Ecosystem fragmentation and habitat loss have been the focus of landscape management due to restrictions on contemporary connectivity and dispersal of populations. Here, we used an individual approach to determine the drivers of genetic differentiation in caribou of the Canadian Rockies. We modelled the effects of isolation by distance, landscape resistance and predation risk and evaluated the consequences of individual migratory behaviour  on gene flow in this threatened species. We applied distance‐based and reciprocal causal modelling approaches, testing alternative hypotheses on the effects of geographic, topographic, environmental and local population‐specific variables on genetic differentiation and relatedness among individuals. Overall, gene flow was restricted to neighbouring local populations, with spatial coordinates, local population size, groups and elevation explaining connectivity among individuals. Landscape resistance, geographic distances and predation risk were correlated with genetic distances, with correlations threefold higher for sedentary than for migratory caribou. As local caribou populations are increasingly isolated, our results indicate the need to address genetic connectivity, especially for populations with individuals displaying different migratory behaviours, whilst maintaining quality habitat both within and across the ranges of threatened populations. Current assessments indicate an approximate 50% loss of individuals every eight years in Alberta  and behavioural migration in the form of seasonal migratory behaviour between seasonal ranges within an individual's home range. Second, we performed individual‐based analysis to examine how multiple topographic and environmental variables  affect genetic distance. Third, we used both Mantel and partial Mantel tests in a reciprocal causal modelling approach . The protected areas of Banff and Jasper national parks are located in the western mountainous region, whereas the higher human impact areas  occur predominantly in the eastern boreal foothill region.The 70,000‐kmA total of 207 adult female caribou  from eight different local populations in west‐central Alberta and eastern British Columbia, Canada Figure , 2013. S2.2Canis lupus , inbreeding coefficient (F) and migration rate (m) were tested and adjusted to numbers over a range of 40%–60% changes. We also determined 95% confidence intervals for migration rates. The output was divided into two matrices (M1 and M2), each representing one direction of contemporary gene flow between two local populations.Directional estimates of contemporary gene flow between local populations were estimated with BayesAss 1.3  and census (Nc) sizes as predictor factors, with pairwise genetic distance  and two dispersal matrices (M1 and M2) as response matrices. Calculations of distance‐based multivariate analyses for linear models were performed in DISTLM5 . Finally, FST was used as another covariate when analysing dispersal (M1 and M2). This allows us to also examine the effect of population size (effective or census) on gene flow, whilst correcting for the influence of drift. In theory, connectivity will be elevated among large populations, irrespective of distances, whereas smaller ones exhibit higher chances to remain genetically isolated, allowing drift to erode genetic variation.A nonparametric multivariate analysis of variance was used to determine the association between local population effective  of woodland caribou in Alberta  spatial (longitude and latitude of sample location), (ii) topographic (vegetation and elevation), (iii) environmental (snow cover) and (iv) local population characteristic variables  and local population ID) . The topographic and environmental variables were calculated based on the average home range  or sets of variables  when considered alone on genetic distance and relatedness matrices using DISTLM5  analyses on each resistance surface using the Landscape Genetics ArcToolbox  on a Euclidean line among all separate individual pairs.We tested multiple hypotheses of genetic differentiation, including absence of spatial structure, presence of anthropogenic barriers, predation risk and unsuitable habitat (assessed with RSFs that excluded predator or human factors). The null model of isolation by distance (IBD) was initially measured by calculating all logarithmic pairwise geographic Euclidean distances. Additionally, landscape resistance and wolf‐predation risk between the three groups of individuals  were estimated using least‐cost path (LCPRSF), predation risk (LCPPRR) and hypothesized human barriers to movement  on the genetic and relatedness distances among caribou was tested using simple Mantel tests  and relatedness (R) matrices. Finally, we employed an improved version of the causal modelling approach , habitat suitability (LCP33.1m < 0.030) to moderate (0.030 < m < 0.100). There were two cases where gene flow was high (0.100 < m); these were from TQN to BRZ and