Nodularia douglasiae based on microsatellite DNA genotypes and mitochondrial DNA sequences. Phylogenetic analysis a fragment of the COI mitochondrial gene indicated that N. douglasiae collected from across the middle and lower Yangtze River drainage are monophyletic with N. douglasiae from Japan, Russia, and South Korea. The results of the analysis of both the mtDNA and microsatellite datasets indicated that the seven collection locations of N. douglasiae in the middle and lower Yangtze River drainage showed high genetic diversity, significant genetic differentiation and genetic structure, and stable population dynamics over time. Moreover, we found that the connections among tributaries rivers and lakes in the Yangtze River drainage were important in maintaining gene flow among locations that N. douglasiae inhabits. An understanding of the genetic structure and diversity of a widespread species like N. douglasiae could be used as a surrogate to better understand the populations of other freshwater mussel species that are more rare in the Yangtze River drainage. At the same time, these results could provide a basis for the protection of genetic diversity and management of unionid mussels diversity and other aquatic organisms in the system.The Yangtze River drainage in China is among the most species rich rivers for freshwater mussels (order Unionida) on Earth with at least 68 species known. The freshwater mussels of the Yangtze River face a variety of threats with indications that species are declining in abundance and area of occupancy. This study represents the first analyses of the genetic structure and diversity for the common and widespread freshwater mussel  Freshwater mussels  are one of the most important faunas in freshwater ecosystems, for their potential to enhance biodiversity and ecosystem functioning –4. HowevThe middle and lower reaches of the Yangtze River are a center for freshwater mussel diversity in East Asia with 68 described species. HoweverNodularia douglasiae  is a relatively common and widespread freshwater mussel in the middle and lower reaches of the Yangtze River drainage, China; the Korean Peninsula; eastern Russia; and in Japan and the Sakhalin islands show high levels of population divergence and structure even at relatively short geographic distances show lower levels of population divergence and structure and only show strong divergence and structure in populations separated by relatively high geographic distances[N. douglasiae are currently unknown, inferences about the dispersal abilities is impossible until experiments to determine potential host fish are conducted.Adult unionids disperse relatively little as adults, with long-distance dispersal being facilitated by hosts during their larval  stage–75. Givehe region–78. Genedistances, while udistances, 80. GivN. douglasiae across the seven collection locations did not have a recent population expansion, and suggested that the current distribution is quite ancient. These analyses also indicate that the population dynamics of N. douglasiae are quite stable. This is not a suprising result given that N. douglasiae is a widely distributed species and often the most abundant unionid species in the region[The mismatch distribution analysis and neutrality tests of the mtDNA sequence data suggest that he region, 61, 81.he region, 61 and he region, 15, 81,N. douglasiae in the middle and lower Yangtze River drainage.Higher levels of genetic diversity among populations of aquatic organisms could improve evolutionary potential for dealing with habitat change, effects of pathogen infection, and other selective forces –84. The N. douglasiae among the seven collection locations was high. There were more haplotypes found in N. douglasiae compared with other some rare and imperiled unionids [The analyses of the COI seqeuences showed that the haplotype diversity of unionids , 69, butunionids , 30.O) expected heterozygosity (HE) estimated for N. douglasiae was somewhat lower than that of the widely distributed Sinohyriopsis cumingii [Solenaia oleivora [N. douglasiae were somewhat higher than that estimated for Solenaia carinata [The genetic diversity estimated from the microsatellite DNA results showed similar levels of genetic diversity to other unionids in the Yangtze River drainage. Mean observed heterozygocity (H98–0.701). HoweverE: 0.478), an endeN. douglasiae collection locations across the middle and lower Yangtze River drainage showed evidence of a recent bottleneck. It is unclear if these bottlenecks resulted from a founder effect due to colonization by a small founding population with low genetic diversity, or if these were the result of severe demographic reductions followed by subsequent recovery in population size. The moderate levels of genetic diversity as revealed by the polymorphic information criterion (0.25<PIC<0.5), may also be evidence for a recent genetic bottleneck.Virtually all This study represents the first analyses of the genetic structure and diversity for this widespread freshwater mussel and the first for a unionid mussel in the middle and lower Yangtze River drainage. Large-scale patterns of genetic structure occasionally differ among unionid species in the same geographic region, 25, 87.N. douglasiae in the Yangtze River region showed robust genetic diversity, and significant and often high genetic differentiation  and limited gene flow among the seven collection locations. Moreover, although the historical population dynamics of N. douglasiae appear stable, loss of hydrologic connectivity among rivers and lakes in the Yangtze River drainage may lead to increased isolation of populations and possibly leading depression and population declines. Genetic structure of common species have been shown to be useful surrogates for predicting genetic structure of rare species in North American unionids[N. douglasiae may assist in understanding general patterns for freshwater mussel populations in the Yangtze River drainage. At the same time, we also propose the urgent need for research on the life history of N. douglasiae and other Chinese unionids with an emphasis on characterizing habitat preferences and host-testing experiments to identify potential host fish species.In this study,  unionids–24. TherS1 FigThe Evanno et al. method, which compares the ΔK between sequential K values (a) and the comparison of the mean of the estimate of the natural log of the probability of the data amongst K values, with the circle centered over the mean and the bar indicating the standard error (b).(PDF)Click here for additional data file.S1 File(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 TableA: the number of alleles; NE: the effective number of alleles; HE: expected heterozygosity; HO: observed heterozygosity; p: significance of HWE test; Fis:fixation index; PIC: polymorphic information content. Bold type indicates significant deviations from HWE expectations after Bonferroni correction (α = 0.0005495). Site codes as in N: number genotyped; N(DOCX)Click here for additional data file.S3 TableBold type indicates significant probability for the presence of null alleles.(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.


Oncorhynchus clarkii henshawi, LCT, Federally listed) remains in the Truckee River watershed of northwestern Nevada and northeastern California. The majority of populations in this watershed were extirpated in the 1940s due to invasive species, overharvest, anthropogenic water consumption and changing precipitation regimes. In 1977, a population of cutthroat trout discovered in the Pilot Peak Mountains in the Bonneville basin of Utah, was putatively identified as the extirpated LCT lacustrine lineage native to Pyramid Lake in the Truckee River basin based on morphological and meristic characters. Our phylogenetic and Bayesian genotype clustering analyses of museum specimens collected from the large lakes (1872–1913) and contemporary samples collected from populations throughout the extant range provide evidence in support of a genetically distinct Truckee River basin origin for this population. Analysis of museum samples alone identified three distinct genotype clusters and historical connectivity among water bodies within the Truckee River basin. Baseline data from museum collections indicate that the extant Pilot Peak strain represents a remnant of the extirpated lacustrine lineage. Given the limitations on high-quality data when working with a sparse number of preserved museum samples, we acknowledge that, in the end, this may be a more complicated story. However, the paucity of remnant populations in the Truckee River watershed, in combination with data on the distribution of morphological, meristic and genetic data for Lahontan cutthroat trout, suggests that recovery strategies, particularly in the large lacustrine habitats should consider this lineage as an important part of the genetic legacy of this species.Currently one small, native population of the culturally and ecologically important Lahontan cutthroat trout ( These fishes were once found throughout the intermontane western USA in multiple-order stream and lake habitats. Habitat losses due to the introduction of non-native trout, water diversions, water pollution and riparian habitat degradation have largely isolated cutthroat trout populations into small headwater reaches or led to local extirpations outright .Significant positive p < 0.04). In a previous analysis [p ≤ 0.005) and multiple populations within the Humboldt  and Quinn rivers .There was no evidence of null alleles in the Bettridge, Morrison, Independence and Heenan lake populations or in the museum samples. The Morrison Creek population was the only population of those tested  that showed evidence of a bottleneck under both the TPM and SMM mutation models . In the river-based Bayesian analysis, k = 5 had the highest statistical support (LnP(D) = −1157.78, Δk = 3.95) and individuals were assigned primarily to single genotype clusters that overlapped with major river drainage of origin  with 61% within individuals, 20% among individuals, 17% among populations and only 2% among regions (the designated ESUs). There was also substantial genetic structure range wide as all pairwise f origin . The Pilf origin ; howeverf origin .Figure 3.3.2.FST among Pilot, Independence and each of the museum sampling locations were also significant . However, not surprisingly the FST estimates among the museum sites had the lowest values (table 4b). The Bayesian genotype clustering results reveal three genotype clusters (LnP(D) = −3513.3, Δk = 596.8) with the museum samples being assigned primarily to two genotype clusters (blue and red) and all Pilot Peak strain individuals being assigned to the red genotype cluster  from Winnemucca Lake.Genetic variance within the museum samples alone was partitioned as 69% within individuals, 26% among individuals, 4% among sampling locations and 1% among three regions . Nm estimates among the museum sites also suggest gene flow between the lower river and Pyramid Lake (Nm = 58) with additional limited gene flow among Tahoe, the lower river and Pyramid Lake . Among the museum sampling sites, three genotype clusters had the best statistical support, which roughly corresponds to the three sampling regions (LnP(D) = –1059.7, Δ4.The lacustrine lineage of Lahontan cutthroat trout historically found in Lake Tahoe and Pyramid Lake was the largest of the inland cutthroat