Aedes aegypti is a devastating disease vector transmitting several important human arboviral diseases. In its native range in Africa, the mosquito can be found in both the ancestral forest habitat and anthropogenic habitats such as villages. How do the different habitats impact the population genetic structure of the local mosquito populations?The mosquito Ae. aegypti from the forest and local villages in La Lopé, Gabon and Rabai, Kenya. The mosquitoes were genotyped at 12 microsatellite loci and a panel of ~25,000 single nucleotide polymorphisms (SNPs), which allowed us to estimate their genetic ancestries and the population genetic structure related to habitats and sampling sites.To address this question, we simultaneously sampled Ae. aegypti, clustering analysis showed that mosquitoes from the same locality (La Lopé or Rabai) have similar genetic ancestry, regardless of their habitats. Further analysis at the local scale also found no strong genetic differentiation between the forest and village mosquitoes in both La Lopé and Rabai. Interestingly, these results from our 2017 samples from Rabai, Kenya contrast to the documentation of genetic differentiation between village and forest mosquito collections from 1975–1976 and 2009. Between-habitat measures of genetic difference (Fst) vary across the genome, with a peak of high divergence observed at the third chromosome only in the La Lopé populations.In the context of the global population genetic structure of Collectively, these results demonstrated that there is little genetic isolation between forest and village habitats, which suggests possible extensive gene flow between them. From an epidemiological perspective, the forest habitat could act as a refuge for mosquitoes against vector control programmes in the domestic settings. Moreover, sylvatic populations could play a role in zoonotic pathogen transferred to humans. Therefore, future studies on disease transmission and vector control planning in the study area should take natural populations into consideration. Aedes aegypti, the main disease vector of several arboviral diseases, including yellow fever, dengue, chikungunya and Zika [Ae. aegypti outside of Africa exhibit strong adaptations to the domestic environments, for example, a strong preference for biting humans and breeding in human-made containers [Anthropogenic habitats such as villages and urban areas have been exploited by many species, including disease vectors , 2. Amonand Zika , 4. Therand Zika . This moand Zika –12. Popuntainers . These dAe. aegypti in Africa and the derived populations outside of Africa [Ae. aegypti formosus (Aaf) in Africa with darker body color and Ae. aegypti aegypti (Aaa) outside Africa with lighter body color [Ae. aegypti. Aaf can be frequently found in the forest in Africa, while Aaa is specialized in domestic habitats in other continents [Aaf has been found in many villages and cities (i.e. domestic habitats) in Africa [Ae. aegypti?Genetic data accumulated during the last few decades consistently suggested strong genetic differentiation between the ancestral populations of f Africa –10, 14. dy color , 16. Altntinents . Howevern Africa –19. ThesAe. aegypti [Aaf populations in Senegal and Cameroon [Habitat differences, in theory, could lead to divergent adaptive selection and reduced gene flow allowing independent genetic drift, both contributing to genetic differentiation. While the latter generates genomic-wide differentiation, the former could lead to accelerated rates of divergence in certain parts of the genome that are either directly under selection or tightly linked to the genomic regions under selection . These r aegypti –25. On tCameroon –19. If tAe. aegypti, we examined the genetic structure of Ae. aegypti in La Lopé, Gabon  and Rabai, Kenya (East Africa). Both locations have Ae. aegypti living in nearly sympatric forest and village habitats, and the two locations are sufficiently distant from each other that they represent two independent habitat shift events. The mosquito populations in La Lopé were previously sampled in 2014 and genetically classified as Aaf [Aaa outside of Africa that were introduced to the east coast of Kenya [Aaf populations in the nearby forest [Ae. aegypti population in Rabai has been proposed as “peridomestic” living in human-disturbed areas such as gardens between the huts and forests, and is similar to what has been observed in village settings at La Lopé. However, the mosquito populations in Rabai, Kenya had not been monitored genetically since 2011 [Ae. aegypti. More importantly, if this co-existence of the forest, peridomestic, and domestic types prevails, it would present a contrast to La Lopé, Gabon  to study the genomic evolution associated with habitat difference.To address this question and to further our understanding of the domestication history of d as Aaf . The stoof Kenya , 11, 12.y forest –28. A thAe. aegypti in each of the two localities shared similar genetic ancestry; (ii) how genetically differentiated mosquitoes are in different habitats; (iii) whether certain regions on the genome show parallel habitat-associated genetic differentiation in the two locations (La Lopé and Rabai). We used 12 microsatellite markers and a high-throughput SNP microarray containing ~25,000 SNP loci, both of which have proven to provide high resolution in detecting the population structure of Ae. aegypti globally as well as pinpointing the source of local invasion [Ae. aegypti.Specifically, the primary goals of this study are to examine: (i) whether forest and village invasion , 29–32. Ae. aegypti larvae from five rock pools near small streams and two bamboo traps. The seven containers were less than 7 km away from each other. Because of the low abundance of Ae. aegypti larvae found in the forest, we supplemented the larvae collection with human landing catches in the forest. We also collected Ae. aegypti in La Lopé village from 11 artificial containers like tires and construction bricks scattered no more than 2 km away from each other. In Rabai, Kenya, Ae. aegypti were found in tree holes in the forest and artificial containers in four villages  about 3–7 km away from the forest  were no more than 1 km apart. Because of the relatively low number of breeding sites produced Ae. aegypti, we collected all available samples and later used genomic methods to identify and remove siblings (described in the following sections).Mosquitoes were collected from natural breeding sites as larvae in La Lopé, Gabon in November and December 2016 and in Rabai, Kenya in April and May 2017. The sampling sites in La Lopé were at the edge of a continuous tropical rainforest in the La Lopé National Park, where there is a small village (i.e. La Lopé village) about 5–13 km from the forest , and domestic (indoor in the village). In Rabai, we also recorded the geographical coordinate of each individual, which is not available in La Lopé.In both La Lopé and Rabai, the mosquitoes were reared to adults in the field for species identification. We kept Ae. aegypti that contains 50,000 loci [We extracted whole genomic DNA from mosquito samples using the Qiagen Blood and Tissue kit  following the manufacturer instructions. All individuals were amplified and genotyped at 12 microsatellite loci, according to Brown et al. . A subse000 loci . Data we000 loci , 34 to r000 loci ; (iv) lo000 loci .Ae. aegypti could lay multiple eggs in one container. Therefore, individuals sampled from the same container could be siblings. To identify them, after the filtering, we calculated individual pairwise kinship coefficient k as described in Loiselle et al. [k > 0.1875, as in Schmidt et al. [Female e et al.  using the et al. . Siblingt et al. . Only onThe final number of samples in the microsatellite and SNP dataset are summarized by sampling sites and habitats in Table Finally, for simplicity, in the rest of the manuscript, we use “populations” to merely refer to groups of mosquitoes collected from the same habitat or sampling site without any prior implication of their genetic identities.Ae. aegypti in both localities, we clustered our microsatellite and SNP data from La Lopé and Rabai with a representative global genetic dataset of Ae. aegypti as references. The reference global microsatellite dataset contains 32 populations, reported in Gloria-Soria et al. [Aaa genetically. We also updated the name of the population “Kisumu, Kenya” which was originally mislabeled as “Kahawa Sukari” in [To first examine the genetic ancestry of the forest and village a et al. . The SNPa et al. , with tha et al. ). This pkari” in , in ordeK) ranging from 1 to 10 and conducted 20 replicates for each K with 500,000 MCMC iterations and 100,000 burn-in. The optimal number of clusters was determined by the delta K method [pophelper v 2.2.8.1 [adegenet v 2.1.1 [For the microsatellite data, we performed a Bayesian clustering analysis using STRUCTURE v 2.3, which generated the probability of each individual attributing to each genetic cluster . We randK method  in STRUCK method . The 20 K method  in pophe 2.2.8.1  in R v 3 2.2.8.1 . In addi v 2.1.1 , 48.LEA to estimate the individual ancestry coefficients [K from 1 to 34. The best K was determined by having the lowest cross-entropy. Finally, the SNP data was also analyzed by PCA in adegenet.The SNP data of all populations were filtered the same way as described in the last section. We performed the “snmf” function (sparse non-negative matrix factorization) in the R package ficients , which pFst) and genetic differentiation were evaluated in GENEPOP v 4.7 [He) and observed heterozygosity (Ho) were calculated in adegenet (microsatellite) and SPAGeDi (SNP). Using the microsatellite data only, we also calculated allelic richness and private allelic richness using HPRARE v1.1 [We next focused on only mosquitoes from the La Lopé 2016 samples or Rabai 2017 samples, which are the main target populations of this study. We first examined how differentiated the mosquitoes living in different habitats or different sampling sites are from each other. The SNP datasets for La Lopé and Rabai consist of 17,694 and 23,068 loci after filtering and removing siblings. Pairwise genetic distance  and LEA (SNP) to summarize the genetic variability in our samples and visualize the genetic distances between individuals.In addition to these descriptive metrics, we estimated the genetic ancestry of the mosquitoes using STRUCTURE (microsatellite) and LEA (SNP), as described above. We also applied principal components analysis (PCA) in k calculated by SPAGeDi (see above) across different categories of individual pairs: within forest , forest-village , within village , and between villages . The last category only applies to the Rabai samples, and both peridomestic and domestic habitats were considered as the village. We did not further distinguish mosquito pairs from the same breeding sites or different breeding sites, because there are very few pairs from the same breeding sites. In addition to the categories by locations, for analysis of the Rabai samples, we also applied an alternative method to categorize the individual pairs by habitats, which resulted in six categories . Differences between categories were tested by analysis of variance (ANOVA) with post-hoc multiple comparisons by t-test and Holm correction for the P-values. We used bootstrap with 1000 iterations to estimate the P-value of each test to account for the non-independence of the pairwise data.To further increase the power to detect any genetic structure in our fine spatial scale (3–13 km) within La Lopé or Rabai, we supplemented our population-level analysis with individual-based approaches. Specifically, we compared the kinship coefficient adegenet between the individual pairwise kinship coefficients (estimated with SNPs) and individual pairwise geographical distance, with 999 permutations. To remove this possible distance effect in the Rabai data, we applied a linear regression model. The model used pairwise kinship coefficients as the dependent variable and the pairwise geographical distance as the predictor. We then extracted the residuals from the model and used it to compare between categories described above. By using the residuals, we controlled for possible distance effects on kinship. We did not apply