Aristeus antennatus is a demersal marine species harvested by bottom trawling in the Mediterranean Sea, the adjacent Atlantic Ocean (AO) waters, and the Mozambique Channel in the Indian Ocean (IO). As it is considered to be a priority species for sustainable fishing, identification of its genetic stocks and the connectivity between them is essential. Using 12 microsatellite loci we detected at least four genetic stocks distributed in the Western Mediterranean (WM), Eastern Mediterranean (EM), AO, and IO and signals for a possible fifth stock in the Alborán Sea. We detected no additional population structuring within the WM. Thus, although the Almería-Orán Front exerts some isolating effect, high genetic homogeneity and gene flow are present within the WM Basin. The IO stock is genetically closer to the AO stock than to the others; thus, the species dispersion route is more likely via the Atlantic Ocean than via the Red Sea. Large effective population sizes suggest population sustainability, but moderate genetic diversity values indicate to proceed with caution. Our genetic results serve as a basis for species conservation to ensure long-term sustainability of this marine resource.The blue and red shrimp  Aristeus antennatus   is a demersal marine decapod that inhabits the muddy bottoms of the continental slope along submarine canyons1. The species is distributed throughout the Mediterranean Sea, in the Atlantic Ocean (AO) waters adjacent to the Strait of Gibraltar from Portugal to Cape Verde, and in the western Indian Ocean (IO) from South Africa across the Mozambique Channel, Zanzibar, and the Maldives Islands; more recently, its presence off the coast of Brazil has been suggested2. Biological studies indicate that this species is the most eurybathic in the Mediterranean Sea, present at depths ranging from 80 to 3,300 m, with a greater abundance in the first 1,000 m depth, where fishing occurs3.The blue and red shrimp A. antennatus is among the most important demersal resources for bottom-trawling fishery in the Mediterranean Sea, with 1,782 tonnes collected in 20144. In fact, since 2006, the General Fisheries Commission for the Mediterranean Sea included A. antennatus on the list of priority species for which fishing regulation plans should be developed5. It is highly appreciated gastronomically and retail market prices are quite high ($70–160/kg) in the Western Mediterranean (WM), where it has been fished most intensely. Outside the Mediterranean Sea, other fishing zones of lesser importance extend to the south of Portugal6, and a new fishing ground was established recently in the Mozambique Channel7.A. antennatus exists at the international level yet. Long-term species sustainability requires adequate fishing and conservation strategies that depend heavily on the identification of the species’ population structure. One of the primary tools for this purpose is genetic analysis, which involves the identification of genetic stocks and connectivity among them8. Such analysis is particularly important to study population processes of marine organisms from inaccessible habitats, such as deep-sea species9.However, no sustainable fishing management policy for A. antennatus in the range of distribution where the species is exploited: the AO, WM, Eastern Mediterranean (EM), and IO2. However, no mtDNA marker study to date has been able to discern genetic structuring at a more regional level between proximal fishing zones12.A previous study using two mitochondrial DNA (mtDNA) markers (16S rDNA and COI) detected four genetic stocks of A. antennatus and the degree of connectivity between stocks. Microsatellite analysis has enabled the identification of stocks in other penaeoid marine shrimp species . However, very few microsatellite markers are available for A. antennatus , and they have not been proven to be useful for the detection of genetic stocks15. This situation led us to develop and characterise 35 new microsatellite loci for A. antennatus using next-generation sequencing13 and in the present study we used 12 of them.A priori, the examination of more variable molecular markers, such as microsatellites, which are sensitive to small but significant genetic differences, should reveal genetic structure at a finer regional scale in populations of 16, affect the population configuration of marine organisms, other factors such as marine geology and ocean currents create many geographic and oceanographic discontinuities that can reduce connectivity, and strongly influence population structure affecting effective species conservation strategies, as has been observed in numerous Mediterranean species . In the Mediterranean Sea, they include the following The Strait of Gibraltar, which forms the dividing zone between the denser Mediterranean waters and the Atlantic Ocean waters.(ii)The Almería-Orán Front, formed at the eastern end of the anticyclonic Eastern Alborán Gyre, between the coasts of the Iberian Peninsula and Algeria.(iii)The Balearic Front and Ibiza Channel, which act in the area between the Balearic Islands and the Iberian Peninsula.(iv)The Strait of Sicily separating the Western Mediterranean Basin from the Eastern Mediterranean Basin.Although human impacts, particularly through fishingOn the other hand, the Red Sea is a possible connection between the Indian Ocean and the Mediterranean Sea, but the Bab-el-Mandeb Strait and the Suez Canal are possible barriers along this route Fig. .A. antennatus and the effect of putative geographic and oceanographic barriers, indicated before, to prevent connectivity among stocks.By using genetic variation at 12 microsatellite loci, the aim of this study is the identification of genetic stocks across the distribution of n = 30) were private alleles present at single locations. Among five sampled regions, Mozambique contributed the largest number of alleles  alleles per locus, of which 19%  of 0.458  and 0.628 , respectively. Mozambique sample presented the highest values in all cases  alleles per locus, average allelic richness (Ar) value of 7.5 , and mean observed and expected heterozygosities  using NeEstimator software often yielded negative values that were interpreted as very large , although even in these cases the upper boundaries of the 95% confidence intervals were infinite  and not significant , rather than within groups (FSC). The first and fourth hypotheses yielded the most significant FCT values and no significant divergence within groups  due to the low homogeneity within the groups created  revealed significant genetic structuring in all hypothesised grouping scenarios, with significant ps Table . For theed Table . These rDiscriminant analysis of principal components (DAPC) enabled differentiation of four groups: IO (Mozambique), AO (Faro), EM (Ionian Sea), and AS + WM Fig. . The horr = 0.67579, p = 0.00842) and the Red Sea route . When we excluded Mozambique and Faro (AO), which is located on the other side of the Strait of Gibraltar, but retained the EM sample, again a significant correlation was obtained , indicating a pattern of isolation by distance (IBD) along the Mediterranean Sea.We detected significant correlation between genetic and geographical distances for both: the Atlantic route  Fig. , which aet al.13, likely because of higher sample size in the present study. Our overall median Ho and He values 20. The Mozambique sample presented the highest levels of diversity , and to a much lesser degree from the Red Sea21.Our analysis yielded higher average Na values  lower limit of CI, the risk of demographic reduction due to inbreeding is low. Nevertheless, as estimates of genetic diversity at our study were not particularly high, caution is warranted because overfishing could result in the loss of essential alleles for adaptation to environmental changes16.The dy Table , to avoiFST values obtained in our study may appear to be low, but they were significant among the principal groups detected  >2 weeks] are significantly affected by oceanographic discontinuities that reduce gene flow between populationsA. antennatus between the AO and Mediterranean Sea  in A. antennatus, as they reported no significant genetic variation between the Atlantic and the Mediterranean populations. Considering that the depth of maximum abundance of A. antennatus adults (600–800 m)3 falls below the maximum sill depth in the Strait of Gibraltar (284 m at a width of ~30 km)21, the dispersal of adults is restricted, although possible planktonic larval drift by surface currents would not36.The observed genetic differentiation of ea Table , 3, 4 fiA. antennatus2. However, our results indicated a slight isolation of the AS sample from the other WM samples  and Palaemon elegans (decapod)39, although it seems to cause no effect on other marine decapod species, including Perapenaeus longirostris (Penaoidea)26.Previous mtDNA analyses failed to identify the Almería-Orán Front as a barrier between populations of es Table , despitees Table . The sizLiocarcinus depurator26 and littoral fish40, respectively, they had no effect on A. antennatus genetic structuring, confirming the results obtained using mtDNA12 and other microsatellite loci15. The detection of several larval stages of A. antennatus in this area36 reinforce the hypothesis that larvae are passively transported by oceanic currents in most of the marine decapods33, and justify the observed high degree at gene flow in the Western Mediterranean  kerathurus30. Our analysis showed genetic differentiation between the WM and EM  and wider (130 km) than the latter21. IBD signals along the WM–EM axis also have been detected in the decapod P. longirostris42. Thus, the EM stock of A. antennatus might be affected by adaptations to the Eastern Mediterranean Basin due to differences in environmental conditions from the Western Mediterranean Basin21.The Strait of Sicily acts as a weak geographic barrier to gene flow in many marine speciesEM Table , Fig. 2,44, our findings indicate that the IO stock of A. antennatus is genetically closer to the AO stock than to the Mediterranean ones 45, prevent connectivity between the Mediterranean Sea and Indian Ocean populations, and the dispersal route of A. antennatus is more likely via the Atlantic Ocean than via the Red Sea. On the other hand, an open question is raised, applicable also to other demersal species for which little information is available, of how we may explain the scant presence of A. antennatus along the Atlantic Coast of Africa2. The species may have a “patchwork” distribution, or demersal populations may remain undiscovered.The IO stock presented the greatest genetic distance Table  and was es Table , 4. ThesA. antennatus, we were able to detect genetic differentiation and identify at least four genetic stocks and the signal for a fifth stock along the species’ distribution. Our results support efforts to achieve sustainability of this resource for future generations via genetic assessment. Currently, overfishing strongly affects marine natural resources16. Taking into account genetic connectivity are fundamentally important to provide a basis for the study of A. antennatus resilience and adaptation to possible environmental changes from human impact46.In conclusion, using a set of sensitive microsatellite loci in A. antennatus is currently exploited across the following five regions: AO, AS, WM, EM, and IO. As the species is traditionally harvested in waters of the WM4, more locations from this region were included in the sampling design  and one singleplex PCR for optimal amplification, following standard procedures with an annealing temperature of 50 °C or 60 °C, as described in Planella et al.22. PCR product sizing was performed with the GeneScanTM 500 LIZ® size standard (Applied Biosystems) on an ABI PRISM® 3130 Genetic Analyzer (Applied Biosystems). Allele scoring was performed with Geneious v. 7.1.9 software47.We performed genomic DNA extraction following the standard phenol-chloroform procedure, and we carried out genotyping for 12 microsatellites developed for Na) and richness (Ar) per locus and per population with FSTAT v. 2.9.3.2 software48, and computed the number of private alleles (Ap) with GenAlEx v. 6.503 software49. We estimated observed and expected heterozygosities (Ho and He), and inbreeding coefficient FIS50 and tested for linkage and HW equilibria using Genepop v. 4.4.3 software51. We assessed test significance using exact tests  and Fisher’s method for global tests. Significance values were adjusted for multiple comparisons using Bonferroni correction. Micro-Checker software52 was employed to identify scoring errors