trout subspecies and after the 1940s was believed to be extinct. The genetic results presented here together with morphological, meristic, growth and behavioural data suggest this is not the case. Genetic analyses support the hypothesis that Lahontan cutthroat trout found in an out-of-basin stream in the Pilot Peak Mountains of Utah are the original lacustrine strain native to the Truckee River basin in the western Lahontan hydrograph basin. However, we also acknowledge the uncertainty inherent in microsatellite data and the difficulty of working with preserved museum samples. That said, this population is the most genetically similar to the Lake Tahoe and Pyramid Lake museum specimens collected in the late ninenteenth and early twentieth centuries prior to the extirpation of these large lake populations, when compared with the majority of extant LCT populations, including Independence Lake, the only other extant population in the Truckee River basin.Furthermore, genetic analyses indicate population genetic structure as well as movement within the basin historically, with Independence Lake supporting a largely distinct subpopulation. Although results from the Independence Lake samples, collected approximately 100 years later than the museum samples, may reflect the effects of isolation and drift in the contemporary Independence Lake population, we did observe comparable levels of allelic richness and heterozygosity per locus between the historic museum samples and contemporary Independence Lake samples. Although bottlenecks have certainly been an issue for Lahontan cutthroat trout populations in the twentieth century due to human perturbations, the museum samples were collected when this area was still sparsely populated and we found no evidence for a genetic bottleneck in the contemporary Independence Lake population. These data suggest the Independence Lake population has maintained genetic variation despite its current isolation and that the genetic differentiation observed probably reflects historical patterns. The Truckee River basin appears to have once supported multiple subpopulations of Lahontan cutthroat trout. Support for this interpretation is based on the observed patterns of population genetic structure and gene flow among extant subpopulations of this subspecies found in the few remaining large, multiple-order interconnected streams systems in the Humboldt River basin ,38,50.These results further support anecdotal information of anthropogenic movement of trout from Lake Tahoe and Pyramid Lake into waters in the eastern Lahontan basin and other out-of-basin waters such as fishless Morrison Creek in the Pilot Peak Mountains in Utah in the early part of the twentieth century. Given the genetic data together with the morphological and meristic characters and evidence of piscivory, which support a lacustrine life history as well as the record growth rates and large sizes being achieved in Pyramid Lake today, the Pilot Peak strain represents an important part of the lacustrine legacy of this subspecies of cutthroat trout.O. clarkii subsp.) subspecies, which would better reflect the lacustrine and fluvial life histories of these fish and be consistent with the observed morphological differences.Under an ESU framework, native fishes should be used to restore populations in historical habitat. Implicit in this aim is that locally adapted fishes have a greater chance of re-establishing populations ,20. OwinThe use of the Pilot Peak strain of Lahontan cutthroat trout in recovery activities in the large lacustrine ecosystems of the western Lahontan basin ESU offers a rare opportunity to not only restore a native lineage to its historical waters, but in doing so also recover the breadth of life-history strategies and the suite of adaptations these fish may have had to these large lacustrine habitats.5.In the ninenteenth and early twentieth centuries, museums worldwide created natural history collections for a diverse array of taxa in order to catalogue and voucher species as they were discovered. For over 500 years, such collections have been used to examine local and regional patterns of morphological variation. In just the past 20 years, however, improved DNA extraction methodologies and new analytical techniques have been applied to these specimens, allowing researchers to place patterns of genetic variation in a historical context . As techIn North American collections, specimens from both aquatic and terrestrial vertebrates, although relatively abundant, are at continued risk due to budgetary and storage constraints . This st


Leucocytozoon caulleryi and study the phylogenetics of L. caulleryi of broiler chickens in endemic areas in Indonesia.The objective of this research was to determine the species and strains of cyt b) gene that determines the species, and the phylogenetics analysis of L. caulleryi that infected broiler chickens in endemic areas in Indonesia, using Mega 5 software.Blood samples were collected from broiler chickens originated from endemic area in Indonesia, i.e., Pasuruan, Lamongan, Blitar, Lumajang, Boyolali, Purwokerto, and Banjarmasin in 2017. Collected blood was used for microscopic examination, sequencing using BLAST method to identify the nucleotide structure of cytochrome b (L. caulleryi were infected broiler chickens in endemic areas in Indonesia. L. caulleryi in one area had very close phylogenetic relations with those in other areas. The genetic distance between L. caulleryi taxa from various endemic areas is very close (<5%).The results showed that Plasmodium sp. and L. caulleryi that infected broiler chickens in various endemic areas in Indonesia.There is a very close phylogenetics among strains of  Leucocytozoon genus. These protozoas live as parasites in the white blood cells. In Southeast Asia, there are two species that cause leukocytozoonosis in chickens the most, which are Leucocytozoon caulleryi and Leucocytozoon sabrazesi. This disease occurs very frequently in chicken farms that are close to water sources. This is because water sources are the natural habitat of Leucocytozoon sp. vectors, which are Simulium sp. and Culicoides arakawae [Leukocytozoonosis is a parasitic disease in poultry which is caused by protozoans from the arakawae .Simulium sp. and Culicoides sp. [In endemic areas, this disease occurs throughout the year. There is a positive correlation between leukocytozoonosis incidences with the season and farm locations. During seasonal changes, from rainy season to dry season and vice versa, the frequency of leukocytozoonosis incidences tends to increase. This is due to the increase in the populations of ides sp. .Leucocytozoon sp. is often unable to be set up to the species determination due to morphological variations which make it hard to characterize the parasite’s morphology.The outbreaks of leukocytozoonosis in broiler chickens in East Java are about 32% and in Central Java about 67%, while the incidences of leukocytozoonosis in local chickens and birds were not reported [Leucocytozoon between species [Leucocytozoon in chickens, compared to other protozoas. Even, there has not been information yet about the phylogenetic analysis that gives information about the varieties of Leucocytozoon that infect chickens in Indonesia up until now.Molecular biology research can assist in species determination and can even show genetical varieties of  species ,5. Up unThe genetic variations that create virulent strains in the microorganisms are thought to be responsible for poultry disease outbreaks. Phylogenetic analysis can be used as the basis for vaccine making for specific species ,7.Leucocytozoon has been conducted to cytochrome b (cyt b) gene [cyt b gene is one of the genes located in the mitochondrial genome that is widely used for molecular analyses. cyt b gene possesses fast and slow developments of codon positions which have conservative parts and ever-changing parts [Deoxyribonucleic acid (DNA) analysis on  b) gene . Series  b) gene ,10. cyt ng parts .The experiments and sampling procedures were conducted under the protocol reviewed and approved by the Ethics Committee at Universitas AirlanggaAll samples were taken from chicken originating from endemic areas in Indonesia in 2012. These areas include Pasuruan (n = 10), Lamongan (n = 6), Blitar (n = 10), Lumajang (n = 4), Boyolali (n = 10), Purwokerto (n = 8), and Banjarmasin (n = 15).Blood samples were collected and stored, and then, samples were fixed and stained for microscopic observation. The remainders of the blood samples were used for the subsequent DNA extraction procedure.Blood samples were extracted using phenol-chloroform. Extracted DNA was dissolved in TE solution and used for nested PCR. The primer design was based on a pre-existing sequence, either from the previous research data or the data contained in GenBank. Two primers were used in the first- and second-round PCR. Nucleotide sequence for first round was 5’- CATATATTAAGAGAATTATGGAG-3’ and 5’-ATAAAATGGTAAGAAATACCATTC-3’ and for the second round was 5’- ATGTGCTTTAG ATATATGCATGCT-3’ and 5’-GCATTATCTGGATGTGATAATGGT-3’ ,13.2, and 0.25 units Taq polymerase and buffer. For the primary PCR, a total of 35 cycles was carried out, consisting of denaturation at 94°C for 30 s, annealing at 50°C for 30 s, and extension at 72°C for 45 s. The second PCR reaction was performed in a total of 40 cycles identical to the first PCR, except the used of 0.4 μM primers, 0.5 units Taq Polymerase, and 0.2 μl of the first PCR product were used as the DNA template.In the first cycle, the total volume for this reaction was 25 μl reaction mixture consisted of 2 μl DNA template, 0.125 mM dNTP, 0.2 μM primer, 3 mM MgClThe amplification product was visualized in 2% agarose gels stained with ethidium bromide. The expected base length is 600 bp in the first round and 503 bp in the second round.Leucocytozoon genome. The PCR sequencing process of cyt b gene used ABI PRISM 310  automated sequencer.This activity was carried out to know the variations of DNA sequence that was produced from the PCR sequencing with a DNA template originating from The reaction was conducted by mixing the DNA template (40 μg), which was the result of amplification and purification, with 1 μL primer (reverse primer) at 2 pmol/μL and 4 μL concentrations of the ready mix (containing Ampli Tag and fluorescent-labeled dNTP). This mix was amplified in a PCR at a temperature of 96°C for 30 s, 50°C for 15 s, and 60°C for 4 min until 25 rounds. The PCR result was centrifuged at 12,000 rpm for 1 min, the liquid of which being discarded, and then centrifuged again at 14,000 rpm for 2 min for drying. The lower tube was then discarded and changed with a new Eppendorf tube.Elution Buffer in the amount of 30 μL was added and centrifuged at 10,000 rpm for 1 min. In this process, a pure DNA had been produced. For the sequencing, a 4 μL Big Dye sequencing kit was mixed with a 4 μL DNA sequencing kit (such as Big Dye Terminator) and 5 μL of pure DNA. Electrophoresis was carried out with 4% of acrylamide gel in a TBE 1 × running buffer using ABI 310 DNA sequencer.Molecular data preparation to build a phylogenetic tree using genetic distance approach was conducted with Mega 5 software . A standLeucocytozoon found in this study was entirely L. caulleryi .The overall results of identification L. caulleryi from Pasuruan 3 had the biggest mutation, followed by Lamongan 1 and Purwokerto 2 . Among those taxa, it