles against environmental stressors or are desirable for the modification of quantitative and qualitative crop traits such as yield, taste, and shape , such information is often lacking. Moreover, there is currently no standardised format or global portal to access data that are specifically gathered on CWRs (Engels and Thormann Musa L.) according to the World Checklist for Selected Plant families (WCSP Musa Germplasm Transit Centre (ITC), with 1100 accessions being duplicated and conserved cryogenically  are one of the worlds’ most important staple foods. With over 150 million tonnes being produced annually, they contribute to the income and diets of hundreds of millions of people FAO , 2019. Ties WCSP , and oveies WCSP . Worldwiies WCSP , of whicMusa Germplasm Information System (MGIS)   belonging to the AB, ABB, AAB, AABB, and ABBB groups -tropical rainforests ranging from Northeast India to South China and northern Vietnam. Although the species is also present in Taiwan, the Ryukyu islands (Japan), Indonesia, Malaysia, the Philippines, and Papua New Guinea (PNG), these occurrences have been attributed to human-mediated introductions . In addition, the absence of M. balbisiana in other parts of Marine Southeast Asia and its scattered distribution in PNG may indicate non-natural introductions in the Philippines, but it is no conclusive evidence  with those from ex situ seed collections, whereas Kallow et al.  in Southeast Asia and Melanesia. With this approach we aim to (i) identify areas in Southeast Asia that require additional collecting; (ii) investigate the extent of the genetic variation in M. balbisiana accessions that are currently stored and maintained in the ITC gene bank; and (iii) examine the importance of passport data for linking genetic data with the putative interpretation of human interference and dispersal routes.Up to now, few studies have focussed on the genetic diversity present among wild n et al.  comparedw et al.  comparedMusa balbisiana samples from different countries were acquired from three different sources (Supplementary Information – Table 1). Firstly, SSR data of Musa balbisiana were retrieved from two previous studies  originating from the Philippines, Indonesia, India, PNG, China, and Thailand were included  without coordinates. Eleven samples only had information on their country of origin, and eight samples had no data on their sampling origin (Supplementary Information – Table 1).Leaf material from field missions and ITC germplasm was dried using silica gel and DNA was subsequently isolated using a modified cetyltrimethylammonium bromide (CTAB) extraction protocol  using the PCR protocols described by White et al.  and three chloroplast markers  or those marked as hybrids in the Musa germplasm information system (MGIS) were excluded from the analyses of genetic structure , the average number of different alleles (Na), the average number of unique (private) alleles (Np), and the number of locally common alleles (frequency ≥ 5% in a population) found in less than 25% of all assessed accessions (Lcomm) were calculated using the “allele frequency data parameters” menu in the GenAlEx 6.51 Excel plugin  in GenAlEx using the “Distance-Based” menu options. For this analysis, all individuals from each population were used and the data were presented both at the population level (one point per population) and at the individual level . As ITC accessions cannot be seen as one population due to their dispersed origin, we refrained from performing an analysis of molecular variance (AMOVA). In another analysis, a similar plot was made including six accessions of unknown origin to see whether their putative origin could be traced back .K), we ran an admixture model to allow samples to be assigned to one or multiple clusters. Unequal sampling of populations may lead to inaccurate estimation of K  markers revealed very little sequence variation between accessions. BLAST resulted in sequence similarities with M. balbisiana ranging from 99.5–100% for all markers. Nonetheless, based on the chloroplast markers, five accessions  were found to be potential hybrids with M. acuminata (> 99.1% sequence similarity).Screening of both nuclear (ITS) and chloroplast  for the samples included in this study , they were retained for all analyses in order to be able to compare results with the studies of Bawin et al. (%P) and average number of different alleles (Na) ranged from 0 to 66.67 and 1.000 to 2.167 respectively , had no locally common alleles. Excluding VNM-S3, no home garden or ITC accession had private alleles.For the subsequent analyses, the dataset was reduced to accessions presumed to be diploids of n et al.  and Mertn et al. . The proM. balbisiana sampled from home gardens in Vietnam (VNM-S5-8 and VNM-N9) (yellow group). Fourth, another group of ITC accessions from various countries of origin and one home garden sample from Vietnam (VNM-S3) were genetically similar (pink group). Fifth, only one accessions from ITC (ITC1527) from Xishuangbanna (China) grouped among accessions from the native distribution area. The same patterns were found in the PCoA graph where individual samples from populations were plotted . Five main patterns were observed in the PCoA graph with the populations from Vietnam, China, PNG, and Japan plotted together with the distinct ITC and home garden accessions Fig. a. First,ITC ITC157 from XiITC ITC157 from XiThe PCoA including the six accessions of unknown origin showed that, apart from one accession (ITC0271), all these accessions grouped together and seemed genetically relatively distinct from other accessions when assessed at the population level Fig. a. HoweveK/K was highest for K = 3 (13.42) and second highest for K = 6 (4.39). The logarithm of the posterior probability of the replicates over each K suggested an optimal K of 19, but the slope of the curve sharply decreased and the standard deviation started to increase for a K larger than 6 . For this reason, K = 6 was used to set an initial alpha of 0.167 (1/6) in the subsequent clustering runs with uncorrelated allele frequencies to obtain a better cluster