due to large allele drop-out, stuttering or presence of null alleles.We calculated allele number (Ne) for each location using NeEstimator v. 2.1 software19 with the Linkage Disequilibrium method under a random mating model, and we determined 95% confidence intervals using the non-parametric jack-knife method. Given the minimum sample size of 38  was inferred. We estimated the frequency of null alleles and computed FST values that were and were not corrected for the presence of null alleles using 1,000 bootstrap replicates.The FreeNA programme53, computing F-statistics derived from hierarchical partitions. Four different a priori grouping scenarios were independently analyzed to test whether geographic and oceanographic discontinuities effectively reduced gene flow across the species distribution  additional groupings with modification of the effectiveness of various barriers. Statistical significance of F-statistics for each scenario was tested with 10,000 permutations. In such approach, the best grouping scenario maximizes FCT by reducing FSC.We conducted a global locus-by-locus AMOVA analysis with Arlequin v. 3.5.1.2 software55 to construct an NJ tree based on Latter’s FST genetic distance56. The robustness of the tree was tested using bootstrap analysis with 1,000 replicates. DAPC is a multivariate model-free type of analysis conducted with no assumption of HW or linkage equilibrium57. According to Pometti et al.58, it allows more rapid and accurate inference of K populations than do Bayesian model–based clustering methods. We conducted DAPC with adegenet v. 2.1.059 to identify genetic clusters among sampled individuals. We also computed membership probabilities based on the retained discriminant functions, and we used a compoplot function to interpret group memberships in DAPC and to infer admixture between clusters.We used Populations v. 1.2.32 software51. We used the Mantel test based on 100,000 permutations and correlated the logarithm of the pairwise geographical distances between locations and pairwise estimates of linearized FST [FST/(1 − FST)], obtained with FreeNA. The geographical distances between sampling sites were measured as the shortest marine map distance (in kilometers) in Google Earth based on two hypothesised dispersal routes: (i) that connecting Mozambique with Atlantic populations through the Cape of Good Hope and along the Atlantic coast of Africa, and (ii) passage through the Red Sea and Suez Canal, and then through the Mediterranean Sea.IBD was tested with the ISOLDE programme in Genepop v. 4.4.360 (https://popgen.shinyapps.io/divMigrate-online/) using Nm61 as a measure of genetic distance. Population samples (or groups of samples) are represented by nodes in the networks. In addition, to test whether the gene flow among regions was asymmetrically significant, 1,000 bootstrap replicates were performed (α = 0.05).Finally, we generated a network graph representing relative directional migration rates between regions, considering the locations adjoining the oceanographic barriers, with divMigrate-onlineSupplementary Information


The Iberian Peninsula is a well-delimited geographic region with a rich and complex human history. However, the causes of its genetic structure and past migratory dynamics are not yet fully understood. In order to shed light on them, here we evaluated the gene flow and genetic structure throughout the Iberian Peninsula with spatially explicit modelling applied to a georeferenced genetic dataset composed of genome-wide SNPs from 746 individuals belonging to 17 different regions of the Peninsula. We found contrasting patterns of genetic structure throughout Iberia. In particular, we identified strong patterns of genetic differentiation caused by relevant barriers to gene flow in northern regions and, on the other hand, a large genetic similarity in central and southern regions. In addition, our results showed a preferential north to south migratory dynamics and suggest a sex-biased dispersal in Mediterranean and southern regions. The estimated genetic patterns did not fit with the geographical relief of the Iberian landscape and they rather seem to follow political and linguistic territorial boundaries. Essentially, these methods allow the study of patterns of spatially-explicit genetic differentiation by modulating genetic distances between samples or populations as a function of the geographic distance2.Recent approaches combining geographic information with genetic data provide robust analyses of the population structure across a landscape8. However, genetic differentiation has also been associated with political-cultural boundaries that limit the gene flow between populations10. Previous studies have shown that major geographic features often lead to strong genetic differentiation at a global scale11, while at a regional/local scale cultural barriers usually modulate the genetic structure of populations14.Studies based on genome-wide autosomal data showed that genetic differentiation among European populations is, in general, highly correlated with geography16 and was probably a reservoir for the repopulation of Europe after the end of the LGM17. Recently, several studies based on ancient DNA shed new light into the genomic history of the Iberian Peninsula. Olalde et al.18 reported the presence of genetic structure in Palaeolithic hunter-gatherer populations. While the adoption of a sedentary lifestyle influenced the genetic variability of Iberians21, in the Neolithic individuals still carried a strong hunter-gatherer component due to admixture with hunter-gatherer populations23. Post Neolithic migrations also affected the genetic landscape of the region. A large replacement of male ancestry during the Bronze Age was inferred24 and it was recently associated with the massive immigration of steppe populations at ~4,500 years ago26. Additionally, two recent studies based on ancient DNA documented gene flow from North Africa toward the Iberian Peninsula at least since the Late Neolithic27. In the last two millennia, the Iberian Peninsula was occupied by Phoenicians, Greeks, Romans, German tribes and, more recently, most of the Peninsula was under Islamic rule (from the beginning of the 8th until the end of the 15th century) that was revoked with the so-called Reconquista where Catholic kingdoms recolonized the territory29. Current Iberian populations reflect this complex admixture of cultures and genetic backgrounds30, being one of the regions in Europe with the highest genetic diversity31. On the other hand, results based on autosomal markers showed a general homogeneity among Iberian populations32 with some local differentiation identified with mitochondrial DNA and Y-chromosome data36. Interestingly, previous studies showed that genetic variation correlates with the geographic distance in the Iberian Peninsula38. However, some of these studies were based on limited genetic information . Altogether, the patterns of genetic variation across the Iberian Peninsula are still not totally clear and an analysis of comprehensive genetic data is required to address this issue.The Iberian Peninsula is separated from the rest of Europe by a range of mountains (the Pyrenees) and from Africa by a small stretch of water . The history of the Iberian Peninsula is characterized by multiple migrations and settlements from diverse population groups. The region was a refuge for populations fleeing from glaciers advance during the Last Glacial Maximum (LGM)Here we extended previous works by investigating genetic structure, genetic gradients and migratory dynamics of humans in the Iberian Peninsula at a fine-scale level through the analysis of a genome-wide dataset of 1,204 individuals belonging to 26 populations, based on current state of the art spatially-explicit models. Our results show that the genetic structure in northern Iberia agrees with the political frontiers established during the first centuries of the Reconquista, while the genetic landscape of central and southern regions do not show this association and present large migration corridors especially throughout the Mediterranean coast.We compiled genome-wide SNP data of individuals belonging to 26 populations from publicly available resources to generate two datasets: a global dataset with all the compiled populations Table , and a sIn order to explore the presence of population stratification in global and Iberian datasets, we performed a principal component analysis (PCA). PCA results obtained from the global dataset showed a clear genetic differentiation between European, North African and sub-Saharan African populations for the first PC PC1), while PC2 could distinguish between northern, central and southern Europeans  on the global dataset. Results showed that most ancestry of Iberians is shared with other European samples, followed by contributions from Africa . We found that isolation by distance models considering migration and migration with admixture presented a better fitting with the observed data, compared with models based on pure isolation by distance with and without admixture  suggested that the EEMS model was robust to describe the observed data  estimated from the autosomes presented several barriers to gene flow (regions of low effective migration rate) splitting the northern regions. It also showed corridors of genetic similarity (areas of high effective migration rate) connecting northern, central and southern regions Fig. . Populat2 = 1.00) but the fitting was weak when considering dissimilarities between demes (R2 = 0.373). Similarly to the what was found with the autosomes, a significant deviation from an isolation by distance model (R2 coefficient = 0.01) was found when the observed pairwise genetic distances between populations were related with geographic distances  . Additionally, a deviation from an isolation by distance model (R2 = 0.074) was found when the observed pairwise genetic distances between populations were correlated with geographic distances .39. Here we extended those studies by considering a more comprehensive dataset of genome-wide genetic information, analysing autosomes and, for the first time, the X chromosome. We found that the characterization of the genetic landscape of the Iberian Peninsula, with spatially explicit approaches, presents subtle structure features. Patterns of genetic differentiation were largely observed along a longitudinal orientation . However, as previously indicated, these results should be interpreted with caution since the patterns found for the autosomes could only be partially replicated when analysing only chromosome 7 markers, and further studies will be necessary to clarify this issue.The presence of genetic structure in the Iberian Peninsula has been described based on the Y chromosome and mtDNA at a regional levelula Figs  and 3, wula Figs , which slar Fig.  with onllar Fig. . Intereslar Fig. , which i44. Additionally, the genetic similarity found between Spanish and French Basques using both PCA  showed a genetic distinctiveness from the other Iberian populations for both autosomes Figs  and S5 a45. Moreover, a study on marital behaviour showed a high proportion of inbreeding that could lead to genetic differentiation from other Iberian populations46. Interestingly, Portugal and Galicia may share their ancestral history40 and we also found results supporting this hypothesis. The geogenetic map , before the annexation by the Visigoths48. Portugal became politically independent in 1143 and expanded rapidly toward the south (Portuguese Reconquista ended by 1249). The establishment of a political border could have promoted some cultural divergences but still important relationships were kept between Galicia and Portugal because of their geographic proximity, similar language and sociological factors. A recent study based on genome data but applying other approaches also showed remarkable genetic similarities between these regions40, in agreement with our findings.The northwest region of the Peninsula  presented a higher than average genetic differentiation when compared to other Spanish populations Figs  and 3. Amap Fig.  shows thIn conclusion, we found that the genetic landscape across the Iberian Peninsula is complex, with contrasting patterns of remarkable genetic dissimilarity in the North and genetic homogeneity in the South. We interpret our findings considering that the geography is not the main factor shaping the genetic landscape of the Iberian Peninsula. Instead the major genetic dissimilarities estimated from our data better fitted with historical, political and cultural barriers