Prosopis rubriflora and Prosopis ruscifolia are typical tree species of chaquenian area forests, which have been subjected to continuous fragmentation caused by cattle raising. This study evaluated P. rubriflora and P. ruscifolia in areas with varying levels of disturbance. We investigated the contemporary genetic diversities of both species in areas with distinct anthropogenic disturbances. Even with a lower heterozygote frequency, disturbed areas can provide important storage for alleles, allowing the maintenance of diversity. The genetic diversity of P. rubriflora was surprisingly similar to that of P. ruscifolia  even with very different distribution ranges of both species. However, P. ruscifolia exhibited a higher intrapopulation fixation index than P. rubriflora. P. rubriflora showed evidence of bottlenecking in 64% of the sampled areas, while P. ruscifolia showed such evidence in 36% of the sampled areas. Additionally, P. rubriflora had two distinct populations due to its disjunctive geographic distribution, whereas P. ruscifolia had a single population that exhibited few signs of population structure in some areas, possibly due to the main pollinators presenting a short range of dispersion. Our results suggest that 42 Chaco areas should be conserved to retain the minimum of 500 individuals necessary to maintain genetic diversity for 100–1,000 generations. This study improves our understanding of these two Prosopis species and provides information for the conservation of their genetic diversities.The Gran Chaco is the largest continuous region of the South American dry forest, spanning Argentina, Paraguay, Bolivia, and Brazil.  Habitat fragmentation may cause inbreeding in the long term due to an increased probability of pollination between related and inbred individuals  strong genetic structures are present in both species due to the behavior of the main pollinating agent, as described in the literature on Prosopis. Thus, the objectives of this study were to evaluate the genetic diversity and to estimate the genetic structures of the sampled areas containing P. rubriflora and P. ruscifolia to examine whether they have been influenced by distinct anthropogenic disturbances.In this context, this study aimed to test the following hypotheses: (i) the genetic diversity of 22.1P. rubriflora and P. ruscifolia were collected in the Corumbá, Nioaque, and Porto Murtinho counties of the State of Mato Grosso do Sul, Brazil  using WGS‐84 datum.The sampled areas were classified according to the observed levels of anthropogenic disturbance based on field observations and surveys by the farm owners. “Conserved areas” represent no to low disturbance levels, such as cattle breeding activity without any evidence of anthropogenic suppression. The “intermediate disturbance areas” have experienced recent suppression in vegetation (10–15 years) or have a predominance of colonizing species, such as Prosopis rubriflora was exclusively located in the following areas: Nioaque County in Assentamento Andalúcia (presenting fragments with lower to intermediate disturbance) and Porto Murtinho County in Fazenda Patolá (areas 1 and 2), Fazenda Retiro Conceição (area 2), Fazenda São Manoel, Fazenda Santa Vergínia, and highway MS‐467, where FPT2 and FRC2 were the only conserved areas, and all the remaining areas presented fragments with higher to lower degrees of disturbance. The remaining areas from which only P. ruscifolia was collected were located in Corumbá County  and Porto Murtinho County , Fazenda Tereré (areas 1 and 2), and Fazenda Santa Cristina, which did not present a continuous conservation status but rather “conserved” fragments surrounded by higher or lower degrees of anthropogenic disturbance. , FazendaP. rubriflora individuals and 308 P. ruscifolia individuals, ranging from 16 to 30 trees per area (Tables We collected the leaves and leaflets of 241 2.2P. rubriflora (eight specific markers and two markers transferred from P. ruscifolia) and 11 SSR markers for the analysis of P. ruscifolia (nine specific markers and two markers transferred from P. rubriflora) as developed by Alves et al. , adherence to Hardy–Weinberg (HW) equilibrium and the frequency of null alleles (NA). LD and HW analyses were performed using Genepop v.1.2 software , allelic richness (Ari), Ar, observed heterozygosity (Ho), expected heterozygosity (He), and fixation index, and endogamy significance (FIS), were calculated based on 110,000 permutations for P. rubriflora and 121,000 permutations for P. ruscifolia with an adjusted nominal level of 5% using the software FSTAT 2.9.3.2  per locus was estimated according to the equation A^e=11−H^e , and the total endogamy (F or FIT). The number of private alleles (Ap) was estimated with GDA v.1.0 software , the primary parameter for outcrossing into populations, was obtained according to the equation t^a=1−F^IS1+F^IS as described by Vencovsky (Ne) was estimated using an equation derived from Cockerham  was estimated indirectly according to the Crow and Aoki 2, and n is the number of samples.The historic gene flow  was performed using the POPR package version 2.5.0  was 500,000 replicates, with 200,000 replicates for burn‐in. The most likely value of ΔK based on Evanno et al. , DAPC analysis uses a nonparametric approach. The results obtained from this analysis are presented as multidimensional scatterplots of the principal components.The Bayesian model of the 33.1P. rubriflora individuals, we were able to identify departure from HW equilibrium based on Fisher’s exact test at loci Prb2 and Prb4 and possible null alleles in AAL’s area  (Table Based on the genotyping of a Tables  and S2. y) Table .P. rubriflora, with an average of 10 alleles per locus in the 11 sampled areas from the Nioaque and Porto Murtinho counties (Table Ari ranged from 3.9 (AAL) to 6.1 (RMS) alleles, and the Ae ranged from 23 (AAL) to 36 (RMS and FSV) alleles. The percentage of effective alleles relative to the number of sampled alleles varied from 47% (FTR1) to 65% (FTR2). We detected 12 Ap, half of which were detected in the RMS region, followed by FSV , FPT1, FTR1, FSM, and AAL (one Ap per area); the remaining areas had no Ap. Forty‐two Ar, ranging from 11 (AAL) to 23 (RMS and FTR), were detected in the sampled areas, with an average of 16 alleles for all areas. Therefore, P. rubriflora contained 45% common alleles, 43% Ar, and 12% Ap  (AAL) to 0.64 (CI95% = 0.47–0.80) (FSC). The average He was 0.59 and ranged from 0.49 (CI95% = 0.34–0.60) (AAL) to 0.64 (CI95% = 0.51–0.73) (RMS) (Table Ho) and the expected frequency of heterozygotes (He) in a panmictic population according to assumptions of the HW equilibrium model  was 0.05 and ranged from −0.08 (CI95% = −0.10 to −0.04) (FSC) to 0.17 (CI95% = 0.08–0.24) (AAL)  to 0.96 (FSC) and averaged 0.86; this result suggests that mixed reproductive systems were functioning in most sampled areas, with a strong allogamous tendency for FSC.The average inbreeding coefficient , varying from 45% (AAL) to 59% (FPT1 and FSM)  and Prs6 (FTR2) , evidence of LD was noted according to pairwise analysis of the following loci: Prb4 × , Prs7 × , Prs6 × , and Prs2 ×  (Table Using the genotyped ) Tables  and S6. 1) Table . The ass1) Table . Therefo1) Table . In thisP. ruscifolia dataset, 138 alleles were detected, with an average of 13 alleles per locus for 11 areas in Corumbá (one area) and Porto Murtinho (10 areas). The number of alleles ranged from 4.4 (EPM) to 6.8 (FFL), with an average of 67 alleles per area (Table Ari ranged from 49 (EPM) to 75 (FFL) alleles and averaged 61 alleles. The Ae ranged from 30 (EPM) to 47 (FFL and FQB) alleles and averaged 39 alleles, producing a variation ranging from 52% (NSA) to 64% (FTR1) compared with the detected allelic number. Additionally, 31 Ap were detected, with eight Ap in FFL, seven Ap in FQB, five Ap in ECD, three Ap in FTR1, two Ap in FTR2 and ROE, one Ap in EPM and FSC, and no Ap in the other sampled areas.In the Ho ranged from 0.46 (CI95% = 0.32–0.58) (FRC1 and EPM) to 0.60 (CI95% = 0.50–0.68) (ROE) and averaged 0.52; He ranged from 0.54 (CI95% = 0.42–0.65) (EPM) to 0.67 (CI95% = 0.53–0.76) (ECD) and averaged 0.60. He was greater than Ho, suggesting that the number of homozygotes exceeded the number of heterozygotes in all analyzed areas.FIS ranged from 0.01  (FSC and ROE) to 0.28 (CI95% = 0.17–0.37) (ECD) and averaged 0.12. A total of 63% of the areas presented values that significantly differed from zero, suggesting an excessive number of homozygotes based on the p‐value (Table t^a ranged from 0.57 (ECD) to 0.89 (FSC) and averaged 0.77; this result suggests that a mixed reproductive system is present in all areas. The value of Ne suggested that 43% of all sampled trees provided genic contributions to the sampled areas, and this percentage ranged from 31% in ECD to 49% in FSC  and zero to four loci with excess He under mutation‐drift equilibrium (H.e.); the p‐values ranged from .000 to .349, as observed in the TPM and SMM results  and seven areas of P. ruscifolia  were reported to be in mutation‐drift equilibrium according to the sign test and Wilcoxon signed‐rank test for both the SMM and TPM. The remaining populations of both species presented deviations based on their p‐values (p < .05).Four sampled areas of 3.4FIS suggested that P. rubriflora has a panmictic genic distribution (0.042) because this value did not significantly differ from zero according