this linear model on the La Lopé dataset as, unfortunately, the geographical information was not available. Instead, we used the raw values of kinship estimates for the La Lopé analysis. If habitat does predict genetic clustering, we would expect reduced gene flow and hence lower genetic relatedness between the forest- and village-living mosquitoes.The relatedness measures could be affected by geographical distances as individuals collected further apart are more likely to be less related. Information about the exact sampling location of each mosquito was available in Rabai but not La Lopé. Therefore, for the Rabai dataset, we examined this potential distance effect using a Mantel test in Aedes mascarensis as the outgroup, using 22,287 SNPs in IQTree v 1.6.12 [n = 1000) to estimate node certainty [ape v 3.5.3 [Lastly, we constructed a phylogeny that consisted of both La Lopé and Rabai samples with v 1.6.12 . IQTree v 1.6.12 , ascertav 1.6.12 , and modertainty . Nodes w v 3.5.3 , 58.Fst between forest and village Ae. aegypti on each small segment of the genome (i.e. a genomic “window”). We then slid this “window” through the whole genome, i.e. a genome scan. For simplicity, peridomestic and domestic samples were grouped as village samples. We implemented this sliding-window analysis in VCFtools v 0.1.14 [Habitat-associated genetic differentiation, if it exists, could vary across the genome. To examine this possibility, we took advantage of the genome-wide distribution of the SNP loci and calculated v 0.1.14  with a wAe. aegypti in La Lopé and Rabai were genetically similar to most other African populations. The best number of clusters was 13 for the LEA analysis using SNP data and two for the STRUCTURE analysis using microsatellite data. However, in both analyses, the first split of all samples  was between most African populations, including our La Lopé and Rabai samples, and populations outside of Africa . These findings based on the SNP data were consistent with STRUCTURE analysis with microsatellite data . In addition, PCA with microsatellite data and SNP data both showed that the 2016 La Lopé samples and the 2017 Rabai samples overlap with the majority of African populations  of mosquitoes from different habitats or sampling sites were comparable  ranged from 0.0066 to 0.0336 when using microsatellite data and from -0.003 to 0.020 when using the SNP data , both SNP-based and microsatellite-based analysis did not find evidence of genetic differences between individuals from different habitats or sampling sites, which was also supported by the PCA . Comparisons between these two categories and the “within village” did not show a statistically significant difference  or categories by habitats . None of the pairwise comparisons between categories were significantly different.In La Lopé, pairwise kinship coefficients were higher in the “within forest” category than the “forest-village” category , clustering analysis and the phylogenetic tree suggest a clear distinction between these two locations . This is consistent with the conclusion of Brown et al. [The initial results of this study first show that mosquitoes in La Lopé, Gabon and Rabai, Kenya have genetic ancestry more similar to most other African n et al.  suggestiFst values of these pairs are small when compared with the genetic variation of Ae. aegypti across Africa [Fst estimates could be affected by our pooling of samples across the four villages in Rabai that are geographically separated  and observed heterozygosity (Ho) of La Lopé and Rabai samples grouped by habitats or sampling sites. Table S3. Pairwise Fst of La Lopé and Rabai samples grouped by habitats or sampling sites. Table S4. Annotated genes in the region of the Fst peak in La Lopé populations. Figure S1. Geographical locations of the global panel of Ae. aegypti populations included in this study. Figure S2. Genetic ancestry of the La Lopé and Rabai populations with a reference panel of global populations of Ae. aegypti. Figure S3. PCA of the La Lopé and Rabai populations with the global panel of populations. Figure S4. Genetic structure of the Rabai mosquito samples grouped by sampling sites. Figure S5. Results of STRUCTURE analysis and PCA using microsatellite data. Figure S6. Individual pairwise kinship coefficients of mosquito samples from Rabai. Figure S7. Phylogeny of the La Lopé and Rabai mosquitoes.


Platanista gangetica gangetica; GRD) is one of the most charismatic species in this freshwater ecosystem. Though a >50% population size reduction has occurred since 1957, researchers and decision‐makers often overlook the persistence  of this species in the highly fragmented GBMK. We define the evolutionary potential as the ability of species/populations to adapt in a changing environment by maintaining their genetic diversity. Here, we review how evolutionary trap mechanisms affect the dynamics and viability of the GRD (hereafter Ganges dolphin) populations after rapid declines in their population size and distribution. We detected six potential trap mechanisms that might affect the Ganges dolphin populations discretely or in combination: (a) habitat modification; (b) occurrence of finite and geographically restricted local populations; (c) ratio of effective to estimate population size; (d) increasing risk of inbreeding depression in genetically isolated groups; (e) at‐risk behavioral attributes; and (f) direct fisheries–dolphin interactions. Because evolutionary traps appear most significant during low water season, they adversely affect demographic parameters, which reduce evolutionary potential. These traps have already caused local extirpation events; therefore, we recommend translocation among populations, including restoring and preserving essential habitats as immediate conservation strategies. Integrative evolutionary potential information based on demographic, genetic, and environmental data is still lacking. Thus, we identify gaps in the knowledge and suggest integrative approaches to understand the future of Ganges dolphins in South Asian waterways.The Ganges–Brahmaputra–Meghna and Karnaphuli (GBMK) River Basin in Nepal, India, and Bangladesh is among the world's most biodiverse river basins. However, human‐induced habitat modification processes threaten the ecological structure of this river basin. Among the GBMK’s diverse flora and fauna of this freshwater ecosystem, the endemic Ganges River dolphin  is one of the most charismatic species in this freshwater ecosystem, which lost 50% of its population size since the last decades. Here, we review how evolutionary trap mechanisms affect the dynamics and viability of this species after rapid declines in their population size. We detected six potential trap mechanisms representing demographics, genetics, environment, and behavioral factors and suggest integrative approaches to understand the future of GRD in South Asian waterways.The Ganges–Brahmaputra–Meghna and Karnaphuli (GBMK) River Basin in Nepal, India, and Bangladesh is among the world's most biodiverse river basin. The endemic Ganges River dolphin ( We also identify research gaps that hinder our understanding of the Ganges dolphin evolution and ecology.2n = 61), review articles (n = 4), notes (n = 2), proceedings articles (n = 16), and book chapters (n = 2) related to the Ganges dolphin population (n = 20), biology (n = 12), ecology (n = 7), anthropogenic threats (n = 12), echolocation and communication (n = 14), phylogenetics (n = 6), evolution (n = 2), biochemistry (n = 8), anatomy (n = 3) and disease (n = 1). We did not find publications devoted to interactive evolutionary potential of the Ganges dolphin; however, two partially dealt with the integrative evolutionary potential of the Ganges dolphin ; (d) increasing risk of inbreeding depression in genetically isolated groups; (e) at‐risk behavioral traits; and (f) direct fisheries–dolphin interactions (Table Ne) to estimate population size (Nc) to quantify the relative rate at which genetic diversity eroded, a fundamental process of evolutionary change  = 0.11  habitat modification ; (b) occurrence of finite and geographically restricted local populations; (c) ratio of effective to estimate population size  required to maintain viable genetic diversity  and fragmented into isolated small subpopulations, inbreeding depression could be the most immediate and significant hindrance to the evolutionary potential of the Ganges dolphin . At‐risk behavioral activities  in combination with different biological traits, such as small litter size, late sexual maturity, and long gestation period, might also reduce persistence of the Ganges dolphin  in New Zealand, the Indo‐Pacific humpback dolphin (Sousa chinensis) in Taiwan and Hong Kong, and the Irrawaddy dolphin (Orcaella brevirostris) in Asia  and Narora (lower Ganges Dam) , a freshwater dolphin endemic to the Yangtze River, China, showcases how such interactions can cause dramatic declines  with <30 individuals, and 95 Indus River dolphins (Platanista gangetica minor) in Pakistan killed in a fishing gear between 1993 and 2012 were all associated with human–dolphin interactions  and Yangtze River dolphin (Lipotes vexillifer). Since we noticed a large fluctuation in population size, we emphasize the importance of understanding this fluctuation effects on demographic structure, which might produce a minimal value of the ratio of effective to census population size. Because of weak selective pressure in a small and isolated population, Ganges dolphin subpopulations might be severely affected by the risk of inbreeding and thus could exhibit a unique genetic structure. Using modern gene sequencing methods would improve our ability to test articulated assumptions, we made in this article (e.g., Parsons, Noble, Reid, & Thompson, Because of conservative policies and limitation of resources, there currently exists a void in the application of genetic tools to explore the population viability of the Ganges dolphin in the GBMK Basin. Initiating a regional intergovernmental project promoting genetic‐based research to examine genetic viability and factors associated with the risk of extinction is essential. As the Ganges dolphin is facing serious population loss issues, use of noninvasive tools, like environmental DNA, for genetic monitoring might be effective (Foote et al., From a conservation perspective, our findings imply that making extinction predictions from a single ecological factor may be risky because of synergistic effects of several factors. Thus, we highlight the integration of genetics, demographics, and environmental factors in future studies, which can aid future research and provide a better understanding of conservation and management purposes. In general, isolated, small subpopulations warrant immediate conservation attention, regionally and internationally. Maintenance of minimum stream flow and restoration and preservation of essential foraging and surfacing habitats appear to be the best methods to limit or prevent any further declines of the Ganges dolphin.5Ne group size, risky behavioral activities, direct dolphin‐fisheries interaction, and habitat modification. As an interactive function of these different mechanisms, we note the reduced evolutionary potential of the Ganges dolphin in the fragmented South Asian waterways as Ganges dolphins are vulnerable to changing environments. Despite this, recent research mostly focuses on discrete conservation issues, and science‐based integrative knowledge of the Ganges dolphin evolutionary potential remains limited. Populations with such spatial disequilibrium can have significant ecological and evolutionary outcomes. Therefore, integration of robust genetic and novel population data, merged with historical information, could significantly aid the trajectory of future Ganges dolphin evolutionary potential. We recommend an integrative approach of demographic, genetic, and environmental aspects, an essential combination to improve our understanding of the future of this charismatic species in the GBMK Basin.The evolutionary potential of the Ganges dolphin in the GBMK Basin may be hindered by several mechanisms like spatial and genetic isolation, small Each named author has substantially contributed to conducting the underlying research and drafting this manuscript.