that influenced migratory patterns and the relationships between populations..et al.49 and 15 Spanish populations retrieved from Botigue et al.30, Henn et al.50 and Fernandez-Rozadilla et al.51), and 4 European populations , Tuscans (TSI) and Finns (FIN) obtained from the 1000 Genomes Project53). Out of Europe we included 4 North African populations  retrieved from Henn et al.50 and one sub-Saharan (Yoruba) from the 1000 Genomes Project53.We examined a genome-wide SNP dataset genotyped on the Affymetrix 6.0 chip, composed of 26 populations reported in several previous studies: 17 Iberian populations  <0.05 were excluded. After applying quality control filters, the global dataset and the Iberian dataset presented a total of 1,204 and 746 individuals, respectively  a pure isolation by distance model, in which populations are stationary (absence of migration) and do not present admixture; (2) a model of isolation by distance with admixture, in which populations are stationary (absence of migration) and can present admixture; (3) a model of isolation by distance with migration, in which population locations are inferred  and cannot present admixture; (4) a model of isolation by distance with migration and admixture, in which population locations are inferred  and can present admixture. For each model we ran 10 independent short chains of 106 iterations each, followed by a long chain of 107 iterations based on the estimates of the last iteration of the short chain with the highest posterior probability. A sample was taken every 104 iterations leading to a total of 1,000 points to estimate the posterior distribution of each parameter. Initial population locations were randomly taken from a uniform distribution of −180 to 180 and −90 to 90 for longitude and latitude, respectively.We investigated patterns of isolation by distance and gene flow throughout the Iberian Peninsula for the autosomes in the Iberian dataset with the Bayesian framework SpaceMix2 to the Iberian dataset. We estimated migration rate surfaces allowing the visualization of corridors and barriers to gene flow. Basically, EEMS considers the stepping-stone migration model to infer migration rates through a Bayesian approach2. The method applies a dense triangular grid that fills the entire landscape and assigns each individual to the geographic neighbour deme of each population to finally provide a map quantifying genetic dissimilarities. We also estimated a matrix of genetic dissimilarities between all 746 individuals with the bed2cliffs method implemented in the EEMS package. For all the analyses we specified a total of 1,000 demes and we performed 5 independent runs with 1.1 × 107 iterations, a thinning interval of 1,000 iterations and 106 iterations as burn-in. Finally, we adjusted migration and diversity parameters to model acceptance rates between 10–40% following the software documentation.We also identified patterns of spatial structure and genetic heterogeneity within the Iberian Peninsula applying the framework EEMSIn order to test the presence of sex-biased migration in the Iberian Peninsula, we compared the patterns of genetic differentiation of the X chromosome with the autosomes applying the EEMS framework. Additionally, we analysed the patterns of genetic differentiation only on chromosome 7, which presents a size similar to the X chromosome (around 150 million bases), to ensure that the higher linkage on the X chromosome or the lower number of SNPs did not affect the results. Therefore we trimmed chromosome 7 to have a similar SNP density to the X chromosome  by applying a LD threshold of 0.205 with PLINK 1.9 (Supplementary Table Supplementary Material


Capra hircus) were domesticated during the lateNeolithic, approximately 10,500 years ago, and humans exerted minor selectionpressure until fairly recently. Probably the largest genetic change occurringover the millennia happened via natural selection and random genetic drift, thelatter causing genes to be fixed in small and isolated populations. Recenthuman-influenced genetic changes have occurred through biometrics and genomics.For the most part, biometrics has concentrated upon the refining of estimates ofheritabilities and genetic correlations. Heritabilities are instrumental in thecalculation of estimated breeding values and genetic correlations are necessaryin the construction of selection indices that account for changes in multipletraits under selection at one time. Early genomic studies focused uponmicrosatellite markers, which are short tandem repeats of nucleic acids andwhich are detected using polymerase chain reaction primers flanking themicrosatellite. Microsatellite markers have been very important in parentageverification, which can impact genetic progress. Additionally, microsatellitemarkers have been a useful tool in assessing genetic diversity between and amongbreeds, which is important in the conservation of minor breeds. Singlenucleotide polymorphisms are a new genomic tool that have refined classical BLUPmethodology (biometric) to provide more accurate genomic estimated breedingvalues, provided a large reference population is available.Goats ( Capra hircus) have been modified by humankind ever sincetheir domestication during the late Neolithic, approximately 10,500 years ago, inthe Fertile Crescent [Goats , ‘intermediate’ (E),‘weak’  and ‘null’ (O) whenαs1-casein is absent. The quantitative effects of goatαs1-casein variants on dairy traits were studied and haveindicated that the difference for protein content between the extreme genotypes AA(favorable) and FF was 4.5 g/kg. These results indicate that milk from AA goats havehigher protein, casein, and fat content, and a higher casein: protein ratio thanmilk from FF goats. A major gene such as αs1-casein can alsoexplain a major portion of the total genetic variation in protein content. One studycompared estimates of genetic parameters for Alpine goats using an animal modelincluding or excluding αs1-casein fixed effect [s1-caseineffect was accounted for, showing that variance at this major gene represents about50% of total genetic variance of protein content in this goat population[s1-casein, genetic correlationsbetween protein yield and protein content were 0.09 and −0.22, respectively[In the beginning of the genomics era in the early 1990’s, research studiesinvestigated the influence of major genes upon productivity traits but has evolvedrapidly –29. Markver time ”. The knver time ,31 and cd effect . The herpulation. Whenigectively,32.s1-casein gene was related to primiparouslitter size in five breeds of Chinese goats. Individuals with the II genotype hadthe largest litter size when compared with ID or DD genotypes [In a recent study in China, a variant within the αenotypes . The aut−15. Thus,microsatellite markers are a very reliable tool for parentage identification. Thenumber of PCR primers is periodically evaluated and the number of recommended primeris now 14  relative tothe total population (T), FIS is the measure of an individual (I)relative to the subpopulation (S), and FST is the measure of asubpopulation (S) relative to the total (T) [IS (inbreeding coefficient) isinterpreted as a correlation measure between alleles rather than a probability,which means that it can have negative values. Positive values of FISindicate that the individual is less heterozygous than expected given thesubpopulation’s heterozygosity, i.e. inbred, and negative values ofFIS indicate that the individual is more heterozygous then expectedgiven the subpopulation, i.e. outbred. High FST (gene fixationcoefficient) implies a considerable degree of differentiation among populations. Inthis Canary Island study, an FST of only 0.04 indicates that most of thebreeds were genetically similar indicating a high degree of interbreeding.Microsatellites have also been used to evaluate genetic distances among breeds, whichis a tool used in the conservation and management of animal genetic resources,especially the conservation of endangered indigenous breeds –41. In awas 0.04 .In a stwas 0.04 . F can botal (T) . FIS  and two introducedbreeds (Saanen and Nubian), was examined using 15 microsatellite markers. A meanIS valuesranged between 0.18 and −0.04, and FST was 0.07. As expected,Alpine, Saanen, and Toggenburg provided one phylogenetic cluster while Jamunapariand Nubian provided two other clusters.In a Thai dairy goat study on genetic diversity, five imported breeds  were evaluated using five microsatellitemarkers . Amean IS values ranged between 0.32 and−0.10. FST was higher for herds (FST S = 0.08) thanfor breeds (FST P = 0.03), indicating similarity between the importedbreeds and the existence of crosses between them. As expected, the genetic distancewas greatest between Moxotó and the imported breeds, which were of similarphylogenetic clusters.In a Brazilian dairy goat study, Moxotó, which is a local goat breedwell-adapted to semiarid Northeast Brazil, was compared to two exotic dairy goatbreeds (Alpine and Saanen) using 11 microsatellite markers . NortheaIn addition to the above usages of microsatellite markers, they have been used toverify dairy goat products . Adulterhttp://www.goatgenome.org/), several research projects involved insequencing the goat genome successfully identified approximately twelve million highquality SNP variants in the goat genome [The successor to microsatellite markers is single nucleotide polymorphisms,frequently called SNPs (pronounced “snips”). A SNP is thesingle-pair difference at a specific location on specific chromosome. For example, aSNP can occur when the nucleotide cytosine (C) is replaced with the nucleotidethymine (T). SNPs occur normally throughout the genome of all livestock species,including goats. Under the umbrella of the International Goat Genome Consortium. These SNP effects were not present in theAlpine breed. In an Eastern European study using significantly lower animal numbers,researchers found 10 candidate genes  for resistance to mastitis andgastrointestinal parasitism [SNPs and GWAS have great potential for traits that are hard or costly to measure, anduddrasitism .Another GWAS study in France examined supernumerary teats (SNT) in goats.Supernu−57 and2.94×10−34, respectively. In the reference data setof 109 Alpine goats, both panels correctly identified all parent–offspringrelationships. Further work by these researchers constructed a 195 SNP panel witheven greater accuracy [As with microsatellite marker, a reduced panel SNP chip has been proposed for use inparentage and pedigree verification . In an Iaccuracy . Thus, aDGAT1) gene onchromosome 14 was identified as a functional and positional candidate gene affectingfat content. A reference sequence of the DGAT1 gene was completedand 29 polymorphisms were found, including two novel mutations, R251L and R396W. Inthe Saanen breed, the frequency of the R251L mutation was 3.5%. In bothSaanen and Alpine breeds, the frequency of the R396W mutation was 13% and7%, respectively. Both mutations were associated with a notable decrease inmilk fat content.GWAS studies can also identify candidate genes affecting production traits. In aFrCombining biometrics and genomics yields genomic selection ,63. Genos1-casein) and yielded a better prediction of genomic breedingvalues than unweighted ssGBLUP [s1-casein alleles [Also, in France, three weighted single-step GBLUP (ssGBLUP) methods were moreefficient for detecting SNPs associated with protein contentfor feed efficiency and body weight in a dairy goat herd . Feed acA recent study in Spain, using 50,649 lactation records on 19,067 goats of theFlorida breed which represented daughters of 4,397 dams and 500 sires, indicatedthat ssGBLUP methodology increased average reliability of the estimations by1.06% over classical BLUP methodology . The corIn the most recent history of the dairy goat, biometric and genomic approaches havebeen used to select animals of highest genetic merit, and therefore, to improvebreeds. Biometric approaches have concentrated upon more accurate estimates ofheritabilities and genetic correlations, the former is used in the EBV and thelatter is used in the formulation of selection indices. EBV and selection indiceshave been used in various countries to improve milk, fat, and protein yields. Thegenomics approach started with microsatellite markers, which have been veryimportant in parentage determination and in genetic conservation studies.Microsatellite markers are an inexpensive means to determine genetic diversitywithin and across breeds. Most recently, genomics has focused upon SNP and thewealth of information that this technology brings.