RPC to NAR, with the highest value occurring from RPC to NAR (0.225). Overall, genetic migration rates between local population pairs were symmetric, with few cases of strong asymmetry; there were higher emigration rates from RPC to NAR and TQN to BRZ than vice versa  were significantly associated with effective population size only (p = .012), explaining 32.3% of the variation. Such an association would not be unexpected as both FST and Ne reflect genetic drift, and as it is unclear whether Ne has changed recently relative to the long‐term average. However, no influence of effective local population or census local population sizes was detected on FST values after controlling for geographic distance. Moreover, no significant correlation was detected among gene flow and effective or census local population sizes, before or after controlling for FST.The local population‐level distance‐based multivariate analysis showed that genetic distances  had a significant effect on genetic differentiation for the marginal tests, accounting for 2.00%–10.85% of its variation , with values for sedentary individuals nearly three times higher  than in seasonally migratory caribou . Landscape resistance was followed by geographic distance and predation risk, whereas the amount of variation explained by anthropogenic barriers was negligible. All correlations between relatedness and pairwise distances were negative and statistically significant . In contrast, significant correlations were found between genetic and Euclidean distances after the removal of predation and anthropogenic barrier matrices (Appendix S4A). Similarly, habitat suitability explained genetic differentiation of individual caribou (Appendix S4B). In analyses predicting the best fit of genetic relatedness, partial Mantel values were significant, indicating that all variables had an effect on relatedness (Appendix S5). Therefore, in addition to geographic distances and habitat suitability, predation risk and anthropogenic footprints significantly affected genetic relatedness.p < .05, Appendix S7A). Additionally, relatedness was better explained by Euclidean distance once the predation correlation was removed. The relationship between the relatedness matrix and predation risk was significant after accounting for landscape resistance .In this study, 126 individuals were identified as being seasonally migratory. The amount of gene flow among seasonally migratory caribou was better explained by habitat suitability when geographic distance was included as a covariate. This signal was not detected for predation risk (Appendix S6A). The partial Mantel values for relatedness were significant for all variables (Appendix S6B). For the sedentary caribou, none of the resistance matrices could explain gene flow when the effect of log Euclidean distance was controlled (RSF) and predation risk (LCPPRR) for all data sets , habitat suitability (LCPs Figure . Both IBs Figure a, whereas Figure b,c. The 4In this study, gene flow appeared restricted to adjacent local populations and was not affected by population sizes and seasonal migration, with most mating occurring among neighbours. This result is corroborated by telemetry data that showed most individual movements occurred within a local population's home range (McDevitt et al., We integrated evolutionary and ecological approaches to better understand the relationships between genetic structure and gene flow with topographic, climatic and vegetation predictors of caribou natural history (DeCesare et al., _Migratoryr = .1532, IBD_Sedenteryr = .4516; Table The evaluation of space, landscape and predation on genetic distances and relatedness revealed that habitat suitability and Euclidean distances both influenced genetic connectivity Table , suggestOur results showed that IBD alone was not sufficient to explain caribou gene flow Figure a. HabitaCrotalus viridis (Weyer, Jørgensen, Schmitt, Maxwell, & Anderson, Despite evidence of caribou avoidance for anthropogenic barriers (DeCesare et al., The complexity of uniform management decisions in small areas is emphasized with the separation of caribou into migratory and sedentary animals. The role of individual caribou, or group of individuals, sharing life strategies is ignored and not incorporated into the species’ MU designation. This methodology relies on evaluating the role of each individual within a local population, as interindividual variation can have important consequences for dispersal and gene flow (McDevitt et al., Our study has clear management implications, as exemplified by our findings regarding the Little Smoky local population. In west‐central Alberta, this local population is at risk of extirpation COSEWIC  with resOur findings provide guidelines to caribou managers on the importance of incorporating genetic connectivity and ecological characteristics, such as migratory behaviour, into caribou management planning (Trumbo et al.,  Click here for additional data file.


