The results of studies of the genetic structure of the Central European wild boar (Sus scrofa scrofa) populationand four breeds of domestic pigs  bred in the Central Black Earthregion of Russia are presented in this work. Based on 12 microsatellite loci, a significant ( p <0.05) decrease in thelevel of genetic variability in bred breeds was shown. The expected heterozygosity and Shannon index were as follows:in the wild boar, Ho = 0.763 ± 0.026, I = 1.717 ± 0.091; in the Duroc breed, Ho = 0.569 ± 0.068, I = 1.191 ± 0.157;in the Landrace, Ho = 0.618 ± 0.062, I = 1.201 ± 0.147; in the Large White, Ho = 0.680 ± 0.029, I = 1.362 ± 0.074; and inthe Yorkshire, Ho = 0.642 ± 0.065, I = 1.287 ± 0.156. The results of checking genotypic Hardy–Weinberg equilibriumbased on the G-test of maximum likelihood demonstrated that the overwhelming majority of loci in the wild boarpopulation were in the state of said equilibrium. By contrast, in pig breed populations, some loci demonstrated asignificant deviation from the indicated equilibrium. In addition, the Yorkshire, Large White, and Landrace populationshad loci, for which the hypothesis of neutrality was reliably rejected based on the results of the Ewens–Wattersontest. The revealed private alleles, characteristic of the wild boar and breeds, can later be used to identifythem. The ordination of the centroids of different herds in the space of the first two principal coordinates based onthe matrix of pairwise estimates of Nei’s genetic distances showed that the most distant populations are the Durocand Boar breeds, and the most genetically close are the Yorkshire and Landrace breeds. The closest to the wild boarpopulation was the Large White breed. The assessment of the effective size, carried out using the method basedon the linkage disequilibrium and the molecular coancestry method, showed that in all studied groups, includingthe wild boar population, the effective size was less than 100 individuals. The low effective size of the wild boarpopulation  is probably caused by the death and shooting of animals due to Pestis africanasuum. According to various estimates, the domestication of the wildboar began 7–9 thousand years ago. During this time, morethan 730 breeds of these animals have been created by man.It is obvious that for such a long “cultural” evolution, variousbreeds, being so-called clean lines, have largely lost the naturalgenetic potential that provides homeostatic mechanisms.As a result, maintaining the stability of existing breeds, likeany artificially created systems, requires significant financialinvestments. In this regard, the study of the genetic potentialof natural populations of wild boars for a possible increasein the resistance of pig breeds  is highly relevant.In this regard, the study of wild boar genetics is now receivingmuch attention, both in Russia and other countries . In addition, due to constant outbreaks of the Africanswine fever (Pestis africana suum) in Russia, the wildboars are regularly shot as potential carriers of this disease.At the same time, the population structure of this animal isnot taken into account, which can cause depletion of the genepool and, against the background of increasing anthropogenicpressure, lead to the extinction of some groups. Examples ofa significant reduction in the population of commercial speciesoccurring due to the data on the state of the gene pool beingneglected are well known .Also, in the practice of molecular genetic laboratories, forforensic purposes, it is often necessary to diagnose tissuesamples from illegally caught wild boars and prove theirbelonging to a wild species, and not to domesticated formsof pigs , or identifywild boar tissue in food . In this regard,the identification of private alleles for natural populations isalso an urgent problem.Microsatellite DNA loci (STR markers), which are tandemrepeats of the noncoding part of nuclear DNA, are very convenientmarkers for studying genetic processes in populations.There are many works on the assessment of population genepools of both domestic pigs and wild boars in different regions.The purpose of this work is to assess the genetic diversityof microsatellite loci in the population of the Central Europeanwild boar (Sus scrofa scrofa) and the most common pigbreeds (Sus scrofa domesticus) bred in the Central Black Earthregion of Russia. It should be noted that such studies have notpreviously been conducted in this region.A total of 320 animals were involved in the study. A sampleof 30 wild boars was taken from the populations of the Oryolregion , Russia. The wild boars werecaught during the hunting season of 2018. For comparison,samples from four populations of different breeds of domesticpigs bred on the farms of the Central Black Earth Region ofRussia were used: Durok – 67 individuals (Belgorod region),Yorkshire – 108 individuals (Kursk region), Landrace –50 individuals (Belgorod region), large white – 65 individuals(Voronezh region). All analyzed animals are Canadianbreeds.All 12 microsatellite loci recommended by ISAG-FAO  and arrangedin one multiplex panel  wereused as DNA markers (Table 1). Primers for PCR were selectedtaking into account the amplification of all 12 loci in one tube.The size of all amplified PCR products, taking into accountall known alleles, was < 300 base pairs.In the domestic pigs, DNA extraction was carried out fromear pits, and in the wild boars – from muscle tissue samples.For this purpose, we used kits with proteinase K DNA-Extran-2 . The PCR reaction was carried outon a Verity amplifier  in 20 μLof a mixture containing 20 ng of genomic DNA, PCR buffer(10 mmol Tris-HCl (pH 8.3), 50 mmol KCl, 2 mmol MgCl2),0.25 mmol dNTP , 0.5 μmol primer, 1 unit Taq DNA polymerase(inhibited for hot start).PCR parameters: 94 °C – 3 min;  – 4 cycles;  – 6 cycles;  – 20 cycles; 68 °C – 30 min. Theheating rate from 59 to 72 °C was no more than 0.3 °C/1 sec.Fragment analysis of the PCR products was carried out onan ABI PRISM 3500 automatic capillary DNA sequencer  using 50 cm capillaries and a POP-7™polymer matrix. Fragment size analysis was performed usingGeneMapper R Software v. 4.1 (Applied Biosystems).For statistical processing of the data obtained we used theGenAIEx v. 6.5  and PorGene 1.32.In Table 2, the allele frequencies of the microsatellite loci usedfor analysis are presented. The data provide insight into thedistribution of various alleles among populations of domesticpigs and wild boars.The presence of private alleles in different populations isshown in Table 3. The results show a high content of uniquealleles in the wild boar population . The highestfrequency of private alleles was found at the loci SW24 andSW72 (0.25 in each). At the same time, the allele 97 at theSW24 locus, and the allele 99 at the SW72 locus are foundmore often than others among private alleles. The pig ofthe Duroc breed is slightly inferior to the wild boar in thenumber of private alleles, while the 105 and 111 alleles atthe SW936 locus  had thehighest frequency of occurrence. The Large White breed isalmost three times inferior in the number of private alleles tothe population of wild boar and Duroc pigs. However, someof the original alleles are found in this group of animals withnoticeable frequency. For example, the allele 141, unique forthis breed, at the SW857 locus was observed in half of theanimals analyzed (frequency 0.5). No private alleles werefound in the Landrace population, and only one private allelewas noted in the Yorkshire population.The wild boar population has significantly high values ofthe indicators of genetic variability in comparison with thepig breeds. The comparison was carried out using the Pearsonχ2 test ( p < 0.05) (Table 4).The level of inbreeding (F ) in the studied groups turnedout to be low, and the populations of Yorkshire, Landrace andwild boar received a negative value due to the prevalenceof observed heterozygosity over the theoretically expectedheterozygosity (see Table 4).The results of checking the genotypic balance of Hardy–Weinberg based on the G-test of maximum likelihood demonstratedthat in the wild boar population the overwhelming majority of the loci were in the state of the genotypicequilibrium of Hardy–Weinberg (Table 5). On the contrary,in the populations of the different breeds of pigs, some of theloci demonstrated a significant deviation from the indicatedequilibrium. In addition, the monomorphic loci were notedin three groups . Also, inthe populations of Yorkshire, Large White, and Landrace, theMC-loci were present, for which the hypothesis of neutralitywas reliably rejected based on the results of the Ewens–Wattersontest  (Table 6). Most ofthese loci were found in the Yorkshire and Landrace breeds.The degree of similarity of the genetic structure of the pigswas assessed using the principal component analysis, whichresulted in the ordination of the centroids of different herds in the space of the first two Main Coordinates based on thematrix of pairwise estimates of genetic distances by M. Nei. According to the data obtained, themost distant populations are the Duroc and Boar breeds, andthe most genetically close are the Yorkshire and Landracebreeds. The closest to the wild boar population was the LargeWhite breed.The effective population size was estimated using the linkagedisequilibrium (LD) method , as well as the molecular coancestry method. The calculations were performed using theNeEstimator V2 software . The results areshown in Table 8.The reliably high values of genetic variability ( p <0.05), notedin the wild boar population, are quite an expected phenomenon.This is despite the fact that the sample from the analyzedgroup was smaller than the samples from herds of domesticpigs. This clearly demonstrates the consequences of panmixisand genetic-automatic processes, which, in combination withthe natural selection, form the gene pools of natural populations.Moreover, in the wild boar population we analyzed, thelevel of actual heterozygosity turned out to be significantlyhigher than in the populations of this animal both in Europeand Asia. At the same time, the Oryol group, in terms of thelevel of genetic diversity, turned out to be similar to the Chineseand Vietnamese wild boar populations (Table 9).On the contrary, the transition of a number of loci to themonomorphic state and the lack of equilibrium accordingto Hardy–Weinberg, noted by us for most loci in domesticpig breeds, is a consequence of long-term artificial selectionwork, as a result of which many alleles of the “wild” typewere lost, which led to the loss of genetic variety. This is alsoevidenced by the significant deviation from neutrality of somemicrosatellite loci in the Landrace, Yorkshire, and Large Whitebreeds, which was revealed by us using the Evens–Wattersontest (see Table 6).Allelic diversity results in a large number of private allelesobserved in the wild boar population. At the same time, theDuroc population, despite the large number of private alleles,turned out to be the most monomorphic among the studiedgroups. This probably indicates a long-term selection of thisred color breed in the conditions of the North American continent,isolated from crossing with other pig breeds, includingwhite breeds of European origin . This can explain the significant genetic distance ofthe Duroc breed, both from the wild boar and the European pigbreeds. The presence of original alleles in the studied populationsmay be used in the future for identifying both the breedof pigs and belonging to a wild boar population.It should be noted that the results obtained are in part consistentwith the data obtained in the work of E.A. Glydyr et al.