to the CI  that significantly differed from zero, suggesting an intrapopulational genetic structure for this taxon according to the CI. The global FST presented CI values that significantly differed from zero for P. rubriflora and P. ruscifolia; this difference indicates a population structure among the areas for both species. P. ruscifolia had a low FST (0.042), reflecting a low level of structure or differentiation (FST < 0.05); P. rubriflora had a slightly higher value (0.057), reflecting a moderate population structure (FST = 0.05–0.15) according to the review by Balloux and Moulin (FIT) significantly differed from zero; this result was expected because this analysis consists of the sum of FIS and FST. The number of migrants (Nm) could be estimated based on the FST values, with high values for both species (3.41 for P. rubriflora and 4.71 for P. ruscifolia). These values suggest that a large Nm helps maintain genetic homogeneity among all the investigated chaquenian areas.The estimated CI Table . Conversd Moulin . The gloP. rubriflora and 94% for P. ruscifolia) is retained within the populations sampled, with smaller portions of 8% and 6% for those two species, respectively  for P. ruscifolia and  P. rubriflora, indicating significant isolation‐by‐distance across all the sampled areas. However, considering only the areas sampled in Porto Murtinho, no such correlation was observed for P. rubriflora  or P. ruscifolia .The Mantel test revealed a positive and significant relationship between geographic and genetic distances based on Nei’s distance , with intermediary levels between FSV and FRC1 (FST = 0.05–0.15) and high (FST = 0.15–0.25) to very high (FST > 0.25) levels of genetic structure (differentiation) between the areas in Porto Murtinho compared with those in the Nioaque county area (AAL).The pairwise ia Table . Based od Moulin , almost P. ruscifolia, the level of genetic structure was low for 65% of the area combinations in both sampled counties and intermediary for 35% of the combinations. An intermediary genetic structure was primarily observed for the ECD area (Corumbá); the distance between this area and the other areas in Porto Murtinho ranged from 189 to 244 km. Even with a moderate level of structure, the distinction between the sampled areas of P. ruscifolia was not clear  based on the Evanno analysis , 80 axes were included, and based on the DA eigenvalues, three discriminant functions were selected, accounting for 73% of the genetic variability. For P. ruscifolia (308 samples obtained), 102 axes were selected, and five discriminant functions were analyzed, accounting for 71% of the genetic variability ; however, this area lacks direct evidence of anthropic disturbance. This population may have been small for a long period and must have experienced both loss and genetic reorganization, which was expected due to genetic drift , esthetic reasons, or difficulties in cutting.For Barreto, . The ECDP. rubriflora and P. ruscifolia; this result was not expected because P. rubriflora had reduced geographic distributions in Brazil and Paraguay compared with P. ruscifolia, which also occurred in Argentina and Bolivia  had the highest level of intrapopulational structure for P. ruscifolia. In addition, the geographic isolation of the severely disturbed ECD area may contribute to intrapopulational inbreeding to maximize the genetic structure. Similar levels of inbreeding were detected by Bessega et al. , ranging from 31% in ECD to 49% in FSC, and 54% of trees (in P. rubriflora), ranging from 45% (AAL) to 59% (FPT1 and FSM), will genetically contribute to the Chaco areas, and the largest oscillations have generally occurred in disturbed areas or smaller populations. According to Frankham et al.  may be at risk for accumulating deleterious mutations due to their isolation. Although the Porto Murtinho areas have reduced effective populations, they have greater connectivity and should not be at risk for inbreeding depression; however, because the total Ne is <500, a minimum of 42 Chaco areas of similar size should be preserved to maintain the evolutionary potential of both species.m et al. , an estiP. rubriflora and 36% of the P. ruscifolia remnants have undergone bottlenecks; however, the excessive H.d. observed from the sign test of most remnant areas suggests a population expansion or the introduction of Ar from immigrants . This result reinforces the high gene flux for most of the sampled Porto Murtinho areas, making determining differences from each other difficult. DAPC revealed a very strong relationships among all the sampled areas in Porto Murtinho for both P. rubriflora and P. ruscifolia, supporting the Bayesian analysis in Structure and consistent across the genetic structure analyses. As such, the low genetic structure among the Porto Murtinho areas regardless of geographic distance could be due to the connections between the areas, which enable a longer gene flow for both species. Migrants may also contribute to the genetic homogeneity in these areas , supported by all the analyses. This structure is likely the result of geographic distance (ca. 218–267 km) and the lack of connection between these areas; this lack of connection severely limits pollen flow and causes long periods of isolation for AAL. This disjunction appears to have resulted from ancient natural factors rather than from recent anthropogenic fragmentation. Prosopis grows poorly in acidic and low‐phosphorus soils  and other Porto Murtinho areas. Although the distance between the ECD area (Corumbá) and the remainder of Porto Murtinho (ca. 189–244 km) was similar to that affecting P. rubriflora, the moderate structure indicates that these regions can be connected, assuring some level of gene flow. However, the continuous fragmentation of chaquenian areas used for cattle farming was expected to increase the structure in this area compared with the remainder of Porto Murtinho. In addition to the observed moderate population structure of P. ruscifolia in the ECD area, a similar structure was estimated between some Porto Murtinho areas by pairwise analysis. These lower‐level yet significant structures may be attributed to the fragmentation of forested areas and the shorter flowering and fruiting period, which, in turn, may contribute to the lower gene flow compared with that of P. rubriflora.The genetic structure of P. rubriflora has two distinct populations in the areas sampled in Mato Grosso do Sul that are produced by an ancient isolation event. Although P. ruscifolia apparently has some connections with the other areas according to the FST pairwise, Nei’s genetic distance, and Structure analyses, this connection appears limited according to the DAPC and Mantel test results. If the anthropogenic disturbance and fragmentation remain constant, the area in Corumbá will become increasingly isolated and possibly present results similar to those observed in the Nioaque area for P. rubriflora, which has reduced genetic variability and a significant population structure.Thus, Prosopis species and other rare species that occur only in this biome, which is increasingly suppressed by anthropic pressures.We expect the results of this study to help with the allotment of subsidies for decision making and the development of conservation strategies for chaquenian areas. These measures will help to preserve the genetic stock for both 5He) of P. rubriflora appears to be similar to that of P. ruscifolia. Although P. ruscifolia has more alleles, most of the alleles are rare and do not increase He to the same degree. The apparent conservation status of an area can be misleading regarding allelic diversity, and even disturbed areas may have high allelic diversity. Evidence of a bottleneck was detected for both species, and P. rubriflora was affected in most of the analyzed areas.The genetic diversity (P. ruscifolia, as expected. Despite the predominance of bees as pollen dispersal agents, a relatively small structure index was observed between the sampled areas; this small index indicates high gene flow because the connection between areas enables pollen flow. Based on the effective population number in this study, 42 Chaco areas must be preserved to preserve the minimum of 500 individuals needed to maintain genetic diversity and retain the evolutionary potential of both species over 100–1,000 generations. The measures suggested in this study should prevent additional environmental damage that may cause extinction, which would negatively affect the local fauna because P. rubriflora and P. ruscifolia provide important food resources.High gene flow was observed between populations, and a strong structure was evidenced only in extreme cases, such as populations at a substantial geographic distance and with a lack of connection. The intrapopulation structure was higher for None declared.Alves FM, Zucchi MI, and Souza AP involved in conception and designed the work; Alves FM and Sartori ALB acquired the data; Alves FM, Zucchi MI, Souza AP, Tambarussi EV, and Alves‐Pereira A developed the analysis and interpreted the results; Alves FM, Zucchi MI, Souza AP, Tambarussi EV, Sartori ALB, Azevedo‐Tozzi AMG, and Alves‐Pereira A drafted the article and reviewed the content; Alves FM, Zucchi MI, Souza AP, Tambarussi EV, Sartori ALB, Azevedo‐Tozzi AMG, and Alves‐Pereira A made final approval of the version to be published. Click here for additional data file.