Staphylococcus aureus has caused a wave of severe skin and soft tissue infections in the United States since it emerged in the early 2000s, but its geographic origin is obscure. Here we use the population genomic signatures expected from the serial founder effects of a geographic range expansion to infer the origin of USA300-NAE and identify polymorphisms associated with its spread. Genome sequences from 357 isolates from 22 U.S. states and territories and seven other countries are compared. We observe two significant signatures of range expansion, including decreases in genetic diversity and increases in derived allele frequency with geographic distance from the Pennsylvania region. These signatures account for approximately half of the core nucleotide variation of this clone, occur genome wide, and are robust to heterogeneity in temporal sampling of isolates, human population density, and recombination detection methods. The potential for positive selection of a gyrA fluoroquinolone resistance allele and several intergenic regions, along with a 2.4 times higher recombination rate in a resistant subclade, is noted. These results are the first to show a pattern of genetic variation that is consistent with a range expansion of an epidemic bacterial clone, and they highlight a rarely considered but potentially common mechanism by which genetic drift may profoundly influence bacterial genetic variation.The USA300 North American epidemic (USA300-NAE) clone of methicillin-resistant  Staphylococcus aureus, which was first noticed in the early 2000s and subsequently became the leading cause of skin and soft tissue infections in the United States. The eastern United States is the most likely origin of epidemic USA300. Relatively few variants, which include an antibiotic resistance mutation, have persisted during this clone’s spread. Our study suggests that an early chapter in the genetic history of this epidemic bacterial clone was greatly influenced by random subsampling of isolates during the clone’s geographic spread.The process of geographic spread of an origin population by a series of smaller populations can result in distinctive patterns of genetic variation. We detect these patterns for the first time with an epidemic bacterial clone and use them to uncover the clone’s geographic origin and variants associated with its spread. We study the USA300 clone of methicillin-resistant  Staphylococcus aureus (MRSA) were from outbreaks of skin and soft tissue infections (SSTIs) in a prison in Mississippi in November 1999 and in a sports team in Pennsylvania in September 2000  type IV element and the arginine catabolic mobile element (ACME), and the ability to express the Panton-Valentine leucocidin (PVL)  pattern  that occin (PVL) , 13. In an clone , 15. Phyan clone . Export an clone –19.Transmission of USA300-NAE is primarily through close person-to-person contact such as within households and among other populations living under crowded conditions and sharing personal items , 21. Onl20–Here we infer the geographic origin of USA300-NAE through the use of genomic signatures that are expected to occur when an origin population expands its geographic range by a series of smaller populations . These s25–28–19–With the resolution provided by genome sequencing, we observe two significant signatures of range expansion of USA300-NAE that independently outline the same region of origin in the eastern United States. We demonstrate that these signatures are genome wide and robust to potential confounders and analysis artifacts. Some unusual polymorphisms accompanying the spread of this clone are also identified, which builds upon prior work , 21. OnegyrA (encoding Ser84Leu) and grlA (encoding Ser80Tyr) and represented 157 (48%) of 330 USA300-NAE isolates  from the Bayesian analysis, the dates for the most recent common ancestor of our sample of USA300-NAE and its FQ-resistant subclade were estimated to be 1992 (1988 to 1996) and 1998 (1998 to 2000), respectively  and geographic distance (great-circle distance) . Pennsylvania was the most likely origin on the basis of a decrease in θπ , a decrease in tip-to-tip distance , and an increase in Ψ by linear regression , but the adjacent population of New York was the origin when the nonlinear regression of Ψ was used   . Restric n = 15) . A clear n = 15) . The Wes n = 15) .