assignment for each individual. Because an ‘optimal K’ can be quite ambiguous and because ΔK/K clearly showed K = 3 as the preferred number of genetic groups, we here report STRUCTURE plots ranging from K = 3 to K = 6 , in general, the main clusters as observed at K = 4 remained clear, with additional sub-structure in some Vietnamese populations.When ree Fig. a, three ree Fig. b, the acree Fig. a. When KM. balbisiana movement, it is important to take into account the origin of the species and current knowledge on the introduction of this species across insular Southeast Asia. Our results provide evidence for the genetic structuring of the accessions in four groups: (i) wild Chinese populations, (ii) wild Vietnamese populations, (iii) most ITC and home garden accessions from Vietnam, and (iv) accessions from PNG.In order to place our observed genetic structure within the current knowledge on the history of K-values. In the current study, the same pattern was observed when accessions from other regions were included in the dataset. The seed collection of Amami (Japan) and ITC1527 from Southwest China clustered with Chinese and Vietnamese populations respectively. A Chinese introduction of M. balbisiana in the 16th century in Japan has already been suggested, bringing great economic benefits to the Ryukyu islands  and the conservation gaps of this collection were highlighted  were missing, its origin was assessed as a wild accession from India (pers comm. Van den houwe). However, its genetic differentiation from other populations sampled from the native distribution area of mainland Asia and its high genetic similarity to ITC0248 “Singapuri” suggests that this accession might also have been translocated and domesticated in other regions. Suckers of this genotype may have been distributed between villages, also in more southern states and Sri Lanka Uma . AdditioM. balbisiana have been reported from northern Thailand. De Langhe et al. , even if populations seem to appear in natural conditions and suitable climate. Similarly, “Pisang Klutuk Wulung” (ITC1587), and M. balbisiana in general, was likely introduced to Java and naturalised there . This suggests that M. balbisiana used in home gardens might have come from cultivated material rather than from wild populations  with cultivated accessions from the Institut National des Etudes et Recherches Agronomique (INERA) Yangambi and from local backyards  showed genetic similarity with some samples from China and two ITC accessions from the Philippines. As M. balbisiana is locally known as Eti-Kehel in Sri Lanka and as ITC0271 was donated to ITC by the Royal Botanic Gardens of Sri Lanka, Sri Lankan origin of this accession can be assumed , represented by only one or very few genotypes or “BB” cultivars . Of these, 145 are labelled as “wild species or subspecies”  or from seed collections from China. These include Vietnamese seed collections of populations of which leaves were used in this study. On the other hand, very limited germplasm from other countries of the native distribution area of M. balbisiana is available at the ITC or at other germplasm centres, though these populations have relatively high genetic variation within- and among-populations compared to feral populations and cultivated material , the same province in which the populations VNM-C1 and VNM-C2 were sampled. Based on the provided coordinates, these ITC accessions were sampled from more densely populated areas in the East of the province in contrast to the populations which were collected in the West, potentially from home gardens instead of a wild population. Two other accessions (ITC1120 and ITC1588) were sampled from Thailand and India, respectively. Both countries are partly covered by the (sub)tropical forests of northern Indo-Burma, which harbour many wild Musa populations . This high genetic diversity in Philippine M. balbisiana may suggest that the Philippines are part of the species’ native distribution area. Nevertheless, it remains unclear whether this high diversity reflects naturally accumulated genetic variation in wild M. balbisiana populations or increased variation in feral populations. Such increased variation in feral populations can be explained by multiple introductions of M. balbisiana into the Philippines or introgression of genetic material from other Musa species (e.g. M. acuminata) into the Philippine M. balbisiana gene pool  provide some support that introgression could have taken place.In addition to the one ITC accession from China, nine other ITC accessions originated from a country in which M. balbisiana populations in countries such as Myanmar, Thailand, India, Laos, but also the Philippines and Indonesia, additional collecting and genetic screening of populations and existing collections (such as the MSB) with plant material from these countries is required to maximize the genetic representation of the species with minimal resources (e.g. with seed collections).The lack of sufficient germplasm from the putative regions of origin of this species results in a poor representation of the total genetic diversity of the species at ITC, especially with only one sample from India and Thailand and none from Laos and Myanmar, all countries of which at least some parts are climatically suitable for the species  cross-pollinated species such as Equally important is making sure passport data of germplasm collections are of high quality for optimal use for farmers or breeders, but also that the material is available for research. Optimally, passport data follow a standard of descriptors such as the standard of the multi-crop passport descriptors from FAO and Bioversity International , often is symptomless but present in the field which could lead to the spread of Fusarium when locally distributed. Screening wild genotypes of M. balbisiana for resistance against Foc might help in prioritising areas for additional germplasm collection. For example, though five Indian BB-type accessions were found highly resistant or immune to Foc race 1 Supplementary file2 (DOCX 35 kb)Below is the link to the electronic supplementary material.