Plasmodium merozoite surface protein-1 (MSP-1) is released into the bloodstream during merozoite invasion, and thus represents a crucial malarial vaccine target. Although substantial research effort has been devoted to uncovering the genetic diversity of MSP-1 for P. falciparum and P. vivax, there is minimal information available regarding the genetic profiles and structure of P. ovale. Therefore, the aim of the present study was to determine the extent of genetic variation among two subspecies of P. ovale by characterizing the MSP-1 N-terminal sequence at the nucleotide and protein levels.N-terminal of MSP-1 gene were amplified from 126 clinical samples collected from imported cases of malaria in migrant workers returning to Jiangsu Province from Africa using a conventional polymerase chain reaction (PCR) assay. The PCR products were then sequenced and analyzed using the GeneDoc, MegAlign, MEGA7 and DnaSP v.6 programs.π) of P. ovale curtisi and P. ovale wallikeri MSP-1 genes (pomsp1) were 0.01043 and 0.01974, respectively, and the haplotype diversity (Hd) were 0.746 and 0.598, respectively. Most of the nucleotide substitutions detected were non-synonymous, indicating that the genetic variations of pomsp1 were maintained by positive diversifying selection, thereby suggesting their role as a potential target of a protective immune response. Amino acid substitutions of P. ovale curtisi and P. ovale wallikeri MSP-1 could be categorized into five and three unique amino acid variants, respectively.The average pairwise nucleotide diversities  contains supplementary material, which is available to authorized users. Plasmodium  are known to cause human malaria under natural transmission . In addition, Tajima’s D, Fu and Li’s D* and F* tests rejected a neutral model of polymorphism occurrence with values for pocmsp1  and powmsp1 , respectively  and powmsp1 , and found that pocmsp1 was more conserved than powmsp1 with 26  and 42  sites of nucleotide diversity, respectively.The life-cycle of the malaria parasite alternates between the human host and the mosquito vector, which is complex with extensive genetic and antigenic diversity across different stages of the parasite’s life . The genantigens . The seqisolates . Hence, P. ovale. Significantly positive values for these statistics reflect an excess of intermediate frequency alleles, which can result from population bottlenecks or balancing selection. The sequences for subspecies P. ovale curtisi and P. ovale wallikeri were further divided into five and three branches that were all within the same evolutionary branches. The MSP-1 N-terminal sequences placed the two subspecies in a distinct bifurcating branch, and the split of pocmsp1 and powmsp1 seems to be relatively more recent. Therefore, the MSP-1 N-terminal sequences of P. ovale curtisi and P. ovale wallikeri support the ancient divergence times of the malaria parasite lineage  were lower compared to that of P. falciparum and P. vivax [P. ovale from diverse geographical origins [P. ovale [The esponses . In addiP. vivax , which m origins . These fP. ovale .P. falciparum has a low level of genetic diversity in areas with low transmission rates and high level of sequence diversity in areas with high transmission rates [pvmsp1 isolated from Thailand northwestern region [pocmsp1 and powmsp1 N-terminal sequences detected in this study showed lower magnitude than that reported for pvmsp1 and pfmsp1 [The intragenic recombination of MSP-1 gene is a major informative pattern at the level of population sequence diversity. The frequency of allelic recombination has important guiding significance for the population structure of parasites . A previon rates . High mun region . The levd pfmsp1 , 35.P. ovale curtisi and P. ovale wallikeri isolates imported from Africa to China based on analysis of the MSP-1 N-terminal sequence. To our knowledge, this is the first report of the genetic diversity, selection signature, and population structure of the N-terminal of pomsp1 gene from an African population. The low level of genetic diversity indicated that these genes are under purifying selection. Therefore, these sequences have potential for vaccine development, which requires further investigation of the immunogenicity and antigenicity of P. ovale MSP-1.This study provides valuable reference information on the genetic diversity of Additional file 1:Figure S1. Neighbor-joining tree of 26 unique alleles of the gene encoding msp1 from 18 Plasmodium parasite species. (TIF 3756 kb)


Apis cerana) is such a species and this study explored the effects of long and narrow geographic distributions on honeybee populations. Genetic differentiation and diversity were assessed across populations of the southeastern Qinghai-Tibet Plateau. A total of 492 honeybee samples from eight sampling sites in four valleys were analyzed for the genetic differentiation and diversity of 31 microsatellite loci and mitochondrial tRNAleu-COII fragments. The following results were obtained: (1) Microsatellite genetic differentiation coefficients (FST) ranged from 0.06 to 0.16, and mitochondrial FST estimates ranged from 0.18 to 0.70 for different sampling sites in the same valley, indicating genetic differentiation. (2) Honeybees in adjacent valleys were also genetically differentiated. The FST of microsatellites and mitochondria were 0.04–0.29 and 0.06–0.76, respectively. (3) Likely a result of small population sizes, the observed genetic diversity was low. The observed impedance of honeybee gene flow among valleys increased both genetic differentiation and population numbers in the Qinghai-Tibet Plateau. This study contributes significantly to the current understanding of the mechanism underlying population genetic differentiation and highlights the potential effects of utilizing genetic resources that are subject to the ecological conditions of the long and narrow geographic distributions of plateau-valley landforms.Many species of high-altitude plateaus tend to be narrowly distributed along river valleys at lower elevations due to a limitation of suitable habitats. The eastern honeybee ( Genetic differentiation of populations is the theoretical basis of the study of species formation, biological evolution, and genetic resources. In response to the blocked gene flow, evolutionary forces, such as natural selection and genetic drift, promote genetic differentiation among populations. Adaptation to their respective environments leads to the development of unique genetic characteristics and traits in differentiated populations Mayr, . As the Apis cerana in the Loess Plateau of northern Shaanxi, China, exhibited population genetic differentiation due to the discontinuous distribution of the populations  was reported , and the Spanish bee (Apis mellifera iberiensis), inhabiting the northeast and southwest sides of the mountains, respectively, have evolved into distinct subspecies , 78 samples from Batang (SCBT), 67 samples from Derong (SCDR), 50 samples from Diqing (YNDQ), 65 samples from Yajiang (SCYJ), 61 samples from Jiulong (SCJL), 67 samples from Muli (SCML), and 66 samples from Xiaojin (SCXJ). To eliminate the influence of captive bee colonies, all A. cerana samples were collected from wild colonies without captively bred queens . Sample tissues were digested using protease K, extracted with phenol, then cleaned up using a column, and finally, DNA was dissolved in 60 μl CE buffer (pH 9.0) from the kit.A total of 31 microsatellite loci were amplified using multiplex PCR kits  were amplified using the rtaq amplification kit . Primer sequences were optimized versions of similar primers that are used to amplify the same sequences in A. mellifera  of microsatellite markers among sampled sites, the degree of genetic differentiation among samples was assessed, and statistical significance was determined using GenAlEx 6.5  of microsatellite data was conducted by GenAlEx 6.5  , polymorphic information content (PIC), and allele number (Na) were calculated with Mstools   , the average number of nucleotide differences (k), and nucleotide diversity (π), DNAsp 5.0 was used . Ne estimator 2.01 was utilized to investigate the effective population size (LDNe), using the linkage disequilibrium method with a random mating model, where the critical value was set to 0.05  Verdu,  were cal) Verdu, . Fragmen) Verdu, , and theA. cerana samples of the same valley exhibited genetic differentiation. In the structure analysis of microsatellite markers, when K = 4 , the Yajiang sample was genetically differentiated from Jiulong and Muli samples in the Yalong R. V. Samples from Diqing also showed genetic differentiation from Batang and Derong samples in Jinsha R. V. When K = 8, genetic differentiation was found between Batang and Derong and also between Jiulong and Muli . Both Acmt01152 and Acmt01001 haplotypes were found to be dominant in Jiulong and Muli. The Acmt01152 and Acmt01001 haplotypes detected in Jiulong comprised 77 and 10% of the population, respectively, and those detected in Muli comprised 24 and 37%, respectively. Analysis of the Batang, Derong, and Diqing (Jinsha R. V.) samples showed that Batang was composed of three haplotypes, of which Acmt01001 accounted for 94%. Derong included six haplotypes, 61% of which accounted for Acmt01001. Diqing was dominated by the two haplotypes Acmt01152 (65%) and Acmt01215 (20%) . The mitFST, Yajiang, Jiulong, and Muli  FST values were 0.06–0.16, while FST values of Batang, Derong, and Diqing in the Jinsha R. V. were 0.07–0.10. Analysis of mitochondrial FST of Yajiang, Jiulong, and Muli in the Yalong R. V. found FST values of 0.23–0.54, and FST values of Batang, Derong, and Diqing in the Jinsha R. V. were 0.18–0.70. Samples collected from within a single valley exhibited significant genetic differentiation, indicating that genetic differentiation could occur within the same valley , indicating the existence of high levels of genetic differentiation among bees of the same valley . For principal coordinate 1, samples collected from Xiaojin (Dadu R. V.) were clearly different from other samples. The principal coordinates 1 and 2 of samples collected from Bomi  were different from other samples, and samples from Batang, Derong, and Diqing (Jinsha R. V.) differed from those of Yajiang, Jiulong, and Muli  with respect to principal coordinate 2 . In the A. cerana colonies (No. 1–16) sampled at the Xiaojin site when K = 4 in the Structure analysis of microsatellite markers, genetic differentiation was observed between Xiaojin (Dadu R. V.), Yajiang , Bomi , and other sampling sites. Similarly, the sampling sites of Batang and Derong (Jinsha R. V.) were also genetically different compared to other samples  and Xiaojin (Dadu R. V.) were dominated by the unique haplotypes Acmt01308 (74%) and Acmt01290 (56%), respectively. In populations from Yajiang, Jiulong, and Muli , Acmt01001 (27%), Acmt01022 (22%), and Acmt01152 (32%) were predominant haplotypes. Furthermore, Acmt01001 (59%) and Acmt01152 (16%) were dominant haplotypes in samples collected from Batang, Derong, and Diqing (Jinsha R. V.) .FST values were calculated to determine the degree of genetic differentiation of samples. Samples from Xiaojin (Dadu R. V.) and other valleys had microsatellite FST values of 0.18–0.29. The microsatellite FST values between Bomi  and other populations were 0.10–0.24, and the microsatellite FST values of samples collected from Batang, Derong, and Diqing (Jinsha R. V.) and those from Yajiang, Jiulong, and Muli  were 0.04–0.20. The mitochondrial FST values between Xiaojin (Dadu R. V.) and the other valleys were 0.26–0.59. Comparison between samples collected from Bomi  and others had FST values of 0.38–0.76, and the mitochondrial FST values between Batang, Derong, and Diqing (Jinsha R. V.) and Yajiang, Jiulong, Muli  were 0.06–0.72 , indicating strong genetic differentiation among honeybees in the different valleys  in valleys of the Alps showed that the snake population in canyons was large and distributed continuously; therefore, gene flow could be maintained and no genetic differentiation was found  decrease. These ecological factors limit population sizes, especially upstream of valleys in the areas of Batang, Derong, Yajiang, and Jiulong. In these areas, the observed populations consisted of <500 colonies. Due to these factors, A. cerana is vulnerable to catastrophic natural events and environmental fluctuations, resulting in discontinuous distribution. The particular distribution of A. cerana makes them prone to genetic differentiation. These characteristics are of great value for an improved understanding of the process and mechanisms that contribute to population differentiation of other species in similar environments.Within the valleys of the Yalong and Jinsha Rivers, three  Wright, , FST valA. cerana survival, populations in adjacent valleys are more likely to undergo genetic differentiation as a result of the blockage of gene flow. This study indeed showed genetic differentiation between A. cerana populations in different valleys of the Qinghai-Tibet Plateau. Based on the conducted PCoA and DAPC, A. cerana in different valleys showed obvious differentiation. Furthermore, the FST of microsatellites and mitochondria ranged from 0.04–0.29 to 0.06–0.76, respectively. According to Wright   (Yu et al., A. cerana in the valleys of the Qinghai-Tibet Plateau is a unique and precious genetic resource.Comparison of zed loci  (Xu et azed loci . SimilarA. cerana is easily blocked, resulting in genetic divergence among populations. The diversity of these populations is relatively high; however, the genetic divergence between populations is low. Comparison with similar A. cerana research indicates that He ranges from 0.2066 to 0.8305 (Chen et al., PIC ranges from 0.28 to 0.81 (Cao et al., Na ranges from 1.81 to 10.90 (Ji et al., Hd ranges from 0.171 to 0.905 (Zhou et al., A. cerana. Here, ecological factors such as elevation and nesting environments have approached the limits of suitability for this species. Under specific environmental constraints, the effective population size of A. cerana is generally lower than 500 colonies. At a given mutation rate, this results in low genetic diversity due to the small numbers of individuals in any given population (Vrijenhoek, A. cerana populations into larger populations, with results in low genetic diversity.In the valleys of the Qinghai-Tibet Plateau, the gene flow of A. cerana in the Qinghai-Tibet Plateau are thus prone to genetic differentiation and exhibit a high level of population diversity. Therefore, the various populations need to be protected and may be utilized as genetic resources. However, both the effective population size and genetic diversity of A. cerana in the valleys are small, making the populations, and the species within this range in general, vulnerable to effects of genetic drift and selection. Potentially, this can result in local extinctions of populations. Consequently, further studies are required to determine how best to protect the genetic resources of these particular A. cerana populations.The populations of This research is not related to biomedical research. In addition, our research object was honeybee, an invertebrate. Commonly, such a study is exempt from ethics approval.BZ, YY, SZ, XZ, XX, and LH participated in the whole research project, including the experimental design and data analysis. WW, LZ, PW, JW, KL, and SW participated in part of the experimental design and sample collection.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.