Rhinogobius gigas is an amphidromous fish endemic to eastern Taiwan. Fishes with the diadromous behavior are expected to have a broader distribution range and higher genetic homogeneity despite that some amphidromous fishes with limited distribution are observed and R. gigas is an additional exception with a limited distribution range. Rhinogobius gigas has been documented to be retained inshore near the river plume with a short pelagic larval duration of 30–40 days, which may account for the endemism of this species. The short marine larval stage of R. gigas may imply a population genetic structure and the aim of the present study is to test whether the population genetic structure is present in R. gigas. To test the population genetic structure, fragments of mitochondrial displacement loop and cytochrome c oxidase subunit I were sequenced to provide molecular inference for genetic structure among populations. Sixty-nine haplotypes were identified among 191 R. gigas from 10 populations of eastern Taiwan and the mean haplotype and nucleotide diversities for all samples were 0.956 and 0.0024, respectively, implying a bottleneck followed by a recent population expansion further supported by Fu’s Fs  and Tajima’s D  values. The phylogenetic analysis revealed lack of genetic structure and the bush-like median joining network without commonly shared haplotypes supports the same scenario. The genetic homogeneity is probably due to the amphidromous life history providing the opportunity for passive larval transportation among the rivers through coastal currents in eastern Taiwan. The endemism to eastern Taiwan may be a consequence of complicated interactions among short pelagic larval duration, interspecific competition and coastal currents. Sicyopterus aiensis, S. sarasini, and Lentipes concolor have PLDs of c. 80 days and show no genetic structure across their distribution range of Vanuatu, New Caledonia, and the Hawaiian Islands, respectively, while S. lagocephalus has PLD of c. 130–260 days and is distributed across the Indo-West Pacific although deep genetic divergence was observed between oceanic basins [Kuhlia rupestris can colonize remote islands and extend its range throughout the Indo-West Pacific at an evolutionary timescale although the speciation may be ongoing [Amphidromy is a life history of diadromous organisms, in which adults inhabit fresh water and their pelagic eggs or larvae drifting into estuaries or open oceans before settling down . Most amc basins . However ongoing ,10. In tRhinogobius is a genus of small gobies distributed in freshwaters of East Asia, including Cambodia, China, Japan, Korea, Laos, Taiwan, Philippines, Russia, Thailand, and Vietnam [Rhinogobius in Taiwan, including R. candidianus, R. delicatus, R. formosanus, R. gigas, R. henchuenensis, R. lanyuensis, R. maculafasciatus, R. nantaiensis, R. rubromaculatus, and R. similis [R. delicatus, R. gigas and R. similis are the only three rhinogobies native to eastern drainages, but recently R. candidianus has been introduced from western Taiwan and becoming dominant. Rhinogobius delicatus inhabits in upper reach while R. gigas and R. similis are commonly found in middle and lower reaches. Rhinogobius gigas is endemic to Taiwan, distributed from the Xuhai Creek to Gengfang Creek in Taiwan, but rarely seen in the north of the Nan-ao Creek  and cytochrome c oxidase subunit I (COI) were sequenced to provide molecular inference for genetic structure among populations. The results will provide inferences for the influence of Kuroshio Current on the phylogeography of amphidromous fishes in eastern Taiwan.The endemic distribution and short marine larval stage of R. gigas was not an endangered species protected by the Wildlife Conservation Act in Taiwan and no experiment was conducted on live individuals.All sampling sites in the present study were not within any protected area and no permission was required for sampling. Specimen collections were performed in strict accordance with the Wildlife Conservation Act in Taiwan. No ethical approval was required for this study because R. gigas is eastern Taiwan with occasional records in the north of Nan-ao Creek and, therefore, not included in the analyses of the present study. A total of 191 individuals of R. gigas were sampled from 10 basins of eastern Taiwan, including Nanao (NA), Sanjhan (SJ), Hualien (HL), Xiuguluan (XL), Beinan (BN), Zhiben (ZB), Taiping (TP), Jinlun (JL), Dazhu (DZ), Dawu (DW) (The distribution of awu (DW) . Most ofGTCGA CTCTC ACCCC TGGCT CCCAA AG-3’) and PU2  and FishR2  following the manufacturer’s instructions. Fragments of mitochondrial D-loop and COI were amplified by polymerase chain reaction (PCR) with universal primers, PK2  for D-lAGAA-3’; ) for COIC for 5 minutes; 35 cycles of 94°C for 60 seconds for DNA denaturation, 50°C for 30 seconds for primers annealing and 72°C for 90 seconds for sequences extension; and a final extension step at 72°C for 7 minutes. The thermal regime for COI included an initial denaturation step preheating at 94°C for 5 minutes, followed by 37 cycles of 94°C for 60 seconds for DNA denaturation, 60°C for 45 seconds for primers annealing and 72°C for 60 seconds for sequences extension, and a final extension step at 72°C for 10 minutes. After checking qualities by electrophoresis, PCR products were purified using the SAP–Exo purification kit  according to the manufacturer’s protocols and then sent for sequencing by a commercial company.PCR reactions were run in a 25 μl total volume including 2 μl DNA sample, 1.2 μl for each primer, 3 μl Pro Taq 10X Buffer , 2 μl dNTP , 0.5 μl Pro Taq Plus  and 15.1 μl ultrapure water. Different thermal regimes were applied for the two genes. The thermal regime for D-loop were: an initial denaturation step preheating at 94°All sequencing results were corrected and edited manually using MEGA X , and blaR. formosanus  were used as outgroup. Median joining network (MJN) [A maximum likelihood (ML) tree was reconstructed using MEGA version X based on Tamura-Nei (TN93) model with combination gamma distribute with invariant sites (G+I), selected by the lowest bias–corrected Akaike Information Criterion (AICc) value in the model test, and a 1,000 replicate bootstrap probability was analyzed . Sequencrk (MJN)  was implD and Fu’s Fs were analyzed using Arlequin ver. 3.5.2 [To test the historical demography, mismatch distribution and neutrality tests including Tajima’s r. 3.5.2 . The Bayr. 3.5.2 , calcular. 3.5.2 . In the r. 3.5.2 .A 1,074-bp concatenated sequence of mtDNA COI (554 bps) and D-loop (520 bps) was analyzed for 191 individuals obtained from 10 sampling sites  in riverFs  and Tajima’s D  values, which failed to reject the null hypothesis of constant population size. According to the Bayesian skyline analysis, all populations of R. gigas had a stable growth rate, not showing significant population dynamics during the past five thousand years .The phylogenetic analysis revealed no genetic structure among the 191 individuals and no long internal branch was found. The bush-like MJN without predominantly shared haplotypes support the general topology of the ML tree Figs  and 3. Tnd years . The TMRRhinogobius maculafasciatus [Aphyocypris kikuchii [Rhinogobius. Therefore, we included D-loop gene, which provided more genetic variation and higher resolution of the genetic structure in this study.According to the results , the anaasciatus , amphidrasciatus –31 and akikuchii . In COI kikuchii , the lowFs and Tajima’s D also support a demographic expansion that may be attributed to the postglacial warmer climate commonly observed in fishes [Fs and Tajima’s D based on COI and the significant demographic expansion was also retrieved (data not shown). Therefore, the different datasets do not account for the incongruence. The TMRCA of R. gigas were dated to 0.13 mya, approximately at the beginning of the last glacial . The se116 mya; .A. kikuchii (except for one population), Hemimyzon taitungensisi, Onychostoma alticorpus and Spinibarbus hollandi [Sicyopterus japonicus [S. japonicus may result from frequent gene flow through pelagic larvae dispersion in the ocean [R. gigas population is monophyletic , and similar to the landlocked population of R. formosanus with a PLD of 24 to 46 days . The sh-1 [R. gigas is endemic to eastern Taiwan, it is unlikely for R. gigas larvae to disperse by the Kuroshio Current and this may be further supported by its absence from the Green Island (approximately 33 km away from eastern Taiwan), Orchid Island (73 km away) and Yonaguni (110 km away) along the Kuroshio Current. Therefore, we hypothesize that larvae of R. gigas may actively select the habitat close to estuaries and avoid to be entrained in the Kuroshio Current, which is approximately several kilometers away from the eastern coasts of Taiwan. Active habitat selection by pre-settlement larvae has been documented in many reef fishes [Sicydium punctatum actively choose low to intermediate salinity at 5-d post-hatch and minimized exposure to salinities >10 ppt [The strong Kuroshio Current plays an important role in facilitating wide distribution of many marine lives along the northwestern Pacific Ocean . Based o-1 ,42, the f fishes  and some >10 ppt .R. gigas may expand its’ distribution via the tidal current dispersal to neighboring rivers in eastern Taiwan and this may result in gene flow accounting for the genetic homogeneity observed in the present study. However, R. gigas is endemic to eastern Taiwan with very rare reports in the distal ends of distribution, the Gengfang Creek and Lanyang River in the north and the Xuhai Creek in the south ..R. gigas Current . More stR. gigas from 10 populations show high haplotype diversity and low nucleotide diversity. Combined with the left-hand side unimodal pattern of mismatch distribution and negative values of Fu’s Fs and Tajima’s D, our results imply a demographic scenario of a bottleneck followed by a recent population expansion. The genetic homogeneity is probably due to the amphidromous life history providing the opportunity for passive egg and/or larval transportation among the rivers in eastern Taiwan. The endemism to eastern Taiwan may be a consequence of complicated interactions among short PLD, active habitat selection by larvae, interspecific competition and coastal currents. Further studies such as comparative phylogeography may be needed to uncover the complicity.In the present study, mitochondrial concatenated sequences of 191 S1 TableNumbers in parenthesis are duplicated copies of each haplotype.(DOCX)Click here for additional data file.