Formica selysi. This species has two social forms controlled by a supergene. The monogyne form has one queen per colony, while the polygyne form has multiple queens per colony. The two social forms co‐occur in the same populations. For both social forms, we found a strong pattern of isolation‐by‐distance across the Alps. Within regions, genetic differentiation between populations was weaker for the monogyne form than for the polygyne form. We suggest that this pattern is due to higher dispersal and effective population sizes in the monogyne form. In addition, we found stronger isolation‐by‐distance and lower genetic diversity in high elevation populations, compared to lowland populations, suggesting that gene flow between F. selysi populations in the Alps occurs mostly through riparian corridors along lowland valleys. Overall, this survey highlights the need to consider intraspecific polymorphisms when assessing population connectivity and calls for special attention to the conservation of lowland habitats in mountain regions.Studying patterns of population structure across the landscape sheds light on dispersal and demographic processes, which helps to inform conservation decisions. Here, we study how social organization and landscape factors affect spatial patterns of genetic differentiation in an ant species living in mountainous regions. Using genome‐wide SNP markers, we assess population structure in the Alpine silver ant,  Formica selysi populations in the Alps occurs mostly through riparian corridors along lowland valleys.We studied how social organization and landscape factors affect spatial patterns of genetic differentiation in an ant species living in mountainous regions. Genetic differentiation between populations was weaker for the monogyne form than for the polygyne form. In addition, we found stronger isolation‐by‐distance and lower genetic diversity in high elevation populations, compared to lowland populations, suggesting that gene flow between  The monogyne and polygyne social forms generally differ in several traits, including colony size and lifespan, sex allocation, dispersal, and colony founding strategy Keller, . Across Several landscape factors tend to restrict gene flow and lead to population structure. First, gene flow may be constrained by geographical distance, causing distant populations to diverge through drift  dispersing by flight , calculated with VCFtools, distinguishes homozygous from heterozygous individuals . We further inferred population genetic structure with hierarchical F‐statistics analyses, and obtained 95% confidence intervals (CI) by bootstraping over loci, as implemented in the R package “hierfstat”  and each of the other distances. Next, we ran a multiple regression of distance matrices  as response variable and geographical distance, elevation distance and the four environmental distances as predictors. These tests were run in “ecodist,” and the significance of the associations tested with 1,000 permutations.We tested for isolation‐by‐distance (IBD) and isolation‐by‐environment (IBE) between pairs of populations, excluding two populations in which fewer than three individuals were sampled , and interaction between the two factors were included as fixed explanatory factors, while a random term accounted for correlation of pairwise distances. Additionally, we tested for the effect of elevation on genetic diversity. Genetic diversity  within each population was estimated with the “hierfstat” package. We ran a linear model with genetic diversity (Hs) as response variable and elevation category (lowland or highland) as well as region as explanatory variables.We investigated if elevation and social organization affected isolation‐by‐distance and population differentiation at a local scale, within regions. For that, we used maximum‐likelihood population‐effects models, which are linear mixed‐effect regression models (LMER) that include a random term to account for correlation of pairwise distances involving a common population  or the polygyne (P) social form in each population, resulting in distances corresponding to three social form combinations between each population pair . To control for sample size bias, we calculated FST with rarefaction and 1,000 iterations of resampling, taking as sample size the smallest number of individuals belonging to one social form in one population of this pair. For example, for the FST between "Derborence‐M" (N = 14) and "Finges‐M" (N = 22), we resampled 10 individuals from each of the two groups, corresponding to the smallest sample size for one social form in this population pair, which is "Finges‐P" , and the interaction between the two factors. We also included a random term to account for correlation of pairwise distances.To test if social organization affects genetic differentiation between populations within regions, we focused on pairs of populations less than 100 km apart. These pairs comprise all populations with three or more individuals, except St. Michel in the Lower Rhône region, which is distant from all other populations Figure . We calcp‐values for the LMER models were obtained using the Kenward Roger approximation with the function KRmodcomp in the “pbkrtest” package . Differentiation among populations within regions was moderate , while differentiation between social forms within populations was low , indicating extensive gene flow between social forms. Overall, individuals clustered by regions, but not by population or social form, at SNPs located outside of the social supergene.In hierarchical 3.1R = .83, p < .001; Figure R = .54, p < .001, Figure R2 = .72; geography: p = .001; elevation: p = .77; temperature: p = .1; precipitation: p = .27; soil: p = .61; vegetation: p = .84). This suggests that the effect of temperature is due to its correlation with geography, and that geography accounts for most of the genome‐wide genetic differentiation across the range.There was a very strong pattern of isolation‐by‐distance at a range‐wide scale , while genetic distances between lowland and highland populations were intermediate . Genetic diversity was higher in lowland populations than in highland populations .Isolation‐by‐distance between populations within regions varied with elevation. The association between genetic and geographical distance was stronger among highland populations than among lowland populations . Distances between monogyne individuals of one population and polygyne individuals of the other were intermediate . The degree of isolation‐by‐distance did not differ according to the social form considered .Social organization affected the genetic distance between populations within regions  was high compared to measures over similar geographical scales in other ant species.Such strong pattern of isolation‐by‐distance between populations (IBD) is uncommon in ants . This difference persisted when considering geographical distance: isolation‐by‐distance was significantly stronger among highland populations than among lowland populations. Sample size was small, and pairwise genetic distances were variable, so further research including more highland and lowland population pairs from additional independent valleys will be needed to confirm this pattern. Second, highland populations were genetically less diverse than lowland populations. Restricted gene flow, founder effects and small effective population sizes at high elevations might explain this pattern ; Data curation ; Formal analysis (lead); Investigation (lead); Methodology ; Visualization (lead); Writing – original draft (lead); Writing – review & editing (lead). Martin Kapun: Formal analysis (supporting); Methodology (supporting); Software (supporting); Supervision (supporting). Patrick Tran Van: Data curation (supporting); Formal analysis (supporting); Methodology (supporting); Software (supporting). Jessica Purcell: Conceptualization (lead); Data curation ; Formal analysis ; Funding acquisition (supporting); Investigation (supporting); Methodology (supporting); Resources (supporting); Supervision ; Validation ; Visualization (supporting); Writing – review & editing (supporting). Michel Chapuisat: Conceptualization ; Formal analysis (supporting); Funding acquisition (lead); Investigation (supporting); Resources ; Supervision (lead); Validation ; Visualization (supporting); Writing – original draft (supporting); Writing – review & editing (supporting).¦Click here for additional data file.¦Click here for additional data file.¦Click here for additional data file.¦Click here for additional data file.¦Click here for additional data file.¦Click here for additional data file.