Evolutionary Applications consists of 10 publications investigating the use of next‐generation tools and techniques in population genetic analyses and biodiversity assessment. The special issue stems from a 2016 Next Generation Genetic Monitoring Workshop, hosted by the National Institute for Mathematical and Biological Synthesis (NIMBioS) in Tennessee, USA. The improved accessibility of next‐generation sequencing platforms has allowed molecular ecologists to rapidly produce large amounts of data. However, with the increased availability of new genomic markers and mathematical techniques, care is needed in selecting appropriate study designs, interpreting results in light of conservation concerns, and determining appropriate management actions. This special issue identifies key attributes of successful genetic data analyses in biodiversity evaluation and suggests ways to improve analyses and their application in current population and conservation genetics research.This special issue of  Fred Allendorf Modern molecular techniques provide unprecedented power to understand genetic variation in natural populations. Nevertheless, application of this information requires sound understanding of population genetics theory. 1As biodiversity loss accelerates and environmental challenges mount, there is a need for quantitative evaluation of the status and trends of intraspecific and interspecific genetic diversity of species and communities. Assessing variation in neutral and adaptive loci can identify genetic threats to populations, species, and communities . These targets are focused on integrating biodiversity awareness, valuation, and measurements into governmental and societal action; they are designed to safeguard ecosystems, species, and genetic diversity against the loss of biodiversity by promoting sustainable processes and management. Specifically, Target 13 states that by 2020 “the genetic diversity of cultivated plants and farmed and domesticated animals and of wild relatives, including other socioeconomically as well as culturally valuable species [emphasis added], is maintained, and strategies have been developed and implemented for minimizing genetic erosion and safeguarding their genetic diversity.”At the time of writing, the proposed Convention on Biological Diversity 2020 Aichi Biodiversity Targets is 2 years away. These targets were developed in Aichi, Japan, in 2010 and provide an overarching framework for the United Nations system and various Nations and Conventions to preserve biodiversity . This massively parallel high‐throughput sequencing approach produces high coverage sequencing reads for many loci and samples  in Tennessee, USA. The goal of the workshop was to help unlock the conservation potential of genomic data for biodiversity studies and lay a foundation for describing, quantifying, and interpreting the complex, multidimensional information contained in new theories and approaches. Next‐generation sequence data analyses will need to be integrated among various research areas such as noninvasive sampling, taxonomic delineations, landscape genetics, forensics, microbiomics, and epigenetic studies as well as interdisciplinary fields such as climate science, phenotypic analysis, and geospatial remote sensing. The workshop therefore included experts comprising empiricists, theoreticians, and method developers from divergent geographic areas, genders, career stages, and expertise to spark cross‐disciplinary discussions. This special issue synthesizes the contributions and discussions made at the workshop to assist those working with NGS data for conservation and management.The workshop participants identified key attributes of successful data analysis in biodiversity evaluation and surveyed and critiqued existing genetic metrics to improve analyses and how they might be applied to current needs. For example, monitoring tools should be able to assess system conditions, diagnose the cause of population or diversity losses , and predict future changes. Moreover, they should ideally be easily measured, simple to apply, readily understood by nonspecialists such as decision makers, and respond to stressors in a predictable manner , with the goal of detecting genetic differentiation among three species. The authors found similar results using both marker types, as long as genetic groups were correctly partitioned based on evolutionary signals. Using >20,000 SNPs, only two to five individuals were required to obtain accurate population differentiation measures with Fst estimates, although incorrect grouping of samples led to a lack of population structure. Thus, while theoretically a small number of individuals can be sufficient, it may be wise to use a larger number to reduce incorrect interpretation of groupings. Further examining the relationship between traditional markers and genomic data, Ferchaud et al.  by comparing microsatellites to SNP data. The two marker types produced similar results, but the ~5,000 SNPs also allowed for investigations into adaptation and deleterious mutations, improving the evidence available for management decisions. In particular, the joint identification of neutral and deleterious mutations could help refine the choice of source and sink supplementations to maximize evolutionary potential and limit mutation load.Gaughran et al.  conducted et al.  conducteCarroll et al.  target tst, Fst, and theta) estimate proximity to fixation in demes as opposed to the degree of differentiation of allele frequencies. The authors use several simple examples to address the misconception that the two metrics are correcting or estimating one another and demonstrated that this assumption can yield invalid inferences. For example, allelic differentiation measures may be more useful in some conservation situations when the relative sizes of the demes differ. And Jost's D, a heterozygosity‐based measurement, was found to be informative to assess genetic divergence between populations. The authors also discuss the importance of considering locus mutation rate, number of alleles, and whether loci are under selection, when using both sets of statistics.Regarding genetic analysis, Jost et al.  provide e) and demographic population size (Nc), Pierson, Graves, Banks, Kendall, and Lindenmayer . Critically, translation and communication of genetic results and implications for natural resource managers still need improvement. For example, NFollowing the NIMBioS workshop, there was a meeting of the IUCN  Conservation Genetics Specialist Group (CGSG) in November 2017 hosted by the Antwerp Zoo, Belgium. Members from 14 countries outlined priorities for both wild and cultivated species in the fields of conservation genetics and applied evolutionary studies. The incorporation of genetic diversity and conservation unit delineation into the assessment of species for assignment to the IUCN Red List of Threatened Species was identified as an urgent need. Detailed protocols are also needed for appropriate monitoring of changes in genetic diversity; specifically, there is still no genetic indicator for Target 13 except for domesticated livestock. Simple and easily interpreted indicators are urgently needed to complete the upcoming the 2020 Targets. The group also recognized that the potential of genomic information is still largely overlooked in policy and conservation management. To address these issues, the CGSG has identified several guidelines to help managers understand the applicability of population genetic studies and better decide when genomic tools would improve conservation outcomes. Lastly, the CGSG has committed to development of guidelines for genetic considerations in Key Biodiversity Areas (sites contributing significantly to the global persistence of biodiversity; IUCN, We would like to dedicate this special issue to Dr. Tim King of the US Geological Survey, Leetown Science Center. Dr. King was a renowned conservation geneticist, making key contributions to the field and promoting the application of genetic data in management decisions. His focus, detail, and thoughtful approach to science paved the way for many of us by highlighting the importance of genetic data in imperiled and invasive species management. Dr. King was also a beloved friend and mentor to many and we endeavor to carry his extraordinary legacy forward.