Schistosoma mansoni, S. haematobium and S. japonicum. Here we present novel data on the population genetic structure of Schistosoma bovis, a highly widespread and prevalent schistosome infecting ruminants, and therefore of veterinary importance.Schistosomiasis is neglected tropical parasitic disease affecting both humans and animals. Due to the human health impact, population genetic studies have focused on the three main human-infecting schistosome species: S. bovis were sampled in the two main abattoirs of Cameroon . Twenty-two cows originating from four distinct localities were sampled and a total of 218 parasites were recovered. All parasites were genotyped using a panel of 14 microsatellite markers and a sub-sample of 91 parasites were sequenced and characterized with the mitochondrial (cox1) and nuclear (ITS) genetic markers.Adult S. bovis is more polymorphic than its sister species, S. haematobium, and that the haplotype diversity is similar to that of S. mansoni while the nucleotide diversity does not significantly differ from that of S. haematobium. The resulting negative Tajima’s D* and Fu and Li’s D* indices could be a signature of population demographic expansion. No S. haematobium/S. bovis hybrids were observed in our populations, thus all samples were considered as pure S. bovis.No significant difference in allelic richness, heterozygosity, nucleotide diversity and haplotype diversity was observed between the populations. Additionally, no strong genetic structure was observed at the country scale. Our data also show that S. bovis. No strong genetic structure was observed at the country scale but some genetic indices could be associated as a signature of population demographic expansion.This study provides novel insights into genetic diversity and population genetic structure of The online version of this article (10.1186/s13071-019-3307-0) contains supplementary material, which is available to authorized users. Schistosoma mansoni, S. haematobium, S. intercalatum and S. guineensis in Africa, and S. mekongi and S. japonicum in Asia  involved in human infections, creating a considerable human health concern [S. haematobium, is endemic in northern Cameroon and other foci are located in the south of the country in Kékem, Loum, Barombi-kotto, Kumba and Marumba [S. bovis and S. curassoni, respectively [Schistosoma species are endemic to Cameroon, interspecific mating between different species have been reported between S. haematobium × S. guineensis and also between S. haematobium × S. mansoni [S. haematobium × S. bovis hybrids. Schistosoma haematobium is a human parasite living in the veins around the urinary bladder while S. bovis is a livestock parasite living in the mesenteric veins around the gut. S. haematobium × S. bovis hybrids are largely present in West Africa, including Senegal, Mali, Benin and Niger [In Cameroon, out of 23 million inhabitants, there are five million people at risk of schistosomiasis, of which two million are infected  with thr concern . Urogeni Marumba –14. Limiectively . Since s mansoni , 17 but nd Niger . This land Niger –21.S. bovis are structured in Cameroon at a large geographical scale. We used genetic approaches, including both mitochondrial (cox1) and nuclear markers (ITS and microsatellites), first to characterize the population genetic structure of S. bovis and secondly to identify potential S. haematobium × S. bovis hybrids in Cameroon. Additionally, we took advantage of the fact that the microsatellite markers used in this study are informative for both S. haematobium and S. bovis to compare the genetic diversity between these two sister species.Here, our aim is to document how populations of S. bovis infection . In Cameroon the majority of cattle breeding areas are situated in the north of the country Fig.  and animion Fig. . Maroua Upon slaughter, the mesenteric vessels of each cow were visually inspected for schistosome parasites. The observed adult schistosomes were recovered using forceps and washed using saline solution. Adult pairs were gently separated into males and females while alive before being individually stored in 95% ethanol for subsequent genetic analyses.cox1 markers to clarify species and hybrid status and investigate mitochondrial genetic diversity. The details of this subsample is presented in Additional file Total genomic DNA from a total of 218 individual schistosomes  from 22 cows were individually extracted using the E.Z.N.A. tissue DNA extraction kit . All specimens were genotyped using a panel of microsatellites markers for population genetic analysis. A subsample of 91 individuals  were further molecularly characterized using both the nuclear ITS and mitochondrial S. haematobium by Webster et al. [S. haematobium and S. bovis are closely related sister species so the microsatellites were known to cross-react with S. bovis . Of the 18 microsatellites tested, 14 successfully amplified on our S. bovis samples and were thus used in the present study. The remaining four markers  succeeded on less than 20% of the specimens and were thus discarded from our dataset. Microsatellite amplifications were performed using the Qiagen® Multiplex PCR kit, . The forward primers were fluorescently labeled using 6-FAM, VIC, NED and PET dyes  as per [Individual samples were genotyped with a set of microsatellites initially developed for r et al. ; however) as per . PCRs wecox1 and ITS gene regions were amplified from the DNA samples using primers defined by Lockyer et al. [2, 5 μl of dNTP (10 mM), 1 μl of each primer (10 μM) and 0.2 μl of Taq polymerase (Go taq®G2 Hot Start Polymerase). Cycling conditions were the same for both genes: 3 min at 95 °C followed by 35 cycles of 95 °C for 40 s, 52 °C for 40 s and 72 °C for 70 s, with an extension of 4 min at 72 °C. After amplification, 4.5 μl of the individual PCR products were stained with ethidium bromide, loaded on a 1.5% agarose gel and visually inspected to check the size and the quality of the amplicons (1100–1200 bp). All positive DNA products were then purified and sequenced in both directions (i.e. using both the forward and the reverse primers) on an Applied Biosystems Genetic Analyser at Genoscreen.The partial r et al.  and Barbr et al. , respectis at each microsatellite locus using FSTAT v.2.9.3.2. [st according to Weir & Cockerham [6 iterations after a ‘burn-in’ period of 250,000 iterations, using an admixture model and the other parameters were set by default. The log likelihood for each K was then averaged over the three runs using the corrsieve package in R. The ΔK-values were then computed in R to determine the most likely K number among the K tested as described in Evanno et al. [6 iterations and setting the same initial parameters as those described previously. The probability for each individual to belong to each K was then averaged over the 10 runs . These Q values were then graphically represented using Clumpp [The genetic diversity of the schistosomes from each origin, defined as populations, was assessed by computing the expected heterozygosity (He), number of alleles (A), allelic richness (Ar) and F2.9.3.2. . These pockerham  using FSockerham . Finallyockerham . In parto et al. . Two K-vg Clumpp  and the g Clumpp  softwareS. haematobium and S. bovis to compare the genetic diversity between the two species based upon the 14 shared markers. To do so, we used a raw dataset from a previous article focusing on S. haematobium [S. haematobium miracidia larvae from both Niger and Zanzibar [Finally, we took advantage of the fact that the microsatellite markers used in this study are informative for both matobium . This raZanzibar . Both, tcox1 sequences to the GenBank nucleotide database. Concerning ITS, because this gene differs at 5 mutation points between S. haematobium and S. bovis, the sequence chromatograms were carefully checked at these mutation sites to identify possible heterogenous ITS sequences [cox1 data, polymorphism indices  were calculated using DnaSP v.6.0 software [Schistosoma haematobium (AY157209.1) as the outgroup. All sequences were uploaded to GenBank (accession numbers MH647122-MH647179).The sequences were checked for possible sequencing errors and concatenated using Sequencher software v.4.5. The resulting cleaned sequences were next aligned using Bioedit v.7.0.9 and ClustalW software. Species assignation was done by comparing ITS and equences . For thesoftware . Signifisoftware . The bessoftware . A phyloχ2 = 4.8, df = 3, P = 0.18; Table Among the 480 cows inspected, 94 were found to be positive 19.5%) for adult schistosome worms. Depending on the animals’ origin, the prevalence varied from 13.5 to 37.5% although there was no significant difference (9.5% for is) are presented in Table χ2 = 4.036, df = 3, P = 0.258), Ar  and Fis . The only statistical difference was observed for the numbers of allele A , which could be attributed to the sampling size disequilibrium between the sites, from n = 28 in Bertoua to n = 71 samples in Garoua. The AMOVA analysis revealed that most of the genetic variation (98.09%) is found within animals and not between animals sampled  presented both lower allelic richness and lower diversity than the whole S. bovis population from Cameroon. The difference was statistically significant for allelic richness only .Table ium from . The twoS. bovis. No double peak was observed in any sequence at the polymorphic sites between S. bovis and S. haematobium. All the cox1 sequences were also identified as S. bovis. Thus, according to these two markers all the schistosomes analyzed were considered as pure S. bovis. A total of 58 cox1 haplotypes (accession numbers MH647122-MH647179) were identified. The haplotype diversity (± SD) and nucleotide diversity (± SD) were 0.961 ± 0.012 and 0.00602 ± 0.00043, respectively. No difference in haplotype and nucleotide diversity was observed according to the sampling site (Table P < 0.01) and Fu and Li’s D*  statistics were significantly negative.Only one ITS genotype was identified among the 91 screened schistosomes and this was identified as AMOVA analysis revealed that 88.83% of the genetic variation can be explained by the variation within animals whereas the remaining is due to variation among animals within sites (8.81%) or among sites (2.36%)  and nuclear markers (microsatellites) indicated that most of the genetic variation observed among parasites is observed within and not between sampled animals. The factors influencing the genetic diversity of schistosomes were nicely depicted in the S. mansoni-Rattus rattus system on an island of Guadeloupe (West Indies) [S. bovis, only interspecific comparison with closely related species using relevant comparable markers can be performed. Previous studies using cox1 gene sequencing show a distinct pattern of genetic diversity according to the schistosome species concerned. At the scale of the African continent, the haplotype diversity (h) and the nucleotide diversity (Π) were 0.94 ± 0.0067 and 0.02553 [S. mansoni and S. haematobium, respectively. Thus the two parameters converge with S. mansoni having both higher haplotype and nucleotide diversity than S. haematobium. Our study shows that S. bovis is more polymorphic than its sister