(2009). In this study, the level of actual heterozygosity in three out of five wild boar populations was found to be higher thanin domestic pigs. However, the average number of the effectivealleles was the same (Ae = 2.6). In terms of the numberof private alleles, the wild boar population also surpassed thedomestic pigs .The calculation of the effective population size based onthe LD method showed that in almost all the studied groups,including the wild boar population, the effective size wasless than 50 individuals. The only exception was the Durocbreed (Ne = 86.1). The data indicate the observed linkagedisequilibrium, probably caused by closely related matingin the analyzed groups of domestic pigs. This genetic drifthas generated a non-random association between alleles atdifferent loci.Calculations carried out using the MC method showedeven lower values. This phenomenon is most likely associatedwith a small number of parental individuals who founded the studied populations. It is worth noting thata similar picture was obtained in the work on assessing theeffective population size of the endangered Gochu Asturceltapig breed .If the results for breeds of domestic pigs were comparablewith data obtained in other studies, where Ne varied in generalfrom 20 to 92 individuals , then inrelation to the wild boar population, the result was somewhatunexpected, since in previous studies Ne ranged from 180 to1477 animals in natural populations .Such a low Ne value noted in the wild boar population ofthe Oryol region can be partly explained by a small sample,however, reasons behind it may be more serious. In particular,it is known that outbreaks of African swine fever (Pestisafricana suum) (https://www.kommersant.ru/doc/4236233),resulting in the death of wild boars are often recorded on theterritory of the Central Black Earth Region, which includesthe specified area. In addition, in order to prevent the spreadof infection, hunting farms are forced to shoot a significantpart of the animals. It is likely that these phenomena affectthe effective size of the wild boar populations.Thus, on the basis of the studies carried out, a clear reduction inthe genetic diversity of the domestic pig breeds in comparisonwith the natural wild boar population was demonstrated. Thepresence of private alleles can further aid in the identificationof wild boar and different breeds of pigs. Low values of theeffective size of the studied groups require attention frombreeders in relation to the breeds of pigs. In particular, thepig breeding companies in the region under study need touse a larger number of producers  to obtainreplacement livestock. 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The Bronze Age is a complex period of social, cultural and economic changes. Recent paleogenomic studies have documented a large and rapid genetic change in early Bronze Age populations from Central Europe. However, the detailed demographic and genetic processes involved in this change are still debated. Here we have used spatially explicit simulations of genomic components to better characterize the demographic and migratory conditions that may have led to this change. We investigated various scenarios representing the expansion of pastoralists from the Pontic steppe, potentially linked to the Yamnaya cultural complex, and their interactions with local populations in Central Europe, considering various eco-evolutionary factors, such as population admixture, competition and long-distance dispersal. Our results do not support direct competition but rather the cohabitation of pastoralists and farmers in Central Europe, with limited gene flow between populations. They also suggest occasional long-distance migrations accompanying the expansion of pastoralists and a demographic decline in both populations following their initial contact. These results link recent archaeological and paleogenomic observations and move further the debate of genomic changes during the early Bronze Age. Rio et al. design and utilise spatially explicit simulations of genomic components to investigate the cohabitation or competition hypothesis of Bronze Age Central European human populations. Their results support cohabitation with limited gene flow between pastoralists and farmers, linking together palaeogenomic and archaeological observations from the literature. The recent developments allowing DNA sequencing from ancient individuals represent a promising method for better understanding this period of complex social, cultural, and economic changes , representing one of the key stages of human prehistory in western Eurasia.While the rise of the farming lifestyle during the Neolithic transition has attracted the attention of population geneticists for decades . These studies indicated that genetic components related to the populations of the Pontic steppe  were introduced during this period. While it is widely accepted that people of steppe ancestry are involved in these genetic replacement events8, it is less clear the extent to which they are related to the Yamnaya cultural complex  that originated in the Pontic steppe at approximately 6000–5300 BP10. Despite the wide range of cultures identified in the archeological record that characterize this period, the YCC is central to the debate due to the spread of its funeral ritual11 and associated genetic background14 over large areas.Recent paleogenomic studies focusing on the British islands and the Iberian Peninsula showed major changes in the human gene pool during the BA. The British island populations experienced a nearly total replacement of the local population at approximately 4400 years Before Present (BP)km between the Atlantic fringes of Europe and the Pontic steppe, with CE being key for studying the connection between these two areas during the BA. Populations from CE have been shown to hold a large amount of steppe ancestry that appeared within a short period of time. “Funnelbeaker”, “Baden” and “Globular Amphora” are diverse CE cultures associated with the fifth millennium BP. They predate the Corded Ware cultural complex (CWC), one of the earliest archeological culture to have been genetically associated with steppe ancestry15. While the origin of the CWC has been questioned in the past9, its connection to the YCC has been strengthened recently18 but is still debated21. On the one hand, the archeological record supports an impact of the YCC on Central European cultures based on mixed assemblages 18. There are similarities in burial rites, but a distinction can be made based on differences in potteries18. On the other hand, recent paleogenomic analyses support a large amount of steppe ancestry in CWC populations24. Thus far, arguments from both the archeology and paleogenomics point to a close relationship between the populations associated with two geographically distant cultural complexes, with the YCC starting earlier but partially overlapping spatially and temporally with the CWC17.There is around 3500 12 interpreted the rapid and large genomic shift in CE during the BA as the consequence of massive migrations from the steppes. The term “massive” does not necessarily mean a high number of individuals and could actually imply a relatively small size of local populations in comparison to the migrating populations from the steppe. However, little is known about the relative population sizes of various communities during the BA, contrary to the Neolithic transition, during which hunter-gatherer populations were replaced by farmer populations able to sustain much larger densities27. Furthermore, the massive migrations hypothesis is contradicted on the archeological grounds3, which shows no sign of massive migrations but rather a more diffused process through a deeper, long-term history of interactions characterized by regular and repeated movements between the Pontic steppe and CE. While there is no doubt that some migration of YCC-related populations occurred from the Pontic steppes westward around 5000 BP, more work needs to be done to understand the demographic processes that have shaped the genetic diversity of human populations during the BA11.Haak et al.Yersinia pestis, the pathogen responsible for the plague. Rasmussen et al.28 proposed that strains of Y. pestis from the BA did not carry the Yersinia murine toxin gene (or YMT gene), which gives rise to flea-borne transmission of the plague and the ability to cause bubonic plague, but Demeure et al.29 proposed that this gene was acquired by a different branch of Y. pestis that split around 5300 BP. One question that emerged is whether Y. pestis would have played a role during the diffusion of pastoralists from the Pontic steppe to CE during the BA31. It could indeed be hypothesized that incoming pastoralists arrived with pathogens to which they were resistant but Central Europeans were not (or less so)28. These pathogens may then have decimated part of the indigenous populations, allowing the genomes of incoming pastoralists to partially replace local genomes without a large number of migrants.Interestingly, the period associated with the YCC and CWC encompasses the time of the most ancient detected traces of 32. Spatially explicit simulations applied to human evolution have been developed to study the European Neolithic transition. They have provided important information, which could not have been identified otherwise, on the genetic consequences of the interactions between early farmers and hunter-gatherers34, including the effects of sex-biased