In this study we investigated the degree of genetic differentiation among two types of Polish cold-blooded horses, namely Sokólski and Sztumski. Both these horse types are a subject of conservation breeding as their populations have been dramatically reduced during recent decades. These cold-blooded horses are considered as separate breeds, with separate stud books, but yet still their genetic differentiation has not been precisely determined, especially on the genome–wide level. In this study, we characterized genetic differentiation of these two horse populations as well as their genetic variation, admixture patterns, and level of relatedness within populations with the use of single nucleotide polymorphism (SNP) genotyping arrays. We also attempted to detect genome regions with the largest genetic differences between those horses by the detection of so-called diversifying selection signals. The results of this study provide initial evidence supporting decisions made during conservation breeding program design and answer some of the questions raised by the breeders of Sokólski and Sztumski horses.The current role of the horse as a companion animal resulted in a decrease of interest in breeding and usage of draft horses. This meant that the population of cold-blooded horses in Poland has been dramatically reduced during the last decades. To avoid impoverishment of the gene pool of the local horse population, a conservation program was established which involves draft horses and other local horse breeds. The draft horses bred in Poland can be subdivided in a few horse types of which the most widespread and consolidated are Sztumski and Sokólski horses. These two subpopulations are phenotypically diversified, however, the overall level of their genetic differentiation seems to be relatively low and not precisely determined, especially with the use of molecular markers. In reference to this, in this study we used Illumina genotyping arrays to describe in detail the genetic differentiation of these two cold-blooded horse populations. We describe the genetic distance between them, as well as within-population variation, admixture patterns, and level of relatedness within populations. We also made an attempt to detect genome regions divergently selected between those horses by the detection of diversifying selection signals. The results of this study provide initial evidence supporting breeding decisions that were made during conservation breeding program design and answer questions raised by the breeders of Sokólski and Sztumski horses concerning the level of their genetic variation and differentiation. The current population of draft horses in Poland was dramatically reduced due to the limited interest in usage and breeding of working horses. To maintain biodiversity and protect the existing gene pool, local draft horses were included in a conservation program which follows the guidelines of the FAO (Food and Agriculture Organization of the United Nations) Global Plan of Action for Animal Genetic Resources . Currently, two subpopulations (types) of a draft horse with separate stud books are maintained in Poland within the conservation program, namely Sokólski and Sztumski . These pThe other studied subpopulation of Polish cold-blooded horses is formed by the Sztumski horses which are the largest and the heaviest of the cold-blooded horses maintained in Poland. Sztumski horses were originally created on the basis of a local population crossbred mainly with Ardennes and Belgian sires. They were bred predominantly in the areas of Powiśle, Warmia, and Mazury, where heavy soil that is difficult to cultivate required particularly strong working horses. Thus, the Sztumski horses are characterized by a larger caliber and thicker fat cover in comparison to the Sokólski horses ,2.Despite these clear phenotypical differences between the Sokólski and Sztumski horses, several concerns have arisen regarding their genetic differentiation, mainly because of some stallion exchange events and past breeding history that was not fully registered in the breeding records. The existing pedigree analysis of 2653 Sztumski and Sokólski horses involved in the conservation programs in 2014  showed that the contribution of the Ardennes sires amounted to 61.9% for Sokólski and 64.2% for Sztumski horses, while Belgian sires amounted to 9.3% for Sokólski and 18.3% for Sztumski. The contribution of Breton founders in Sokólski populations was equal to 4.38%. The average inbreeding coefficients were 1.56% and 1.54%, whereas effective population size (Ne) was 493 and 482, in these populations, respectively. The average pedigree completeness for five generations was 98.24 for Sokólski horses and 98.79 for Sztumski [n = 107; SOK) and Sztumski  horses  < 1.0E-06 in each breed separately were excluded. The final SNP panel of 52,023 markers was scattered across the horse genome (EquCab2.0) with an average inter-marker distance of 43.0 kb.SNP filtering was performed as previously described . Only geST distances [ST values obtained using Plink software v1.90b4 (--fst case-control command) [ST values were subsequently ranked and the top 1% of the highest observations pointed to windows corresponding with the most pronounced selection signals. Overlapping windows with the highest FST values were then merged and genomic regions formed by these merged widows were extended on both sides by 25 kb to account for extended linkage and potentially linked genes. The resulting genome regions spanning the strongest diversifying selection signals were analyzed in detail to identify encompassed genes and associated biological processes using the UCSC Genome Browser [ST coefficients into a population-specific component (beta) shared by all loci, and a locus-specific component  shared by all populations using logistic regression. Departure from neutrality at a given locus is assumed when the locus-specific component is necessary to explain the observed pattern of diversity . A positive value of alpha suggests diversifying selection, whereas negative values suggest balancing or purifying selection. This leads to two alternative models for each locus, including or not the alpha component to model selection. For each locus, a reversible-jump Markov chain Monte Carlo MCMC statistic explores models with and without selection  and estimates their relative posterior probabilities [The horse types’ genetic differentiation was evaluated using pairwise Fistances  measurin Browser , ENSEMBL Browser . The gen Browser . In thisST-based approach were analyzed using HaploView 4.2 [The linkage disequilibrium patterns and haplotype block structures at the genome regions with the strongest signs of divergent section identified using initial FView 4.2  softwareView 4.2 .To additionally evaluate the genetic structures of the studied populations, we employed principal components analysis (PCA) based on animal genotypes and inter-individual distance matrix calculated as 1 − IBS (identity-by-state) similarity, with IBS defined as the probability that alleles drawn at random from two individuals at the same locus are the same. The obtained IBS distances were then used to create cladograms based on a neighbor-joining (NJ) method  and visuTo further validate the population structure and the patterns of admixture among populations, Admixture software  was usedGRM), (ii) method-of-moments F coefficient estimate (similar to FIS), (iii) F coefficient based on the correlation between uniting gametes (FU) [ROH, including ROH segments with lengths above 1 Mb) [e) were estimated with the use of NeEstimator v2.1 [A population structure was additionally evaluated by calculating four genomic inbreeding measures: (i) the usual variance-standardized relationship minus 1 (Ftes (FU) , and runve 1 Mb)  using Plve 1 Mb) . The firve 1 Mb) ) and evave 1 Mb) . Effectitor v2.1  softwareAfter initial SNP filtering 52,023 markers were retained for further analysis. SNP panel polymorphism parameters were described in our previous study [ST was low and mean and weighted mean FST values were 0.012 and 0.014, respectively, suggesting that about 98% of genetic variation within the analyzed horse types can be attributed to inter-individual variation. The genomic inbreeding coefficients were comparable for both populations and depending on the applied measure ranged from 0 for FGRM to about 13% for FIS. The average FROH values for ROH segments with length over 1Mb were also similar for both horse types and showed values of about 11%  had size from 278 to 826 kb and covered in total 4.3 Mb of the genomic sequence  had positive Alfa coefficient corresponding to the presence of positive selection   within the region on ECA3. The analysis of F(MIR140)  on the t(MIR140) . The regnd RAB30 . The regnd RAB30 . A commoStandard methods of genetic diversity evaluation and management have proved to be effective in conservation of animals’ genetic diversity during several years of animal breeding. Nevertheless, together with the development of molecular and computational sciences, new methods of genetic variability evaluation have become available which may help in more efficient conservation program managing and a more effective breeding decision-making process. These new methods use tools of advanced molecular and population genetics allowing deep analyses of within- and among-population variation and the analysis of population history as well as providing reliable quantitative measures of genetic differentiation even for poorly-characterized populations . AdditioThe current breeding program of the Sokólski and Sztumski horse assumes separate sections of stud books within cold-blooded horses since 2008) and aims at clear separation of these populations as separate breeds. This is dictated by the fact that these horses possess their historical, genetic, and phenotypic specificity, resulting from the used breeding material on the basis of which they were produced as well as different environmental conditions which shaped their present character 008 and a.ST = 0.03) [NFAT5, NQO1, NOB1, WWP2, and MIR140 genes. While phenotypic differences between Sokólski and Sztumski horses involve mainly body size and constitution, the presumed candidate genes should be connected with e.g., growth, development, and/or metabolism. In this light, the exact role of the genes detected in the region is difficult to explain without building a complex theory unsupported by this study’s results. Similarly, within the another strongly divergently-selected region on ECA7, we detected inter alia CCDC90B, ANKRD42, PCF11, and RAB30 genes, with functions (A previous attempt to characterize genetic differentiation of Sokólski and Sztumski horses with the use of microsatellite markers resulted in similar low estimates of the average genetic distance between those populations (at F = 0.03) . This ge = 0.03) . What is = 0.03) . Moreoveunctions . which cAltogether, the performed analysis shows that despite a generally low level of genetic differentiation, Sokólski and Sztumski horses are characterized by visible differences in their genetic profiles which may result from both discrete fluctuations of allele frequency across the genome as well as from the differentiation of specific genomic loci being probably associated with the presence of gene variants with a large effect on the differing phenotypical traits of these two horse types. Based on these observations, we can conclude that further maintaining of separate stud books is reasonable and will allow preserving and increasing of the observed genetic differentiation among the studied draft horse populations.