On-going climate change will drastically modify agriculture in the future, with a need for more sustainable systems, in particular regarding animal production. In this context, genetic diversity is a key factor for adaptation to new conditions: local breeds likely harbor unique adaptive features and represent a key component of diversity to reach resilience. However, local breeds often suffer from small population sizes, which puts these valuable resources at risk of extinction. In chickens, population management programs were initiated a few decades ago in France, relying on a particular niche market that aims at promoting and protecting local breeds. We conducted a unique comprehensive study of 22 French local breeds, along with four commercial lines, to evaluate their genetic conservation status and the efficiency of the population management programs.Using a 57K single nucleotide polymorphism (SNP) chip, we demonstrated that both the between- and within-breed genetic diversity levels are high in the French local chicken populations. Diversity is mainly structured according to the breeds’ selection and history. Nevertheless, we observed a prominent sub-structuring of breeds according to farmers’ practices in terms of exchange, leading to more or less isolated flocks. By analysing demographic parameters and molecular information, we showed that consistent management programs are efficient in conserving genetic diversity, since breeds that integrated such programs earlier had older inbreeding.Management programs of French local chicken breeds have maintained their genetic diversity at a good level. We recommend that future programs sample as many individuals as possible, with emphasis on both males and females from the start, and focus on a quick and strong increase of population size while conserving as many families as possible. We also stress the usefulness of molecular tools to monitor small populations for which pedigrees are not always available. Finally, the breed appears to be an appropriate operational unit for the conservation of genetic diversity, even for local breeds, for which varieties, if present, could also be taken into account.The online version contains supplementary material available at 10.1186/s12711-022-00746-2. During the last decades, climate change has been demonstrated to occur as a result of human activities and to have a growing impact . ClimatePoultry are among the most important contributors to animal production worldwide, with the highest increase in output of all livestock species over the last decades, in particular in developing countries . FollowiIn this context, considering the impact that global change could have on breeding goals with respect to climate, resources or diseases, traditional local poultry breeds may represent a valuable reservoir of genetic diversity for future breeding . Yet, thIn France, local breeds often have an important heritage value and are often named after the name of the region or city they originated from. Some of these local breeds have benefited from the development of designations of origins or quality brands since the 1950s  and the 1960s  as documented by . These qIn this study, we conducted a large and unique survey of 24 representative French local chicken populations using both molecular (57K single nucleotide polymorphism (SNP) genotyping) and demographic (pedigree and management features) data to (i) evaluate the genetic diversity status of French breeds and (ii) assess the impact of population management programs that have been put in place on genetic diversity. For comparison purposes, we also included data from four commercial lines to provide a comprehensive view of the diversity patterns associated with different kinds of population management. Based on such an assessment, we identify some lessons and make recommendations for the management of genetic diversity that could be extended to any genetic conservation program. In particular, we highlight the suitability of molecular tools to monitor the genetic diversity of local populations for which pedigree information is scarce.The aim of the choice of the local breeds included in our study was to represent the diversity of origins and localities of French breeds as much as possible. These populations are generally described by a standard phenotype that is a marker of their identity. These breeds usually do not compete with each other since they are often associated with a specific niche market in a particular region. Breeding goals are defined collectively at the breed association level based on weight, laying rate, etc. Nevertheless, the standard of a breed is an important part of the breeding goals and is maintained based on phenotypic traits such as body shape, body size, feather color, and comb shape. Only individuals that have extreme phenotypes or morphological defects are eliminated.http://centrebechanne.fr/) in 2013. The CSB is in charge of the breeding program for 15 populations  and of the conservation program for four breeds . The CSB has the full responsibility of the breeding program for eight of the 15 breeds,  and acts on behalf of the breed associations for the others. Thus, the nucleus flocks at the CSB are representative of each breed or population and are used to provide chicks to farmers for small-scale production. Breeding units consist of one sire mated with three to four unrelated hens (based on available pedigrees). At collection time, eggs are individually identified according to the mother hen, to be able to construct the pedigrees. A particular effort was made to try to maintain almost all the initial sire and hen families that were present at the creation of the flock across generations. Group 1 also included a selected population of the Marans breed (MAG), which consists of three lines bred by a single breeder for commercial production of eggs with a typical dark brown eggshell. All breeding schemes and mating plans were designed with SYSAAF, a professional union (https://www.sysaaf.fr/sysaaf_eng/), that is also in charge of data storage and management (phenotypes and pedigrees). SYSAAF is in charge of the estimation of breeding values, with the aim to limit inbreeding rate and coancestry of the populations for its members . Tailor-made solutions have been developed to optimize breeding schemes and mating plans in order to limit inbreeding rates while applying selection. They rely on a constrained optimization procedure based on simulated annealing [We distinguished three groups of breeds based on population management  that have not yet been included in a management program with pedigree recording, so these animals were sampled from two to six independent farms, depending on the breed, from 2013 to 2014, and no pedigree information was available. One of these populations corresponds to the Marans chickens (MAR) that are raised by fancy breeders and have the same origin and similar eggshell color as the MAG population