appeared that okerto 2 .L. caulleryi strains from those areas was compared with other Leucocytozoon sp. and Plasmodium sp. , which its morphology was similar to L. caulleryi.It means that Plasmodium sp. whose morphology and PCR products resemble L. caulleryi could be explained ecologically due to the evolution of L. caulleryi in several endemic areas caused by the interference of humans who traded poultry [The finding of  poultry .L. caulleryi from different regions  have the same nucleotide sequence (0% genetic distance) but among them exhibit different morphologies. This may be due to changes in protein caused by environmental influences or stress that may alter a protein configuration called folding protein [Leucocytozoon between the cities.Some isolates of  protein . The lowL. caulleryi from Pasuruan 3 has the most mutation, followed by Lamongan 1 and Purwokerto 2. Mutations can lead the changes of parasite characters, such as the pathogenicity and the variation of its life cycle. Therefore, its need a continuous observation of the case leukocytozoonosis in Pasuruan.Among the taxon, L. caulleryi was done by Culicoides sp. Therefore, there should be further studies to find the factors that caused the spread of L. caulleryi among the hosts and across areas. Even though L. caulleryi from Pasuruan 3 was outside the groups of other areas, the genetic distance was still < 5%.Pasuruan 3 isolate was not in the same group with isolates from Pasuruan 1 and Pasuruan 2 although they were originated from the same area. One of the causes was probably the same with the above explanation. Meanwhile, the spread of L. caulleryi from various endemic areas with L. caulleryi (AB 302215) on average was < 5%. The genetic variety of intraspecies in this research were supported the occurrence of morphological variation. The less than 5% genetic distance in cyt b gene portrayed intraspecific species variation, while in Leucocytozoon parasite, this < 5% genetic distance showed interspecific species genetic variety. These data were in line with another study that stated that two parasite species in poultry, Haemoproteus sp. and Plasmodium sp., also with genetic distance of less than 5% for the cyt b gene, had different morphologies [The results of the research showed that the intraspecies genetic distance of hologies .cyt b gene show the interspecific species variety in many groups of hemosporidians that infect poultry, and thus, it can be used to explain the nature of the phylogenetic tree of this gene. It should also be noted that genetic diversity of the cyt b genes in some species of poultry parasite whose morphological characteristics have been identified indicates genetic distance < 5%, even some species have a genetic distance of < 1% [cyt b gene can be developed to identify species in the hemosporidia group cautiously. It is suggested to combine molecular data with microscopic one.It is possible that the criteria of genetic distance of < 5% in cyt b gene of poultry hemosporidia and help to build parasitic taxonomy based on molecular data [Data of the genetic distance between species that morphologically clear were very important. Increasing data in this field may underlie phylogenetic tree genetic of lar data .L. caulleryi species in various areas was analyzed with phylogenetic tree analysis that showed the close genetic relationship as the cause of leukocytozoonosis in broiler chickens, including with L. caulleryi from GenBank.The closeness among L. caulleryi from Pasuruan 3 showed the most distant genetic relationship, which was allegedly caused by a mutation in several points. This mutation may due to drug exposure or the existence of chickens brought from other areas to Pasuruan. The mutation had caused changes in the characteristics of this parasite, that is, the pathogenicity and its life cycles.L. caulleryi in these areas were due to various factors. One of which was probably caused by living animal transportation across regions.The spread of isolates which was gotten from a phylogenetic analysis requires further studies. The isolates from Banjarmasin 1 and Banjarmasin 2 , for example, belonged to the same group with isolates from Blitar, Pasuruan 1, and Pasuruan 2 (East Java). The structural similarities of L. caulleryi and Plasmodium sp. from Blitar and Lumajang, whereas this parasite has similar morphology with L. caulleryi. The two Plasmodium sp. isolates were close to L. caulleryi and both were from the same strain. The Plasmodium sp. found in this research had a lower genetic distance with L. caulleryi than with other species of Leucocytozoon.The phylogenetic tree in Leucocytozoon had to be supported with genetic data to reveal the level of varieties and to describe the taxonomy of Leucocytozoon.This is an indication that the morphological data of Plasmodium sp. had a different genus with Leucocytozoon sp. Therefore, there should be a rearrangement of taxonomic concept that needs to adjust molecular data with morphological. L. sabrazesi also attacks chickens, even though the host is the same, but it has a very distant phylogeny with L. caulleryi . There is a very close phylogenetics among strains of L. caulleryi that infected broiler chickens in various endemic areas.ES and WMY carried out the main research works, WMY performed the statistical analysis and analyzed the main data in the experiments, and ES and WMY approved the final manuscript. All authors read and approved the final manuscript.


The OWA genetic divergences (0–0.4%) are compatible with intra-specific divergence, showing evidence of both ancient and contemporary admixture between the Pacific and Atlantic populations, enhanced by high asymmetrical migration from the Pacific to the Atlantic. The estimated divergence between Atlantic and Pacific anchovies (0.67 [0.53–0.80] Ma) matches a severe drop of sea temperature during the Günz glacial stage of the Pleistocene. Our results support an alternative evolutionary scenario for the OWA, suggesting a coastal migration along south Asia, Middle East and eastern Africa continental platforms, followed by the colonization of the Atlantic via the Cape of the Good Hope.Small pelagic fishes have the ability to disperse over long distances and may present complex evolutionary histories. Here, Old World Anchovies (OWA) were used as a model system to understand genetic patterns and connectivity of fish between the Atlantic and Pacific basins. We surveyed 16 locations worldwide using mtDNA and 8 microsatellite loci for genetic parameters, and mtDNA (cyt  Speciation in the marine realm is mostly related to geological and climatic events that have occurred throughout different periods3. Pleistocene climatic events promoted fluctuations in sea surface temperatures, sea level, ice sheet coverage and changes on the global oceanographic circulation patterns, having impact on living species distribution, diversity, population structure and speciation  species complex10 proposed that the whole group should be considered a single species and molecular studies revealed that some of the described species have no genetic support. No significant genetic differences were found between E. encrasicolus and E. eurystole14 and the existence of shared mtDNA haplotypes between E. encrasicolus and E. capensis15 or between E. australis and E. japonicus16, seriously compromise the current taxonomy of this group. The lack of genetic divergence among disjunct distributions contrasts with partial reproductive isolation and parallel genetic differentiation among sympatric ecological morphotypes17 or with the complex population structure observed at regional spatial scales in the European anchovy22. Also, E. encrasicolus mtDNA is divided in two lineages (clade A and B), but the paraphyly of these mtDNA lineages generated further debate15.The OWA exhibit shallow genetic divergences between putative species and several conflicting aspects in the taxonomy and molecular classification of the group exist. Based on morphological characters, Whitehead and his colleagues16 and promoted inter-oceanic migrations16. Anchovies are thought to have colonised the Atlantic Ocean through the southern Indian Ocean16. Anchovies in the Atlantic Ocean likely experienced several extinction-colonization cycles at the extremes of the distribution driven by Pleistocene climate shifts16. Moreover, the western Atlantic was colonized after the last glacial maximum (LGM) from anchovies dispersing from western African populations of the European anchovy14. Recently, a mitochondrial-based analysis of E. encrasicolus  indicated that some of these individuals were found under selection, possibly related to adaptation to cold temperatures23. In the Pacific Ocean, the Japanese and Australian anchovies diverged between 105 ka (thousand years ago) and 420 ka24. The genetic signature of Pacific anchovies revealed persistence on separated hemispheres over several glacial cycles, although more recent dispersals were identified16. Thus far, studies involving OWA phylogenetic assessments were based on allozymes and on a small fragment of the mitochondrial cytochrome b gene 16.Pleistocene climatic swings influenced anchovies range shifts, lead to trans-equatorial dispersals during episodes of global cooling or through deep cold waterb and 8 nuclear microsatellites  to further analyse evolutionary relationships within OWA and provide a novel perspective on the level of connectivity between the Atlantic and Pacific anchovies. We dated main lineage splitting events within Engraulidae and propose a new biogeographic scenario for the OWA.In this study, we used a large portion (1044 bp) of the cyt FST value of as low as 0.001 using the microsatellite data set  varied between 0.797  and 0.894 (USA) in the Atlantic Ocean and between 0.849 and 0.899 in the Pacific Ocean, while the observed heterozygosity (HO) varied between 0.653 (Guinea-Bissau) and 0.825  in the Atlantic Ocean and between 0.699 and 0.742 in the Pacific Ocean . Our comparison of candidate models of gene flow between populations, clearly rejects panmixia, and showed that migration from the Pacific to the Atlantic (model 2) fitted our microsatellite data best (Table M = 1.5).Estimates with microsatellite data of the mutation scaled population size parameter were higher in the Pacific Ocean than in the Atlantic Ocean  was generally high, ranging from 0.917 to 1.000 in the eastern Atlantic Ocean (from Norway to South Africa), 0.909 in the western Atlantic, and from 0.709 to 1.000 in the Pacific Ocean (Table π) was low, ranging from 0.4% (USA) to 1.9% (South Africa) in the Atlantic Ocean and from 0.1% to 1.1% in the Pacific Ocean  and the japanese anchovies (E. japonicus) showing the lowest genetic differentiation between sea basins .Estimates with mtDNA data of the mutation scaled population size parameter were the same between ocean basins (Θ = 0.1). Our comparison of candidate models of gene flow between populations, clearly rejects panmixia and showed that the full gene flow model (model 1) fitted our mtDNA data best Table , with hib) were identical  and ML  analyses arrived at similar topologies . Phylogenetic relationships within Engraulidae, either with ML or BI approaches, were generally unresolved and it was not possible to confidently establish the sister group of the Engraulis spp. complex  and nuclear introns (RAG1 and RAG2) fragments of the family Engraulidae estimated the age of the most recent common ancestor (MRCA) of the OWA at 0.67 Ma ago [0.53–0.80]  recovered two lineages (clades A and B) within E. encrasicolus that did not group together16. The assumption of paraphyly for this species was uniquely based on a neighbour joining analysis that grouped the clade B with E. japonicus with relatively low statistical support (BP 53%). A more recent study showed that some individuals belonging to the E. encrasicolus mitochondrial clade B were under selection23 and therefore it is pivotal to understand if selection plays an important role in shaping the evolutionary and demographic history of this complex. All phylogenetic analyses (ML and BI) presented here that included more sequences (55 Engraulidae species) and a larger fragment (1044 bp) than previous studies, returned very similar results, regardless the effect of selection over the analysed portion of the cyt b. Moreover, unlike previous results, our ML analyses retrieved E. encrasicolus as monophyletic  and the southeastern Atlantic and Pacific Oceans . The DAPC 31, given the high sea surface temperatures (SST’s) in the coastlines of eastern Africa and southern Asia. Until now, few records of OWA were reported in warmer habitats10, but recent studies indicated that higher SST’s are not a limiting factor for the OWA, as they can be found in tropical waters both in the Atlantic and Pacific oceans23. The colonisation pathway proposed by Grant and Bowen16 would imply more than 8000 km of open-ocean migration between the western Australia and South Africa without any stepping-stones. Anchovies are coastal pelagic fishes that live in average up to three years and it would be extremely unlikely that anchovies survived to a trans-oceanic migration of this magnitude in a single generation with no stopovers32. Open-ocean areas usually exhibit low productivity and scarce food resources33, which would create additional difficulties to large-scale migrations. Moreover, anchovies from Australia and southeastern Atlantic are not closely related in any of the phylogenetic analyses performed thus far  coastal fish may disperse along large distances and frequently cross biogeographic barriers while maintaining high levels of genetic diversity and low genetic differentiation among populations; (ii) the dispersal route from the Pacific to the Atlantic Ocean is shared by other coastal fish species . A small portion of white muscle or fin was preserved in 96% ethanol and stored at −20 °C. Total DNA extraction, polymerase chain reaction (PCR), purification of the PCR product, sequencing for a fragment of the mitochondrial cyt b (1044 bp) and microsatellite genotyping of eight loci were performed as described in Silva et al.14. Sequences of the nuclear recombination-activating genes RAG1 (1480 bp) and RAG2 (1221 bp) and of the mitochondrial 16S rRNA (800 bp) from OWA putative species used in the Bayesian dating analysis were obtained as described in Bloom & Lovejoy35.Samples of OWA likely representing five putative species were collected from 16 locations from both Atlantic and Pacific oceans  implemented in the adegenet package39 was used to evaluate statistical power for detecting pairwise genetic differentiation at FST levels ranging from 0.00 to 0.10. We simulated the divergence of three subpopulations, corresponding to (1) European anchovy , (2) E. japonicus and (3) E. australis, from a single ancestral population through genetic drift to a given overall FST value defined by controlling effective population size (Ne) and number of generations (t). To best reflect the assumingly large Ne of anchovy, we let Ne = 10 000 and varied t from 0 to 2078 for simulating different levels of differentiation. After the simulation, each subpopulation was sampled at n = 381 and divergence from genetic homogeneity was tested with χ-exact test. This procedure was repeated 100 times and the proportion of significant outcomes was used to estimate statistical power for detecting pairwise genetic differentiation.The program POWSIM 4.0b sequences were aligned using ClustalX 2.0.3 with default settings, implemented in Geneious 5.440, checked and trimmed manually. Sequences were reduced to haplotypes using Collapse 1.241. Number of individuals (N), number of haplotypes (nh) and haplotype (h) and nucleotide diversities (π) were calculated in Arlequin 3.5.1.242 using the cyt b data set.Cyt N), average number of alleles (Aavg), observed heterozygosity (HO) and expected heterozygosity (HE) were calculated for each location and for each locus with Genodive43. Net evolutionary divergence between putative species of OWA was calculated on MEGA 544 using the Maximum Composite Likelihood model. The rate variation among sites was modelled with a gamma distribution (shape parameter = 1.48).Summary statistics, number of individuals  and Pacific Ocean . We tested four variations of the two-population (Atlantic-Pacific) migration model: bidirectional migration , strict Atlantic to Pacific migration (three parameters), strict Pacific to Atlantic migration (three parameters) and panmictic model that assumes the Atlantic and Pacific are part of a panmictic population (one parameter). Testing the directionality of gene flow is justified because the dominant ocean current between the ocean basins, the Agulhas flow, runs westerly from the Indian to the Atlantic Ocean and is thought to play a limiting role in marine dispersal in the opposite direction53. Initial values were calculated using FST. Mutation rates were set to be constant among loci. The Migrate-N run parameters were calibrated on the full model for convergence of the Markov chain Monte Carlo sampling method. The prior distributions were uniform for mutation-scaled population size parameters theta (θ), that are four times the product of the effective population size and the mutation rate, and mutation-scaled migration rates M, that is, immigration rate scaled by the mutation rate, over the range of θ = 0.05–0.5 and prior migration rate M = 0–100 for mtDNA, and θ = 10–100 and M = 0–50 for microsatellites. These settings resulted in converged posterior distributions with a clear maximum for each estimate. The Bayesian run for mtDNA consisted of one long chain with a total of 15 million states visited and 50,000 states recorded for the generation of posterior distribution histograms for each locus after discarding the first 10,000 genealogies as burn-in; for all loci, a total of 48 million states were visited and 160,000 samples were recorded. For all the analyses we used an adaptive heating scheme with four simultaneous chains using different acceptance ratios ; the analyses were run on a cluster computer using 4 compute nodes per run. The Bayesian run for microsatellites consisted of one long chain with a total of 6 million states were visited and 20,000 states were recorded for the generation of posterior distribution histograms for each locus after discarding the first 10,000 genealogies as burn-in; for all loci, a total of 48 million states were visited and 160,000 samples were recorded. For all the analyses we used an adaptive heating scheme with four simultaneous chains using different acceptance ratios ; the analyses were run on a cluster computer using four compute nodes per run. Overall loci information was combined into a single estimate by Bézier approximation of the thermodynamic scores as described by Beerli & Palczewski54. We averaged the Bézier score over three different runs and used as input to evaluate multiple models using Bayes factors55.We used the coalescent-based program Migrate-Nb gene . At least one representative species of Engraulidae per genus (except Papuengraulis) and nine randomly chosen specimens from each of the five currently recognized OWA species were included in the phylogenetic analyses, with the exception of E. capensis from which only four specimens were available 57 implemented in Modeltest 3.758, selected the GTR+I+Γ as the evolutionary model that best-fitted the data set. The inferred parameters were used in maximum likelihood (ML) and Bayesian Inference (BI) analyses. BI analyses were conducted with MrBayes 3.2.159. Metropolis-coupled Markov chain Monte Carlo (MCMC) analyses were ran for 20,000,000 generations with sample frequency of 2000. Final trees were calculated after a burnin of 1,000 generations. PhyML 3.060 was used to estimate the ML tree and to test by non-parametric bootstrapping the robustness of the inferred trees using 1,000 pseudoreplicates.Phylogenetic relationships within Engraulidae were based on a fragment of the mitochondrial cyt 16 recovered E. encrasicolus as paraphyletic (specimens assigned to the species grouped into two clades that did not group together). To test if natural selection could interfere with phylogenetic inference, we performed all phylogenetic analyses using the above data set that only included individuals that do not show any evidence of being under selection and repeated the procedures using another data set  that included individuals presenting a mutation in codon 368 of the cyt b as identified in Silva et al.23.Previous work61 based on a concatenated dataset of four partial fragments of mtDNA  and nuclear  genes. We included sequence data of 49 Engraulidae lineages/taxa35, likely representing 14 genera (out of 16: the monospecific Lycothrissa and Papuengraulis genera are not represented), from which 5 are OWA  were recovered within the latter. Hence, to perform the dating analysis we only selected a single representative of E. encrasicolus from clade A and two from clade B, both without the mutation at codon 368. We used the BirthDeath model for the tree prior that assumes that at any point in time, every lineage undergoes speciation at rate λ or goes extinct at rate μ62, and three calibration points. One refers to the earliest record of Engraulidae [6–12] million years (Ma) from the Miocene - Lower Pliocene of Cyprus63. The second calibration corresponds to age estimated for the divergence between Anchovia clupeoides and A. macrolepidota [2.8–3.1] Ma due to the closure of the Panama seaway64. The third calibration corresponds to E. japonicus [2–0] Ma from Kokubu group, Japan65. Calibrations using the two fossils were modelled with a lognormal distribution, where 95% of the prior weight fell within the geological interval in which each fossil was discovered. For the Engraulidae [12–6] Ma, the parameters of the lognormal calibration prior were: 95% interval: mean in real space: 1.4, offset: 6.0 and log stdev: 1.0. For E. japonicus [2–0] Ma, the parameters of the lognormal calibration prior were: 95% interval: mean in real space: 0.465, offset: 0 and log stdev: 1.0. For the divergence between Anchovia clupeoides and A. macrolepidota we used a calibration according to Lessios et al.64 where the closure of the isthmus of Panama occurred between 3.1–2.8 Ma. Lognormal calibration was set to: 95% interval: mean in real space: 0.071, offset: 2.8 and log stdev: 1.0. MCMC analyses were run for 20,000,000 generations with a sample frequency of 20,000, following a discarded burn-in of 2,000,000 steps. The convergence to the stationary distributions was confirmed by inspection of the MCMC samples using Tracer 1.666.To estimate the OWA origin and date lineage-splitting events within Engraulidae we used a Bayesian relaxed molecular-clock approach as implemented in Beast 2.1.3ML, BI and dating analyses were performed on the R2C2 research group cluster facility provided by the IT Department of the University of Algarve.b (1044 bp) were deposited in GenBank . Sequences of the nuclear recombination-activating genes RAG1 and RAG2 and of the mitochondrial 16S rRNA used in the Bayesian dating analysis were deposited in GenBank (accession numbers: KX824115–KX824124). The accession numbers of the sequences retrieved from GenBank corresponding to the remaining Engraulidae specimens used in all phylogenetic analyses are indicated in Tables Sequences of the mitochondrial cyt Supporting Information