migrations and variable admixture rates5. Although focusing on the Neolithic transition, a couple of studies34 also incorporated migrations from the steppes in their simulations that were applied to allele frequencies. The spatially explicit approach implemented in the program SPLATCHE335 offers an adequate tool for addressing further questions related to these topics as it allows the simulation of two layers of interacting populations that admix at various levels over time and space . In the BA context, one layer would represent the Central European populations characterized by sedentary farmers, while the second layer would represent the incoming populations from the Pontic steppe, characterized by herding and the use of horses. The farmer and pastoralist layers would thus represent structured populations having different ways of life and possibly other distinct characteristics . SPLATCHE3 can generate virtual population samples with the same sizes, locations, and dates as the real samples, from which the proportions of alleles coming from one or another population source  can be computed for multiple loci .The population dynamics and interactions of populations during the BA in CE remain unclear. It has been shown that eco-evolutionary simulations of paleogenomic data could provide insights into these processes12. We aim to clarify the conditions under which contacts between the local farmers and incoming pastoralists during the BA resulted in the sudden appearance of large steppe ancestry components in CE13. We explored various scenarios of the expansion of pastoralist populations from the Pontic steppe, where the YCC originates, westward and their admixture with the local farming populations of CE. Our scenarios include i) symmetrical gene flow between populations, ii) competition within and between populations, iii) short- and long-distance dispersal, iv) various effective population sizes, and v) demographic decline in one or both populations after their initial contact to represent the effects of a deadly disease, such as plague. Our goal was to reconcile paleogenomics and archeological observations.In our study, we used complex spatially explicit simulations Fig.  combined12 (Table 37) was used to evaluate the probability of the investigated scenario and to estimate the parameter values.Thirty thousand simulations with different combinations of parameters were performed for each investigated scenario. At the end of each simulation, the proportion of genes coming from either the initial pastoralist population or from the farming population was measured in each virtual sampled genome. These samples were taken at the same place and time as the observed samples“F”), the pastoralists (scenario “P”) or both (scenario “F&P”) at the time when pastoralists arrive in CE. These three scenarios are compared to a fourth “control” scenario, where no demographic decline is simulated.We tested three scenarios to simulate the effect of a disease reducing the population size of the farmers . The four scenarios without competition have the highest posterior probabilities, with the “F&P” scenario being the most likely (41.5%), followed by the “F” (19.5%), “P” (15.8%), and “control” (14.7%) scenarios. Assuming direct competition, the most likely scenario is the “P” scenario (7.4%), which is the only one of the direct competition scenarios with a posterior probability >1%. Pairwise Bayes factors provided in Supplementary Table P”, which ranges from “anecdotal” (1.98) to “moderate” (5.59). The confusion matrix shows that there is 24% confidence that the “F&P” is correctly identified  is much higher than the probability of a model with direct competition , with a “strong” evidence for model without competition as revealed by the Bayes factor (10.22), and 83% probability of being identified correctly  than the two other scenarios involving a single population decline , and the less probable “control” scenario where there is no demographic decline (posterior probability of 12.4%). Pairwise Bayes factors provided in Supplementary Table F&P” against the other three scenarios, but the differences between “control”, “F,” and “P” is “anecdotal”. In total, the posterior probabilities of the scenarios including a demographic decline amount to 88% against only 12% for the scenario without demographic decline. This implies that the incorporation of demographic declines in the model improves its fits to the observed paleogenomic dataset. The confusion matrix shows that 37% of the “F&P” scenario is correctly recovered . When comparing between the “control” and “F&P” scenarios only, the posterior probability of the “F&P” scenario (99.4%) is much higher than that of the “control” scenario (0.7%), with a “strong” evidence for the former as revealed by the Bayes factor (10.14), with 66.2% of “F&P” scenario being correctly recovered (PR = 0.66).We performed another model comparison using only the scenarios without competition and by fixing the layer of genetic sampling when possible  to optimize the exploration of the parameter space  without competition.We estimated demographic parameter values through an ABC analysis using the most likely scenario (“LDD), the strength at which the demographic decline is applied (SDD), and the parameters representing the number of migrants between demes within the farmer layer (NmF) and within the pastoralist layer (NmP). Smaller values within the prior distributions of these four parameters are more likely to result into simulations that are compatible with all the sampling times and locations . This implies that LDD is necessary in our simulation framework for the expansion of pastoralists to be consistent with the dates of the appearance of their associated genetic input in CE.There are four demographic parameters that are affected by the demographic filtering in the simulations . These are the long-distance dispersal rate , the long-distance dispersal rate  and the strength of the population decline . The absolute number of migrants between demes within the farmer layer  and within the pastoralist layer  also provides interesting information  rather than the migration rate . The cross-validation shows that only γ can be retrieved with accuracy , the migration rates (mF and mP), and the generation when the population decline ends (EDD), do not show clear signals, and we consider them uninformative (PE > 0.88). Even though noninformative, these parameters add stochasticity to the complex processes modeled here and we consider them useful to include.As displayed in Fig. on Table . The difOur spatially explicit simulations support the expansion of pastoralists from the Pontic Steppe towards Central Europe during the Bronze Age, with rare—but necessary—long-distance migrations (~2% of the migration events), as well as side by side cohabitation with Central European farmers with limited admixture (1% of the contacts). In addition, the parameter estimation shows that the steppe ancestry components estimated in Bronze Age Central European populations are best explained with local farmer and incoming pastoralist populations having similar sizes; furthermore, the results indicate that both populations suffered an important demographic decline in CE after their initial contact (~65% decline), despite its exact timing and duration remain undetermined.34. This asymmetrical introgression of neutral genes between a local population at demographic equilibrium and an incoming population in demographic and spatial expansion is due to their different population dynamics, that lead to a rapid dilution of genes from the incoming population into the gene pool of the local population, if admixture is not severely prevented36. Consequently, a high steppe ancestry far from the place of origin of pastoralists can be explained by a very low amount of gene flow between both populations, as estimated in our study by an admixture rate of around 1% . With higher admixture rates, genes from the initial pastoralist population would have been rapidly diluted in the gene pool of the local populations encountered during their expansion toward CE. This dilution effect is partly counteracted by long-distance dispersal (LDD), which reduces the number of admixture events and thus the amount of local introgression in the dispersing population38.Previous studies showed that in the context of range expansion, a low level of admixture with a local population is sufficient to explain the high introgression of local genes in the incoming population39, as observed in CE12, western Russia40 or in Britain6. However, this does not seem to be sufficient here to explain the variance in steppe ancestry, since incorporating an important demographic decline just after the pastoralists reached CE better fit the observed data.Furthermore, a low level of admixture could potentially explain heterogeneous introgression levels32. For comparison, the continuous admixture rate estimated between farmers and pastoralists during the BA is lower than that estimated between hunter-gatherers and farmers during the Neolithic (8.8%32). However, contrary to our best scenario of cohabitation during the BA, admixture between hunter-gatherers and early Neolithic farmers was simulated during a much shorter cohabitation period before the disappearance of the foraging lifestyle due to competition with the farmers. The effective cohabitation time in the current study corresponds to a minimum of around 60 generations while it was around 15 generations in the simulation of the Neolithic32. Note that those values vary across simulations depending on the combination of parameters. This explains why a lower admixture rate over a longer period (this study) leads to similar introgression as that observed with a higher admixture rate over a shorter period (simulations of the Neolithic transition32).The estimated admixture value means that approximately 1% of contacts between pastoralists and farmers resulted in a change in the lifestyle of one interacting individual or its descendants, thus contributing to mixing genomic legacies from Central European and Pontic steppe populations. The admixture rate is the most informative parameter among those investigated, similar to what has been found when modeling the Neolithic transition with a similar methodology41.Our results support the occurrence of LDD at a low rate (2.3%), meaning that approximately 2% of migrations would have occurred over long distances. LDD helped pastoralists reach the sampled locations in CE at the corresponding dates  for both the farmers and pastoralists , indicating that gene flow is extensive within both populations. These values tend to be in the higher range compared to other Nm estimations for human populations from genetic data44. However, we will not interpret these absolute values extensively, since our cross-validation shows that the estimates for the parameter Nm lack precision and that there are many differences across studies, such as deme sizes, as well as spatial and temporal contexts. What is interesting, however, is the impossibility to differentiate between the number of migrants estimated from the farmer and pastoralist populations. This suggests that the “massive” change that occurred in the genomes of CE populations during the BA does not necessarily mean that local farmers were overwhelmed by a large immigrant population and that a continuous process of migration without much admixture could also explain this genomic pattern.We estimated a large number of migrants between demes  because they do not necessarily overlapThe model without direct competition, which is supported by our analysis, does not contradict the different economic subsistence strategies of pastoralists and farmers. Populations with different lifestyles could indeed cooperate, leading to their coexistence over long periods, which would not appear as a cultural “change” from the archeological point of view but would be associated with a cultural complex , such as the Corded Ware complex (CWC) associated with the populations analysed in our study. However, we have tested only two extreme situations (with and without full competition) and cannot exclude more nuanced processes of competition between local farmers and incoming pastoralists in CE.20. The fact that a strong, fast, and well-delimited cultural change associated with cultures from the steppes did not appear in CE at the same time as large genomic components related to a steppe ancestry could be explained by the coexistence of pastoralists and farmers on a large geographical scale.Our results thus do not contradict the proposal that moving people can result in a homogenization of material culture, such as the CWC, while communities remain distinct for specific combinations of cultural characteristics/material5), and the effect of social structure49 . Second, we used ancestry proportions as proxies of genomic data to estimate demographic parameters. The use of ancestral components somewhat limits the molecular information available from paleogenomes for making demographic inferences, but the dataset used fits the kind of information that is simulated by the program SPLATCHE3 particularly well . There is a strong correlation between those two quantities , we were limited by the number of population samples that we can used in our study namely due to two technical constraints: the impossibility of obtaining ancestry proportions in the same deme at several different times with SPLATCHE3 and the exponential increase in combinations of sampling layers . Nevertheless, following studies13 confirmed the general pattern described in Haak et al.12 about a sudden appearance of large steppe ancestry components in CE during the BA and we verified the representativeness of the data analysed against a larger dataset available from multiple sources  and it opens new avenues in the spatial analysis of ancient DNA.Our model is a simplified representation of societal and biological mechanisms related to processes that would have occurred in CE during the BA. It is meant to improve the understanding of those processes as a complement to other approaches. Despite improving on the realism of panmictic models by incorporating spatially explicit features, our modeling framework still has limitations that are worth mentioning. First, we tested only a limited number of scenarios. For instance, we did not consider the spatiotemporal heterogeneity of the environment and other processes associated with population dynamics, such as the temporal variance of the population growth rate, the effect of matrilocality or patrilocality . We started this work with the hypothesis that Y. pestis may have been the cause of a population decline, but the literature concerning the epoch approximately 4500 BP reveals that this period is marked by climatic, social, migratory, and economic changes that could potentially have affected the demography and genomic background of BA populations. Below, we discuss a list of possible causes that could explain a large population decline occurring at approximately 4500–4,000 BP in CE, encompassing the time when the samples in the dataset we used are dated from.Disease: Our initial hypothesis was that early BA populations from the steppes would have been resistant to an ancient strain of Y. pestis, while Central European populations would have been vulnerable. We thought that this could have contributed to the sudden appearance of large inputs from populations from the steppes to the genomic profiles of Central European populations. Under the hypothesis that the spread of pastoralist populations was favored by a disease that decimated local farmers only, we were expecting a demographic decline in the farming population but not in the pastoralist population, corresponding to scenario “F,” which received low support (16.7% posterior probability). If the demographic decline observed in the scenario with the highest support  had been due to a disease, it would thus have affected both populations, not only the farmers. Note that scenario “P,” in which only the pastoralist population is affected by a demographic decline, is as probable as scenario “F,” with a posterior probability of 16.4%.Climate: Brutal climatic events, such as the one occurring approximately 4200 BP may have had major impacts on human populations, including in the European continent57 and its effects could have started hundreds of years earlier. A demographic decline starting around 4450 BP, as in our simulations, could potentially be explained by climatic events degrading environmental conditions and leading to reduced resources accessible to both the pastoralists and farmers.Resource depletion: The exploitation of the ecosystem, together with growing material needs, could lead to the fast degradation of the environment and to a demographic collapse. Technological improvements can provide an enhanced carrying capacity in the short term, although eroding the resources and reducing the carrying capacity in the long term58. Ecosystem loss might overcome its resilience due to increasing stress induced by humans, leading to a critical disequilibrium that may cause the collapse of the whole system59. Economic stratification can also independently lead to a population decline by inducing economic inequalities and the loss of resources61. For instance, Svizzero62 showed that the BA prosperity in CE, due to metalworking, required a large amount of primary resources that led to an economic crisis called the “Dutch disease.” This may explain the later collapse of the Únětice Culture, which is more recent than the dataset used in this study. In our case study, the populations could have affected their common resources in the long term, affecting both carrying capacities (scenario “F&P”). This could be considered a form of indirect competition equally affecting both parties.Direct conflicts: Direct conflicts between farmers and pastoralists could explain the demographic decrease in CE during the BA. These events could affect both populations or only one population . It was suggested that populations associated with the Bell Beaker culture, also carriers of a large proportion of steppe ancestries, are responsible for population replacement at approximately 4500 BP on the British Islands6. Because Bell Beaker communities were also present in CE around this period, they may have been the cause of the demographic decrease in this area. However, this is speculative, as we have no evidence supporting such conflicting events in CE.Biased sex ratio of reproducers: Another social hypothesis involves a biased sex ratio among reproducers. Indeed, a biased sex ratio among individuals contributing to reproduction, passing from 1.0 to 0.25, would result in an effective population size reduction of 60%64, while the census population size would remain the same. Goldberg et al.65 suggested BA migration driven by males  approximately 5000 BP. Moreover, Heyer et al.66 showed that the transmission of reproductive success can greatly reduce the effective population size in one or the other sex, an effect that could be detected by analysing patterns of genetic variation on the Y and X chromosomes. While we analysed autosomal genetic patterns, the inference of a large population decrease in CE may be explained by biased sex ratio or by changes in cultural practice affecting each sex differently68.Our results show that a large steppe ancestry component in Central European BA populations can be explained by a model of pastoralist expansion with long-distance dispersal combined to a limited gene flow with local populations. This low admixture rate is necessary to avoid a fast dilution of steppe ancestry in the gene pool of local populations encountered by the Pastoralists during their migration toward Central Europe. Furthermore, we found that a further demographic decline is necessary to explain the spatiotemporal heterogeneity of this steppe ancestry among the samples analysed. Note that the observed heterogeneity in steppe ancestry could be partly dependent on the samples, and additional data are needed to further confirm this observation. We estimated the amplitude of this demographic decline to be very strong (between 60 and 75%), but we did not obtain much information about its start and duration , and immigrant populations from the Pontic steppe, hereafter referred to as pastoralists (“P”), were performed with a version of the program SPLATCHE335 allowing for LDD with the two-layer option. This program simulates the demography and migration of populations through space and time by taking into account the type of interactions between them , various types of migration (short- and long-distance), the spatiotemporal heterogeneity of the environment, and the resulting molecular diversity based on a serial coalescent approach.Spatially explicit simulations of the interactions between sedentary Central European populations, hereafter referred to as farmers  in the first grid of demes (layer F). This is followed by the expansion of pastoralists from the Northern Caucasus area starting at the end of the Neolithic (5600 BP)10 in a second