species S. haematobium  but also shows that the haplotype diversity is similar to that of S. mansoni (h = 0.961 ± 0.012) while the nucleotide diversity does not significantly differ from that of S. haematobium (Π = 0.00602). Moreover, we found that the Tajima’s D* and Fu and Li’s D* computed on our S. bovis dataset were significantly negative, which could constitute a signature of population demographic expansion [S. mansoni and S. haematobium in Yemen  . In part Indies) . Because 0.02553  and 0.36 0.02553  for S. mxpansion . These rrom zero ). This mrom zero , 45.S. mansoni for which populations are generally strongly genetically structured with high Fst values even at small geographical scales. Such strong genetic structuring has been evidenced in several countries including Kenya [S. haematobium populations [S. haematobium [S. mansoni [S. bovis populations. First, as previously mentioned, the absence of anthelminthic treatments in cattle might foster the expansion of S. bovis populations. The effective population size for S. bovis is expected to be large enough to limit the effect of possible genetic drift and population differentiation. Secondly, host dispersal is known to promote parasite dispersion, which in turn might homogenize populations at the genetic level. Dispersion of S. bovis is mainly driven by cattle movements across the country through transhumance practices. Transhumance is a common practice where pastoralists move their livestock seasonally across different grazing lands [versus dry and cold versus hot) could result in significant changes in whether transmission of infectious diseases is prevented or facilitated by movements [Whatever the method or the marker  used, we did not find any evidence for inter- or intrapopulation structuring despite considerable geographical distances separating the populations. This pattern clearly differs from that of ng Kenya , Brazil ng Kenya , Uganda ng Kenya , Ethioping Kenya  and Seneng Kenya . Conversulations , 51. Basulations  revealedmatobium  is poorlng lands . While tng lands , they alng lands  and haveng lands . The speovements . In CameS. bovis. No strong genetic structure was observed at the country scale but some genetic indices could be associated as a signature of population demographic expansion. More studies on genetic diversity and population genetic structuring of animal schistosomes are needed to infer the role of the definitive hosts on parasite gene flow.This study provides novel insights into the population genetics of Additional file 1:Table S1. Details of the schistosomes from different cows that were molecularly characterized using the nuclear ITS and mitochondrial cox1 markers. (DOCX 13 kb)Additional file 2:Table S2. Microsatellite dataset. (XLS 47 kb)Additional file 3:Figure S1. ΔK-values calculated by Evanno’s method detecting K = 2 and K = 4 subpopulations as the most genetically probable within the 14 loci analyzed. (DOCX 32 kb)Additional file 4:Figure S2. Mitochondrial cox1 maximum likelihood tree based of the S. bovis samples. The code for each sample corresponds to the sampling site-the animal number-the sex of the schistosome (M or F) and the parasite number. (DOCX 21 kb)


Allium subgenus Cyathophora coincidentally distribute across this biogeographical divide. Intriguingly, Allium fasciculatum of subgenus Amerallium co-occurs in the distribution range of subgenus Cyathophora. To illuminate the role of this biogeographic divide on the genetic divergence, we genotyped 466 individuals of 52 populations of subgenus Cyathophora and 110 individuals of 19 populations of A. fasciculatum using three chloroplast DNA fragments, whole nrITS and nine nuclear microsatellite loci, supplemented with the present environmental space and paleo-distribution modeling. Our phylogeographical evidence recovered the concordant east–west genetic breaks both for subgenus Cyathophora and A. fasciculatum along the 500 mm isohyet. The divergence time estimations and environmental niche differentiations suggested this east–west genetic breaks could have been triggered by the climatic-induced vicariance during the early Pleistocene. Noticeably, this split within subgenus Cyathophora could have been deepened by the morphological vicariance from the eastern umbel to the western spicate, while that within A. fasciculatum could have been obscured due to the pollen flows from the east to west caused by the postglacial expansion. The genetic structures and ecological niche modelings (ENMs) recovered the distinct responses to the Quaternary climatic oscillations for species constricted to different climatic zones, further highlighting the profound effect of the climatic differences and tectonic uplifts on the genetic diversification. Overall, our findings offer strong evidence for the existence of a biogeographic divide between the eastern monsoonal and the west continental climatic zones of the QTP nearly along the 500 mm isohyet.The Qinghai-Tibet Plateau (QTP) has been biogeographically divided into the eastern monsoonal and the western continental climatic zones along the 500 mm isohyet. However, this biogeographic hypothesis has been rarely tested using a phylogeographic approach. The members of the genus  The Asian monsoon system, as a by-product of the Qinghai-Tibet Plateau (QTP) uprising, has become an increasing attraction not only because of its controversial onset, also its profound effect on the reorganization of the Asian climate system . With thSophora davidii  subgenus Cyathophora species extend their distribution range across the eastern monsoon and western continent climatic zones along the 500 mm isohyet. Strikingly, the clear morphology differences occur between populations restricted to these two zones: the eastern species comprising A. mairei (AMA), A. rhynchogynum, A. cyathophorum (ACY), A. farreri (AFR), and A. tetraploidum  presents spicate inflorescence  belonging to subgenus Amerallium, sympatrically distributes with A. cyathophorum in the eastern QTP, and with the A. spicatum in the western QTP  can test whether the climatic differences could have an influence on the species’ responses to the Quaternary climatic oscillations. Therefore, combined with the present environmental space and the ecological niche model (ENM) of species distributions, we conducted the phylogeographical analyses for subgenus Cyathophora and A. fasciculatum using three chloroplast fragments , whole nrITS, and nine pairs of microsatellites, aiming to (i) examine the common genetic breaks along the 500 mm isohyet; (ii) test the relevance between climatic differences and the genetic breaks; (iii) address the influence of climatic differences on species’ response to Quaternary climatic oscillations.Among those endemic plant components in the QTP, the genus rescence . Thus, ttern QTP , howevericulatum . In thisA. fasciculatum and subgenus Cyathophora. Of which, 110 individuals for 19 populations of A. fasciculatum, and 466 individuals for 52 populations of all members of subgenus Cyathophora: 41 individuals of 7 populations of A. mairei, no individuals for A. rhynchogynum due to the difficulties to access, 200 individuals of 20 populations of A. cyathophorum, 59 individuals of 7 populations of A. farreri, 57 individuals of 6 populations of A. tetraploidum, and 109 individuals of 12 populations of A. spicatum  and the whole nrITS were amplified using the common primer pairs for the individuals of A. mairei and A. fasciculatum. One individual of Allium macranthum was amplified to confirm the monophyly of A. fasciculatum in the cpDNA-based phylogenetic analyses. For trnDGUC-trnTGGU and trnLUAA-trnFGAA, the PCR began with 94°C for 4 min, followed by 35 cycles of 94°C for 45 s, 60°C for 1 min, and 72°C for 1 min with a final-extension for 10 min at 72°C. For rps16 and nrITS, the most conditions followed the above one, except the different denaturation temperatures, respectively, at 52°C and 55°C . Three chloroplast DNA fragments , the observed heterozygosity (HO), the expected heterozygosity (HE), the average genetic diversity within the population (HS) and fixation index Fis. For each population, the genetic diversity was assessed across all loci using AO, HO, HS, Fis, and allele richness (RS). Linkage disequilibrium (LD) among loci was tested in FSTAT using Delta’ and R2. Genetic groups in the nSSR dataset were identified by a Bayesian model-based clustering approach using STRUCTURE v2.3.4 (K (=1–20) with 100,000 MCMC replications and 10,000 used as burn-in steps. To determine the true number of gene pools indicated by the optimal K-value, we monitored the mean of logeP(D), with independent runs for each K  and the number of population subdivision (NST), with running 1,000 random permutations for the significance of NST > GST. The average genetic diversity within populations (HS), total genetic diversity (HT) were also computed. An analysis of molecular variance (AMOVA) was conducted using ARLEQUIN version 3.5 to quantify variation in DNA sequences (cpDNA and nrITS) and nSSRs using Φ- and R- statistics, with 10,000 permutations to test the significance  was then conducted to estimate the variation of principle components (PC1 and PC2) and each determinant bioclimatic variable within the taxa of interest using R lmPerm package to identify the bioclimatic variables which determined species’ divergence  and geographical/environmental distances by running 10,000 permutations in R vegan package.To assess the effect of geographic distances and environmental conditions  to the observed genetic differentiation, the correlations between pairwise Frdinates  using GEc. 21 kyr before present (BP)] and the Last inter-glacial , respectively. These determinant bioclimatic variables exhibiting pairwise Pearson correlation coefficients r < 0.7 of each group  was performed in MAXENT v3.3.1  to predich group  were userve AUC; .Cyathophora and A. fasciculatum, with 46 and 24 chlorotypes (A. mairei (HT/HS = 1.0/0.137), A. farreri (HT/HS = 0.847/0.303), and A. fasciculatum (HT/HS = 0.913/0.408) showed much higher HT than HS, and concordantly have the significant phylogeographic structures . The similar genetic structures were also detected in the east (HT/HS = 0.539/0.287) and west groups (HT/HS = 0.921/0.479) of A. fasciculatum. By contrast, the species with no such significant differences between HT and HS, presented the non-significant phylogeographic structures , i.e., in A. spicatum (HT/HS = 0.789/0.541) and A. tetraploidum (HT/HS = 0.257/0.170), and even no such phylogeographic structure  in A. cyathophorum (HT/HS = 0.826/0.381)  was greatly larger than that of the eastern genealogies (ranging from 0.03 × 10-3 to 0.24 × 10-3)  has a consensus length of 2,249 bp and 2,242 bp, respectively, for subgenus orotypes  based onorotypes  and 42 vorotypes . It was 6/0.381) . The nuc × 10-3) . Neutralall taxa .Cyathophora and A. fasciculatum , A. tetraploidum , the east group of A. fasciculatum  and the west group of A. fasciculatum   and A. spicatum , and pattern labeled by population-specific chlorotypes was detected in A. mairei (A. cyathophorum (ZG24) and A. tetraploidum (JR4)  , 3B. How. mairei , 4A. C2 um (JR4) , 4A.ST ≥ 0.59, all P < 0.001), except