ST = 0.059). Pairwise FST values indicated least differentiation between Solapuri and Madgyal (0.025). In terms of genetic distances, Kolhapuri and Lonand were most closely related (Ds = 0.177). The most probable structure clustering of the five studied populations was at K = 5. The study showed a fair congruence between the dendrogram constructed on the basis of Mahalanobis distances and Nei’s as well as Reynolds genetic distances. The findings gave highest conservation priority to Lonand and least to Solapuri ecotype.Characterization of Indian livestock breeds has mostly been limited to single breed/population focused on either physical description of traditionally recognized breeds/populations or to their genetic description. Usually, morphological and genetic characterization has taken place in isolation. A parallel morphological characterization of genetically identified breeds or genetic characterization of morphologically described breeds is mostly missing, and their conservation priorities have largely been based on solely considering degree of endangerment. This study uses parallel approach based on morphometric and genetic differentiation for classification of five sheep ecotypes of Maharashtra state, and sets their conservation priority using threat parameters, current utilities/merits and contribution to genetic diversity. A total of 1101 animals were described for 7 body measurements for morphometric characterization. From this sample set, 456 animals were genotyped for 25 microsatellite markers for genetic characterization. Conservation priorities were assessed combining genetic and non-genetic factors. All studied traits varied significantly among ecotypes (p<0.05). All morphometric traits exhibited substantial sexual dimorphism except ear length. Males were 42% heavier than females. Madgyal sheep were the largest amongst the five ecotypes. In the stepwise discriminant analysis, all measured traits were significant and were found to have potential discriminatory power. Tail length was the most discriminatory trait. The Mahalanobis distance of the morphological traits between Kolhapuri and Madgyal was maximum (12.07) while the least differentiation was observed between Madgyal and Solapuri (1.50). Discriminant analysis showed that 68.12% sheep were classified into their source population. The Sangamneri sheep showed least assignment error (22%) whilst Solapuri exhibited maximum error level (41%). A total of 407 alleles were observed, with an average of 16.28 alleles per locus. Sufficient levels of genetic diversity were observed in all the ecotypes with observed heterozygosity values exceeding 0.47 and gene diversity values exceeding 0.76. About 6% of the total genetic variation was explained by population differences (F The Global Action Plan for Animal Genetic Resources recognizes that “a better understanding of the characteristics of livestock breeds is necessary for guiding decision making in the development of farms and breeding programs”. GeneticIndian sheep breeds have generally been named after their place of origin and some based on their prominent characteristics. These have been traditionally described and classified based on their utility, and on the basis of agro-ecological regions . In receThe sheep of Deccan plateau, between 16° to 20°N latitude and 72° to 78°E longitude, called Deccani were considered a breed –23 and wMaharashtra state (15°55' and 22° N and 72°5' and 80°9' E) is located in the Deccan plateau region of India. The altitude of the plateau varies from 450–750 m above mean sea level. The state is divided into 36 administrative districts grouped into 6 divisions. The studied sheep ecotypes are native to and distributed through five districts as shown in The study involved drawing of ~ 5 ml blood from jugular vein aseptically from sheep with the consent of the flock owners in the presence of the trained veterinarians. There is no specific legislation for blood sample collection and hence no approval was necessary. Nevertheless, the study has the approval of Institute Animal Ethics Committee (IAEC) of Indian Council of Agricultural Research –National Bureau of Animal Genetic Resources, Karnal (PME Ref. 11/2017-18).A total of 456 blood samples of unrelated animals were collected from five sheep ecotypes across their distribution tract. Samples were taken from distinct flocks. Number of samples per flock ranged between 2 to 4 depending upon the size of the flock. The sheep owners do not maintain any pedigree records of the animals.Genomic DNA was isolated and purified using the standard phenol chloroform extraction protocol. Genetic variation was assayed as described earlier  using 25Statistical analyses on morphometric data were performed using SAS software, version 9.3 . The anaa), observed heterozygosity (Ho), expected heterozygosity (He), FIT , FST (measurement of population differentiation) and FIS (within-population-inbreeding estimate) were calculated using FSTAT ver 2.9.3 [Allele sizing was performed using GENEMAPPER software. Allele frequencies, observed number of alleles  +Wi, where zi is the extinction probability, Di is Weitzman marginal diversity and Wi is the current merit of the ith ecotype.Contribution to between-breed diversity was computed by estimation of Weitzman values  based onework of –44 as UiLeast-squares means and standard errors for morphometric traits are given in p < 0.0001). The largest distance was between Kolhapuri and Madgyal (12.07) and the least was between Madgyal and Solapuri (1.50).Results of stepwise discriminant analysis are presented in The UPGMA based dendrogram , construA total of 407 alleles were found across 25 loci in the 456 individuals sampled from the 5 sheep ecotypes . The moso) for all the ecotypes ranged from 0.47 (Sangamneri) to 0.62 (Lonand) whereas the gene diversity values varied from 0.76 (Sangamneri) to 0.81 (Solapuri). All sheep ecotypes were genetically diverse at the 25 loci as evident from the high allele (>6) and gene (>0.6) diversity values. The allelic richness, independent of the sample size revealed highest mean allelic richness in Solapuri (8.25) and least in Sangamneri (7.27) sheep. Marker wise observed and expected heterozygosity in each breed is being given as supplementary information (ormation .ST) and genetic distance (DS) matrices are given in ST values between populations were statistically significant (p<0.05).The ecotype pair-wise FST values ranged from 0.025 to 0.054, thereby revealing least differentiation between Solapuri and Madgyal and highest differentiation between Sangamneri and Madgyal. The average genetic differentiation (FST) between the ecotypes was 5.9% (P<0.05) revealing moderate discrimination between the ecotypes  might be due to its development under selective breeding. Madgyal is an improved sheep whose origins are obscure . Some shThe allele diversity based on mean number of alleles and mean effective allele number revealed high genetic polymorphism in the investigated sheep populations. The genetic diversity is also reflected in the phenotypic variability between these ecotypes. This magnitude of genetic diversity can be attributed to the highly polymorphic microsatellite markers used (PIC>0.5). These estimates are higher than those reported for other Indian sheep breeds , 17, 51 FIS value revealed heterozygotes deficiency in the five ecotypes. Possible reasons could be presence of null alleles; locus may be under selection; or population subdivision. However, distinguishing among these is generally difficult. Due to non-availability of pedigreed animals with the farmers for analysis, it was not possible to demonstrate the presence of null alleles. Inbreeding is an unlikely explanation because of the presence of high gene diversity in the populations and observing the practice of replacing the breeding rams in the farmer’s sheep flocks. The deficit of heterozygotes may partly be explained by Wahlund effects at our level of sampling i.e. sampling at random from several flocks in several villages [A significant positive villages . Furthervillages . Similarvillages .ST (coefficient of multi-locus genetic differentiation fixation index) were moderate  with 5.9% of total genetic variation attributable to breed differences and the remaining 94.1% corresponding to differences among individuals. This could be due to geographic proximity, migration and cultural similarities amongst the major sheep rearing communities. Similar reasons were attributed to low differentiation (4.6%) between the Ethiopian sheep breeds [The population differences shown by global analysis of Fp breeds , 18. Thep breeds ), Alpinep breeds , Nigeriap breeds , Algeriap breeds , Moroccap breeds ) and Ausp breeds  sheep brST, genetic distances and PCA revealed similar results, with Sangamneri ecotype differentiated from other sheep in the study. Genetic closeness was observed between Lonand and Kolhapuri as well as Solapuri and Madgyal. The phylogenetic relationship between the ecotypes was supported by the cluster analyses. The clusters defined by STRUCTURE [The frequency-based assignment test  assigns TRUCTURE  and by tTRUCTURE  showed cThe measure of the marginal loss of diversity for each ecotype using the Weitzman approach enables the ranking of breeds for conservation purposes . This apThe study defines the morphometric and genetic standards of Deccani sheep ecotypes. Morphometric as well as genetic differentiation established five differentiated groups. A fair congruence between the dendrogram constructed on the basis of Mahalanobis distances and Nei’s genetic distances was observed. The gene diversity observed was moderate, a distinctive trait of India sheep breeds. It may notably be the result of the lack of artificial selection pressure and high level of genetic admixture in the breeds. Our study outlines conservation priorities of Deccani sheep ecotypes by combining contributions to genetic diversity, breed merits and threat status. The study could be used as a model to define conservation priorities of Indian sheep breeds and could contribute to a National conservation plan of Indian livestock breeds.S1 TableHere is the ecotype-wise information about the total number of animals used for morphometric characterization and for genotyping.(DOCX)Click here for additional data file.S2 TableHere is the information on these parameters on the 25 microsatellites used in the study.(DOCX)Click here for additional data file.S3 TableThis table provides observed and expected heterozygosity in each ecotype on used 25 microsatellite markers.(DOCX)Click here for additional data file.S4 TableThe exact p-values using Markov chain  were estimated using GENEPOP  version (DOCX)Click here for additional data file.S1 FileThis file contains the allele frequency data on 456 animals genotyped on 25 microsatellite markers. The animals belong to five sheep ecotypes of Maharashtra, India.(XLSX)Click here for additional data file.