Apis mellifera) is a key pollinator and has in the last decades suffered significant population decline. A combination of factors, including decrease in genetic diversity and introduction of Varroa mites, have been suggested to be responsible for these losses, but no definitive cause has yet been appointed. In Europe not only have wild colonies been severely affected, but managed hives have had a massive decline in numbers. To test the hypothesis that honeybees’ genetic diversity has decreased in the recent past, we used reduced representation genome sequencing of 40 historical honeybee specimens collected in Natural History collections across Europe and compared them to genomic data from 40 individuals from extant populations (collected post 2006). Our results are consistent with the existence of five evolutionary lineages as previously described, and show a decrease in genetic diversity between historical and extant individuals of the same lineage, as well as high levels of admixture in historical specimens. Our data confirm that a loss of genetic diversity has occurred during the last century, potentially increasing honeybees’ vulnerability to contemporary ecological and anthropogenic stressors.The European honeybee ( The number of wild or feral honey bees is less known, with many believing or assuming that they no longer exist. Recent work however suggests that feral honey bees still colonize beech forests in Germany, and probably much of Central Europe2.Honeybees are one of the most important pollinator species and the most widely used insect, managed for its pollination services and production of honey. The number of managed honeybee colonies in Europe has generally decreased since the 1960′s, at least in Central Europe5, it was only when beekeeping techniques were perfected in the seventeenth and eighteenth centuries that it became possible to maintain large bee colonies giving rise to modern apiculture6. More recent practices, such as the commercial mass rearing of queens, artificial selection of behaviours favouring honey production, and the presence of thousands of bees in limited spaces, may have altered the natural processes and affected the genetic diversity of domestic and wild  hives, increasing their susceptibility and the transmission rate of diseases between bees7. There is an ongoing debate about whether European honeybees are domesticated  or not10. Traits favourable to beekeepers, such as docility, lack of propensity to swarming, honey yield, and others may be selected for, but as it is difficult to have controlled mating, this is usually done through the import of stock from other areas, where these traits are more frequent. This has consequences for wild populations, as due to the wide freedom honeybees have even when in artificial hives9, factors influencing one of them will have a similar effect on the other.Even though bees have been used to produce honey and for pollination purposes for over 7,000 years, since at least Ancient Egypt civilizationsApis mellifera12 described based on geography and morphological variation; morphometric and genetic studies have consistently showed that there are only four or five major evolutionary lineages, with one or two of them only occurring in Africa. Honeybees were first aggregated into four major lineages  based on morphometry and biogeography13. Lineage A is present in Africa and in the Iberian Peninsula, O in the Middle East, M in Northern and Western Europe, and C in South Eastern Europe. The existence of a fifth lineage from north-eastern Africa named Y was proposed and supported using mitochondrial DNA14. The approximate distribution of the lineages in Africa, Europe, and Western Asia is shown in Fig. Apis mellifera is of an Asian origin, as all other Apis species are endemic to Asia. However, genetic diversity is higher in Africa, which has led some authors to speculate on an African origin for the species14.There are at least 28 subspecies of 16. After the release of the honeybee genome17, several specific pathways involved in immunity18, and olfaction, and gustation19 have been identified.Early genomic work on honeybees focused on molecular determinants in behaviourConsidering the important role of honeybees for the pollination of monocultures, that are essential for human food security and variety, and other ecological services performed by honeybees, there is dire need for a better understanding of changes in genetic variability of (semi)natural populations and how these may be related to domestication and artificial selection.22. Museum specimens have been widely used to provide insight into past genetic diversity, domestication, taxonomic placement, and migration of several species24 . However, specimens were not always collected considering the preservation of genetic material, and DNA degrades as a function of temperature and time. For this reason, it is necessary to use ancient DNA (aDNA) techniques to retrieve usable DNA from these specimens. We therefore use the term ancient DNA not as a reference to the age of our specimens, but to the likely state of degradation of the molecules. The low percentage of endogenous DNA  in museum samples still poses limitations to aDNA shotgun sequencing experiments, as most sequences yielded would not be from the specimen. DNA capture-enrichment methods, in contrast, allow targeted sequencing by selective enrichment of sequences of interest prior to sequencing, hence increasing the depth of sequencing over target regions and lowering costs per target25.Museum collections are ideal for this purpose, as they provide a temporal series of honeybees from different areas of their natural range, from which trends can be derived, and they are invaluable sources of chitinous material from which DNA can be retrieved without conferring any external damage to the specimenAs honeybees are thought of being at least partially domesticated, they were not usually collected with purpose by naturalists, but rather as incidental bycatches when collecting other insect species thought to have greater natural history value. Curation and annotations were therefore sparse and incomplete. Despite this, several historical collections exist. We took advantage of this, and generated genomic data on historical European honeybee specimens, allowing us to explore the genetic diversity of the species over the last 150 years. In this study, we assess the genetic diversity of honeybees across Europe from different time periods using historical museum collections and ancient DNA (aDNA) techniques.Apis mellifera covering most of the natural range of honeybees in Europe were obtained from museum collections. As collections were not digitized at the time of sampling, all information from labels were recorded manually, including when available: date of sampling, location name, geographical coordinates, sex, and name of collector. None of the collections had unique identifiers (voucher numbers) for these specimens, so internal identifiers were used consisting of two letter country code, four-digit year of sampling, and, if more than one specimen matched the country/year combination, one letter (a through e). This is the only information available for these specimens, and we therefore do not know if their origin is from feral, wild or managed colonies. As bees in managed hives can freely move around their area, gene flow between managed and feral hives is most likely unrestricted, so we believe this does not affect the interpretation of our results. Subspecies/lineage information was not known a priori when selecting specimens, so sampling attempted to cover a wide geographic and temporal span.Pin-dried specimens of 20 and detailed in Campos and Gilbert21. Probes for in-solution, hybridization capture enrichment kits (MYcroarray) were designed for randomly selected gene locations present in gene set AmelOGSV3.226, but also for immune27, sensory19, and behavioural15 biochemical pathways , and for paralogs using BWA. We used ANGSD 32 for quality filtering and data processing. In all ANGSD analyses, we required a minimum mapping quality of 30 and minimum base quality score of 20. We calculated error rates using an outgroup individual and an error free individual. We randomly selected a modern sample from lineage M from Poland (SRR957058) as the error-free individual, and the outgroup individual used was a modern Apis cerana (SRR957079). We used mapDamage33 to display nucleotide misincorporation patterns and rescale the quality scores in the bam files. After rescaling we recalculated error rates and compared them with the previous estimates. The rescaled sequences were used in subsequent analyses. We calculated genome-wide coverage in the modern individuals and depth of coverage within the capture regions for both modern and historical individuals. Five historical samples were excluded from further processing, as they had an average coverage below 0.5 ×. Genotype likelihoods were estimated based on the aligned reads and associated mapping, and sequencing quality scores for all individuals.Sequencing data were analysed using a set of custom scripts and software (SI Appendix). Adapter sequences were trimmed and filtered for N’s and reads shorter than 30 bp were removed using AdapterRemoval34. As the presumptive number of evolutionary lineages in Apis mellifera is five, we ran NGSadmix for K between two and nine. The evolutionary history of the individuals was inferred using Neighbor-Joining35. Haploid genotypes from ancient and modern samples were obtained by randomly sampling one read per position of each of the samples with ANGSD. The tree was built using the program RapidNJ36. FigTree v.1.4.4  was used to visualize the tree. To compare the genetic diversity of our dataset with other published honeybee datasets, we built two haplotype networks using mitochondrial DNA sequences. In addition to the modern and historical samples, we used other Apis mellifera mitochondrial sequences downloaded from the NCBI website (Table 37. Cytochrome b (CytB) and the sequence spanning from the beginning of the Cytochrome Oxidase I to the end of Cytochrome Oxidase II (COI-COII) were extracted separately from the alignment, according to the NCBI sequence KM458618.138. Haplotype networks were reconstructed using TempNet39. The procedure was repeated using the non-admixed individuals from subsets as defined in Supplementary Table We used NGSadmix version 32 to test the number of genetically distinguishable populations in our dataBased on NGSadmix results for K = 5 and geographical location, we grouped individuals