Rhinolophus ferrumequinum) is among the most widespread bat species in Europe but it has experienced severe declines, especially in Northern Europe. This species is listed Near Threatened in the European IUCN Red List of Threatened Animals, and it is considered to be highly sensitive to human activities and particularly to habitat fragmentation. Therefore, understanding the population boundaries and demographic history of populations of this species is of primary importance to assess relevant conservation strategies. In this study, we used 17 microsatellite markers to assess the genetic diversity, the genetic structure, and the demographic history of R. ferrumequinum colonies in the western part of its distribution. We identified one large population showing high levels of genetic diversity and large population size. Lower estimates were found in England and northern France. Analyses of clustering and isolation by distance suggested that the Channel and the Mediterranean seas could impede R. ferrumequinum gene flow. These results provide important information to improve the delineation of R. ferrumequinum management units. We suggest that a large management unit corresponding to the population ranging from Spanish Basque Country to northern France must be considered. Particular attention should be given to mating territories as they seem to play a key role in maintaining high levels of genetic mixing between colonies. Smaller management units corresponding to English and northern France colonies must also be implemented. These insular or peripheral colonies could be at higher risk of extinction in the near future.The greater horseshoe bat ( Rhinolophus ferrumequinum) colonies in the Western European part of its distribution. We found high levels of genetic diversity and large population size in the European mainland and lower estimates in England and northern France. We suggest that a large management unit corresponding to the European mainland population must be considered. Smaller management units corresponding to English and northern France colonies must also be implemented because they could be at higher risk of extinction in the near future.In this study, we used 17 microsatellite markers to assess the genetic diversity, the genetic structure, and the demographic history of the greater horseshoe bat ( Indeed, bats are very sensitive to climate change and human activities  is particularly relevant to address conservation issues from population genetics. This insectivorous species—which seasonally uses hibernation and maternity roosts—has experienced dramatic declines, particularly in Northern Europe  where it is now considered rare or extinct . The reasons for the disappearance of the populations of R. ferrumequinum are difficult to identify but it is likely that anthropogenic factors  are responsible for it , that resulted from the expansion of a single population originating from a Western Asian refugium , as it is currently done on the basis of political constraints. We combined several population genetics approaches based on a dense sampling of R. ferrumequinum maternity colonies in the Poitou‐Charentes region and we also added further samples in France, encompassing the northern edge of R. ferrumequinum distribution, Spanish Basque Country, England, and Tunisia. This sampling scheme, based on concentric circles of sampling, should be able to identify at which geographical scales R. ferrumequinum gene flow may decrease with distance. It should also allow testing for gene flow disruption that might result from population divergence associated with geographic elements such as seas or mountains. We estimated the genetic diversity and genetic effective size of colonies. These are important criteria to assess population viability, so that they may be useful to infer conservation priority. We expected lower levels of genetic diversity and higher levels of genetic differentiation at the edge of R. ferrumequinum distribution range. Indeed, peripheral populations are likely to suffer from reduced gene flow, genetic drift, and small effective population size, compared to central populations , Rhone, France). All methods were approved by the Museum National d'Histoire Naturelle (MNHN) and the Société Française pour l'Étude et la Protection des Mammifères (SFEPM). Authorization for bat captures in Spanish Basque Country was provided by the corresponding regional Ministries and Councils. Capture and handling protocols followed published guidelines for treatment of animals in research and teaching , 28 were captured in one maternity colony in the Spanish Basque Country summer 2012), 36 were captured in two maternity colonies in England (summer 2018), and 22 were captured in one maternity colony in Tunisia  using a 3‐mm diameter biopsy punch. Samples were either preserved in 95° ethanol and quickly stored at 4°C until DNA extraction or preserved in silica‐gel , 2 µl of extracted DNA, and 0.2 µl of each primer . We used the following PCR conditions: 95°C for 15 min, 35 cycles of 94°C for 30 s, 57°C for 90 s and 72°C for 60 s, and a final elongation step at 60°C for 30 min. Twenty microliters of H2O was added to 10 µl of each product in a new plate, and 2 µl of this final solution was mixed with 15 µl of formamide and 0.08 µl of GeneScan 500 LIZ. The samples were genotyped using an ABI3130 automated sequencer and the GeneMapper v.5.0 software. Two independent readings were performed by different people to minimize genotyping errors.DNA was extracted from each wing sample using the EZ‐10 Spin Column Genomic DNA Minipreps Kit for Animal (BioBasic) following the manufacturer's instruction with a final elution of twice 50 µl in elution buffer. We amplified 17 microsatellite loci using primers modified from those previously designed for p‐value thresholds after FDR correction  were calculated following Castro and Singer , the expected (He) heterozygosity, using FSTAT v.2.9.3.2  were calculated following Castro and Singer , using the ML‐RELATE software  as population references for estimating r within each colony.We estimated the fixation index 2.5FST . In order to control for potential effects of null alleles on genetic differentiation, we also estimated pairwise FST corrected for null alleles using the “Excluding Null Alleles” (ENA) correction implemented in FreeNA . However, in the present study, given the fact that three recovered clusters . We used the R package pophelper  and does not require predefined population genetic model. It is based on a spatial network in which pairwise genetic distance between georeferenced samples is attributed to ellipses. A grid of hexagonal cells covers the study area and each cell receives the weighted arithmetic mean of the pairwise genetic distance associated to the ellipses intersecting the cell  was analyzed with the regression of the genetic distances between colonies  and the ancestral scaled population size (θanc = 4Nancµ). Ne and Nanc are the current and ancestral diploid population sizes, µ is the mutation rate per generation of the loci, and D is the time scaled by current population size (D = T/4N) at which an already existing population experienced a demographic change. The mutation model used was a generalized stepwise mutation model (GSM) which is characterized by a geometric distribution of mutation steps with the parameter pGSM. We used the ratio Nratio (N/Nanc) to characterize the strength of demographic events. In the case of a contraction, the Nratio is <1, and in the case of an expansion, it is >1. The change in population size was considered significant when the 95% confidence intervals (95% CIs) of the Nratio did not include the value “1.” Runs were performed independently for each colony and for each genetic cluster that was previously identified. We used 1,000 to 20,000 trees per point, 600 to 800 points, and eight iterations by run. When no signal of demographic change was found, we used the OnePop model implemented in MIGRAINE to estimate the scaled effective population size θ = 4Neµ of stable and panmictic population.We first inferred the demographic history of We then inferred contemporary levels and directions of migration between the main genetic clusters using the program BAYESASS v3.0.4 , and at loci RHD103 , Rferr27 , and Rferr01 . Nevertheless, we did not detect any deviation from HWE in any colony. Fifteen out of the 2,418 pairs of loci (0.62%) exhibited significant linkage disequilibrium, but the loci involved were not consistent among colonies. Therefore, we did not exclude any other loci.3.1Fis, Ar, He) based on the 14 validated microsatellites is presented in Table p = .017, Fis = 0.009). Local Fis estimates  ranged from −0.037  to 0.120 . All tests of departure from local HWE were nonsignificant after FDR correction . The English colonies showed significant lower estimates of allelic richness  and expected heterozygosity  than the French ones  but similar He estimate . The Tunisian colony “GHA” showed similar levels of allelic richness and expected heterozygosity than the French colonies . Private allelic richness was low in all colonies except the Tunisian one  and a decreasing proportion of related individuals. In the northern French colony “MON,” we observed a more uniform proportion of unrelated and relatively closely related individuals (r ranging between .0 and .3). When considering pairwise relatedness between individuals from different colonies, we still observed high levels of relatedness  were very similar, and analyses without juveniles did not substantially change FST estimates. Therefore, we only reported the uncorrected estimates for the dataset including juveniles. The pairwise FST estimates between colonies and associated G‐tests are presented in Table FST < 3%, 65.24% of the G‐tests with p < .05; Table FST than the other colonies . Among the French colonies, “MON” was also the colony that exhibited the highest levels of genetic differentiation with the English , Spanish Basque , and Tunisian colonies . The Spanish Basque colony “LEZ” was more genetically differentiated from colonies in England and Tunisia  than from colonies in France (FST < 3%).K statistics ) showed similar values for K = 2 and K = 3 , between France and Spanish Basque Country , and between France and Tunisia . The northern French colony “MON” was more genetically differentiated from the other French colonies . Moreover, considering only differentiation between pairs for French–English, French–Basque, and French–Tunisian colonies without including the northern French colony “MON,” the isolation by distance relationship was not significant , with an estimated origin of this demographic change D = 0.811 [2.17 e−06–2.201]. None of the other colonies or genetic clusters showed significant signature of demographic change  for all stable colonies, and θ estimates ranged between 1.854 and 6.465  and for the northern French colony “MON” (θ = 3.030). Other colonies and pool of colonies exhibited similar levels of θ estimates (from 4.066 to 6.465). Kruskal–Wallis tests and post‐hoc pairwise comparisons of θ estimates using Wilcoxon test (with FDR correction for multiple testing) revealed a significant difference of θ estimates between the western French–Spanish Basque and the English genetic clusters (p = .002) and nonsignificant differences between the other clusters (p > .115). We observed a significant negative relationship between the estimated θ of each colony and the distance of the colony to the centroid of our sampling . As our results emphasized particular signatures for the northern French colony “MON” all along the study, we also estimated migration rates considering “MON” apart from the other French colonies. Our results revealed significant unidirectional migration rates from all western French colonies to the northern French colony “MON” (m = 0.2700 ± 0.0276).The five runs implemented in BAYESASS provided similar results and converged well, with values ranging from 44.1R. ferrumequinum distribution  of this population. We found homogeneous θ among the western French and Spanish Basque colonies and for the pool of these colonies. This result was congruent with our finding of one large population with each colony representing a replicate of the whole population. We could not transform this estimate θ into an effective number of individuals (Ne) because it requires fixing the mutation rate of microsatellite markers in this species. This is a crucial issue because estimates of mutation rates can greatly vary between and within species and any inaccuracy in the mutation rate estimate is propagated in the estimation of Ne  gene flow disruption associated with western Pyrenees in this study may therefore suggest that this mountain may not impede dispersal or that R. ferrumequinum is able to move over hundreds of kilometers, as exemplified by the low levels of genetic differentiation observed at large geographical scales, the inference of significant migration rates between the western French–Spanish Basque maternity colonies, and the high levels of relatedness observed between individuals sampled in distant colonies.In the absence of important landscape barriers such as seas or, potentially, mountains, we showed that R. ferrumequinum western French and Spanish Basque colonies. Several demographic processes may underlie this genetic mixing. First, mating dispersal at large distance would lead to extracolony copulations and to the relaxation of colonies' genetic borders  and dispersal, in particular the one of males. Ringing data from R. ferrumequinum from across Europe suggest the species is mostly sedentary but with occasional movements over 100 km. Indeed, although rare, there are documented movements of 180 km in Spain, 320 in Hungary . In these latter colonies, we detected lower levels of genetic diversity (He up to 20% lower) and smaller estimates of ϴ  than in the western French–Spanish Basque population. They were also more genetically differentiated than the western French–Spanish Basque ones, as revealed by FST estimates and isolation by distance and clustering analyses. Some of these particular colonies might be insular (England), but are commonly located near the edge of R. ferrumequinum distribution range. Contrasting levels of genetic diversity between insular and continental populations are common in animals  exhibited genetic patterns that were similar to those observed in the English colonies . This colony was also the only one that exhibited a high proportion of strongly related individuals. Overall, these results suggested that this colony experienced strong genetic drift, due to a small effective population size and limited gene flow with other western French–Spanish Basque colonies. This situation could be explained by a lack of favorable habitat surrounding the colony, leading to limited dispersal and reduced extracolony mating. It could also be explained by the location of the colony at the northern limit of R. ferrumequinum from North Africa would be necessary to assess whether it might apply to all colonies located on this southern part of the Mediterranean Sea.The last particular situation emphasized in this study concerned Tunisia. The level of genetic diversity detected in this colony was close to the levels observed in the western French–Spanish Basque colonies, but private allele richness was higher. Moreover, this Tunisian colony exhibited a signature of demographic expansion, although we have to be cautious with this result as the confidence interval of the estimated time since the expansion started was extremely large. Larger and denser sampling of colonies of 4.4R. ferrumequinum experienced severe declines there, so that some populations might be at higher risk of extinction and deserve special management attention  and French administrative regions are needed to improve the knowledge and conservation of this (and other) bat species in France. This large population could be resilient to local disturbance because of its strong interconnection between colonies. However, we cannot exclude that some particular colonies within this population might be vulnerable. It is therefore still important to pursue colony surveys at local scales and also to standardize monitoring procedures at the national and international scale  that are needed for bats to complete their yearly life cycle. Further collaborations between NGOs and academics are required to evaluate these relationships between landscapes, movements at different scales, mating, and genetic mixing, through the development of joint ecological and genetic approaches.None declared.O.T., D.P., N.C., M.L., and J.‐B.P have conceived the study; M.L., J.‐B.P, and D.P. have organized the fieldwork; M.L., J.‐B.P, O.F.‐C, J.D., O.T., A.L., N.C., I.G., F.M., S.P., and D.P. have helped in collecting the biological material; O.T. and A.L. have conducted the DNA analyses; O.T., R.L., and S.P have analyzed the data; O.T., N.C., and D.P. have drafted much of the manuscript. All authors read, criticized, and approved the final manuscript. Click here for additional data file.