π , but not with tip-to-tip distance or Ψ. In a multivariate analysis of the decrease in θπ with the distance from Pennsylvania and accounting for human population density, the range expansion signature remained significant  and human population density was not a significant covariate . In summary, these results show a pattern of USA300-NAE genetic variation that is consistent with a process of geographic range expansion from an eastern U.S. origin that cannot be explained by heterogeneity in the temporal sampling of isolates or by human population density.We also examined whether USA300-NAE genetic diversity could be explained by human population density. Variation in human population density, which is analogous to variation in the carrying capacity of USA300-NAE, as humans are the natural host of this clone, is expected to make the detection of a range expansion more difficult , 36. Humπ and an increase in Ψ, respectively, gave an origin in the Pennsylvania region  at least 80% of the time, but the signatures rapidly decayed with further downsampling  with a positive gradient from Illinois . No correlation was detected between the gyrA allele frequency gradient and state-wide estimates of FQ use in humans   and BratS. aureus  were included in this study . All iso methods . Ciprofl methods . Over ha methods ; these i methods  was incl methods .de novo. Read and assembly information for each genome is presented in Sequencing was performed with Illumina  MiSeq instruments. Genomic DNA was isolated with Qiagen DNeasy Blood and Tissue kits. Sequencing libraries were prepared with Illumina Nextera XT DNA sample preparation kits (250-bp paired-end reads). MiSeq Reporter was used to demultiplex and trim adapters from the sequence reads. CLC Genomics Workbench v7  was used to filter reads for minimum quality , align reads with the reference genome of strain TCH1516, exclude ambiguously aligned and duplicate reads, and assemble genomes Aligned reads were coordinate sorted, realigned around insertion-deletion polymorphisms (indels), and compressed in size with Picard v1.85 and GATK v2.3.9 . RepetitP value of 0.05.An ML phylogeny was generated with PhyML v3.0  under anA time-stamped Bayesian phylogeny was generated with BEAST v1.7.5  under anST  and the U.S. Census Bureau (https://www.census.gov/data/tables/time-series/demo/geographic-mobility/state-to-state-migration.html), respectively. The comparisons of all isolates (2001 to 2011 isolation dates) were done by using 2001 to 2011 passenger data and 2005 to 2011 (earliest available) migrant data, whereas the comparisons of recent isolates (2007 to 2011 isolation dates) used 2007 to 2011 passenger and migrant data. The numbers of passengers or migrants, respectively, between any two U.S. states are asymmetrical and can differ by orders of magnitude but are significantly correlated , so (log) totals were used. Mantel and partial Mantel tests with 10,000 permutations were performed with the R package ecodist (Genetic differentiation between populations was measured with Hudson’s FST  across b package . Geograp package . The num ecodist .w), which is more sensitive to recent mutations  and was therefore inappropriate for our study since the diversity would reflect sample size rather than distance from the origin. Average pairwise difference (θπ), which is more sensitive to older mutations  and was therefore used in our study. The average tip-to-tip (patristic) distance on the ML tree, which is analogous to θπ, was also uncorrelated with sample size  and was used in our study. Nucleotide diversity and Tajima’s D were calculated with the R package pegas  with the Spatial Analyst Toolbox and a default grid size of 0.78.Given the evidence for nonequilibrium populations, we first validated appropriate measurements of genetic diversity to study range expansion. Watterson’s estimator . A multivariate regression analysis of nucleotide diversity with distance from Pennsylvania was performed with the built-in R stats package .The sequence reads obtained in this study were submitted to the NCBI Sequence Read Archive (BioProject


Plasmodium vivax multidrug resistant 1 gene (pvmdr1) codes for a transmembrane protein of the parasite’s digestive vacuole. It is likely that the pvmdr1 gene mutations occur at different sites by convergent evolution. In here, the genetic variation of pvmdr1 at three sites of the Mesoamerican region was studied. Since 1950s, malarious patients of those areas have been treated only with chloroquine and primaquine.The P. vivax were obtained in southern Mexico (SMX), in the Northwest (NIC-NW) and in the northeast (NIC-NE) of Nicaragua. Genomic DNA was obtained and fragments of pvmdr1 were amplified and sequenced. The nucleotide and amino acid changes as well as the haplotype frequency in pvmdr1 were determined per strain and per geographic site. The sequences