according to the most likely lineage they belonged to: lineage A in South Africa; lineage Y in Kingdom of Saudi Arabia and Yemen ; lineage C in Malta , Italy, Croatia, Slovenia, Austria AT1971), Switzerland, Bulgaria, Germany, and Denmark; lineage M in Austria, The Netherlands, Sweden, England, France, Luxembourg, Portugal, Spain, Scotland, and Poland; and lineage O in Jordan and Lebanon  Historic vs Modern C, (ii) Historic vs Modern M North, and (iii) Historic vs Modern M South.We estimated the population scaled mutation rate θ and the neutrality test statistic Tajima's D according to the method described in Korneliussen et al.Apis cerana specimen used as outgroup, while in the rest of the modern specimens, depth of coverage ranged from 12 to 27 × . Depth of coverage within the targeted regions was on average 20 times higher than in other sites in the genome, ranging from a threefold to a 66-fold increase , which correspond mainly to post-mortem deamination C->T and G->A  were selected , compared to historical ones (π ≅ 0.003). Genetic diversity influences a wide range of phenotypes in honeybee colonies, from expression of antimicrobial compounds, resistance to pathogens, thermoregulation, foraging behaviour and colony defence8, all essential to colony survival, and response to environmental stress, with lower genetic diversity reducing the variation of these phenotypes as well. Tasks within a colony, such as defence and hygienic behaviour, are performed by a small subset of workers descendent from only some patrilineal lines8. Differences in propensity for certain tasks are believed to be influenced by genetics. For example, hygienic and non-hygienic colonies have a difference in gene expression in Cytochrome P450 gene and a limited number of other genes47. This means that when genetic diversity is decreased the number of workers in a colony performing some tasks may decrease or less specialized workers will perform such tasks, decreasing the efficiency of the colony48. This may originate from high selection pressure selecting for traits based only on queen performance but ignoring the genetic contribution of drones or failing to maintain sufficient levels of genetic diversity within a colony.Despite the limited sample size, we think that our estimates of genetic diversity are reliable, as both simulation and empirical studies indicate that a large sample size is not required when analysing a large number of SNP markers49, and the level of genetic recombination48.We hypothesize that management practices that increase relatedness between colonies, as well as a reduction of number and density of colonies due to, for example, a decrease of suitable habitat availability, are the main factors contributing to the observed decrease of genetic diversity. At the population level, genetic diversity can be affected by selective sweeps, background selection, temporal fluctuations in the direction of selection on segregating alleles50. It has also been found that abiotic factors, such as temperature and land use, are associated with both density of colonies and genetic diversity51.In addition, decreasing density or fewer colonies, demographic expansions, as well as habitat fragmentation would also lower genetic diversity. Colony density in wild populations in Europe is much lower than in African savannahs, despite harsher environmental conditions. This has been associated with more intensive beekeeping in EuropeDomestication and professional breeding aim at selecting individuals with specific traits, consciously or unconsciously narrowing genetic variation. Artificial selection on managed hives, however, would only have an indirect effect on wild colonies when drones from managed hives breed with wild queens, or new queens from a managed hive establish a new colony in the wild. This is very frequent, as beekeepers do not track all bees in their colonies. In any case, in many European countries, there are much less wild colonies than managed ones due to the lack of suitable nesting places, so gene flow between them has probably masked any genetic difference between them.52 where Harpur and colleagues find the within-colony diversity to be higher in managed colonies than in wild ones, while our results show global patterns of decline within lineages. It should be emphasized that the two are studying different levels of genetic diversity, theirs within-colony diversity in admixed populations, and ours on a meta-population level looking only at non-admixed individuals. It is not surprising that Harpur et al. found higher diversity within managed colonies, as beekeepers will bring in stock from other parts of the world, what they called the progenitor populations, and these will admix with the local populations despite beekeeper’s intentions, creating higher diversity descendants as de la Rua highlights in a reply to that work53. Our results indicate that despite this within colony gain, global patterns of diversity decreased within a short time span (from 1960–1984 to 2013 in lineage C and 1917–1973 to 2013 in lineage M).Our results seem to contradict those of a study from 201228, another gene of the same family, in current honeybees. Major Royal Jelly Protein is a family of nine genes and one pseudogene located in tandem on a 60 kb cluster located on chromosome 1154. This family of genes encodes secretory proteins that are the major protein content of Royal Jelly, a nutrient-rich substance produced by nurse bees used to feed the larvae, which is only found in some genus of Hymenoptera55. MRJP seems to have evolved recently deriving from the Yellow family of genes and it seems to have diversified independently in each species where it has been found. In honeybees, MRJP is mostly expressed in workers (particularly nurses) but also other castes57, and besides being involved in the production of Royal Jelly, it has also been associated with brain function58, caste determination and many aspects of eusociality54. These functions seem to derive from Royal Jelly’s function in establishing division of labour in the colonies through determining the development of larvae into queens and worker. However, their biochemical function is not determined at the moment. MRJP 4 is down regulated in honeybee heads after infection54. Given that Royal Jelly Proteins affect many aspects of behaviour, nutrition and development, and that this pattern of selection is found not only when analysing modern bees alone28, but also when comparing historical bees to modern bees, we speculate that domestication can be responsible for the selection signal. While MRJP7 is but one gene within the MRJP/Yellow family, we speculate that selection on this gene, due to its association with nutrition and development, could be caused by selective pressure from beekeeping practices such as the desire for higher honey production or more fertile queens.We detected signs of selection in MRJP7 and in another gene of unknown function. In a recent genomic analysis, Harpur et al. also detected positive selection in MRJP 7 and MRJP 4The observed decrease in genetic diversity could potentially have an impact on the ability of colonies to react and survive to current and upcoming threats, such as pathogens, pesticides, and climate change. The distribution of evolutionary lineages and admixture proportions in honeybees is not fully understood yet. Several geographical regions are under-sampled, such as most of Africa and areas of the Middle East. The fragmented nature of the sampling carried out in most honeybee genetic studies, has made the lineage nomenclature inconsistent, making comparisons among studies difficult, unreliable or impossible. Mapping with better precision the distribution of each lineage and areas of current and past admixture would help us to better understand the population dynamics of honeybees. Natural history collections with proper annotations of sampling locality and date prove once again to be an essential resource to study temporal trends and provide a glimpse of evolutionary processes occurring in historical times.Supplementary informationSupplementary Table S1Supplementary Table S2Supplementary Table S3


Pistacia atlantica Desf., has great importance in the ecological landscape of North Africa, due to its adaptive plasticity, as well as its use as a rootstock in the cultivation of the economically important species, Pistacia vera L. The conservation and valuation of this species require sampling and an assessment of its genetic variability. For the first time in North Africa, the inter-simple sequence repeats (ISSR) molecular marker has been used in genetic-diversity assessment and in the population relationships of P. atlantica subsp. atlantica. The ISSR markers tested showed 74.1% polymorphism, while molecular variance (AMOVA) analysis revealed a high percentage of the total genetic diversity of 55.7% among the four populations studied. Cluster analysis with neighbor-joining (NJ) and principal coordinate analysis (PCO) divided the study sites into four distinct groups according to their geographical locations . Isolation by distance or Mantel test gave a positive correlation of r = 0.86 between geographical and genetic distances. The results in this study indicate an absence of gene flow, implying that conservation efforts should be taken separately for each population.The Atlas Pistachio tree,  Pistacia, Anacardiaceae family consists of at least 11 dioecious species . A cluster analysis was performed using the neighbor joining (NJ) method with arithmetic averages and viewed with Tree View software . PCO analysis was made with FAMD version 1.3  using the Jaccard’s similarity index .10 transformed values of Nem among populations were linearly regressed against their geographical  distances (in km). The significance of the regression slopes was evaluated by Mantel’s test [http://ibdws.sdsu.edu/~ibdws.To test for isolation by distance , pairwisl’s test  with 10,This work shows that Algeria still contains a rich genetic diversity for the Atlas Pistachio, which is likely to be valued. Despite the strict allogamy of this species and its highly dynamic gene flow, the geographical remoteness of the study areas indicates that this species still maintains a diversity of genetic entities. This inter-population diversity would certainly be remodeled by natural selection under the diversity of the environmental conditions of the different prospected areas. The results also indicate that the rate of intra-population genetic variability is negatively correlated with the increase in density of the populations studied.