for A. spicatum with a weaker one . For the east and west groups of A. fasciculatum , 25%  of the total variation was partitioned to between groups, and 65% explained by variation among populations within group , as well as the divergence among the western clades of A. fasciculatum (4.34–1.74 Ma). The intraspecific divergence and intra-clade divergence for these species mainly were dated back to the largest Quaternary glaciation in the QTP (c. 1.2–0.6 Ma)  . For exa 0.57 Ma . For the 0.53 Ma , and tha 0.68 Ma .Cyathophora species , except in A. mairei   . Neutralall taxa .Cyathophora, indicating well biparental east–west break. However, the eastern dominant R47 of A. fasciculatum was found in the western populations, which geographically are adjacent to the 500 mm isohyet  , in A. tetraploidum , in A. spicatum , and in A. fasciculatum  , and A. mairei displayed a population-specific pattern (A. cyathophorum (GT21) and all populations of A. tetraploidum  , suggest, 65–67) . The sin. 67.3%) . Like cp pattern , 4B. R1 aploidum .ST ≥ 0.53, all P < 0.001), except for A. tetraploidum  and A. spicatum  with a weaker such structure , 29%  of the total variation was partitioned between groups, and 83% explained among populations within group . The nSSR-based AMOVA demonstrated significant population structure for each species . For the east and west groups of A. fasciculatum, 24%  of the total variation was partitioned between groups, and 40% explained among populations within group  (K = 3)  in representing species (A. cyathophorum (ACY1 and ACY2) and A. spicatum were further subdivided (ASP1 and ASP2)   and thre (K = 3)  accordinK values . The geo species , wherebynd ASP2) . The geoiculatum , wherebynd West2 . Observand West2 , as thatnd West2 . Likewispulation .A. tetraploidum having nine ones due to its narrow distributions , isothermality (Bio3) and precipitation of driest month (Bio14), while lower temperature seasonality (Bio4) and precipitation seasonality (Bio15), whereas west group comprising A. spicatum and the west populations of A. fasciculatum, occupies a contrast ecological niche compared to the east group. The niche overlap was also found in the east and west groups of subgenus Cyathophora , isothermality (Bio3) and precipitation of driest quarter (Bio17), while the west group has an ecological niche with higher mean diurnal range (Bio2), precipitation seasonality (Bio15) and altitude  and precipitation of driest month (Bio14), while the west group possesses an ecological niche with higher mean diurnal range (Bio2) and altitude  exhibited slightly overlap, while the allopatric species A. mairei vs. A. farreri showed clearly ecological niche differentiation. By contrast, the allotetraploid species A. tetraploidum showed great niche overlap with its potential diploid parents A. cyathophorum and A. farreri, and a larger niche breadth compared to the diploid parental progenitors , while PC1 was significant difference in the Tukey’s HSD tests (P = 0.0137), but not in the AVOVA test (P > 0.05). For such east–west split in subgenus Cyathophora, Bio1, Bio2, Bio15, Bio17 and altitude were significantly different in both tests (P < 0.001). For such split within A. fasciculatum, Bio1, Bio12, Bio14, Bio15 and altitude were significantly different in both tests (P < 0.02). There were non-significant differences of the determinant bioclimatic variables in the sympatric species including the west group of A. fasciculatum – A. spicatum, and the east group of A. fasciculatum – A. cyathophorum (both P > 0.05) (Cyathophora (r = -0.063 P = 0.456), and A. fasciculatum . Likewise, no significant IBEs were detected within A. fasciculatum  and Bio3  could be the most relevant variables explaining the population genetic structures of subgenus Cyathophora (The significant difference of determinant bioclimatic variables in the specific group was approximately consistent between the AVOVA tests and Tukey’s HSD tests . For the > 0.05) . The Manthophora .A. fasciculatum, A. spicatum, and the eastern subgenus Cyathophora species, with the AUC = 0.967 ± 0.009, 0.986 ± 0.003 and 0.981 ± 0.005 (mean ± SD), respectively. The model predicted suitable habitats for these units were highly coincident with their actual geographic range  (c. 1.2–0.6 Ma), but coincidently falls into the climatic condition differentiation in the QTP around 3.6–1.2 Ma  , 3, whic6–1.2 Ma , suggestt groups  determint groups  both in iculatum . Noticeaiculatum , probabliculatum . Howeverthophora . Considethophora   and total genetic diversity (HT) in the ancestral areas than those in the recolonized areas both for subgenus Cyathophora  and A. fasciculatum  (S = 0.479) and a larger effect population size inferred from the nucleotide diversity (π = 0.93 × 10-3) were found in the western ancestral group of A. fasciculatum, when comparing to the eastern derived group (HS = 0.287 and π = 0.14 × 10-3) (Cyathophora have lower average genetic diversity within populations (HS ≤ 0.381) and smaller effect population size (π ≤ 0.24 × 10-3) when comparing to the western species A. spicatum (HS = 0.541 and π = 0.50 × 10-3)  , 3. Base × 10-3) . Convers × 10-3) . The BEATP e.g., .Cyathophora  and A. fasciculatum . Compared to cpDNA and nrITS, microsatellite markers usually have faster mutation rates and thus are more related to more recent population history. The intraspecific divergence within subgenus Cyathophora , probably highlighting the importance of Quaternary glaciations to their recent diversification in the QTP , A. tetraploidum (C1/R1) and A. fasciculatum (C56/R47)   , 3 most  the LGM , the gre the LGM  and sign the LGM , suggest the LGM . For A. lineages , 5B posslineages . However > 0.05) , possiblgenitors , meaninggenitors , probabliculatum , neverthhophorum . Considehophorum , 3A, it A. spicatum and A. fasciculatum when responding the glacial crisis (A. spicatum (A. fasciculatum (A. fasciculatum and A. spicatum (A. fasciculatum (A. mairei and the west group of A. fasciculatum (A. spicatum and A. fasciculatum during the LGM indicate their refugia would locate at eastern Himalayas with lower ELAs (A. spicatum and A. fasciculatum from the Quaternary freezing damages in the western QTP, agreeing with some tolerant cold organisms, such as Aconitum gymnandrum (Juniperus tibetica complex (Hippophae thibetana (Orinus thoroldii (Eospalax baileyi (Of the west species, the ENMs presented the same contraction–expansion pattern, respectively, for the suitable habitats of l crisis , which cspicatum  and the iculatum . Howevericulatum  and AMOVspicatum , notwithspicatum . It deemiculatum . This priculatum , most liiculatum , 3A. Thewer ELAs . This stmnandrum , Juniper complex , Hippophhibetana , Orinus horoldii  and Eosp baileyi . It is i baileyi  (Shi et  baileyi , which iin situ, most probably due to the development of the dispersed mountain glaciations. By contrast, the western species mostly could have largely contracted to the eastern Himalayas where the larger ice sheet could have limited due to the drought in the western QTP. In this way, the distinct responses to the Quaternary climatic oscillations could have been recovered for species restricted to different climatic zones. Additionally, the early divergence trigged by the tectonic motions and genetic introgression could have a deep effect on the species’ genetic structures.Taken together, it is inferred that the eastern species have the species specific glacial refugia and consequently resulted in the diversifications Cyathophora and A. fasciculatum, along with the present environmental space, robustly support the existence of a biogeographic divide between the eastern and western QTP along the 500 mm isohyet. The causative factor for this genetic divide is suggested to be the climatic differentiation in the eastern and western QTP. Interestingly, this climate-mediated split within subgenus Cyathophora could have been deepened due to the prezygotic sterility resulted from the morphological vicariance from the east umbel inflorescence to the west spicate inflorescence, while that within A. fasciculatum could have been obscured due to the pollen flows from the east to west caused by the postglacial expansion. It is recovered that the tectonic motions could be responsible for the ancestral divergence, and the Quaternary climatic fluctuations for the recent divergence. Strikingly, distinct responses to the Quaternary climatic fluctuations have been revealed in the components of the eastern and western QTP, which could be closely related with the climatic differences. Overall, our findings highlight the importance of orogeny and climatic changes in the QTP in shaping species diversification during the Pliocene–Pleistocene.Our chloroplast and nuclear DNA results for subgenus ML, DX, CX, and YD conducted fieldwork. ML and XH conceived the research. ML and YZ performed the experiments. ML, DX, YY, and YD analyzed the data. ML wrote the manuscript. All authors read and reviewed the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Chelonia mydas) rookeries influenced population composition at a major juvenile feeding ground in the southern Caribbean  using genetic and demographic analyses. Genetic divergence indicated a strong temporal shift in population composition between 2006–2007 and 2015–2016 . Juvenile recruitment  from the north-western Caribbean increased from 12% to 38% while recruitment from the eastern Caribbean region decreased from 46% to 20% between 2006–2007 and 2015–2016. Furthermore, the product of the population growth rate and adult female abundance was a significant predictor for population composition in 2015–2016. Our results may reflect early warning signals of declining reproductive output at eastern Caribbean rookeries, potential displacement effects of smaller rookeries by larger rookeries, and advocate for genetic monitoring as a useful method for monitoring trends in juvenile megafauna. Furthermore, these findings underline the need for adequate conservation of juvenile developmental habitats and a deeper understanding of the interactions between megafaunal population dynamics in different habitats.Understanding the population composition and dynamics of migratory megafauna at key developmental habitats is critical for conservation and management. The present study investigated whether differential recovery of Caribbean green turtle ( Mixed aggregations in key developmental habitats present unique challenges to conservation and management because anthropogenic impacts potentially affect the future generations of multiple populations2. The conservation risk of population overlap is exacerbated for vulnerable populations, such as small populations of threatened species. Understanding the dynamics and composition at key developmental habitats, such as juvenile feeding grounds, is thus of paramount concern to conservation and management2.Different populations of the same species of migratory megafauna may depend on the same key habitats during parts of their life cycle. Developmental habitat shifts between successive life stages result in the mixing of offspring from different breeding populations in juvenile feeding grounds in many marine taxa including fishes5. Dispersal