Candida glabrata is a component of the mycobiota of both humans and yellow-legged gulls that is prone to develop fluconazole resistance. Whether gulls are a reservoir of the yeast and facilitate the dissemination of human C. glabrata strains remains an open question. In this study, MLVA genotyping highlighted the lack of genetic structure of 190 C. glabrata strains isolated from either patients in three hospitals or fecal samples collected from gull breeding colonies located in five distinct areas along the French Mediterranean littoral. Fluconazole-resistant isolates were evenly distributed between both gull and human populations. These findings demonstrate that gulls are a reservoir of this species and facilitate the diffusion of C. glabrata and indirect transmission to human or animal hosts via environmental contamination. This eco-epidemiological view, which can be applied to other vertebrate host species, broadens our perspective regarding the reservoirs and dissemination patterns of antifungal-resistant human pathogenic yeast.The opportunistic pathogenic yeast  Candida glabrata is one of the most frequently identified yeast species of the human gut mycobiotaC. albicans23C. glabrata infections are characterized by a high (40–70%) fatality rate, especially in immunocompromised patientsC. glabrata has not been well characterized. Apart from humans, this yeast is a common commensal organism of many species of pet birds, such as cockatiels, parakeets, lovebirds and cockatoos or migratory birds such as the common whitethroat (Sylvia communis) and the spotted flycatcher (Muscicapa striata)688101112131617 high 40–0% fataliLarus michahellis) population numbers have greatly increased throughout the Mediterranean littoral concurrent with an increase in anthropic refuse production. Consequently, they have displayed an increase in interaction with the environment in general and in particular with humans and other animals192021In recent decades, yellow-legged gull , thereby indicating, as expected, a significant effect of genetic structure on population differentiation. In contrast, the between-host FST was 0.03 (P < 10−5), which indicates a relatively low C. glabrata population differentiation due to genetic structure between gull and human isolates. The MST (C. glabrata isolates. Neither host (gull or human) nor study site was associated with any genetic cluster. In particular, gull and human isolates were distributed between both genetic clusters, with an exception for the 11 isolates collected from gulls at La Grande-Motte, which all belonged to the first cluster , 9% (P < 10−5) and 1% (P = 0.11) of the genetic variance, respectively. We also calculated FST for three patient populations and five gull populations; the between site pairwise FST values are specified in FST values was 0.037 (P = 0.04) between populations from Montpellier and Marseille  between Riou and La Grande-Motte (C. glabrata genetic differentiation (P = 0.023).AMOVA revealed that study sites, within-host and among-host populations explained 90% ; 9 and 14  [18.0–57.5%] vs. 50%, 95%CI [29.5–67.5%], P = 0.53) were isolated from gull or human hosts, respectively. The absence of genetic clustering according to the host or fluconazole susceptibility is depicted in the MLVA-based MST tree  or humans were genetically similaret al.C. glabrata populations isolated from patients in Paris and Montpellier, which are 800 km apart, displayed genetic differentiation . Using the same MLVA scheme as in the present study, Dhieb et al.FST = 0.359, P < 10−5) between C. glabrata populations isolated from patients in France or Tunisia. Although the geographical scale of our study was much more limited than in the previous studies, we detected either relatively high or low genetic differentiation according to the study sites. As we found evidence for both dispersion and differentiation, our study was indeed adequately scaled to dissect transmission profiles and detect reservoirs.Overall, this study highlights the absence of significant genetic differentiation between C. glabrata is a component of the human gut mycobiota. We hypothesize that yellow-legged gulls inadvertently ingest yeast such as C. glabrata with their food, which might be contaminated with human excreta in highly anthropic marine and terrestrial environments. Compared with other sea birds, yellow-legged gulls are highly synanthropic. This is a major reason that gull populations have grown concomitantly with human-made environments, including human refuse sites, along the Mediterranean littoralC. glabrataC. glabrata originating directly (via bird droppings in their direct environment) or, more frequently, indirectly by ingesting food that has been contaminated with bird droppings. The genetic homogeny of human and bird isolates clearly suggests that yellow-legged gulls play a role in the diffusion of C. glabrata acquired from an anthropic environment. In line with this hypothesis, yellow-legged gulls transmit and spread potential human pathogens in various environments. Indeed, Bonnedahl et al.Escherichia coli isolates not far from Pierre Blanche. Similarly, we demonstrated the presence of genetically homogeneous fluconazole-resistant C. glabrata populations in both gulls and humans in Marseille. Therefore, our findings show that yellow-legged gulls act as a carrier, reservoir and disseminator of C. glabrata. These birds may thus contribute to the transmission of yeast to humans and likely other animal hosts. Moreover, the likelihood of infectious disease transmission via yellow-legged gulls has therefore increased due to the marked demographic growth of gull populations along the Mediterranean littoral. Our findings also demonstrate that yellow-legged gulls represent a reservoir of antifungal-resistant Candida strains and disseminate antifungal-resistant isolates.Humans may be infected with C. glabrata by eating or drinking in environments contaminated with human excreta. The yeast eventually evolves to be incorporated into their gut mycobiota and is spread via gull feces to human environment. Moreover, yellow-legged gulls represent a reservoir of fluconazole-resistant C. glabrata isolates, which may have a potential impact on public health, including food spoilage control and potable water source protection.The close proximity and interaction between very dense human and yellow-legged gull populations in cities of the Mediterranean littoral facilitates the circulation of microorganisms between the two hosts. Gulls likely ingest C. glabrata isolates. One hundred eleven samples were isolated from feces collected on the soil at five yellow-legged gull breeding colonies as previously described by Al-Yasiri et al.C. glabrata samples were isolated from patients at three university hospitals in Marseille, Montpellier and Nimes, which were located in the same geographical area and sampled during the same time period as those isolated from gulls. All isolates were subcultured on Malt extract agar . The samples were identified via the MALDI-TOF MS (matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry) technique, as previously describedTM, Becton Dickinson, France) to verify isolate purityIn this study, we analyzed 190 C. glabrata isolates were typed with eight microsatellite markers  as previously described by Brisse et al.TM EasyMAGTM (bioMérieux) systemTM 480  instrument with LightcyclerTM 480 Probes Master . The loci, primer sequences, fluorophores and hybridization temperatures are described in TM safe DNA gel stain . Next, 1 μl of 1:100 diluted PCR products was mixed with a solution containing 25 μl HiDi formamide  and 0.5 μl Gene ScanTM 500 LIZTM size standard . The fragment length was determined via capillary electrophoresis using an ABI 3130 Genetic Analyzer  and analyzed using GeneMapper software v4.0 .All poppr R packageE5, ; Simpson’s (lambda) diversity indexSeveral indices of clonal diversity were estimated using the STRUCTURE v. 2.3.1 was used to identify genetically distinct clusters via estimation of the proportion of membership (Q) of each isolate in each cluster. This software applies a Bayesian model-based clustering approachFST) and Analysis of MOlecular VAriance (AMOVA) using ARLEQUIN v3.5 software. The effect of geographical distances on genetic differentiation was tested via Mantel test. MLVA-based Minimum Spanning Tree (MST) was constructed using BIONUMERICS software v7.1.The among- and between-population differentiation due to genetic structure was estimated via Slatkin’s linearized pairwise fixation index  M27-S4 documentCandida parapsilosis) and ATCC 6258 (Candida krusei) quality control strains.How to cite this article: Al-Yasiri, M. H. et al. Opportunistic fungal pathogen Candida glabrata circulates between humans and yellow-legged gulls. Sci. Rep.6, 36157; doi: 10.1038/srep36157 (2016).Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Ovis canadensis nelsoni) in three differing regions of the southwestern United States as an example. We conducted genetic sampling and least-cost path modeling to optimize landscape resistance models independently for each region, and then simulated the spread of an adaptive allele favored by selection across each region. Optimized landscape resistance models differed between regions with respect to landscape variables included and their relationships to resistance, but the slope of terrain and the presence of water barriers and major roads had the greatest impacts on gene flow. Genetic simulations showed that differences among landscapes strongly influenced spread of adaptive genetic variation, with faster spread (1) in landscapes with more continuously distributed habitat and (2) when a pre-existing allele  rather than a novel allele  served as the source of adaptive genetic variation. The combination of landscape resistance models and genetic simulations has broad conservation applications and can facilitate comparisons of adaptive potential within and between landscapes.Landscape genetic studies based on neutral genetic markers have contributed to our understanding of the influence of landscape composition and configuration on gene flow and genetic variation. However, the potential for species to adapt to changing landscapes will depend on how natural selection influences adaptive genetic variation. We demonstrate how landscape resistance models can be combined with genetic simulations incorporating natural selection to explore how the spread of adaptive variation is affected by landscape characteristics, using desert bighorn sheep ( The field of landscape genetics has greatly enhanced our understanding of the influence of landscape composition and configuration on gene flow and genetic variation of organisms . The mosMuch of our understanding of how landscape characteristics could influence the spread of adaptive variation comes from theoretical models, such as those that explore effects of population subdivision on the rate or probability of fixation of a beneficial mutation. For instance, beneficial mutations spread at a slower rate in structured populations  and reacSelection strength also plays a role in determining how adaptive variation spreads across populations , 16. TheOvis canadensis nelsoni) in the southwestern United States. We genotyped >850 individuals at neutral markers from three landscapes that vary with respect to habitat configuration and factors influencing gene flow, optimized landscape resistance models independently for these regions, and used genetic simulations to determine how differences among landscapes affect the capacity for spread of adaptive genetic variation within landscapes.Landscape resistance models based on neutral genetic variation can play an important role by providing realistic, empirically-supported backdrops for simulating the spread of adaptive genetic variation across landscapes. Individual-based, spatially-explicit genetic simulators now allow us to incorporate natural selection in the simulation of gene flow and demographic processes across resistant landscapes e.g., , 19). Wi. Wi19]).Desert bighorn sheep occupy some of the hottest and driest portions of the southwestern U.S, and their distribution is strongly limited by availability of reliable surface water and steep terrain to allow escape from predators . HabitatClimate projection models predict increases in temperature and aridity in the southwestern U.S. in coming decades , 25, whiIn this study, we explored two scenarios under which adaptive genetic variation could arise and spread throughout a region to facilitate climate change adaptation . In the first, a novel allele favored by selection was introduced at one location–for instance, via a mutation or the intentional translocation of individuals with a novel genotype–and subsequently spread outward from this origin point . In the second scenario, an allele that was already present throughout the region at low frequency became