of Group 1. However, a larger number of color varieties was observed in the Marans flocks kept by six fancy breeders, who are motivated by the conservation of this breed with no large commercial production.Group 3 included four commercial lines as control populations to assess breed identity and possible introgression events. For these purposes, DNA samples from two fast-growing broiler lines  were obtained from the DNA collection that was set up for the Aviandiv EU project in 1999  and werehttps://doi.org/10.15454/1.5613785622827378E12), and a material transfer agreement was signed with each breed association and each individual farmer.The average number of individuals studied per population was 56, and ranged from 29 for the MAN breed to 96 for the ‘label’ chicken line SGB, with a total of 1512 individuals across breeds. Family structure was considered in order to sample distinct families as much as possible. Blood and DNA samples for 1350 animals of the 24 local populations (Group 1 + Group 2) were stored under the project name BioDivA at the @BRIDGe biological resource center of the CRB-Anim infrastructure  at the start of the program from the number of founders and their sex-ratio  and call rates of at least 5% for both markers and individuals (–mind and –geno functions). This resulted in a set of 46,940 autosomal SNPs for 1512 individuals, with a total call rate of 99.86%.Genotypes were obtained using the 57K Illumina Beadchip . After m–indep-pairwise function for sliding windows of 30 SNPs and a step size of 10 SNPs, and a r2 threshold of 0.8 above which markers were considered in high linkage-disequilibrium. This resulted in a pruned dataset of 45,209 autosomal SNPs.For some analyses, we also constructed a pruned dataset from which linked markers were removed using the Plink 1.9 software by applying the –het function) as follows –fst function of the Plink 1.9 software were also computed on the pruned dataset between populations and within populations according to breeders or lines for MAN, HER, MAG and MAR.The level of diversity at the population level was investigated by computing six summary statistics with the Plink 1.9 software: (i) the mean individual inbreeding coefficient computed using allele frequencies estimated across all populations and all groups, (ii) the mean individual inbreeding coefficient computed using allele frequencies estimated for each population, (iii) the mean observed heterozygosity, (iv) the expected heterozygosity, (v) the average MAF, and (vi) the proportion of fixed alleles in the population. The two first indices were computed using the method of moments estimation procedure  on the p and Fis , respect–homozyg function of the Plink 1.9 software with the following parameters: a minimum ROH size of 500 kb, a minimum number of 30 SNPs in a ROH, a density of at least one SNP every 50 kb, and allowing for one missing and heterozygous SNP per window of 50 SNPs. The proportion of ROH in the genome was converted into inbreeding coefficients following MacQuillan et al. [Runs of homozygosity (ROH) were computed using the n et al. .For the Group 1 breeds, pairwise correlations were computed for genetic diversity measures of the populations with each management program feature ( package  to corre–distance square ibs function of plink 1.9), which was then used to compute and plot an unrooted neighbor-joining tree using the APE R package [Genetic distances between all individuals were computed as an identity-by-state distance matrix  and the emmeans , ggplot2Estimates of entclass1pt{minimaentclass1pt{minimaThe within-population genetic diversity was characterized by the mean of six indices Fig.  and see. CorrelaRuns of homozygosity (ROH) were detected and converted into inbreeding coefficients, F-ROH Fig.  and see and . Estimates of pairwise Fst were all significantly different from 0 , HER and MAN breeds. These breeds exhibited significant Fst values between the different flocks, representing either varieties or breeders, and are described in detail in the following.–4). The global Fst between the seven breeders was 0.14 (s.e. 5.6 –4), while that between the six breeders of the MAR group was 0.06 (s.e. 4.1 –4). Another clustering was also observed for the Marans breed based on the phenotype of feather color, for which we observed an Fst of 0.17 (s.e. 5.7 –4). When the MAR and MAG populations were considered separately, the between-color Fst was 0.11  for the MAR population and 0.18  for the MAG population, as shown in Fig. –4;  between them.The MAR (Group 2) and the MAG (Group 1) subpopulations of the Marans breed were clearly separated, while the LAY population (Group 3) was surprisingly intermingled with the MAG group, for which different sublines could be distinguished. The Fst between the MAG and MAR clusters was 0.14 , the three free-range slow-growing Bresse lines under moderate selection , and the subpopulations of the Marans breed clustering together with the commercial layers . Within the Bresse sub-cluster, the B22 line was the most inbred, which is due to its relatively smaller number of founder hens compared to the other Bresse lines (about 200 for B22 vs 350 for B55 and B11). In addition, the founders of B22 were chosen based on a specific phenotype, i.e. a pale comb color, which likely originates from a subset of families in the ancestral Bresse population. Finally, selection for a pale comb phenotype was applied in the B22 population only, which may have led to stronger genetic drift and inbreeding.The most probable explanation for the clustering of LAY with the Marans breed, and more specifically the MAG lines, is that a crossbreeding event between commercial Rhode Island red layers and ancestors of the MAG lines occurred in the past, motivated by the need to improve the laying rate of these lines. However, crossbreeding between commercial layers and some Marans individuals cannot be excluded either, with the motivation to darken eggshell color of commercial Rhode Island red layers. A similar situation was observed with the GG and the HER breeds, because one fancy breeder of HER chickens introduced GG individuals in its breeding scheme to improve their phenotype and the genetic diversity of his flock.Geographical clustering, as in the case of the CNF and CHA breeds, which both originate from the same French region, suggests a history of exchanges between neighbouring farmers at market places. However, other breeds from the same region do not exhibit such clustering, as could have been expected for the NDB and GDT breeds. Indeed, we did not find any sign of isolation by distance, probably due to multiple introductions and origins . HistoriOur data allowed us to compare the impact of different breeding and management programs on genetic diversity. Diversity indicators did not differ significantly between the highly selected (Group 3) and the managed local breeds (Group 1), both groups exhibiting a high level of genetic diversity. In