of marine species is governed by different processes at different life stages. Ocean currents influence dispersal of early life stages and temporal fluctuations in ocean currents can produce changes in the population composition at juvenile feeding grounds4. In addition, juvenile recruitment to feeding grounds can fluctuate over time due to environmental stochasticity affecting the production and survival of offspring4 as well as long-term declines in reproductive success5. Human activities too can cause negative and positive changes, both through destructive activities as well as conservation measures. In the extreme case that human activities result in the local extinction of breeding populations, these extirpated populations will no longer produce recruits. By contrast, increases in reproductive success, for example due to conservation measures, can enhance recruitment6 and possibly change the dynamics at juvenile feeding grounds.However, little is known about the temporal dynamics at key developmental habitats even though recruitment is unlikely to remain constantChelonia mydas). Past human exploitation and habitat degradation has decimated sea turtle populations globally9. In the Caribbean, the present abundance of green turtles is at less than one percent of pre-exploitation levels based upon historical data9. Recent studies have highlighted that some rookeries are showing signs of population recovery following past sea turtle conservation measures14. Protection of nesting females has led to substantial increases in nesting trends, i.e. the number of nests produced, in several rookeries13. Increasing nesting trends as high as 14% per year have been reported in rookeries in Florida14. Rookeries in the eastern Caribbean seemed to have recovered at a slower pace compared to the north-western Caribbean14. Adult female abundance at the Aves Island, Venezuela, rookery has been increasing by approximately 5% per year during recent decades13. Little is known, however, what effects this population recovery may have on the dynamics and composition at juvenile feeding grounds.A prime example of where the dynamics and composition at juvenile feeding grounds could be altered by conservation measures are green turtles . If recovery rates differ among genetically diverged rookeries, temporal genetic heterogeneity is expected to increase with time, i.e. reflecting a directional change in mtDNA diversity. However, investigating changes in recruitment over longer timeframes is warranted because juveniles can spend up to 15 years at feeding grounds before moving to deeper feeding grounds (e.g.23), which could lead to autocorrelation in mtDNA diversity between successive years. Autocorrelation in mtDNA diversity can be further diminished by investigating changes in recruitment using only recent recruits, i.e. small juveniles that presumably arrived recently at a feeding ground.The hypothesis that the population composition at juvenile feeding grounds changes due to differential recovery rates can be tested by studying temporal genetic heterogeneity at feeding grounds using mitochondrial DNA (mtDNA). There is strong population structure in mtDNA diversity among sea turtle rookeries as a result of natal homing in adult females24 located in Lac Bay  in the southern Caribbean. Decadal changes in population composition were investigated by estimating temporal genetic heterogeneity and assessing changes in juvenile recruitment between 2006–2007 and 2015–2016. Decadal changes in juvenile recruitment were correlated with rookery recovery rates that were estimated as the product of population growth rates and adult female abundance to account for variation in population size among rookeries. The genetic and demographic analyses were conducted at both the level of individual rookeries and rookeries grouped into three regions reflecting mtDNA stocks recognized in sea turtle conservation and management25.The present study assessed the relationship between differential recovery rates and changes in the population composition at a major juvenile green turtle feeding ground26. Adding 41 recaptured individuals increased the final sample size to 373 mtDNA control region sequences. The most common mtDNA haplotypes were CM-A03 (52%) and CM-A05 (27%) followed by CM-A01 (9%) and CM-A08 . A significant and positive correlation was detected between pairwise estimates of genetic divergence among years . Including only <50.0 cm SCL juveniles in the analysis increased the genetic divergence between 2006–2007 and 2015–2016  and resulted in a stronger correlation between genetic divergence . However, the elevated genetic divergence could be due to sampling variance and smaller sample sizes (Table The sequence of 474 base pairs of the mtDNA control region was determined in 332 juvenile green turtles (30.0–75.0 cm maximum straight carapace length (SCL); mean ± SD = 50.66 ± 9.71 cm) sampled in Lac Bay between 2006 and 2016 Tables –S2. Re-sars Fig.  and timence Fig.  and timees Table .Figure 1C = −0.22) and north-western Caribbean . Estimates of ΔC were generally close to zero for individual rookeries, with the exception of French Guiana (ΔC = −0.18) and southern Florida (ΔC = 0.44). Rookery contributions were estimated with wide uncertainties for individual rookeries  and decreased recruitment from the eastern Caribbean  between 2006–2007 and 2015–2016  and reproductive output  was estimated for the north-western Caribbean region (Table r = 0.066), south-western Caribbean (r = 0.017) and southern Atlantic (r = 0.035) regions. Nesting trends in Suriname were not linear but seemed to increase mainly during the last decade  was higher than that estimated for the eastern Caribbean  due to the high adult female abundance at the Tortuguero rookery in Costa Rica (Table The highest population growth rate r = 0.36 and rede Table . The expρ was estimated at 1.00 (P = 0.083) at the regional level for both <75.0 cm SCL and <50.0 cm SCL juveniles. The monotonic association between ΔC and Nr, i.e. the ranking of ΔC and Nr matched perfectly at the regional level but was nearly significant, which was probably the consequence of only four data points that resulted from grouping rookeries into regions. At the level of rookeries, the monotonic association between ΔC and Nr was non-significant for both <75.0 cm juveniles  and <50.0 cm juveniles . However, logistic regression suggested Nr was a nearly significant predictor for whether recruitment increased or decreased for <75.0 cm SCL juveniles (P = 0.09), but not for <50.0 cm SCL juveniles (P = 0.25).Spearman’s correlation coefficient N (P = 0.047) and geographical distance D (P = 0.094) was found to best describe mean rookery contribution estimates in 2006–2007 for <75.0 cm SCL juveniles (Table Nr (P = 0.012) was best-supported in 2015–2016 (<75.0 cm). However, a model incorporating only D (P = 0.066) represented the best fit in 2006–2007, while the null model (i.e. no predictors) was best-supported in 2015–2016. Though D was non-significant at an α of 0.05 in the best-supported models in 2006–2007 for both size classes, this was likely an effect of the relative small number of data points (i.e. 18 rookeries) in the multiple linear regression. In addition, excluding D resulted in poorer model performance  at an age of 6 to 10 years depending on their rate of growth33. Changes in juvenile recruitment to Lac Bay are therefore expected to lag up to a decade behind changes in nesting trends. Given that nesting trends in Suriname have started to increase during the last decade, it is possible that juvenile recruitment from the eastern Caribbean will increase in the near-future. This does not explain why recruitment from the eastern Caribbean decreased despite long-term stability in nesting trends. However, nesting trends may be a poor proxy for reproductive output. For example, hatching success may be very sensitive to environmental fluctuations, such as changes in sand temperature at nesting beaches34. Nesting trends may therefore appear stable, but nonetheless result in few recruits due to low hatching success.The decreased recruitment from the eastern Caribbean can possibly be explained by a difference in the timing of nesting trends. Nesting trends were stable in Suriname, the largest green turtle rookery in the eastern Caribbean, between the 1970’s and the 2000’s and started to increase during the last decade, while nesting trends in the Archie Carr National Wildlife Refuge green turtle rookery in Florida increased since the 1990’s36. This was recently demonstrated in a study of juvenile green turtles in Japanese feeding grounds, where the contribution of local rookeries was higher for larger (i.e. 50–70 cm SCL) juveniles35. In the present study, recruitment differed between the two size partitions (i.e. <50.0 cm and <75.0 cm) within 2006–2007 and 2015–2016. Recruitment from the south-western Caribbean was higher overall when all juveniles were analysed, while recruitment from the north-western and eastern Caribbean was higher for small juveniles. These differences are potentially due to juvenile natal homing, reflecting the emigration of larger juveniles of north-western or eastern Caribbean origins from Lac. In addition, this implies that studying the recruitment dynamics of small juveniles is warranted to understand the link between population dynamics at feeding grounds and rookeries if larger juveniles shift between feeding grounds.Sea turtles may shift to other feeding grounds between different size classes as a result of juvenile natal homing28 raises the possibility that the Lac Bay feeding ground is at or near carrying capacity. Species can respond to increased densities by dispersing to other feeding grounds37 and it has been argued that dispersal in green turtles may in part be density-dependent33. Increased abundances at feeding grounds may lead to an increased propensity to disperse as local abundance nears carrying capacity and competition for resources increases33. For example, the exceeding of local carrying capacity in a juvenile feeding ground in the Bahamas resulted in net emigration of individuals over subsequent years until abundance stabilized33. If a feeding ground is at carrying capacity and dispersal propensity is equal for all individuals within a feeding ground, the equilibrium population composition is expected to be determined by the relative levels of recruitment from various source populations. Population composition may therefore change as a result of recruitment from one rookery outweighing recruitment from another rookery. In other words, rookeries contributing a larger number of offspring to a feeding ground may increasingly dominate shared feeding grounds over time, akin to a ‘displacement effect’. This displacement effect could in part explain decreased recruitment despite stable nesting trends in the eastern Caribbean. Further monitoring of juvenile recruitment and abundance at feeding grounds in relation to population dynamics and reproductive output at rookeries is warranted to investigate whether such a displacement effect occurs.The stable abundance of green turtles4. By contrast, if long-term effects such as declines5 or increases in