Aedes koreicus is a mosquito species native to Asia that has recently successfully invaded new areas in several European countries. Here, we provide important data on Ae. koreicus establishment in Pécs (Southern Hungary). Mosquito surveillance was carried out weekly between 2016 and 2019 at 10 different sites located throughout the city from May to September. We conducted a statistical analysis to evaluate the most important abiotic factors driving Ae. koreicus abundance. We then calibrated a previously developed temperature-dependent mathematical model to the recorded captures to evaluate mosquito abundance in the study area. We found that too high summer temperatures negatively affect mosquito abundance. The model accurately replicated the observed capture patterns, providing an estimate of Ae. koreicus density for each breeding season, which we interpolated to map Ae. koreicus abundance throughout Pécs. We found a negative correlation between mosquito captures and human density, suggesting that Ae. koreicus does not necessarily require humans for its blood meals. Our study provides a successful application of a previously published mathematical model to investigate Ae. koreicus population dynamics, proving its suitability for future studies, also within an epidemiological framework. Aedes species are of greater concern due to their competence as vectors of several pathogens such as, for instance, dengue, chikungunya, and Zika viruses [Aedes species [Many invasion events by different alien mosquito species have occurred in Europe in recent decades . Therefo viruses . Improve species ,4,5,6. H species ,8. For i species .Aedes invasive mosquito species, the most successful one is Ae. albopictus , which is now widely spread in the southern part of the continent. In particular, it has established itself in the southern parts of Spain and France, Italy and most of the outer regions of Balkan Peninsula [Ae. koreicus  was found in Europe (in Belgium) for the first time [Among eninsula . In 2008rst time . This sprst time , but it rst time . BecauseDirofilaria immitis [Ae. koreicus to find potential bacterial candidates that could be used in control strategies against this mosquito vector and its pathogens [In Hungary, it was found for the first time in 2016 in Pécs, a medium-sized city located in the southwestern part of the country, close to the country’s border with Croatia . Since t immitis , which c immitis . Gut micathogens .Ae. albopictus, limited knowledge is available regarding the potential for the establishment and spread of this species in Europe. Most of the information referring to the biology and behavior of Ae. koreicus  originated from its native territories [Ae. koreicus, affecting, for instance, the developmental time and survival of immature stages and adult longevity [However, compared to ritories ,15,20. Songevity . In partongevity , explainAe. koreicus within a temperate European city by providing new data on the species abundance in space and time in Pécs, Hungary. For this purpose, we applied a previously published mathematical model [Ae. koreicus density over the whole city. This kind of model has been widely used to investigate mosquito dynamics, also within an epidemiological framework [In this study, we aimed to analyze the dynamics and distribution of al model , which iramework ,23,24,25ramework ,26,27,282 and has a total population of 142,873 [As shown in  142,873 . The reg 142,873 .2 attractant. The sampling was carried out from 2016 to 2019 during the general mosquito season from May to September. Traps were operated once per week overnight (6:00 p.m.–6:00 a.m.), for a total of about 20 sampling sessions per year. The captured adult mosquitoes were identified morphologically on the species level [Ae. koreicus were used for further statistical analyses.For mosquito monitoring, Heavy-Duty Encephalitis Vector Survey (EVS) Traps  were used at 10 sampling sites in an urban area a, with des level ,33 usingFor each trap location, daily average temperature data were obtained from the gap-free Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) maps at a resolution of 250 m . TemperaAe. koreicus abundance was investigated through nonparametric correlation tests and generalized linear models (GLMs) assuming a negative binomial distribution for the response variable. Response variable, denoted by Y, is the yearly total number of Ae. koreicus adult females collected in each sampling site. GLMs were used to assess the effect of temperature on mosquito abundance by alternatively considering as explanatory variable X two different averaging windows for LST, namely spring (April–May) and summer (June–August). The two models can therefore be represented by the following equations:Ae. koreicus adult females collected in each sampling site.Because of the few adults recorded in 2016 (see results), we decided to carry out our quantitative analysis only starting from 2017. The relationship between socioenvironmental factors and temperature and Ae. koreicus dynamics for each population stage  explicitly considering temperature, as it drives most of the developmental and survival rates. More specifically, such rates are estimated through temperature-dependent functions calibrated on the experiments presented in the same study, allowing also for some uncertainty [K driving the carrying capacity for the larval stages. α is assumed to be equal among different years, K is assumed to be year-specific. Thus, there are 4 parameters to estimate that form the set of unknown parameters Ψ, where y and m run over the considered years and trapping sessions respectively, M is the total number of trapping sessions carried out during one year, n is the observed number of Ae. koreicus adults averaged over the 10 traps, and C is the number of captures predicted by the population model with parameters Ψ. A comprehensive description of the model and its parameters, including their laboratory estimation, and additional details on the calibration technique can be found in [We then calibrated a previously published mathematical model, defined as a “population model” , to the ertainty . The popfound in .Ae. koreicus abundance throughout the study area. More specifically, we assumed that a trap on average covers an area Ω = πr2, where r is the average Ae. koreicus daily flight range (150 m as previously used in [M(t) of mosquito abundance for Ω for any day t. So, if A is the area of the whole considered region presented in M(t) for the ratio A/Ω, we can obtain an estimate of the total number of mosquitoes present in the city on day t. We then redistributed this value over each hectare of the study region through a standard universal kriging procedure using human density as a predictor, as we found a significant relationship between this quantity and the total number of trapped mosquitoes (see Results).Finally, by considering the results of the statistical analyses and of the entomological model, we mapped  used in ). ConseqThe statistical analyses, elaboration of model outcomes, and kriging interpolation were carried out in R v3.6.0  using paAe. koreicus over all sites were 6, 61, 161, and 87 in 2016, 2017, 2018, and 2019, respectively. We observed substantial heterogeneity between traps, for instance, trap G captured in total only 1 adult in 2017, whereas trap J captured 80 specimens over the four considered years , meaning that about 1.5% of the host-seeking females were captured in one day. As expected, the larval density dependent factor 2018 see .Ae. koreicus distribution over the city of Pécs. Average predicted summer density ranged between 8 and 57, 87 and 179, and 3 and 97 females per hectare in 2017, 2018, and 2019, respectively. The negative relationship with human density was more marked in 2017 and 2019, whereas for 2018, Ae. koreicus density was estimated to be larger and more homogeneous throughout the city, as expected, given the higher number of trapped mosquitoes.Mosquito city abundance was evaluated at a 100 m resolution by universal kriging considering human density as a predictor, since we found a significant relationship between these two quantities. As shown in Ae. koreicus in a temperate European country where the species was recently introduced and established. We calibrated a previously developed temperature-dependent population model [Ae. koreicus abundance especially toward the end of the season, possibly due to the lack of a diapausing mechanism, a biological feature not yet investigated in detail for this species. We estimated a lower capture rate with respect that reported in [2 traps deployed in Pécs might be less effective for capturing Ae. koreicus. In urban environments, the BG trap has a significantly better trapping efficacy compared to the EVS trap, including diversity of mosquito species and number of mosquitoes per trapping period [In this study, we provided new data on the invasion of on model  on the mon model , the modorted in  for BG tg period . Thus, tAe. koreicus survival. Such association was observed in Northern Italy as well; during field collections carried out over three years at four different locations, the higher numbers of trapped mosquitoes were recorded in the coldest sites [Ae. koreicus abundance. According to our estimates , and if the same asymmetrical competition occurs between these two species, this might help explain different abundances observed between years.Another important biological factor for population abundance might be competition for resources. For instance, a weak larval interspecific competition has been demonstrated under laboratory conditions between e former . In the Ae. koreicus is known to be better adapted to urban environments than Ae. japonicus  [Ae. koreicus can complete its life cycle feeding on animals other than humans, as already suggested by samplings in Northern Italy during which larvae of this species were found far from human settlings [Although d, 1901) ,12, our ettlings . Prelimiettlings . HoweverAe. koreicus abundance and human density is reflected also in the interpolated maps , but not in the study area . Enhanced entomological surveillance, such as the one deployed in Pécs, and clear guidelines and protocols for integrated vector management are thus urgently needed [Aedes-borne diseases as it takes advantage of entomological monitoring data to build a population abundance dynamics that could be used to inform disease transmission risk models [Hungary finds itself exposed to invasion from not only  country . Ae. japy needed ,46. The k models . However