J in terms of the frequency M of the most frequent allele. The method not only enables simpler derivations of known bounds on J in terms of M, it also produces analogous bounds on entropy statistics for genetic diversity and on homozygosity-like statistics that range in their emphasis on the most frequent allele in relation to other alleles. We illustrate the constraints on the statistics using data from human populations. The approach suggests the potential of the majorization method as a tool for deriving inequalities that characterize mathematical relationships between statistics in population genetics.The homozygosity and the frequency of the most frequent allele at a polymorphic genetic locus have a close mathematical relationship, so that each quantity places a tight constraint on the other. We use the theory of majorization to provide a simplified derivation of the bounds on homozygosity  Population-genetic summary statistics—functions computed from data on genetic variation—provide a central tool in population-genetic data analysis. Summary measures of allele frequencies, including measures of genetic similarity and diversity, are often computed from data, leading to much subsequent interpretation and additional computation.Recent studies have demonstrated that pairs of population-genetic summary statistics often have a close mathematical relationship, so that values of one quantity strongly constrain the values of a second quantity Hedrick . For exaosenberg  then tigosenberg .To further advance the study of mathematical properties of population-genetic statistics describing similarity and diversity of alleles in a population, we investigate the application of the theory of majorization—a mathematical and statistical approach concerned with evenness of arrayed structures  Let majorizesk from 1 to I, For each If  If f is differentiable, F that is decomposable into individual, identical convex functions in each of its arguments satisfies the Schur–Ostrowski criterion. An analogous statement holds for Schur-concave functions in relation to concave functions. We state this claim formally.We will have occasion to use a particular case of the Schur–Ostrowski criterion. The first derivative of a differentiable convex function f is (strictly) convex, then F is (strictly) Schur-convex. Similarly, if f is (strictly) concave, then F is (strictly) Schur-concave.If This corollary, in which the part about strict convexity appears on p. 92 of Marshall et al. , is convf is concave, then the inequality is reversed. If f is strictly convex or strictly concave, then equality holds if and only if the list of values  Let J and H by M. In the next section, we apply Karamata’s inequality to establish bounds on these statistics as functions of the fixed largest allele frequency. In establishing Theorems With these tools, we are now ready to study the constraints placed on M of the most frequent allele in two settings. First, we consider an unspecified number of distinct alleles (ii)with equality if and only if (ii)Equality of F with the upper bound in (ii) occurs if f is strictly convex, then this is the only configuration at which equality holds. If f is strictly convex, then the inequality in (i) is strict.Let F that can be expressed as a sum of convex functions f of each argument and that satisfies other mild conditions is bounded below by f(M), where M is the largest component of the vector F is being evaluated, and it cannot exceed the value given by evaluating F at the vector M.Theorem f is continuous and strictly convex, with nonnegative range. It satisfies F is bounded, as f, if certain conditions are satisfied, then the sum To see how Theorem f is convex, thenLet n, f  Choose M; m. We prove that after this procedure is performed, f and vectors f is convex, denoting Let  Theorem  to convej largest components of jM. For any j largest components of We now verify that f, F that follows from Corollary For the equality condition, note that equality in (ii) requires f on f is strictly convex and f is strictly convex, (i) Observe that for any M is the reciprocal of an integer, then M is the reciprocal of an integer, Note that for convenience, in the statement of Theorem Having proven Theorem  ConsideTo obtain Theorem f is continuous and convex. Suppose f is strictly convex, then it is achieved only at this configuration. Moreover, if all other conditions on f hold but f is concave, then the inequalities in Eq. Let M.Let M and Consider vectors f is convex, by Corollary F is Schur-convex. By Karamata’s inequality α] Eq. . First, I is fixed, then the constraint placed by M on I. Note that as I, 1] of permissible values of M, we recover the result of Proposition 7iii of Reddy and Rosenberg  Suppose M is the reciprocal of a positive integer, the lower bound reduces to Here, we adopt the convention that f, for the lower bound, recalling that f is strictly concave, equality with the upper bound is obtained if and only if We apply Theorem We can also obtain a more general result for the Rényi entropy The Rényi entropies, though not generally concave for It follows from the definition of Schur-concavity that the strictly Schur-concave (Bounds for Rényi entropy) Suppose H as a function of M for several choices of I, illustrating the constraints placed on H by both M and the number of distinct alleles I. As discussed above, H is unbounded if the length of the allele frequency vector is unspecified. By fixing the length of the frequency vector, we are able to obtain a finite bound on H; for small values of I, H is tightly constrained as a function of M.Figure H as a function of M by measuring the area of the region of permissible values of H. Given I, the upper bound on H cannot exceed M  must lie. Because a positive term is subtracted from Dividing I. With the graph of Figure H, and M in the full collection of individuals. The missing data rate was 3.7%  contains permissible pairs of values for M and H , describing their placement within the permissible range; the analysis shows that the bounds are useful for clarifying constraints on data points. Outliers in the plots uncover loci of potential interest, with the ratio We have found in our data analysis that values near the bounds are obtained by empirical allele frequencies; the loci generate values of I, their extrema are achieved at the same frequency vectors.Our contributions have utilized majorization and the Schur-convexity and Schur-concavity of the functions used in calculating statistics in population genetics. Each statistic we studied captures the intuitive property that for a fixed sum of frequencies and a fixed maximal number of nonzero components, the least majorized vector, containing only a single nonzero entry, has the lowest value of a diversity index and the highest value of a similarity index, and the most majorized vector, containing a maximal number of equal entries, has the lowest value of a similarity index and the highest value of a diversity index. Indeed, a much stronger result holds, in that these statistics or their additive inverses preserve the majorization order of input vectors, and for fixed The connection of majorization to the measures suggests that one aspect of assessing if a new proposed diversity or similarity measure is sensible is an evaluation of whether it or its additive inverse preserves majorization. The homozygosity and Shannon–Weaver statistics have this property, as do the M identified by Rosenberg and Jakobsson (M is specified. We have also observed examples of these theoretical results in computations with data from human populations. We suggest that the fact that the majorization approach obtains new results on statistics as fundamental as variant forms of homozygosity and the Shannon–Weaver index hints that it might have considerable potential to contribute to mathematical bounds on additional population-genetic statistics.This study both contributes new results and brings methods from another context to the study of well-known population-genetic statistics. We have highlighted that the strong dependence of homozygosity on akobsson  and Reddakobsson  can be e


Octopus tetricus, which has extended its distribution several hundred kilometres polewards associated with the southwards extension of the warm East Australian Current along south-eastern Australia. The historical distribution and the range extension zones had significant genetic differences but levels of genetic diversity were comparable. The population in the range extension zone was sub-structured, contained relatively high levels of self-recruitment and was sourced by migrants from along the entire geographic distribution. Genetic bottlenecks and changes in population size were detected throughout the range extension axis. Persistent gene flow from throughout the historical zone and moderate genetic diversity may buffer the genetic bottlenecks and favour the range extension of O. tetricus. These characteristics may aid adaptation, establishment, and long-term persistence of the population in the range extension zone.Shifts in species distribution, or ‘range shifts’, are one of the most commonly documented responses to ocean warming, with important consequences for the function and structure of ecosystems, and for socio-economic activities. Understanding the genetic signatures of range shifts can help build our knowledge of the capacity of species to establish and persist in colonised areas. Here, seven microsatellite loci were used to examine the population connectivity, genetic structure and diversity of  Such range shifts occur in the form of range contractions, relocations, or extensions that have ecological and socio-economic impacts4, with already significant consequences on human well-being5. Most of our understanding of how genetic and evolutionary processes may relate to marine range shifts is based on terrestrial invasive or range shifting species8 and on theoretical studies11, with only a handful of empirical marine studies13. However, a range of different barriers influence the connectivity and genetic patterns of marine populations in comparison to terrestrial populations; these include water masses, currents, eddies, and coastlines, among other factors. As a consequence, the nature and magnitude of genetic patterns and evolutionary processes related to marine range shifts may differ from those that occur in the terrestrial realm. Therefore it is important to perform marine field-based studies that can increase our understanding of genetic patterns and evolutionary processes related to marine range shifts and help validate theoretical models.In response to the accelerated warming of the oceans, marine species can experience changes in their patterns of geographic distribution and abundance as they track their preferred temperatures12. Selection, the reproductive exchange between individuals, dispersal capacity and the presence of barriers to dispersal, can all influence genetic diversity15 and survival at the trailing edge, at the centre of the distribution, and at the leading edge of range shifts10. Populations at the trailing edge may be negatively affected due to low genetic variability unless they receive genes from better adapted populations6. Populations at the centre of the distribution are likely to have more genetic variability due to gene flow from neighbouring populations that are better adapted to different conditions16. Genetic diversity at the leading edge of range shifts can increase provided there is gene flow from different areas of the distribution16, with maximum survival predicted