favored by selection due to a change in environmental conditions–for instance, a shift in climate regime–and subsequently increased in frequency throughout the region . We simulated each of these scenarios  in three regions that differ with respect to habitat configuration and factors influencing landscape resistance for bighorn sheep, and we compared rates of adaptive allele spread among regions and scenarios while varying selection strength. We hypothesized that the rate of spread would be faster (1) in regions with more continuously distributed habitat because gene flow would be less limited by the need for high-cost dispersal between disjunct populations, (2) under the pre-existing allele scenario because local populations would be more likely to initially contain the adaptive allele and thus be less reliant on outside immigration to achieve adaptive allele spread locally, and (3) when selection for the adaptive allele was stronger.Methods included the following major components: (1) collecting and genotyping DNA samples from individuals in three regions at 14–16 neutral microsatellite loci, (2) developing a suite of candidate landscape resistance models that describe how landscape variables influence gene flow, (3) using genetic data and least-cost path modeling to test the fit of candidate resistance models and identify an optimal model for each region, (4) simulating the spread of an adaptive allele in each region during 100 years of gene flow influenced by landscape resistance, with mate selection and dispersal determined as probabilistic functions of cumulative cost across optimized regional resistance surfaces, and (5) comparing results among regions for three selection strengths and two initial spatial distributions of the adaptive allele. We discuss each component in detail below.This study considers desert bighorn sheep populations in three regions of the southwestern U.S. that differ substantially in habitat configuration. The southern Mojave Desert region  of southeastern California and southern Nevada contains bighorn sheep habitat distributed in discrete mountain ranges within a matrix of less hospitable flats . PopulatThe Grand Canyon region  in northern Arizona is dominated by the Colorado River flowing between Lake Mead and Lake Powell within Grand Canyon National Park . BighornThe Death Valley region  of the northern Mojave Desert along the California-Nevada border is centered on Death Valley National Park. Bighorn sheep populations occupy habitat patches that are relatively discrete and separated by flat, arid valleys, but are generally larger and more linear than in MOJA ; thus, DWe obtained DNA mainly via non-invasive sampling of fecal pellets, and from a small number of tissue and blood samples from live captures, hunter kills, or carcasses found in the field. We used both newly-collected samples (2011–2013) from a larger project to assess genetic diversity around ten national parks in the southwest U.S. , and samSamples were analyzed using similar protocols in three genetics labs, corresponding to the three sampling periods: Oregon State University , White Mountain Research Station , and University of California, Berkeley . A total of 20 microsatellite loci were used across labs, but only 14–16 loci were used in each lab, with 10 loci common to all three labs . Descripar, PCA-based genetic distance) in previous studies , divided by the sum of relative likelihoods of all models). We selected a top model from the candidate set using the median Akaike weight across iterations as our model selection criterion. A simple Euclidean distance surface  was included in the set of candidate surfaces for each variable to serve as a null model of isolation by distance (IBD). Because previous research has suggested that log-transforming cost-distances may improve linearity [R2.For each random subset, we fitted a linear regression model of genetic distance as a function of cost distance for each candidate resistance surface and calculated Akaike Information Criterion (AIC). We performed 10,000 iterations of this procedure, fitting all candidate models to the same random subset of pairwise data in each iteration and compared models using Akaike weight  for all possible combinations of landscape variables, using the optimized univariate resistance surface for each variable. Any variable for which the optimized univariate surface did not perform better than IBD  was excluded from all candidate multivariate resistance surfaces. To allow for the possibility of interactions between variables , we also created candidate multivariate models using univariate surfaces with the same shape of resistance curve as the best univariate surface, but with a different maximum resistance value. For instance, if the optimized univariate surface for the NDVI variable indicated a concave-down, negative relationship with a maximum resistance value of 50, we also created multivariate surfaces including concave-down, negative relationships with maximum resistance values of 10 and 100 for NDVI. We could not test all possible combinations of univariate models because allowing all univariate model parameters to vary for each landscape variable in multivariate models would have resulted in an excessive number of multivariate models. Other methods have been proposed to maximize the amount of the multivariate hypothesis space explored , but all methods are constrained to some extent by computational limitations. We compared multivariate surfaces using the bootstrap AIC approach described above. Because four or fewer landscape variables were more informative than the null model of IBD in each region, the number of multivariate models remained reasonable.A and a neutral allele a. We tested three different strengths of selection for the adaptive allele: a 10 percent (“weak selection”), 20 percent (“moderate selection”), or 30 percent (“strong selection”) increase in offspring survival of the AA genotype relative to the aa genotype. We assumed additive dominance, whereby survival of the Aa genotype was intermediate (h = 0.5) to the two homozygotes.After identifying the best landscape resistance model for each study region using landscape genetic analysis of neutral markers, we used the computer program CDPOP v1.2  to simul2 in each region, resulting in 1,684 individuals for DEVA, 624 for GRCA, and 1,576 simulated individuals for MOJA. We arrived at this density by summing population size estimates for the MOJA and DEVA regions  and dividing by the total area of occupied habitat within these two regions; a population estimate was unavailable for GRCA, so we assumed that the average bighorn sheep density in the other regions was a suitable estimate for GRCA. The assumption of constant density of individuals within and across regions was preferable to using actual population sizes because (1) population information was unavailable or outdated in many areas; (2) bighorn sheep population sizes can change dramatically over short time scales, especially in metapopulation systems such as the Mojave Desert, or in the event of a disease outbreak; thus, current population estimates may only remain accurate for a short portion of the simulation time frame; and (3) we wanted to investigate the effects of differences in landscape configuration and resistance among regions without the variation introduced by differences in local population density.We used maps of occupied desert bighorn sheep habitat provided by state wildlife agencies to assign individual locations, which remain fixed throughout simulations in CDPOP. Individuals were randomly placed within occupied habitat at a constant density of 0.2 individuals/kmA) and neutral (a) alleles, respectively, in each regional population. We selected a single individual near the center of each region and identified the closest two percent of neighboring individuals in the landscape, based on cost distance. Among this subset of individuals, we randomly assigned half of the pooled alleles to be the A allele, and all remaining alleles within the region to be the a allele, creating a small cluster of AA, Aa and aa genotypes at Hardy-Weinberg equilibrium frequencies within a regional population that was otherwise homozygous for the aa genotype. These clusters of adaptive alleles were approximately 15 km in diameter in all three regions, and spanned portions of two populations each in DEVA and MOJA, and a small portion of the single continuous population in GRCA. We used this cluster strategy rather than initializing simulations with a single copy of an adaptive allele, as would occur immediately following a mutation, because a single allele would quickly be removed from the population by genetic drift in most cases, even when selection was strong. Thus, this scenario might exemplify examining spread of a local adaptation or variant. For the pre-existing allele scenario, simulating a change in selective coefficient for an allele already present at some frequency across the region, we initialized genotypes with regional allele frequencies of 0.05 and 0.95 for the A and a alleles, respectively, and each allele randomly distributed among individuals in the region.For the novel allele scenario, we initialized genotypes with allele frequencies of 0.01 and 0.99 for the adaptive  and scenario . Mating and dispersal movements followed an inverse-square function of cost distance. Although bighorn sheep exhibit sex-biased dispersal , prelimiWe first ran simulations with a maximum dispersal threshold of 534,861 cost units, the cost distance beyond which genetic distance and cost distance were no longer correlated within the GRCA region; this was the smallest of such estimates for the three regions, using the best multivariate resistance models to estimate cost distance. Because maximum dispersal distance of bighorn sheep has not been precisely estimated and could influence the relative rate of spread of adaptive alleles, we repeated all simulations at ½ the original dispersal threshold  and at twice the original dispersal threshold  to bracket a range of likely dispersal thresholds; we hereafter refer to these threshold values as “low”, “medium”, and “high” dispersal thresholds. These cost distance thresholds correspond to Euclidean distances ranging between 2.4 and 13.6 km if individuals traveled through average-resistance terrain in each region; however, actual distances traveled in simulations could be much further than this because we assumed individuals traveled along least-cost paths. We allowed males but not females to mate with replacement in order to approximate the polygynous mating system of bighorn sheep. The population included 17 annual age classes, with age-specific mortality and fecundity rates estimated from the literature , 73–75. fA) at every year by averaging results from the 50 MC replicates for each combination of selection strength and scenario. We plotted 95% confidence bands for fA in each region as a function of time and compared confidence bands for differences in the rate of adaptive allele spread among regions.We calculated the mean adaptive allele frequency  or pair of loci (for LE) was consistently out of equilibrium across populations, suggesting that these deviations most likely resulted from population substructure rather than non-neutral loci or non-independent loci. We therefore retained all loci in subsequent analyses.R2. A Gaussian slope model was preferred over a linear model or break-point model, although the parameters of the Gaussian model  differed between regions. Presence of water barriers was associated with increased resistance to gene flow and was the variable with the greatest explanatory power in GRCA, but was not supported in the remaining two regions. Similarly, presence of major roads was associated with increased resistance and was an important variable in MOJA but not in DEVA or GRCA. Distance to water (positively associated with resistance) was supported in DEVA and GRCA, and NDVI (negatively associated with resistance) was supported in DEVA and MOJA, but these variables only explained slightly more variation than Euclidean distance in these regions. Forested areas and anthropogenic development were not supported in any region and were excluded from multivariate optimization. Models with unlogged cost distances were preferred for all variables in all regions, with the exception of the isolation by distance model in GRCA.The number of randomly sampled pairs ranged among iterations from 109 to 112 for DEVA, 123 to 125 for GRCA, and 185 to 188 for MOJA. Landscape variables that were supported by univariate optimization  differed among regions . Slope wThe optimized multivariate model for GRCA included slope, water barriers, and distance to water. For MOJA, the optimized multivariate model included slope, roads, and NDVI. The univariate slope model was preferred over all multivariate models for DEVA. R2: cost distances