Over millions of years, vertebrate species populated vast environments spanning the globe. Among the most challenging habitats encountered were those with limited availability of oxygen, yet many animal and human populations inhabit and perform life cycle functions and/or daily activities in varying degrees of hypoxia today. Of particular interest are species that inhabit high-altitude niches, which experience chronic hypobaric hypoxia throughout their lives. Physiological and molecular aspects of adaptation to hypoxia have long been the focus of high-altitude populations and, within the past decade, genomic information has become increasingly accessible. These data provide an opportunity to search for common genetic signatures of selection across uniquely informative populations and thereby augment our understanding of the mechanisms underlying adaptations to hypoxia. In this review, we synthesize the available genomic findings across hypoxia-tolerant species to provide a comprehensive view of putatively hypoxia-adaptive genes and pathways. In many cases, adaptive signatures across species converge on the same genetic pathways or on genes themselves [i.e., the hypoxia inducible factor (HIF) pathway). However, specific variants thought to underlie function are distinct between species and populations, and, in most cases, the precise functional role of these genomic differences remains unknown. Efforts to standardize these findings and explore relationships between genotype and phenotype will provide important clues into the evolutionary and mechanistic bases of physiological adaptations to environmental hypoxia. Some of the most challenging environments for terrestrial life are those in which oxygen availability is limited. For terrestrial vertebrates, such hypoxic environments occur in high-altitude niches, where ambient oxygen levels are reduced relative to sea level due to lower barometric pressure, and in underground burrows, wherein animal respiration combined with poor gas diffusion through the surrounding soils results in decreased oxygen availability. A key distinction between these environments is that hypoxia is constant at high-altitude, whereas in underground burrows, hypoxia varies both in its intensity and consistency. Despite the challenges that hypoxia imposes on physiology, humans have inhabited hypoxic high-altitude regions for 10, 000 of years, while other mammals have evolved to adapt to life under conditions of hypoxia over millions of years or were brought to high altitudes via human intervention  within a few hundred to several 1000 years ago . Therefovice versa)  .On the demand side of the hypoxia/energy balance reside systemic and cellular adaptations that minimize energy demand, either through reduced behaviors , shutting down non-essential tissues to preserve energy for oxygen-sensitive organs, or by reducing energy demand at the cellular level by turning off specific processes  . ExaminiUntil somewhat recently, most studies have relied exclusively upon physiological and/or molecular approaches. While these studies have provided remarkable insight into the scope and variability of physiological responses and cellular adaptations to hypoxia, it is only with more recent genomic analysis of hypoxia-adapted human and animal populations that we have begun to understand the genetic underpinnings of these adaptations, their evolutionary origins, and, in a very few cases, links between genetic mutations and specific physiological and molecular characteristics.In this review, we focus on the genomic aspects of hypoxia-adapted animals and humans from whom such information is available  and identify overlap in common genes reported as top candidates for selection. The most commonly reported adaptation-associated genes are those related to the control and function of the hypoxia-inducible factor (HIF) pathway and/or downstream genes of this family of transcriptional regulators. This commonality has received considerable attention in comparative analysis of hypoxia-mediated genetic adaptation; however, many other genes have been identified across species, suggesting that there are multiple genetic avenues by which molecular and physiological adaptations to hypoxia might have evolved . AdaptatEPO transcription, to produce erythropoietin (EPO) protein postnatally in the kidney (HIF1A (HIF-1α), EPAS1 (HIF-2α), and HIF3A (HIF-3α), each encode a number of protein isoforms via alternative splicing adding additional functional capabilities (Hypoxia inducible factors (HIFs) are the master regulators of cellular response to hypoxia. These transcriptional factors are involved in many processes such as cellular metabolism, angiogenesis, erythropoiesis, regulation of bone and connective tissue development, and fetal development . The HIFe kidney . While te kidney . The genbilities .HIFs 1-3 share a conserved domain structure consisting of an N-terminal basic helix-loop-helix (bHLH) DNA binding domain, two Per-Arnt-Sim (PAS) domains, an oxygen-dependent degradation domain, and two transactivation domains . HIF-1β,EPO, VEGF (angiogenesis), ALDOA, ENO1, and LDHA (glycolytic enzymes)  ]. In addenzymes) .EPAS1, the gene the encodes HIF-2α and EGLN1, the gene that encodes PHD2) ; these genes were further associated with relatively lower hemoglobin concentration in Tibetans  are reported to result in a gain of PHD2 function, which leads to increased HIF degradation under hypoxic conditions and erythroid progenitor disruption  is absent and 380G > C (Cys127Ser) is found at low frequency in Andeans from Cerro de Pasco, Peru  cluster region and EDNRA)  .VEGFB and ELTD1 were identified within the strongest regions of selection in the Colla population from the Argentinian Andes  and suggested these genes, rather than those in hypoxia-response pathways, are the strongest targets of selection in Andeans , previously reported as a top selection candidate in Andeans , are reported as top candidates in Amhara Ethiopians  have orthologs in Drosophila that afford tolerance to hypoxia , different variants with putatively distinct functions are reported as targets of selection among highland human and other animal populations.Many genes originally highlighted as adaptive targets in human high-altitude studies have also emerged as top candidates for genetic adaptation in other species under various conditions of environmental hypoxia. For example, as in high-altitude human populations, adaptations involving hematological values are found in most hypoxia-adapted high-altitude animal populations. Specifically, high-O alpacas , Andean  alpacas , deer mi alpacas , 2009, a alpacas  , and so analysis of genetic adaptations to altitude in these populations must take this into account. Nonetheless, the study of domesticated high-altitude mammal populations provides an opportunity to contrast adaptive strategies to hypoxia between relatively short- and long-term  exposures to low oxygen environments .EPAS1 in the high-altitude population. Specifically, BIEC2– 310909 (rs69041973) is highly different between the lowland and highland species, although this SNP is intronic and has no known function. Unfortunately, little is known regarding physiological adaptations to hypoxia in this species and so it is difficult to draw direct comparisons between human studies in this case; however, the commonality of the occurrence of mutations in HIF-related genes in high-altitude populations is important to note and will be a recurring theme in this section.One good example of such anthropocentric-driven short-term adaptation to chronic hypoxia in domesticated mammals can be found in feral horses of the Andean Paramo. This region of the Andes is a challenging environment with large temperature, humidity, and rainfall variance, high radiation, and minimal food availability. As a result, ungulates that had naturally evolved to live in this niche have largely shifted their range to more temperate regions. Conversely, populations of feral horses, which were originally introduced by Spanish conquistadores in the 1500’s, have thrived in this niche and the success of these introduced horses over the past ∼200 generations provides an important opportunity to evaluate genetic changes driven by this relatively short-term population-history exposure to hypoxia at altitude. Importantly, genes in Andean feral horse populations can be easily compared to those of their lowland Iberian ancestors . A recenEPAS1 and also in SIRT7, which negatively regulates HIF1 and HIF2 , which is endemic to the Tibetan plateau , which were also brought to altitude as a domesticated animal by humans , were associated with increased energy metabolism (HIF1AN), ANGP4 (ANGPTL4), which is involved in angiogenesis, and ADAM9 (ADAMTS9), which is an important regulator of HIF1  . Genetictabolism , presuma of HIF1 . Each of of HIF1 . InteresGallus gallus), which is an aboriginal breed found on the Tibetan plateau, is flightless and adapted to life at high altitude. Compared to low altitude chickens, the Tibetan chicken is smaller, and has higher hematocrit and hemoglobin concentration and also a higher hemoglobin-oxygen affinity  were recently sequenced and compared to those of lowland ectotherms , which typically live at altitudes of >4, 500 m and are generally considered to be the most high-altitude-adapted lizard known (P. putjatia) revealed that UBE2D1 was positively selected for in the high-altitude population  found similar results  or thermoregulation. Indeed, hypoxia and hypothermia often have opposing effects on development, with the former favoring small body size and low metabolic rates and the later favoring the opposite. Therefore, physiological and genetic studies of high-altitude populations may be confounded by these opposing evolutionary drivers, clouding the impact of hypoxia-alone on genetic mutations and physiological adaptations. Fortunately, hypoxic environments are commonly found beyond high altitude niches. For example, underground burrows and frozen lakes and ponds and are lowland niches in which many hypoxia-tolerant species are found, across the animal kingdom. A key caveat in this comparison, however, is that lowland hypoxic environments are likely not consistently or uniformly hypoxia; denizens of these niches likely experience gradients of hypoxia and even periods of normoxia in their day-to-day activities. This periodic intermittent hypoxic exposure results in divergent stresses to which these animals must adapt  and therefore likely exerts a different selection pressure than life in hypobaric hypoxia; the effects of normobaric and hypobaric hypoxia further remain unclear and require additional standardized analyses . HoweverHetercephalus glaber), which are among the most hypoxia-tolerant mammal presently identified and tolerate minutes of complete anoxia, hours at 3% O2, and days to weeks at 8% O2 (Of particular interest are naked mole-rats (at 8% O2 . Naked mat 8% O2 , which rat 8% O2 . These oAs mentioned above, genes in the HIF pathway have been targets of selection across multiple species. How these variants relate to phenotypes within and across species will provide import insight into the primary and secondary effects of adaptations and maladaptation to hypoxia  and requFrom studies of high-altitude adapted human and animal populations it has become abundantly clear that life in hypobaric hypoxia has driven the convergent evolution of specific genetic pathways across very disparate animals. In species that have lived at altitude for shorter periods of time, admixture from other species that have lived at altitude for longer has played a key role in accelerating these adjustments. Intriguingly, the occurrence of hematological adaptations that enhance blood flow seems to be a common theme at both the physiological and genetic levels, which are easily studied across species. In recent years, genetic analyses have in some ways outpaced physiological studies as high-throughput population-wide approaches become more widely utilized. However, the physiological impact of many of the mutations identified in high-altitude populations and species are unknown, and it is highly likely that adaptations beyond hematological function will prove to be important in tolerating hypoxia at altitude. Indeed, it is very likely that adaptations at the metabolic and cellular levels are critical to tolerating life in hypoxia. Conversely, numerous studies have examined physiological adaptations to hypoxia in a systemic context in a wide variety of comparative animal species; however, genetic information is lacking for many of these species. As such, the next major step in this field will be to marry genetic, omic, and physiological knowledge to fully elucidate beneficial physiological adaptations to hypoxia and their evolutionary origins.MP and TS designed the report. All authors produced figures and drafted and contributed comments to the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.