superimposed grid of demes (layer P). We started the expansion of our first simulated pastoralists at 5600 BP. In this manner, by 5300 BP the extent of these simulated pastoralist populations is similar to the actual Yamnaya cultural horizon. Generation 0—the beginning of a simulation—corresponds to the onset of the Neolithic, while the present corresponds to generation 400—the end of a simulation—assuming a generation time of 25 years. The model takes into account direct competition and admixture between the two layers and the effect of a demographic decline in every demes of each layer after their initial contact in CE. As shown schematically in Fig. m) and the long-distance dispersal rate (parameter LDD). Short-distance migration cover 100 km while long-distance migration covers on average 800 km. The population density within each deme increases at the growth rate (parameter r) until reaching a carrying capacity (parameter K). The population sizes and carrying capacities in SPLATCHE3 are given by the number of gene copies. Each population  has its own demographic and migratory characteristics. The “assimilation” model of SPLATCHE3 was used35 where the admixture rate regulates the gene flow between demes from the same location and different layers (parameter γ), going from 0 (no mixing) to 1 (full mixing). Moreover, SPLATCHE3 takes into account intrademic competition, which is represented by the effect of carrying capacity on the logistic growth regulation within each deme of both layers. In addition, the Lotka–Voltera competition model73 was used to test for direct competition between pastoralists and farmers, which may be due to warfare or common resource exploitation. The parameter “CompetitionModel” was set to 0 without competition or 1 with competition.SPLATCHE3 represents each population  as a layer of interconnected demes of 100 by 100 32. In the absence of further information, parameters of the pastoralist layer were taken from the same prior distributions as those of the farmers .For the various scenarios described below, 30,000 demographic simulations were performed, where each simulation used a different combination of parameters taken independently from prior distributions  the “F” scenario, in which the two populations mix, with a further population decline in the demes of the farmer layer only , iii) the “P” scenario, in which the two populations mix, with a further population decline in the demes of the pastoralist layer only  and iv) the “F&P” scenario, in which the two populations mix, with further population declines in the demes of both the farmer and the pastoralist layers (which could result from a disease affecting both populations in a similar way). Note that this population decline could be due to any other factor responsible for reducing population size, not only pathogens.Accordingly, we designed four different scenarios representing different alternative situations: i) the “EDD) and its strength as a ratio of K (SDD).In the scenarios in which population decline occurs after contact between farmers and pastoralists in CE, two parameters are involved: the generation at which the population decline ends , corresponding to the period when a pandemic caused by a strain of 75). The plague is less lethal in its bubonic form (30–60% death rate) than in its pulmonary form . A subset of values was used because on the one hand, lower values would render the various scenarios difficult to differentiate, while on the other hand, higher values would produce simulations with empty demes at corresponding sampling times, a situation we know does not fit current data. Moreover, the upper limit of the prior is in accordance with the maximum historical values estimated for the Black Death (80% reduction of the population size)76.The chosen prior for the reduction in the population size due to plague is a subset (50–80%) of the lethal range proposed by the World Health Organization (WHO) without medical treatments , and a scale parameter, θ (corresponding to the inverse of the rate parameter β = 1/θ). The maximum prior probability for an emigrant to travel more than one deme away from its current deme (LDD event), as well as the average number of demes traveled by migrants (µ) and the gamma distribution shape parameter (α) values were retrieved from Alves et al.41. The other variables were corrected to take into account the difference in deme size . Moreover, the exploratory simulations qualitatively showed that pastoralist populations have very little probability of reaching CE without long-distance dispersal (LDD), except with the help of specific combinations of demographic parameters, such as a large admixture rate. We therefore decided to use a prior distribution for the LDD rate (0–5%), which includes the absence of LDD, as we do for the admixture rate (0–3%).77 by using the IntCal13 atmospheric curve78 . For each simulation and each sampling location, sampling dates were randomly drawn from a uniform prior distribution, with the limits taken as the average minimum and maximum dates of all the paleogenomes from the same location.For each simulation, we sampled 10 populations  may result in different steppe ancestry proportions. Obviously, samples coming from the farmer layer tend to show lower steppe ancestry proportions than samples taken from the pastoralist layer and not considering this ambiguity could have affected the estimation of the admixture rate. To account for this issue and avoid making any a priori decisions on the lifestyles associated with the observed samples, we collected samples from both layers, and then we made all 1024 combinations of the 10 samples by alternatively taking each sample from one of the two layers (210).SPLATCHE3 uses virtual layers to represent the two populations, which are differentiated by their lifestyle . Because the Central European BA is a culturally complex period, the attribution of observed samples to one or the other lifestyle is not trivial, because it depends on the related archeological context, which can be ambiguousTo reduce the number of improbable simulations and save computational time, we used the layer id  as an input parameter for the ABC analysis to estimate the most likely layer for each sample, and we fixed this layer for the samples in which the signal was sufficiently unambiguous. A value of 0 indicates sampling in the farmer layer, while 1 indicates sampling in the pastoralist layer. When the posterior distribution of a sample was above 0.5 for the lower value of the 99% HDI, we took that sample in the pastoralist layer, while we took it in the farmer layer when the upper value of 99% HDI was under 0.5. When the four demographic scenarios without competition allow us to fix a given sample to the same layer, we fixed this layer for that sample. By using this method, we were able to define the most likely layer for four (out of 10) observed samples  from a deme are not considered for the next step (the ABC estimation). This serves as a demographic filter, and has the advantage of discarding the less probable combinations of parameters that are not in accordance with archeological records . Moreover, some sets of demographic parameters lead to the possibility of sampling in one layer but not in the other, leading to a drastic decrease in the final number of layer combinations.Each set of demographic parameters leads to many sampling combinations, whether the samples may be drawn from both population layers or from a fixed one (as for the four abovementioned examples). By performing 30,000 simulations (each one made of different sets of demographic parameters) for each scenario, we ultimately have a maximum possible number of 30,000 × 2The number of remaining simulations after the demographic filtering can be counted in millions for each scenario without competition  and in hundreds of thousands for each scenario with competition . Finally, a subset of 100,000 simulations is randomly drawn from the whole set of simulation results for each scenario for further ABC estimation, as datasets of millions of simulations are too large for the “abc” R package to run in an acceptable time. We checked that the results are robust to the random choice of 100,000 simulations  for model M1 against model M2 and interpret its value using the scale by81 adjusted from82: with BF12 = 1-3, the evidence in favor of M1 is “anecdotal”, with BF12 = 3–10 it is “moderate”, “strong” with BF12 = 10–30, “very strong” with BF12 = 30–100 and “extreme” with BF12 > 100. To evaluate the precision of the parameter estimation, we used the prediction error (PE) from the “abc” package, computed as: th replicate and All the approximate Bayesian computation (ABC) analyses were performed using the package “abc” version 2.1) in R.1 in R80Further information on research design is available in the Supplementary materialReporting Summary


One hypothesis proposes that different pathogen-driven selective pressures between urban and non-urban landscapes leads to adaptations in host immune genes. Here, we examined urbanization-related differentiation at the key pathogen-recognition genes of vertebrate adaptive immunity-the major histocompatibility complex (MHC)-in a common waterbird, the Eurasian coot (Fulica atra). Samples were collected from an old urban population (established before the 1950s), a new urban population (established in the 2000s), and two rural populations from central Poland. We found strong significant divergence (as measured with Jost’s D) at the MHC class II between the old urban population and the remaining  populations. Also, there was a moderate, but significant divergence at the MHC between the new urban population and two rural populations, while no divergence was found between the two rural populations. The total number of MHC alleles and the number of private (population-specific) MHC alleles was lower in old urban populations, as compared to the rural ones. These patterns of differentiation at the MHC were not consistent with patterns found for neutral genetic markers (microsatellites), which showed few differences between the populations. Our results indicate that MHC allele composition depended on the level of anthropogenic disturbance and the time which passed since urban colonization, possibly due to the processes of genotype sorting and local adaptation. As such, our study contributes to the understanding of genetic mechanisms associated with urbanization processes in wildlife.Urban sprawl is one of the most common landscape alterations occurring worldwide, and there is a growing list of species