explained less than a quarter of the variation in genetic distances in all regions. The multivariate models for GRCA and MOJA represented only a modest increase in explanatory power over the best univariate model. In the DEVA and MOJA regions, Euclidean distance alone explained at least two thirds as much variation as the best multivariate resistance model. However, distance was a less powerful predictor of genetic differentiation in GRCA, where the barrier effect of the Colorado River explained the majority of variation in genetic distances  was positively associated with selection strength and dispersal threshold in both simulation scenarios. We observed greater increase in fA in landscapes with more continuously distributed habitat under both scenarios. Relative differences in fA among regions  tended to be larger under the novel allele scenario.Adaptive allele frequency  was slow to emerge (25–50 years under most conditions) and was more pronounced when selection was stronger and maximum dispersal threshold was larger. GRCA clearly exhibited higher fA than DEVA and MOJA for all dispersal thresholds when selection was moderate or strong. Differences in fA among DEVA and MOJA were only evident when the high dispersal threshold was used and selection was moderate or strong; for all other combinations of dispersal threshold and selection strength, there was no appreciable difference in fA between DEVA and MOJA. Where differences were evident, fA tended to be higher for DEVA than for MOJA, consistent with our hypothesis of faster spread of the adaptive allele in regions with more continuously distributed habitat.Under the novel allele scenario, relatively small increases in n period . Even unfA over time under the pre-existing allele scenario than the novel allele scenario, with fA reaching nearly 0.35 by year 100 under some conditions  rather than a novel allele  served as the source of adaptive genetic variation.Our resistance model optimization suggested that slope and strong dispersal barriers, including major water bodies and interstate highways, were the dominant landscape factors influencing gene flow for desert bighorn sheep. These results are consistent with previous research demonstrating strong effects of slope and highways on bighorn gene flow in the Mojave Desert , 21, andThis example illustrates an important and well-known limitation in landscape genetic analyses: features that influence gene flow, but are not highly variable within the landscape, are often not supported in landscape resistance models . This liEuclidean distance explained at least two thirds as much of the genetic differentiation among individuals as the final optimized model in DEVA and MOJA, suggesting that isolation by distance is strong in these regions. However, distance was a relatively poor predictor of genetic differentiation in GRCA, which was likely driven by the unique juxtaposition of suitable habitat and a major barrier in GRCA: the steeply sloped Grand Canyon provides a long, narrow, and continuous habitat patch for bighorn sheep, but is bisected by the Colorado River running through the bottom of the canyon and serving as a strong barrier to movement. Thus, two individuals on opposite sides of the river that were sampled only hundreds of meters apart may have less chance of mating than two individual on the same side of the river that were sampled tens to hundreds of kilometers apart.The simulated rates of spread of adaptive genetic variation in our three study regions largely supported our expectation of faster spread in landscapes with more continuously distributed habitat. In fact, our results may actually underestimate differences among the regions because population density was assumed to be constant through time and between habitat patches in CDPOP simulations, but densities tend to fluctuate in space and time in real populations. Such fluctuations are likely to be most dramatic in patchy landscapes like the MOJA region that exhibit metapopulation dynamics , 32. ThefA were generally consistent with our expectations: fA increased faster when selection was stronger or dispersal threshold was larger. The effect of dispersal distance on fA values was much smaller than the effect of selection in most cases. Our high dispersal threshold was four times larger than our low threshold, but fA values tended to be only marginally higher using the high threshold. We suspect that this is because we used an inverse square dispersal function resulting in very common short-distance movements and very rare long-distance movements, such that increasing the maximum dispersal distance may have had only a small influence on the average distance moved by an individual. Exploring dispersal functions was beyond the scope of this study and should be left for future theoretical work.The effects of selection strength and dispersal threshold on fA increased faster for DEVA than MOJA with the medium dispersal threshold, but the opposite was true with the low dispersal threshold. This interplay between dispersal threshold and selection strength is a potentially complex topic that warrants further investigation.In some cases, we observed interesting interactions between selection strength and dispersal threshold. The degree to which differences among regions arose during simulations depended on the combination of selection strength and dispersal threshold considered, with regional differences quite pronounced for some combinations and minimal for others. In a few cases, the choice of dispersal threshold even reversed the conclusion regarding the relative spread of an adaptive allele in the two relatively patchy regions; for instance, under strong selection in the pre-existing allele scenario, fA under the novel allele and pre-existing allele scenarios. This may be partially due to the fact that initial allele frequencies differed between the scenarios (0.01 versus 0.05), as they were intended to simulate different processes by which adaptive alleles could be introduced and spread throughout a landscape. However, examination of the spatial spread of the adaptive allele under each scenario suggests limitations on inter-patch dispersal imposed by landscape resistance were also likely responsible for the difference between scenarios. As an example, fA within those patches was close to 1. This reflects the presence of high-resistance terrain  separating patches and making inter-patch dispersal events rare. GRCA, with its highly continuous habitat, exhibited much greater geographic spread, although limited to the side of the Colorado River on which the adaptive allele was initially present. Under the pre-existing allele scenario, however, spread of the adaptive allele was much more extensive in all three regions. This occurred because the adaptive allele was initially present in all patches within each region, and thus increases in fA could occur solely through intra-patch dispersal that did not require traversing high-resistance terrain. This also explains why regional differences in fA were much smaller under the pre-existing allele scenario: habitat patchiness played much less of a limiting role when inter-patch dispersal was not needed to introduce the adaptive allele to new populations.We observed large differences in the trajectory of Given the major differences between the two scenarios, it is helpful to consider the circumstances that could lead to each scenario and the implications of each for adaptation. Novel alleles arise naturally in populations through mutation, but the likelihood of such a mutation giving rise to regional adaptation to climate change or some other stressor is probably low because (1) mutation rates are generally small, so novel alleles should arise infrequently , especiaWe limited our simulations to 100 years because we sought to explore adaptive changes occurring on a temporal scale relevant to conservation and management. Previous research in our study system showed that the effects of barrier construction changed genetic population structure in fewer than 50 years . A briefWe explored a very simplistic selection model in which fitness was dependent upon an individual’s genotype at a single locus exhibiting additive dominance. However, most quantitative traits are determined by multiple genes , 88, andThe results of our resistance modeling have important implications for management of connectivity among desert bighorn sheep populations. Beyond the simple distance between individuals, slope and major dispersal barriers (highways and large waterways) were the primary determinants of landscape connectivity. From a conservation perspective, this may be encouraging because the slope of terrain should be negligibly influenced by climate change or anthropogenic development, and barriers to bighorn sheep dispersal can often be mitigated through construction of crossing structures . NDVI afA was considerably faster for the pre-existing allele scenario, but even after 50 years of strong selection, fA remained below 0.2 in all regions. Furthermore, this was probably optimistic because we initialized the pre-existing allele scenario assuming that the adaptive allele was already distributed across the entire region and present in all populations, which is unlikely to be true in real-life situations. These results highlight the need to maintain standing genetic diversity through conservation measures that promote stable, well connected populations. The GRCA region may be particularly important in this regard because of its large population size and continuously distributed habitat, which could allow for both the maintenance of pre-existing diversity and the rapid spread of adaptive alleles.Our gene flow simulations suggested that the spread of adaptive genetic variation is likely to occur slowly for desert bighorn sheep, even in places where connectivity has not been compromised and natural selection strongly favors an adaptive allele. In patchy systems like the MOJA region  where many populations are small, genetic drift can overwhelm selection , 92. TheUsing equal movement thresholds for males and females was necessary to achieve stable population dynamics in our simulations, but was not biologically realistic because bighorn populations tend to exhibit sex-biased dispersal . Our estThe slow pace of selection is partly a reflection of the relatively long generation time of bighorn sheep; the 100-year period of our simulations may seem long from a wildlife conservation and management perspective, but is exceedingly short from an evolutionary perspective, representing fewer than 20 bighorn sheep generations. This has two important ramifications. First, relying on existing genetic variation and natural gene flow to promote adaptation to climate change by desert bighorn sheep may not be a realistic conservation option given the rapid forecasted rate of change. Second, if the introduction of novel adaptive alleles to a region via translocation is desired, in may be necessary to target multiple locations, particularly in areas such as MOJA and DEVA with distinct subpopulations, to achieve sufficient regional spread within a time frame relevant to conservation.Our genetic simulations assumed that habitat configuration did not change during the simulation period. Because bighorn sheep habitat is strongly limited by topography and surface water availability, major geographic shifts in response to climate change are unlikely. However, if forage quality declines or water sources dry up due to increasingly arid climate conditions, currently occupied habitat could become unsuitable in some areas. Future research could explore how projected climatic changes are likely to affect habitat configuration and the potential for adaptive allele spread in the study regions.The framework we have presented here–combining optimization of resistance models and genetic simulations–could be applied by conservationists and managers in a number of ways to help species cope with climate change and other threats to population persistence. It could be used to identify the most effective locations in a region to translocate individuals possessing favorable genotypes with respect to traits such as thermal tolerance or disease resistance, with the goal of maximizing the subsequent spread of adaptive alleles. This approach should not necessarily be limited to large mammals, or even animals; for instance, outplanting resistant tree stock has become a standard practice for restoring forests affected by introduced pests and pathogens , and ourS1 Appendix(PDF)Click here for additional data file.S2 Appendix(PDF)Click here for additional data file.S1 Dataset(CSV)Click here for additional data file.S2 Dataset(CSV)Click here for additional data file.S3 Dataset(CSV)Click here for additional data file.S1 Fig(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 Fig(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.