to occur if the gene flow is from areas with similar conditions compared with the newly colonised areas17.Maintaining genetic diversity is essential if populations are to persist during range shifts associated with oceanic warming6. Consecutive genetic bottlenecks or founder effects, where the size of the population is reduced or where only few individuals establish in new areas and subsequently become isolated, can result in genetic drift and inbreeding depression20. There are exceptions where invasive populations have overcome the negative impacts of the loss of genetic diversity via multiple introductions that aid the increase of genetic recombination, confer high phenotypic plasticity to the founder population, and strengthen its ability to respond to natural selection22. For instance, multiple introductions of individuals appeared to help sustain genetic diversity of the invasive long-spined sea urchin Centrostephanus rodgersii at the southern Tasman Sea off Australia’s east coast12. This area is warming up to four times faster than the global average23, partly caused by the strengthening of the warm East Australian Current (EAC) which flows polewards from the southern Coral Sea along the south-east coast of mainland Australia24. Over the past 60 years the EAC has strengthened and extended approximately 350 km further south towards the temperate east coast of Tasmania26.Genetic diversity can decrease due to genetic drift under scenarios of limited gene flow, genetic recombination and selection against poorly adapted genotypes28. One of these range-shifting species is the gloomy or common Sydney octopus Octopus tetricus . This commercially important octopus has a short life span (~11 months)29, high reproductive capacity30 and a planktonic paralarval phase. The paralarval phase is likely to last 35–60 days as demonstrated for the closely related O. vulgaris32 under laboratory conditions34. The historical geographic range of O. tetricus is in shallow-waters from southern Queensland to southern New South Wales (NSW)37. Octopus tetricus was detected for the first time off Victoria around the year 2000 and off north-eastern Tasmania around 2006  to describe the population genetic structure of O. tetricus along eastern Australia, including extension areas; (2) to quantify the genetic diversity of the range extension area versus other population components; (3) to determine the level of gene flow between historical and range extension areas; (4) to identify the source populations that contribute to the range extension area; (5) to determine if there is evidence of genetic bottlenecks that may negatively affect the persistence of O. tetricus at the extended areas; and (6) to describe the recent evolutionary history of this range extending species.Examination of the genetic structure, level of connectivity and genetic diversity of range shifting populations will provide valuable information on the genetic signatures during range shifts and may allow forecasting of successful colonisations as a function of gene flow and genetic diversityOctopus tetricus, were genotyped for seven microsatellite loci  suggested five clusters .Phylogenetic analysis of the mitochondrial gene Cytochrome Oxidase subunit I (COI) demonstrated that individuals of the groups Ot1 and Ot2 correspond to Allelic richness was moderate with more than seven alleles at each site; the number of private alleles and heterozygosity in the range extension zone were higher than for the historical distribution zone, and inbreeding coefficients were not significant Table .Table 3DST estimations provided further detail of the population structure and complemented DAPC and Structure results. There was no significant differentiation between Nambucca Heads, Swansea, and Merimbula . Thus, from the FST estimations we inferred that the common Group Ot1 was comprised of sub-group 1 , sub-group 2 , and sub-group 3 . The distinct Group Ot2 was comprised of the sub-group 4 . The FST analysis at the Group level confirmed that the common Group Ot1 was significantly different from the Group Ot2 . The Mantel test indicated that the level of genetic differentiation among collection sites was not significantly correlated to geographic distance (P = 0.258) or year of collection (P = 0.096). Accordingly, the Spearman’s correlation analysis suggested that only two loci were significantly correlated to year of collection . Two loci were associated to depth of sampling  (Supplementary Table S10). The AMOVA detected significant percentages of genetic variation among collection sites .FMigration rates were asymmetric between sites, with higher migration rates from Merimbula to the other sites (Table Heterozygosity excess as an indication of genetic bottleneck was detected under the Stepwise Mutation Model (SMM) at all sites, except for the sign test for Nambucca Heads. Under the Two-Phase Mutation Model (TPM), heterozygosity excess was detected at Tasmania. Genetic bottleneck effect was also detected at the distinct Group Ot2 and at the Group Ot1 under the SMM and the TPM. Tests under the Infinite Allele Model (IAM) and the mode-shift test did not detect genetic bottleneck effects at any site (Table Ne = 286 (N4b) to Ne = 7,930 (N4) at t2 (95% CI). Sub-group 1 experienced a recent genetic bottleneck (t0b), with a posterior decrease in the size of its population from Ne = 8,570 (N1b) to Ne = 7,220 (N1). Sub-group 2  diverged from sub-group 1 at t2 (95% CI), whereas sub-group 3  diverged from the admixture of sub-groups 1 and 2 at t1 (95% CI)   Fig. –S20. PosThe key findings of this study are that genetic differentiation was detected between sites present within the historical distribution and the range extension zones, and also within the range extension zone. The population was sub-structured in one common group along eastern Australia and Tasmania (Group Ot1), and a distinct group mainly comprised of individuals from Tasmania (Group Ot2). Migration rates were asymmetrical with greater migration occurring from the historical distribution zone towards sites at the range extension zone. The genetic diversity at the range extension zone was comparable to that detected at the historical distribution area. Genetic bottleneck effects were detected at sites within the historical distribution and range extension zones. Population history simulations suggest that the distinct group diverged from the common group relatively early in the history of the population.38, fisheries records39, and citizen science monitoring40 suggest that O. tetricus has recently extended its distribution polewards into Tasmanian waters. Moreover, this range extension was classified with “high” level of confidence41 and we detected genetic signatures that are characteristic of range shifts9. The presence of the common Group Ot1 throughout the historical distribution and the range extension zones is supported by the detected gene flow between all sites and by shared haplotypes in O. tetricus between NSW and Tasmania using mitochondrial DNA32. In contrast, Ot2 individuals were common in Tasmanian waters but uncommon along the east coast of mainland Australia. This finding is consistent with the increase in size of the Group Ot2, detected by the Approximate Bayesian Computation analysis (ABC), probably from a genotype that is relatively uncommon in the historical distribution zone. The differentiation and expansion in size of the Group Ot2 was likely favored by high self-recruitment, rapid population turnover and high reproductive capacity30. These characteristics provide species with high evolutionary capacity43 and may facilitate long-term persistence in range extension zones44. However, the range extension of O. tetricus appears to be rather complex. A genetic break was detected in Tasmanian waters between the Group Ot2 and Ot1 Tasmanian individuals. This genetic break is beyond the scope of this study and requires further investigation to understand the mechanisms that may cause limited mixing of both groups despite coexisting in the same area, e.g. differential selection, non-random mating or short-term departures from local panmixia. In addition, the ABC simulations estimated that the Group Ot1 appeared in Tasmanian waters more recently than the Group Ot2, which diverged early in the history of the population. These signatures suggest that O. tetricus historically diverged between Tasmania and mainland Australia, and secondary contact occurred due to the recent expansion of the mainland population polewards towards the south.Systematic marine life censuses10, the ABC analysis suggested a decrease in size of the population at the equatorward sites. A relatively high level of heterozygosity suggests that Tasmania is within the leading edge of the range extension; this finding is consistent with the genetic pattern observed at the leading edge of range shifts of Quaternary trees in response to climate change6. The range shift of O. tetricus thus appears to consist in the polewards relocation of the population, with characteristics of recent and rapid demographic expansions9. Similarly, populations of C. rodgersii undergoing rapid range expansions had high levels of genetic diversity and heterozygosity at the leading edge of the range12. Constant gene flow from along the entire distribution aids sustained genetic diversity in extension areas21, which can confer phenotypic plasticity to founder individuals and the ability to respond to natural selection22. Accordingly, discrete morphological differences were found in male O. tetricus between the east coast of mainland Australia and Tasmania32.Following theoretical expectationsO. tetricus is expected to be shaped by the dynamics of the EAC26, which is likely the main driver of larval transport along the east coast of Australia28. With an assumed planktonic duration of 35–60 days, as estimated for O. vulgaris34, paralarvae of O. tetricus can be transported the linear distance between Nambucca Heads and Tasmanian waters  at an average seawater flow of 55 cm·s−1 24. Our findings are supported by the connectivity detected between the east coast of mainland Australia and Tasmania32. However, the use of Euclidean distances may be a poor predictor of gene flow45 because the effect of the coastline creates circuitous, turbulent, and nonlinear flow, which in addition to eddies present along the east coast of Australia may result in patchy larval dispersal48.The population structure and connectivity of O. tetricus49. Constant gene flow from along different environmental conditions within the historical distribution zone, and subsequent interbreeding, can assist in maintaining genetic diversity, create new gene complexes51, and buffer against any new set of environmental stressors in the range extension zone52. Moreover, Merimbula was the main source of migrants towards the range extension zone. This site is located at the historical polewards edge of O. tetricus range and shares more similar environmental conditions (i.e. temperature) with the range extension zone than other sites further equatorward. Such similarities may further facilitate the capacity of founder individuals to respond to selective pressure at the range extension zone17. Maintenance of genetic diversity by sufficient gene flow is therefore likely to contribute to the establishment, early success and persistence of O. tetricus in the extension zone21.Collection sites of this study are exposed to a gradient of temperatures from NSW  to north-eastern Tamania . Such a gradient of environmental conditions also may have aided in shaping the population structure