that are recognised to have adapted to urban life. To be successful, processes of urban colonization by wildlife require a broad spectrum of phenotypic ( Such inJunco hyemalis) was found to host significantly higher numbers of ectoparasites and had more pox lesion scars compared to non-urban populations from montane habitats  are known to have enhanced immunocompetence compared to non-urban conspecifics (Parus major) has provided strong evidence for the upregulation of adaptive immune responses in urban individuals , which can result in a low level of genetic diversity, including the diversity of the immune receptors responsible for antigen recognition   and the major histocompatibility complex (MHC) . In genethogens) . Even mithogens) . In genethogens) . Thus, Mte fauna .Lynx rufus) whose habitats differed in urbanization level (Vulpes vulpes) that colonized Zurich, Switzerland, showed depleted neutral and functional (immune-linked) genetic diversity following a founder event (Canis latrans) after the recent colonization of the New York metropolitan area, USA  linked microsatellite loci was found between populations of bobcats , from areas that varied in urbanization level. For this purpose, we genotyped MHC class II genes in four populations of the Eurasian coot from central Poland: (1) an old urban population ; (2) a new urban population ; and (3) two non-urban populations (Sarnów and Żeromin). Since the process of urban colonization by the Eurasian coot is still in progress in central Europe, the species may be considered a good model to study genetic adaptations across urban and non-urban populations with different histories. Here, we test whether urbanization processes are related to changes in the MHC gene pool and if they lead to reduced immunogenetic diversity at the population level. We hypothesized that differences in urbanization level altered the allelic composition of MHC genes between urban and non-urban Eurasian coot populations, with more pronounced differences observed in the old versus new urban population. We additionally hypothesized that the process of urban colonization would be associated with reduced MHC diversity, either due to local adaptation  or demographic history and genetic drift .Our study aims to examine the differentiation and diversity of MHC genes in a common waterbird, the Eurasian coot (https://stat.gov.pl/en/), and area (517.24 km2); it is also characterized by high population density . The city was colonized by coots in the mid-20th century, with a stable breeding population of coots being reported in highly urbanized parts of Warszawa as early as in the 1960s  and two rural (Sarnów and Żeromin) populations of Eurasian coot from central Poland during 2012–2018 . Warszawhe 1960s . During he 1960s  in the c994–2002 . Since t994–2002 . Trespasn = 103 individuals in total). Birds were caught with noose traps put in the nests and made from monofilament nylon. Several birds were caught by hand (exclusively in urban populations). Approximately 75 μl of blood was taken from a tarsal vein from each bird and stored in 96% ethanol at 2 °C until further analysis. Each bird was ringed and marked with a plastic neck collar, which allowed us to avoid recapturing the same individuals. Genomic DNA was isolated from blood samples with GeneJET Genomic DNA Purification Kit  following the manufacturer’s protocol. All methods were carried out in accordance with the current laws of Poland  and were approved by the Local Bioethical Commission for Experiments on Animals in Łodź (nos 40/ŁB 620/2012 and 15/ŁB/2016).Blood samples were taken during the reproductive season from 20–30 adult breeding individuals per population (Tribonyx mortierii)    , eight µL of deionized water, 0.5 µL of each primer  and one µL of genomic DNA solution (20–80 ng of DNA). Fragment size analysis was conducted with ABI/Hitachi 3,500  sequencer. The size of alleles was assessed with GeneScan TM 600 LIZ Standard  using Geneious 7.1.7 software . All loci were successfully genotyped in all individuals. To exclude any major genotyping errors allele calling was checked twice and genotyping of technical replicates showed high (>95%) reproducibility of alleles.To assess the level of neutral genetic variation in our study populations, we used ten microsatellite loci . Nine mirtierii) , Kira8, elegans) , and B10icensis) . The lasicensis) . PCR ampp > 0.05 after Bonferroni correction), as assessed with the exact tests (p > 0.05 after Bonferroni correction), as tested with FSTAT 2.9.3 software  , eight µl of deionized water, 0.5 µl of each primer , and one µl of genomic DNA solution (20–80 ng of DNA). To allow identification of samples, amplifications were completed using fusion primers containing Illumina Nextera Transposase adapter sequences , seven-bp barcodes that indicated sample identity, and original MHC primers. Following MHC genotyping focused on class II genes, which were previously reported to show high level of polymorphism in natural coot populations . For ampRaw Illumina MiSeq data were processed using Amplicon Sequencing Analysis Tools (AmpliSAT) web server  and proci.e., total number of alleles and the number of private alleles per population) were calculated for MHC and microsatellite loci using GeneAlEx v.6.5 software. For microsatellite loci we also calculated mean observed heterozygosity for each population using the same software, while for the MHC we calculated population-specific sequence polymorphism measures  using DnaSP v6.10.03  and individual microsatellite heterozygosity between populations were tested with general linear mixed models (GLMM) in glmmADMB package  in the data and to assign individual genotypes to these clusters (separately for MHC and microsatellite data). Since it was not possible to assign MHC alleles to loci, we encoded each allele as a dominant biallelic locus, following K values between one and five. We used the admixture model of ancestry, correlated model of allele frequencies, and sampling location as prior information. The number of Markov chain Monte Carlo iterations was set to 5,00,000 and the length of burn-in period was set to 2,00,000. Each analysis was replicated ten times. The number of genetic clusters for each type of marker was inferred by a comparison of mean posterior probabilities for different K values L(K) , as implemented in the Structure Harvester 0.6.94 (K) against the ΔK algorithm  had three MHC alleles and the mean number of MHC alleles was 2.89 ± 0.08 per individual.After clustering and filtering of Illumina sequences, we found that 115 MHC class II alleles were retained across all populations. These corresponded to 113 unique amino acid sequences. Reproducibility of alleles after processing was 100% , except for Kira-16, where 48 alleles were recorded . The obsi.e., the number of MHC alleles per individual) between the populations  were similar across urban and rural populations .We found strong population differentiation at the MHC and the average pairwise Jost’s D was 0.441. The strongest differentiation at MHC genes was recorded between the old urban population (Warszawa) and all other populations, although the new urban population (Łódź) also showed significant differentiation from both rural populations . The twoST . PairwisK), although ΔK were similar for K = 2 and K = 3 (K) and ΔK values . Two genetic clusters were inferred for the MHC genes, as based on the mean posterior probabilities L and the three others-the new urban (Łódź) and two non-urban populations (Sarnów and Żeromin). Finally, there was an apparent loss in MHC allele diversity in both urban populations when compared to rural ones. These findings were not consistent with the patterns of neutral genetic diversity and differentiation between the populations (across a panel of microsatellite loci), suggesting that processes shaping MHC variation were not primarily driven by demographic processes.vs. adjacent non-urban) framework. This was possible due to the other type of material collected , which did not allow efficient MHC genotyping because of low quality DNA extracts. Here, we used blood sampled from captured individuals, which on one hand allowed us to obtain reliable MHC genotypes, but on the other hand it limited our sampling and reduced statistical power to detect inter-population differentiation. It must be acknowledged, however, that the results on microsatellite and MHC variation within this study were obtained under identical sampling scheme and, thus, can be reliably compared. Taking this into account, contrasting patterns for neutral genetic variation (a lack of differentiation) and adaptive immunogenetic variation (a significant differentiation at the MHC) suggest that the latter cannot be solely explained by stochastic mechanisms associated with genetic drift or demographic processes, which may take place during urban colonization.MHC genes are an important model for studying the adaptive genetic differentiation in wildlife populations, as co-evolution with pathogens is commonly referred to as the main mechanism responsible for the occurrence of MHC polymorphism . Our resi.e., when a group of individuals colonizing novel areas is genetically not representative for the source population; The urbanization process may be associated with a random reduction in genetic diversity as a result of the bottleneck effect  are more likely to colonize novel habitats, including urban areas  and the non-urban populations . This may suggest an adaptation to a specific (perhaps relatively homogenous) assemblage of urban pathogens and it is consistent with empirical studies showing altered composition of parasite faunas in an urbanized landscape and a negative association of parasite richness with urbanization level  on MHC differentiation. Also, our populations were not evenly distributed in space, as the old urban population was located relatively far from the remaining three  populations. This may have suggested that any genetic differentiation of the old urban population could have arisen due to isolation by distance, although this scenario has not been confirmed by the analysis of microsatellite data .Our capability to infer the exact mechanisms responsible for the observed MHC differentiation was further impaired by some methodological limitations. First, we had no replicates for old and new urban populations and, thus, our analyses yielded no statistical power to directly test for the effect of population history (Athene cunicularia) has shown that there is limited gene flow between adjacent urban and rural populations , but not between different urban populations , as a results of strong habitat differences which diminished gene flow and amplified genetic drift  that maintain clusters of adaptive loci . Currently, there is a scarcity of studies testing for immunogenetic adaptations to urban habitats in birds and our results provide an important contribution to this topic, as well as advancing our more general understanding of genetic mechanisms associated with urbanization processes in wildlife. Although our results revealed associations of MHC allele composition with habitat urbanization and the time which passed since urban colonization, we are aware that our conclusions based on a limited number of populations should be confirmed using longitudinal data collected at broader geographical scale and across a larger number of urban and non-urban population replicates. We also acknowledge that future research should complement our findings with pathogen data from different habitats and, ideally, should examine fitness consequences associated with different MHC composition in urban and non-urban environments. Nevertheless, we believe that our study provides important foundational information that future work can build upon, not only in coots, but also pertaining to other urban colonizing wildlife. As our knowledge of how species are evolutionarily responding to increasing rates of urbanization grows, knowing the role immunogenetic adaptations play will provide key insights into urbanized taxa and what it takes to survive in a human world.10.7717/peerj.12264/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.12264/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.12264/supp-3Supplemental Information 3Click here for additional data file.


Lumnitzera consisting of two species (L. racemosa and L. littorea) with nearly sympatric ranges across the Indo-West Pacific (IWP) region by sequencing three chloroplast DNA regions (for both species) and genotyping 11 nuclear microsatellite loci (for L. littorea). Consistent with findings in studies on other mangrove species, we found that both L. racemosa and L. littorea showed relatively high genetic variation among populations but low genetic variation within populations. Haplotype network and genetic clustering analyses indicated two well-differentiated clades in both L. racemosa and L. littorea. The relationship between geographic and genetic distances and divergence time estimates of the haplotypes indicated that limited dispersal ability of the propagules, emergence of land barriers during ancient sea-level changes, and contemporary oceanic circulation pattern in the IWP influenced the current population structure of the two species. However, the position of genetic break was found to vary between the two species: in L. racemosa, strong divergence was observed between populations from the Indian Ocean and the Pacific Ocean possibly due to land barrier effect of the Malay Peninsula; in L. littorea, the phylogeographic pattern was created by a more eastward genetic break along the biogeographic barrier identified as the Huxley’s line. Overall, our findings strongly supported previous hypothesis of mangrove species divergence and revealed that the two Lumnitzera species have different phylogeographic patterns despite their close genetic relationship and similar current geographic distribution. The findings also provided references for the management of Lumnitzera mangroves, especially for the threatened L. littorea.Mangroves are ecologically important forest communities in tropical and subtropical coasts, the effective management of which requires understanding of their phylogeographic patterns. However, these patterns often vary among different species, even among ecologically similar taxa or congeneric species. Here, we investigated the levels and patterns of genetic variation within  Approximately 70 woody plant species belonging to about 30 genera in 20 families are generally recognized as being mangroves . They arRhizophora (Xylocarpus (Many recent molecular studies in the Indo-West Pacific (IWP) region, one of the two mangrove diversity centers of the world , commonly known as the black mangrove, is a non-viviparous mangrove genus distributed in the IWP region  Does the Malay Peninsula also serve as a land barrier in L. littorea, as demonstrated in L. racemosa? (3) Are there any other cryptic barriers that could have affected the current distribution and patterns of genetic variation of L. littorea and L. racemosa? We start by inferring the period of divergence between Lumnitzera and its closest relative, and then compare the genetic structures of its member species across their distributions using maternally inherited chloroplast DNA (cpDNA). Further, a set of 11 bi-parentally inherited nuclear DNA markers are also used to infer fine-scale genetic patterns in L. littorea, especially contemporary gene flow mirrored by more recent demographic history.In this study, we aimed to address the following questions: (1) What is the pattern of genetic variation across the distribution of L. racemosa and 27 populations  of L. littorea across the IWP region  of P region . Only yoP region .trnT-trnL, trnG-trnS, and atpB-rbcL, were found to produce single clear amplified bands and contained the most polymorphic sites . Thermocycling conditions were: initial denaturation at 94°C for 4 min, followed by 30 cycles of 94°C for 40 s, 55–60°C for 1 min and 72°C for 1 min, and a final extension of 72°C for 8 min. The amplified fragments were checked through agarose gel electrophoresis and sequenced on an ABI 3730XL DNA Analyzer (Applied Biosystems).For cpDNA sequences analysis, after preliminary screening of 31 available primer pairs at different chloroplast regions, three cpDNA intergenic spacer regions, ic sites . TherefoL. littorea were only considered for genotyping at microsatellite loci since detailed genetic structure of L. racemosa in the IWP has previously been studied using multiple nuclear genes (L. littorea (unpublished data). Among these, 11 loci that showed stable amplification and high levels of polymorphism were selected for subsequent genotyping (Taq DNA polymerase (TransGen Biotech). Thermocycling conditions were: initial denaturation at 94°C for 4 min, followed by 30 cycles of 94°C for 40 s, 53°C for 1 min and 72°C for 1 min, and a final extension of 72°C for 10 min. The amplified fragments were checked through agarose gel electrophoresis and genotyped on a Fragment Analyzer Automated CE System  following the manufacturer’s protocol. Raw data were exported, and the number of alleles and allele sizes per locus were called using ProSize ver. 2.0 (Agilent). The amplified microsatellite sequences were also subjected to DNA sequencing on an ABI 3730XL DNA Analyzer (Applied Biosystems) for verification. Individual samples of eight populations could not be amplified successfully, therefore, a subset  from 19 populations of L. littorea were genotyped at these loci , aligned using CLUSTAL X ver. 1.83  and checLaguncularia racemosa as the outgroup, following 7 generations were performed and sampled every 3,000 generations with the first 10% of samples discarded as burn-in. TRACER ver. 1.6  and species  levels. Average gene diversity within populations (HS) and total gene diversity (HT) were estimated for all populations using PERMUT ver. 1.0  for each species were calculated and compared through a permutation test with 1,000 permutations to evaluate the presence of phylogeographic structure using the same program.Using the cpDNA data in DNASP ver. 5.10, the haplotype diversity (Hd) and nucleotide diversity (π) were calculated according to ver. 1.0 . The coeχ2-test) generated due to small sample size and/or involvement of rare alleles (p = 0.05) using the score test (U-test) for which Markov chain algorithm was employed with 10,000 dememorization steps and the algorithm was run for 20 batches with 10,000 iterations per batch. To estimate the level of genetic diversity within the populations, the observed number of alleles (NA), effective number of alleles (NE), Shannon’s information index (I), and observed (HO) and expected (HE) heterozygosity were calculated using POPGENE ver. 1.32 (R) was computed using FSTAT ver. 2.9.3 (IS) for each population was estimated using GENEPOP whereas the percentage of polymorphic loci (PPB) were calculated with GENALEX ver. 6.5  values were first estimated using FSTAT and was further standardized (F/ST) using the RecodeData ver. 0.1  value was obtained, was identified as the optimum grouping of populations.Population structure of the species was evaluated with and without location information of the individual populations. Using the cpDNA sequence data, we first used the Bayesian Analysis of Population Structure (BAPS) version 6.0  to groupL. littorea, genetic clusters were determined using a Bayesian model-based clustering method implemented in STRUCTURE ver. 2.3.4 (K = 1–10) under the admixture model and correlated allele frequency model without a priori information of the geographical information of the populations. Each run consisted of 1,000,000 replicates of MCMC after a burn-in of 1,000,000 replicates. The optimal K was determined using the ΔK parameter according to Using the nSSR data of r. 2.3.4 . To obtaAnalysis of molecular variance was performed using ARLEQUIN ver. 3.5  to evaluL. littorea for the ABC analysis due to presence of null alleles (see section Genetic diversity), which can potentially bias conclusions of the species’ demographic history  was conceptualized to estimate the divergence time within the population group ELR. In the second model (hereafter ABC2), three simple population demographic scenarios were tested to examine the changes in effective population size for both species and population groups  framework implemented in DIYABC ver. 2.0  followin history . To keep history . For L. n groups . These sRag; Mismatch distribution analyses (MDA) were performed on the cpDNA data, using ARLEQUIN, to test whether the species as a whole or the populations groups had undergone recent population expansion. The validity of the expansion model was tested using the sum of squared deviations (SSD) between the observed and expected mismatch distributions. The smoothness of the distribution curves was evaluated using Harpending’s raggedness index HRag; . In addiL. littorea were further used to identify presence of a bottleneck event. In BOTTLENECK analysis, implemented using BOTTLENECK ver. 1.2.02  and five variable sites (three substitutions and two indels) in L. racemosa and