particular, Fis values were almost null for both groups 1 and 3, indicating good management of matings, regardless of the level of diversity maintained in these populations. This was surprising since we expected low levels of genetic diversity in the local populations due to their limited population sizes, resulting in genetic drift and inbreeding. One exception in Group 1 is the MAG population that consists of three separate lines, which suggest combined effects of isolation of each line, a limited number of founders, and a higher selection intensity. There was, however, a trend towards less fixed and rare alleles for the commercial than the local populations, although this difference was not significant probably because of the LAY population, as layer populations are often much less genetically diverse than broilers because of a smaller base population.We did observe a significant difference in Fis between the unmanaged (i.e. Group 2) and the managed breeds (Groups 1 and 3), with higher positive Fis values for three managed breeds , which can be explained by a Wahlund effect . This suCombining the information available for Group 1 allowed us to evaluate the management programs and the relevant parameters to consider in the future. In particular, we found a significant positive correlation between the number of generations since the start of the program and the current effective population size based on pedigree data, even when correcting for the initial effective population size at the start of the program. This correlation revealed effective management to limit inbreeding across generations, with smaller ROH and thus older inbreeding in populations with an earlier start of the management program. Obviously, the success of such a population management program relies also largely on the initial diversity in the population, as revealed by the positive correlation between initial and current effective population sizes or length of ROH. We also found that the number of sires was the most limiting factor, since it determines the number of families in the mating plan, while there were in general more females than males at the start of the program in each breed. Thus, in a hierarchical mating plan , as those used in poultry breeding, the number of males is the most limiting factor with the strongest impact on the level of inbreeding, since they are responsible for the genetic bottleneck of the population. This was confirmed by the current sex-ratio being more biased toward females than the initial sex-ratio. Thus, the focus should be on conservation of the maximum number of families, in particular in terms of sires.While Fis informs us about the management of populations given the allelic diversity of the population, Fit allows us to measure the absolute genetic diversity of population as a deviation from its expectation based on approximate ancestral allele frequencies. Indeed, we found a positive correlation of Fit (and a weaker one of Fis) with the proportion of fixed alleles, similarly to what Muir et al.  observedGiven that pedigree recording can be a daily constraint, we also show that molecular indicators can be used to efficiently monitor the conservation program at planned intervals by checking inbreeding and co-ancestry. For populations that are sub-divided in multiple flocks, for instance, Lopez-Cortegano et al.  suggesteOur results confirm that the size of the founder population is a critical parameter to conserve genetic diversity. However, a well-planned management makes it possible to maintain genetic diversity even in populations that started with a small number of founders. Indeed, Gicquel et al.  showed tWe have demonstrated that a high level of genetic diversity exists in a large set of French local chicken breeds and, thus, that these breeds represent potential valuable genetic resources for the future. Indeed, such a large genetic diversity is an obligatory feature to cope with global changes and achieve more sustainable production through better adaptation to variable environmental conditions. We have also shown that an appropriate population management or breeding program can reconcile moderate production performance with conservation of genetic diversity, both within and between breeds, although performance and genetic diversity of populations are generally in conflict with each other. Indeed, avoiding genetic relatedness in the mating plan limited the increase in inbreeding that is frequently observed in other fancy breeds, while maintaining numerous breeds across the country. This is due to the particular French niche markets that are associated with a strong local appropriation of breeds to a territory and top-quality products from chickens that are often raised under free-range conditions in populations for which selection pressure is mild. We have highlighted that the number of families, combined with an increase in population size, has a greater impact on inbreeding rate than sampling a large number of male and female founders. Implementing such population management relies on careful pedigree recording and organization of breeders into either one large breeding nucleus or several smaller ones that are well connected to each other. This ensures the conservation of a large part of the initial genetic diversity. Finally, we have shown the usefulness and accuracy of ROH-based estimates and molecular tools, in general, for the evaluation and routine monitoring of genetic diversity, in particular in the absence of complete pedigrees or when only few samples are available, which is often the case for local breeds.Additional file 1: Table S1. Within-population genetic diversity estimates; raw data for Figs. Additional file 2: Table S2. Marginal estimated means of each diversity index per group of populations. The letters and grey color stand for significant pairwise differences between groups (Tukey correction for multiple tests).Additional file 3: Figure S1. Correlation matrix of population molecular or demographic diversity indices for breeds involved in a management program (Group 1). Values correspond to Pearson’s correlation coefficients. Colored cells stand for significant correlations (p < 0.05) either positive (blue) or negative (red), the intensity depending on the strength of the correlation between estimates. White cells represent non-significant correlations. Nb.Generation stands for the number of generations since the start of the management program and Fixed for the proportion of fixed alleles.Additional file 4: Figure S2. Matrix of pairwise Fst between populations. Darker blue indicates a stronger pairwise Fst.Additional file 5: Figure S3. Mean assignment probability of populations to each of the two clusters. The Asian and European clusters are colored in red and blue, respectively.Additional file 6: Figure S4. Unrooted neighbor-joining tree of the Hergnie breed. The colors of the surrounding circle represent the different breeders.