of 53, likely facilitating the population connectivity between historical and range extension zones. However, the accelerated warming along the east coast of Australia is anticipated to shorten the paralarval phase of O. tetricus. A shorter paralarval phase may reduce dispersal capacity, population connectivity55, and recovery of genetic diversity in the range extension area. Effects of ocean warming on reproductive seasons, frequency of paralarvae releases, swimming capabilities, paralarvae settlement windows, habitat suitability, and paralarvae mortality may also influence the connectivity of populations46. Therefore ‘seascape genetics’ studies must include oceanographic, life history, ecological and physiological data, in the context of ocean warming and changes in oceanic circulation into the examination of the population connectivity. This approach may provide better understanding of the structure, connectivity, genetic diversity, and capacity of populations to prevail in the new sections of their geographic distribution56. Octopus tetricus has a life-span of 11 months29 and allele frequencies could change between years of collection. To strengthen the robustness of our conclusions, future research should include larger samples sizes and from consecutive years within sites to assess stability of allele frequencies through time.The polewards dispersion of larvae is projected to intensify gradually over the next decades along the east coast of AustraliaO. tetricus is characterized by a genetically diverse range extension zone. Constant gene flow from a diversity of source areas along the entire distribution may promote relatively high genetic diversity and counteract genetic bottleneck effects at extension areas. Therefore, the genetic signatures examined in this study suggest that O. tetricus is well placed to be able to persist in its range extension zone provided that no demographic or environmental effect negatively affects the population.This study suggests that the range shift of 57.This research was conducted under the permits no. A11591 and A13740 approved by the University of Tasmania Animal Ethics Committee and in accordance with the ‘Australian code of practice for the care and use of animals for scientific purposes’FV Farquharson using black plastic shelter pots (0.3-m long × 0.1-m high × 0.1-m wide) laid on the seafloor at depths of 35–46 m. Octopus were euthanized by commercial fishers and immediately put in watery ice in an insulated container. A tissue sample was taken from the arm of every specimen and octopus carcasses were returned to the fishers. Octopus from Ulladulla were collected during diving activities whereas specimens from all other sites along the coast of mainland Australia were collected by hand while snorkelling at depths of 1–3 m. These animals were anesthetized by immersion in a 2% MgCl2 solution, a tissue sample was taken and octopus were released after recovery. All tissue samples were fixed in 95% ethanol.Octopus were collected from inshore waters along the NSW coast at Nambucca Heads  and Swansea  during February 2014; and at Ulladulla , Merimbula , and Eden  during May 2013. Sites along the Victorian coastline were Mallacoota  and Cape Conran  with samples collected during May 2013. Samples also were collected off north-eastern Tasmania  during April, September and December 2011. The centre of the known historical distribution included Nambucca Heads, Swansea, and Ulladulla; sites at the polewards edge of the historical distribution were Merimbula and Eden, whereas sites at the range extension zone were Mallacoota, Cape Conran, and Tasmania  , 0.075 µL of 10 mM forward primer, 0.25 µL of 10 mM reverse primer, 0.20 µL of 5pmol/µL fluorophore labelled primer, and 1 µL (18–37 ng) of DNA. PCR conditions were modified slightly to optimize PCR products for some samples, such that 1 µL of 25 mM MgCl2 (Promega) was added in place of water. The number of cycles was reduced from 35 to 30, and the final extension was reduced from 5 to 3 min. Capillary separation of PCR products was performed by the Australian Genome Research Facility Ltd (AGRF). Genotypes were scored by eye using Geneious Pro v. 5.6.461. PCRs were repeated up to three more times for individuals with unclear or missing single-locus genotypes before being categorized as missing data and scored as 000000 (n = 25).PCR reactions were performeda) Table . Each PC62 was used to search for evidence of allele dropout and stuttering for each locus. High frequencies of null alleles are commonly observed in marine invertebrate species including molluscs65. The presence of null alleles can lead to overestimation of FST in cases of low levels of gene flow and significant population differentiation. Therefore, following the Expectation Maximization algorithm66, FreeNA67 was used to estimate null allele frequencies detected as false homozygotes. Null alleles were then corrected by re-naming them as 999 . This method greatly reduces the bias of FST caused regardless of the frequency of null alleles, the level of gene flow, and the number of loci67.Micro-Checker v. 2.2.368 was used with the package ‘adegenet’69 in RStudio v. 0.99.43570 to test separately for population structure along the east coast of Australia and on a range of subsets of the dataset, i.e. (a) at all sites within mainland Australia, (b) at the historical distribution only, (c) at the range extension zone only, (d) Tasmania only, and (e) not including the distinct Group Ot2. The admixture and correlated allele frequencies models were also implemented in Structure v. 2.3.471 to examine population structure. The number of clusters (K) explored was 1–10, with 10 independent runs of 500,000 Monte Carlo Markov Chain (MCMC) replicates and a burnin length of 50,000. The Evanno method72 implemented within Structure Harvester v. 0.6.9373 was used to evaluate the results, which were graphically displayed using Distruct v. 1.174.The DAPCO. tetricus and not representative of a cryptic species .A phylogenetic analysis was performed to corroborate that individuals of the different groups detected from population structure analyses were in fact 75 was used to test for genotypic linkage disequilibrium among loci within sites , and for departures from Hardy–Weinberg equilibrium using the score test for heterozygote deficiency with level of significance determined by the Markov chain method . Arlequin v. 3.5.1.375 was also used to estimate the number of alleles (NA), observed (HO) and expected (HE) heterozygosity and inbreeding coefficient (FIS). Allelic richness (AR) was rarefied to 17 samples using FSTAT76. The number of private alleles (NPA) was calculated using Convert v. 1.3177.Descriptive statistics were estimated for each collection site with ≥17 samples, as well as for the groups inferred from the population structure analyses. Arlequin v. 3.5.1.3ST was calculated using Arlequin v. 3.5.1.375 between all sites with ≥17 samples. A Mantel matrix correlation test was performed in Genepop web v. 4.279 to examine if genetic differentiation (FST) was explained by isolation by geographical distance (km) between collection sites . Isolation by year of collection was also tested for between collection sites, i.e. 2011 (Tasmania), 2013 , and 2014 (Swansea and Nambucca Heads). A Spearman’s correlation analysis also was carried out to discount any effect of the sampling scheme by testing for an association between loci and year and depth of collection. In addition, AMOVA implemented in Arlequin v. 3.5.1.375 was used to test for partitioning of genetic variation among collection sites.Pairwise F80 was implemented to assess admixture81. Migration rates (Δm), allele frequencies (Δa), and inbreeding coefficients (Δf) were given a mixing parameter value of 1 each for the analysis at the collection sites level, and a mixing parameter value of 0.2 each for the analysis at the groups level. We used 50,000,000 iterations, a 5,000,000 burnin length, and a 1000 interval between samples. Trace output convergence was assessed using Tracer v. 1.682. Recent migration rates (i.e. within the recent 2–3 generations) were then estimated between collection sites, as well as between groups inferred from the population structure analyses.BayesAss v. 3.0.183. Heterozygosity excess was tested with the software Bottleneck v. 1.2.0284, using 10,000 iterations for each site with ≥17 samples, and for the groups inferred from the population structure analyses. The tests used were: (i) The “sign test”83, (ii) the 2-tailed “Wilcoxon sign rank test”85, and (iii) the “mode-shift test”86. These tests allow the detection of the heterozygosity excess due to the faster loss of alleles at low-frequency class (<0.1) than alleles in 1 or more intermediate allele frequency classes86. For instance, the mode-shift test detects the L-shaped distribution of allele proportions observed at mutation-drift equilibrium, and bottlenecked populations that do not present the L-shaped distribution86. Microsatellite loci may tend to evolve under a model more similar to the SMM87 than to the IAM88. However, the SMM, the IAM, and a model in between both models, the TPM (using default settings)89 were examined. Examination of the three mutation models allowed greater caution regarding the detection of genetic bottlenecks.Heterozygosity excess is a genetic signature of genetic bottlenecks because alleles are usually lost faster than heterozygosity during a bottleneck as the mutation-drift equilibrium is lostST), datasets of historical and/or demographic events were simulated under a total of eight different scenarios using a coalescent-based ABC algorithm implemented in DIYABC v. 2.190. The ABC approach relies on gene genealogy, demographic history and ancestral polymorphisms to explain evolutionary causes of present-day genetic variation, and thus can be used to discriminate among putative evolutionary scenarios. This approach is commonly used to distinguish the effect of contemporary recurrent gene flow from the effects of historical connectivity91. The ABC algorithm compares summary statistics of simulated datasets for each historical scenario and/or demographic events with summary statistics of the observed data. The posterior probability and distribution of parameters of each scenario are estimated and alternative scenarios can be compared.To reconstruct the history of divergence, migration and admixture events between the sub-groups identified based on the significant levels of genetic differentiation  from the first step. In the second step, three scenarios based on the best fitted topology selected in the first step were examined to refine changes in effective population size. These scenarios assumed a common origin of the sub-groups with successive divergence to test the hypothesis of recent colonization due to range extension. Thus, in the second step scenarios 6 and 7 considered changes in the effective number of founder specimens after bottlenecks and in the effective population size of cluster 4 at different generation times. Scenario 8 also considered changes in the effective number of founder specimens after bottlenecks and in the effective population size of other clusters (e.g. cluster 2).92.To perform the ABC analysis a reference table with 3,000,000 coalescent-simulated data sets for each scenario was built, from which summary statistics parameter values were used in prior distributions (Supplementary Tables The datasets generated during and/or analysed during the current study are available in the DRYAD repository, [10.5061/dryad.t483s94].Supplementary information