L. littorea, respectively (L. racemosa and three haplotypes (LH1–LH3) across the 329 samples analyzed in L. littorea. No haplotype was shared between the two species. Haplotype frequencies for each population are presented in The aligned sequences of the three chloroplast intergenic regions, ectively . In combLumnitzera sp. were clustered into two major groups corresponding to the two species. The two Lumnitzera sp. diverged in around 10.95 MYA  while RH1 was related to RH2 (clade LR-II). The L. littorea group was also sub-divided into two clades, showing that LH1 and LH2 (clade LL-I) were more closely related to each other than to LH3 (clade LL-II).The chloroplast DNA phylogenetic trees constructed using Bayesian inference and Maximum-Likelihood (ML) method  showed cL. racemosa and L. littorea, which showed apparent eastern vs. western geographical distributions in both species  following the phylogenetic clade LR-I , corresponding to the phylogenetic clade LL-I, which was separated by at least four mutational steps from LH3 [eastern-LL (ELL) group], corresponding to the phylogenetic clade LL-II  or showing a mixture of them (one population), while most populations from the Pacific Ocean were fixed for one of the eastern-LR haplotypes  were fixed for one of the western-LH haplotypes (17 populations for LH2 and 2 populations for LH1) or showing a mixture of them (two populations), while only five populations located in the east periphery of the Pacific Ocean were fixed for the eastern-LH haplotype  than L. racemosa populations  across all sampled populations was much higher than the average intra-population diversity HS (0.068 for L. racemosa and 0.031 for L. littorea), suggesting that the majority of cpDNA diversity is distributed among populations. Population differentiation was high in both L. racemosa  and L. littorea . The trend was similar at population group level for both species in which western group had higher genetic diversity and genetic differentiation than eastern group populations (ST was not significantly higher than GST either for the entire IWP or for the population groups separately (p > 0.05). Genetic diversity estimates for individual population have been given in Haplotype diversity was estimated to be 0.606 for ulations . PermutaL. littorea, 74 alleles were found ranging from 4 to 12 alleles per locus (with an average of 6.73). The average expected (HE) and observed (HO) heterozygosity across all L. littorea populations were 0.416 and 0.190, respectively , whereas the LKW population showed the lowest genetic diversity . All populations except one (DRA) had positive FIS values (U-test) showed heterozygote deficiency for all populations except DRA  and the individuals of CQA population having the RH2 haplotype formed another cluster . The four clusters identified in BAPS were consistent in SAMOVA, except the admixtured populations STT and CTT belonged to two different clusters  with individuals from the populations of SJM and TYT (K = 5 when FCT = 0.992) and the pattern is consistent with BAPS except the admixtured populations, namely SJM and TYT, formed two different clusters  values  reached the plateau with the maximum variation between runs  were considered , eastern-LR2 group , western-LR1 group , and western-LR2 group , western-LL1 group , and western-LL2 group  could be attributed to the differentiation among population clusters  populations from the western and eastern coasts of Malay Peninsula, (2) populations from northwestern (LKM and KPM) and northeastern (SDM) Borneo, and (3) populations from western coast of Malay Peninsula and Indonesia  separating one South China Sea population (TLC) from the rest , (2) between Palawan and Philippines (IBP), and (3) between populations in Borneo and Indonesian New Guinea  for L. littorea  from N1 (WLR1) + N2 (WLR2), and N1 from N2 were estimated to be 69,600  and 6,480  generations ago, respectively  from N1 (WLL1) + N3 (WLL2), and N1 from N3 were estimated to be 84,000  and 22,200  generations ago, respectively , the highest values of posterior probability were obtained for scenario 7  for littorea . Neitherlittorea  and the littorea  showed tectively . With an20 years , the divectively . The divL. racemosa, the median values of t were 19,800 , 38,900 , and 16,200  generations ago  generations ago for WLL1, corresponding to 1.022 MYA , the highest value of posterior probability was found for scenario 3 (population bottleneck model) across the population groups of both species . No signions ago , corresp.022 MYA .L. racemosa, the mismatch distribution for all populations was multimodal (Rag (both p > 0.05), and significantly negative Tajima’s D, however with positive and non-significant Fu’s Fs  but not of SSD (p < 0.05). The neutrality tests also showed positive Tajima’s D and Fu’s Fs values  could explain the expansion model, the observed curve for the WLL1 populations was not of a typical unimodal distribution  deviated from mutation-drift equilibrium . Previous studies have found co-occurrence of the hybrid species Lumnitzera × rosea with the parent L. racemosa and L. littorea mostly along the eastern coast of the Malay Peninsula  and L. racemosa (HT = 0.625) were much lower compared to other mangrove species studied using cpDNA markers, e.g., another non-viviparous Excoecaria agallocha  could be attributed to differentiation among these population groups. However, while both species had the eastern-western clustering pattern, the boundary for the clusters was more eastward for L. littorea compared to L. racemosa, signifying that the demographic forces could differently influence population structuring of the congeneric species.Although such local diversity may occasionally be inflated by genetic admixture from other differentiated populations, we found little evidence of genetic exchange between geographically and genetically distinct populations of L. racemosa haplotypes and the clustering algorithms revealed that populations from the Indian Ocean and the Pacific Ocean belonged to two highly divergent groups, as found in many other mangrove species of this region  and the propagules have very low (15 days) buoyancy period  diverged in Late Pliocene (in around 1.68 MYA), suggesting a possible vicariant history caused by isolation, as observed in many plant and animal taxa . FurtherL. racemosa, haplotype sharing between the ELL populations was unlikely to be assisted by present-day LDD of L. littorea propagules. The South Equatorial Counter Current of the Indonesian Throughflow (ITF) could facilitate propagule dispersal between the east coast of Australia and New Guinea, however, further northward dispersal gets hindered due to presence of the Halmahera Eddy  and Palawan . The STRUCTURE analysis also revealed genetic admixture in Palawan populations, suggesting nuclear gene flow through dispersal of pollen grains between these two regions. Similar asymmetrical mobility between pollen and seed gene flow leading to discordance of phylogeographic structure has been reported for other species as well . The nSSera Eddy , therebyLumnitzera species compared to other mangrove species of this region and a strong population structure with restricted genetic exchange in between the geographic regions. The haplotype diversity was centered in a few geographic areas of the species’ distribution range. Further, high amount of genetic variation among populations of the two species indicated high level of inbreeding within genetically similar individuals present in fragmented demes. In line with the observation for other mangroves, fragile habitat, and environmental conditions as well as anthropogenic impacts are continuously threatening the survival of these species. Indeed, L. littorea has been found to become endangered in many areas of its distribution range. For example, the species can only be found in the limited areas in the nature reserve in Hainan Island of China. Habitat fragmentation and isolation of population coupled with poor reproductive capacity in L. littorea, and high amount of selfing and restricted dispersal ability of L. racemosa are threatening the species’ survival in near future. In this context, the genetic information retrieved from this study can provide some conservation insights.Our study revealed low genetic diversity of L. littorea and RNT, STT, CPT, and CTT of L. racemosa should be prioritized for conservation actions. Restoring the disturbed habitats, implementing community-based conservation programs, and long-term monitoring of genetically isolated populations could be effective strategies to maintain genetic diversity and prevent irrevocable genetic erosion in these areas. Besides, long-term in situ conservation programs, like creating and maintaining natural reserves, are necessary and monitoring of natural populations can be effective conservation strategies for populations with both high and low genetic diversity. Due to the impediment of genetic exchange by the natural barriers, translocations among the genetically isolated units might be a feasible potion to supplement locally depleted and/or extinct natural resource (Lumnitzera species, low genetic diversity and strong population structure have been found for other mangroves inhabiting the IWP region (High genetic variation among populations in both species indicated that each population should be conserved separately. Populations with high genetic diversity, e.g., SJM, TYT of resource . Howeverresource . Thereforesource  and therP region . The conL. racemosa and L. littorea. The position of genetic break was found to vary between the two species, indicating that the two Lumnitzera species have different phylogeographic patterns despite their close genetic relationship and similar current geographic distribution. It is important to note here that variable number of sample sizes might have affected the estimated diversity parameters (Our study revealed relatively low genetic diversity and prominent population structure in both rameters . Howeverrameters , the estrameters . HoweverThe representative sequences of cpDNA haplotypes and SSR loci have been deposited in Genbank under the accession numbers MW244372-MW244392 and MT274028-MT274038, respectively.WG and YH conceived the idea and designed the study. WG, HW, SQ, HF, and YL collected the samples and data. WG, AB, and HW analyzed the data and interpreted the results. WG, AB, HW, WN, and YH wrote the manuscript text. All authors reviewed the manuscript and have approved the final version of the article.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.