Pinus species that can be caused by one of two distinct but closely related pathogens; Dothistroma septosporum and Dothistroma pini. Dothistroma septosporum has a wide geographic distribution and is relatively well-known. In contrast, D. pini is known only from the United States and Europe, and there is a distinct lack of knowledge regarding its population structure and genetic diversity. The recent development of 16 microsatellite markers for D. pini provided an opportunity to investigate the diversity, structure, and mode of reproduction for populations collected over a period of 12 years, on eight different hosts in Europe. In total, 345 isolates from Belgium, the Czech Republic, France, Hungary, Romania, Western Russia, Serbia, Slovakia, Slovenia, Spain, Switzerland, and Ukraine were screened using microsatellite and species-specific mating type markers. A total of 109 unique multilocus haplotypes were identified and structure analyses suggested that the populations are influenced by location rather than host species. Populations from France and Spain displayed the highest levels of genetic diversity followed by the population in Ukraine. Both mating types were detected in most countries, with the exception of Hungary, Russia and Slovenia. Evidence for sexual recombination was supported only in the population from Spain. The observed population structure and several shared haplotypes between non-bordering countries provides good evidence that the movement of D. pini in Europe has been strongly influenced by human activity in Europe.Dothistroma needle blight (DNB) is an important disease of  Pinus spp., both in planted and native forests, worldwide. The disease has a long history of having damaged plantations in the Southern Hemisphere dating back to the 1960s  is recognized as one of the most important diseases of he 1960s , but durhe 1960s . Dothists  M. Morelet and Dothistroma pini Hulbary that are most effectively distinguished based on molecular identification  project documented, as far as possible, the geographic distribution, hosts and mating type distribution of these two Dothistroma species , Minnesota and Nebraska in the United States  Holmboe  investigate the genetic diversity and population structure of the pathogen including countries or specific locations where the pathogen has been reported in Europe, and 2) determine its mode of reproduction and likely means of dispersal in Europe.Collections of  studies , and to Pine needles that displayed DNB symptoms were collected between 2008 and 2019 from 30 locations in 11 countries of Europe . AdditioFungal tissue was freeze dried and DNA extracted using a Zymo Research ZR fungal/Bacterial DNA MiniPrep™ kit  as described by https://www.qiagenbioinformatics.com/products/clc-main-workbench/) was used to create consensus sequences using the forward and reverse sequences of the ITS region for each isolate. All consensus sequences were compared in a BLAST analysis against the GenBank database  to confirm the identity of each isolate. To determine the ITS haplotype for each confirmed isolate of D. pini, sequences were compared to those reported in CLC Main workbench version 8.0 . The fragments were amplified using the same cycling conditions described by Sixteen labelled microsatellite markers  were use®  size standard and 12 μl formamide. Fragment analyses of the prepared reactions was conducted at the University of Pretoria in South Africa with an ABI PRISM 3500xl capillary auto sequencer (Thermo Fisher Scientific). Allele sizes were scored using GENEMAPPER® Software version 5.0 .PCR products were pooled in two panels for fragment analysis as described by poppr (Alleles scored for each marker were combined to obtain a multilocus haplotype (MLH) for each isolate. Individual isolates were considered clones if they had the same combination of alleles for each marker analyzed. The R package poppr  was usedpoppr (AR) and private allelic richness (PAR) were determined using ADZE .The optimal number of clusters was estimated with StructureSelector . Structuadegenet package in R studio . The optimal number of principal components retained in the analysis was determined by cross-validation using the xvalDapc function.The R studio  was usedR studio  to additp < 0.05.An Analysis of Molecular Variance (AMOVA) test was implemented in GENALEX version 6.5 . The tesD. pini isolates was determined by using the primers of 2, 1.25 μl 10x PCR reaction buffer and the volume was adjusted to 12.5 μl with sterile SABAX water.The mating type of the ® Veriti® 96 well Thermal cycler . The cycling conditions for all microsatellite fragments included an initial denaturation step at 95°C for 4 min, 10 cycles consisting of 94°C for 20 s, a 45 s annealing step with the temperature set according to the protocols by ® LE agarose gel for 50 min at 90 V and viewed under a UV light using the GelDoc™ EZ Imager . When using the MAT1-1 and those with a size of 480 bp were assigned as MAT1-2. The MAT1-1 and 288–323 bp for MAT1-2.PCR reactions were carried out on an Applied Biosystemsp-values (http://www.biostathandbook.com/exactgof.html) was used to test if the mating type ratios deviated from a 1:1 ratio (at p < 0.05) in the non-clone-corrected dataset, which provides evidence of random mating. The index of association (IA) (d)   and rBar(r¯d)  was used)  of the 16 European countries where D. pini has been reported. The isolations were made from plant material obtained from 10 different Pinus species or sub-species with P. nigra being the most common of these  were identified in the 345 isolates analyzed  of which MLH 83,  was shard MLH 47  with an Collections from France had the greatest number of MLHs, followed by the isolates from the Ukraine. When considering populations with a sample size of six and higher, Hungary had the fewest MLHs (five) followed by Russia, Slovenia and Switzerland, which had six each . When coR) and rarefaction of MLGs. The genotypic evenness (E.5) observed in the populations from Russia and Spain were the closest to having equal abundance. Using Nei’s unbiased gene diversity, the Russian population had the highest gene diversity (0.546) followed by those from Spain (0.494), Ukraine (0.379) and France (0.344). This could be due to the uneven sample sizes obtained at the different locations because the algorithm does not correct for small population sizes. Populations from Slovenia and Switzerland had the lowest genotypic diversity and genotypic richness. Countries for which only one or two isolates were available  were not considered in the analyses.Varying levels of genotypic diversity and genotypic richness were observed for the isolates considered in this study . PopulatPAR) (16.51%) followed by those from France (5.50%) and Slovakia (4.59%). Populations from Russia, Serbia, Slovenia and Switzerland had the lowest number of private alleles (0.90%). Within Slovakia, private alleles were from Arboretum Mlyňany, Jahodná, Košice and Zvolen and in France the private alleles were only from Souesmes  clusters, which indicates that this method failed to detect population structure. LnP (K) suggested K = 10 as the optimal scenario. The four Puechmaille methods suggested that 5–8 clusters are most likely the optimal number of clusters depending on the threshold that was set . The STRFor both the K = 8 and K = 10 scenario, the DAPC  and STRUThe DAPC clusters  were mosThe AMOVA results  indicateMAT1-1 individuals were detected and in Serbia only MAT1-2 individuals were present in isolates from Subotica Sands. Similarly, although both mating types were present in the Slovakian collections, either MAT1-1 or MAT1-2 individuals were detected at each of the 10 locations sampled in this country. In Ukraine, the population from Nova Zburivka included only one individual that was MAT1-2 and in Mykolaiv Kinburn, only MAT1-1 individuals were detected. Although both mating types were found in these countries, random mating was statistically supported only in the populations from Spain, Switzerland and Ukraine as well as in the sub-populations from Souesmes and La Bouyale in France, Deliblato Sands in Serbia, and Hola Prystan, Tsjurupinsk and Mykolaiv Kinburn in Ukraine. In isolates from the Czech Republic, Romania and Russia only MAT1-1 individuals were present and in those from Belgium, Hungary, and Slovenia only MAT1-2 individuals were present . Analysis of the non-clone-corrected dataset also supported evidence of sexual recombination in Serbia . This result is however not plausible as the data for both Deliblato Sands and Subotica Sands in Serbia were pooled for this analysis and therefore do not reflect that single mating types were observed at each of these locations.Testing linkage disequilibrium using the clone-corrected dataset, with the index of association and rbarD, provided evidence for sexual recombination only in the population from Spain (D. pini in Europe. Even though extensive sampling was conducted in the area over a 12-year period, due to the low incidence of D. pini, sampling was relatively unstructured and sample sizes were relatively small. This was also emphasized in reports in Switzerland (D. pini was less frequently detected than D. septosporum. Nonetheless, it was clear that D. pini is not new to the European continent and that movement of the pathogen was facilitated through anthropogenic activities.This study provided the first insights into the population structure and genetic diversity of tzerland  as well tzerland  where D.D. pini populations considered in this study grouped in four main geographic clusters including one in Western Europe, two in Central Europe, and one in Eastern Europe. Variable population diversity was observed between countries, with France, Spain and Ukraine having the highest levels of genetic diversity and the presence of both mating types. This suggests that D. pini has most likely been present in those countries for a long period of time and is in agreement with the identification of D. pini in France from herbarium specimens dating back to 1907 and 1965 (D. pini is taking place in Europe, possibly through plant trade (Based on population structure analyses, the and 1965 . In contnt trade .D. pini were present in many populations considered in this study, but evidence for sexual recombination was supported only in the population from Spain. The fact that some isolates of the same MLHs were of different mating type suggests that sexual recombination could be occurring in other European populations of D. pini. This is not unusual and has been found in pathogens such as Teratosphaeria destructans (Verticillium dahliae, a clonally reproducing pathogen, having individuals of opposite mating types that were indicative of cryptic or ancestral sexual recombination events (Both mating types of tructans  as well n events .Dothistroma pini has a limited host range and is currently confined to a particular latitudinal geographical range both in Europe as well as in North America. The majority of the isolates in the present study were from several sub-species of Pinus nigra with few collections from P. coulteri, P. jeffreyi, P. mugo, P. ponderosa, P. schwerinii and P. sylvestris. Many of the single isolates from hosts other than P. nigra were from urban areas or arboreta and not from the native ranges of the host trees. This suggests that D. pini is most likely not native to the areas where it was collected in Europe and could have been introduced to the continent. This is in contrast to the more commonly occurring D. septosporum that is hypothesized to be native to the P. sylvestris forests in Northern Europe (n Europe , Easternn Europe .D. pini is known to occur is North America (D. septosporum and D. pini were conclusively separated based on phylogenetic inference in 2004 (D. pini has been confirmed in only seven states in the Central regions of the United States (D. septosporum in four states (D. pini from the United States, and potentially other unsampled areas such as Asia. This would facilitate an opportunity to compare populations across continents, using either microsatellite markers or whole genome comparisons, in an effort to understand global pathways of spread and potential native areas. The extensive data assembled in the present study will provide a solid foundation for these comparisons.The results of this study have provided no clues to the possible center of origin of the pathogen. The only other area of the world where  America . Dothist America . However in 2004 . Thus, td States  and D. sr states . The tecr states  should sD. septosporum has spread from the Northern Hemisphere to many Southern Hemisphere countries but that the closely related D. pini has not done so. This could be related to host range where D. septosporum has mainly been a problem on P. radiata in the Southern Hemisphere (P. tecunumanii in Colombia (Dothistroma species have relatively wide host ranges and as greater numbers of Pinus spp. are being tested and propagated in Southern Hemisphere countries, it seems plausible to suggest that D. pini poses an important threat to these resources. Based on experience with D. septosporum as well as the increasingly important pine needle pathogen Lecanosticta acicola (D. pini as was found in this study, there is good reason to emphasize the importance of quarantine when moving Pinus germplasm between countries and continents.An intriguing question pertaining to DNB is why misphere , althougColombia . Both Do acicola , and app