hatching success, lead to differential juvenile recruitment to feeding grounds over time, genetic signals of ‘directional change’ in mtDNA diversity over multiple years, e.g. a decade, are expected. Short-term and long-term effects can therefore possibly be disentangled by assessing the direction and nature of changes in genetic heterogeneity during longer time intervals, as was done in the present study. The degree of temporal genetic heterogeneity observed during this study correlated strongly with time and suggested a directional change in mtDNA diversity. The temporal genetic heterogeneity we observed in Lac Bay was similar to levels recorded among different green turtle feeding grounds . These findings suggest the observed temporal genetic heterogeneity is in part explained by long-term changes in juvenile recruitment though the presence of short-term effects could not be rejected. However, short-term and long-term effects are not necessarily mutually exclusive4.Short-term fluctuations in mtDNA diversity between successive years in sea turtles can be a result of stochasticity in reproductive output39, during the study period could, in theory, lead to a false-positive signal of juvenile recruitment. However, this is unlikely to explain the findings in the present study since no temporal heterogeneity in mtDNA has been observed at any rookeries so far42, although these studies only lasted two to three years. Temporal heterogeneity in mtDNA diversity has been reported at a loggerhead sea turtle rookery in Florida but could simply be due to sampling variance43. Second, possible changes in mtDNA diversity at the regional level during the study would require gene flow among distant rookeries which is unlikely given the high degree of natal homing to rookeries observed in nesting female sea turtles21.Genetic changes within rookeries, e.g. via genetic drift44. Green turtle rookeries in the Caribbean differ substantially in mtDNA haplotype composition at the regional level but less so within regions. Accordingly, the estimated contribution from individual Caribbean rookeries was subject to a high degree of uncertainty in this study and complicated making inferences at the level of individual rookeries. Sequencing longer fragments of the mtDNA control region45, mitochondrial short tandem repeats46 or mitochondrial genomes47 could possibly reduce uncertainties in mixed stock analysis. However, this would require generating novel genetic data for all major Caribbean green turtle rookeries, which was beyond the scope of this study.Genetic assignment methods such as mixed stock analysis have reduced statistical power to determine the origin of individuals when putative sources are genetically similar48, even though nesting beaches constitute only a small, albeit vital, part of the life history of sea turtles. The increasing numbers of juveniles originating from rookeries that are showing signs of recovery observed in the present study are encouraging and highlights the success of current attention to sea turtle nesting beaches. In addition, the present study demonstrates genetic monitoring may represent a useful method for monitoring trends in juveniles, which may provide an early warning signal for declining reproductive success29 and improve our understanding of sea turtle metapopulation dynamics48. However, sufficient feeding habitat quality and quantity is required for juveniles to mature and contribute to future generations48, and fortunately feeding habitats are increasingly receiving attention49. Climate change, invasive species and habitat degradation continue to threaten developmental habitats of sea turtles52. In Lac Bay, the invasive seagrass Halophila stipulacea has expanded rapidly during recent years53 and a recent study demonstrated green turtles facilitate the expansion of H. stipulacea through selectively grazing on the native seagrass Thalassia testudinum28. If developmental habitat quality and quantity are not maintained, increased abundances of juveniles may elevate intra-specific competition for resources and increase risks of overconsumption, e.g. overgrazing of seagrass meadows54, and habitat collapse55. An understanding of the interactions between nesting trends at rookeries, recruitment and dynamics at feeding grounds as well as an understanding of the ecological interactions within feeding grounds is required to ensure adequate protection of both adult breeding and juvenile developmental habitats in endangered marine megafauna.Effective conservation measures are key in an era characterized by an accelerated loss of biodiversity driven by anthropogenic activities. Sea turtle conservation and management has typically focused upon protecting nesting beaches56 or in 70% ethanol, stored locally at −4 degrees Celsius (°C) and archived at −20 °C upon arrival at the University of Groningen, the Netherlands. Total-cell DNA was extracted using the Gentra Puragene® Tissue Kit (QIAGEN Inc.) following the manufacturer’s instructions.All fieldwork was conducted under ‘Openbaar Lichaam Bonaire’ permit nr. 558/2015-2015007762 granted by the ‘Executive Council of the Public Entity of Bonaire’ to Sea Turtle Conservation Bonaire (STCB) in accordance with the required animal care protocols. As a member of the Wider Caribbean Sea Turtle Network (WIDECAST), STCB uses best-practice standardized protocols for sampling and handling sea turtles. Tissue samples originated from juvenile green turtles (maximum straight carapace length (SCL) below 75.0 centimetres (cm)). Green turtles were captured by hand or nets in Lac Bay in Bonaire between 2009 and 2016. Tissue samples were collected from the dorsal neck epidermal area using a sterilized scalpel blade or a 6 mm biopsy punch (Integra™ Miltex®), preserved in 6 M sodium chloride with 25% dimethyl sulfoxide58. An initial PCR amplification was conducted using the primers CM15412F  and CM16333R  followed by a PCR amplification using the primers CM15791F  and CM15984R . PCR amplifications were conducted in a 10 μL reaction volume containing 1 μM of each primer, 1X standard Taq reaction buffer (New England Biolabs® Inc.), 0.2 μM of each dNTP, 0.4 units of Taq DNA polymerase (New England Biolabs® Inc.), autoclaved Milli-Q® H2O and between 5 to 15 ng extracted DNA. The PCR conditions consisted of an initial cycle of 2 minutes at 94 °C followed by 32 cycles each of 30 seconds at 94 °C, 45 seconds at 55 °C and 30 seconds at 72 °C, and a single final step of 5 minutes at 72 °C. Excess primers and nucleotides were removed from the amplifications prior to cycle-sequencing by addition of one unit of shrimp alkaline phosphatase  and five units of exonuclease I (New England Biolabs® Inc.) as described by Werle et al.59. Cycle-sequencing was performed with primers CM15412F (for the first fragment) and CM15791F (for the second fragment) using an ABI BigDye® Terminator v3.1 Cycle Sequencing Kit (Applied Biosystems Inc.) following the manufacturer’s instructions but using 1/16 of the BigDye® Ready Reaction Mix. The PCR conditions consisted of 25 cycles each with 10 seconds at 96 °C, 5 seconds at 50 °C and 4 minutes at 72 °C. Excess nucleotides and primers were removed by ethanol/EDTA precipitation. The cycle-sequencing products were re-suspended overnight in deionized formamide and the order of cycle-sequencing products was resolved by capillary electrophoresis on an Applied Biosystems 3730xl DNA Analyzer™ (Life Technologies Inc.) at the University of Groningen.A 474 base pair fragment of the mitochondrial DNA control region was amplified by nested PCR (polymerase chain reaction) amplificationsunpublished). Individual mtDNA sequences from each sample were assembled, the consensus sequence estimated from the Phred quality scores and subsequently aligned to a mtDNA control region green turtle reference sequence (GenBank accession number: JN632497.1) and truncated to 474 bp using MIRA ver. 4.9.5.260. Assembled sequences were aligned with MUSCLE ver. 3.8.3161 in SEQOTRON ver. 1.0.162 using the default parameter settings. Aligned sequences were visually inspected and manually corrected. A total of 96 randomly chosen DNA extractions were re-sequenced to assess sequencing consistency rates.MtDNA control region consensus sequences were assembled from one or more DNA sequences per DNA extraction using a custom software pipeline  in 2010 and recaptured again in 2015 was represented as three observations of the same individual, one in each observed year.The data generated in the present study were combined with mtDNA sequence haplotype frequency data from juvenile green turtles (SCL < 75.0 cm) sampled in Lac Bay during 2006 and 200764 and nucleotide diversity64 and genetic divergence (ϕST65) between 2006–2007 and 2015–2016 and among years was estimated using ARLEQUIN ver. 3.5.2.266. The statistical significance of ϕST was estimated from 10,000 random permutations of the data. The correlation between genetic divergence and temporal distance, measured in years, was estimated using a Mantel test67 as implemented in ARLEQUIN68. Population genetic analyses were repeated using only recent recruits .MtDNA haplotype diversity69. Published mtDNA control region haplotype  data from Atlantic green turtle rookeries77 were used as source populations (Appendix 20 and ‘southern Atlantic’ and (2) individual rookeries representing source populations. Additional mixed stock assessments were conducted using only small juveniles (<50.0 cm SCL) that presumably represent recent recruits and are more informative of recent changes in recruitment dynamics. A uniform prior was used where the prior contribution was equal among each source, i.e. prior contribution was set to 1/k where k was the number of putative sources, to avoid bias in subsequent demographic analyses. The employed MCMC settings are listed in Table 69. Temporal changes in juvenile recruitment were estimated as:Temporal changes in juvenile recruitment were inferred estimating the contribution of Caribbean rookeries Fig.  to the LAppendix . Two mixAppendix ; ‘north-Ci was the mean contribution estimated for 2006–2007 and Cj was the mean contribution estimated for 2015–2016.where et al.14) within rookeries  and Weber et al.11 (Ascension Island). Regional annual population growth rates were estimated as weighted mean annual population growth rates for rookeries within regions.where rand the adult female abundance N (Nr) was used as a proxy for reproductive output in order to account for variation in adult female abundance among rookeries. The association between ΔC and Nr, was tested using nonparametric Spearman rank-order correlation in R ver. 3.5.378. In addition, we tested how well Nr predicted whether recruitment increased or decreased over time at the level of rookeries (i.e. as a binary response variable) using logistic regression in R ver. 3.5.3. Multiple linear regression was used to test the effect of N, geographic distance  and Nr on mean rookery contribution estimates during 2006–2007 and 2015–2016 for both size classes. Model selection was performed via a stepwise algorithm using the Akaike Information Criterion (AIC) in R ver. 3.5.3.The product of the annual population growth rate Supplementary information Appendix 1