of pvmdr1 obtained from the studied regions were compared with homologous sequences from the GenBank database to explore the P. vivax genetic structure.Blood samples from patients infected with P = 0.0001). The pvmdr1codons 925–1083 gene fragment showed higher genetic and haplotype diversity in parasites from NIC-NE than the other areas outside Latin America. The haplotype networks suggested local diversification of pvmdr1 and no significant departure from neutrality. The FST values were low to moderate regionally, but high between NIC-NE or NIC-NW and other regions inside and outside Latin America.In 141 parasites, eight nucleotide changes (two changes were synonymous and other six were nonsynonymous) were detected in 1536 bp. The PvMDR1 amino acid changes Y976F, F1076FL were predominant in endemic parasites from NIC-NE and outbreak parasites in NIC-NW but absent in SMX. Thirteen haplotypes were resolved, and found to be closely related, but their frequency at each geographic site was different (pvmdr1 gene might have diversified recently at regional level. In the absence of significant natural, genetic drift might have caused differential pvmdr1 haplotype frequencies at different geographic sites in Mesoamerica. A very recent expansion of divergent pvmdr1 haplotypes in NIC-NE/NIC-NW produced high differentiation between these and parasites from other sites including SMX. These data are useful to set a baseline for epidemiological surveillance.The  Plasmodium vivax causes most malaria cases in Latin America .mdr1sequence was used as reference: XM_001613678. Sequences forward and reverse were aligned using ClustalW Multiple Alignment of BioEdit v7.0 [The Salvador I strain  dit v7.0  and revidit v7.0 . Statistpvmdr1, haplotype networks were constructed using TCS 1.21 [In order to explore the regional and global parasite relationships based on TCS 1.21 . Mutatiopvmdr1 gene in parasite populations of Mesoamerica, the number of synonymous (s) and nonsynonymous (ns) nucleotide changes and the difference in the rate of nonsynonymous versus synonymous changes (dN˗dS) were determined by using the Nei Gojobori proportion method with 1000 bootstrap replicates in MEGA v6.0 software [. vivax genetic relationships from different locations, the FST values were estimated by using the two parameters of Kimura in dnaSP; values ranged from 0.0 to 1.0. A value of 0.0 indicates that populations are equal and 1.0 indicates they are completely different [Indexes of nucleotide diversity were calculated in dnaSP v5.1. To test whether natural selection and gene flow shaped the evolution of software . Tajima’ifferent , 26.pvmdr1 gene fragment, synonymous and nonsynonymous nucleotide changes, and haplotype frequency were clearly described: IND, n = 25 [DNA sequences for the global genetic analysis. Groups of DNA sequences that comprised codons 925–1083 (including positions 976/1076) were obtained from the NBCI Gene Bank. Brazil (BRZ): n = 7, EU333973-9 ; n = 15,, n = 25 . Hondura, n = 25 . Nepal  : haplotype m1 GG-GTACTT was predominant (93.2%) in SMX; m3 AA-GTTTTC was highly frequent (78.2%) in NIC-NW; m2 GA-GTTTTC and m4 GG-ATATTC were at 62.7 and 22%, respectively, in NIC-NE. Other haplotypes were detected in two or all sites, although at a very low frequency. For instance, haplotype m1 was also detected in one isolate from NIC-NE and in another from NIC-NW, m6 and m10 were observed at SMX and NIC-NE, and m5 was found in NIC-NW and NIC-NE. One haplotype (m8) was exclusive to SMX and resembled the Sal I strain sequence. Another three haplotypes  were exclusive to NIC-NE and two (m7 and m12) to NIC-NW  with the nucleotide information at variant codons 500, 529–927, 958, 976, 1021, 1070 and 1076 resolved 13 P. vivax mdr1 comprising mutations at codons 500, 529–927, 958, 976, 1021, 1070 and 1076 for parasites from the study sites is shown in Fig. The haplotype network of Parasites from NIC-NE and mostly from the Mesoamerican region showed a nucleotide diversity (π = 0.0036) similar to that found at other geographic origins as well as the global level π = 0.0028). Moreover, the minimal number of recombination events were from very low to cero    and the highest for Ecuador (0.949), but in either case, it was found to be no significant. Although a higher number of nonsynonymous than synonymous changes were detected, the 7) Table .P. vivax mdr1, the haplotype network evidenced the relationship of 19 global haplotypes (g1 → g19) identified in 610 sequences , FST values were from low to moderate .te Table . BetweenP. vivax and/or P. falciparum [Plasmodium species seem susceptible to CQ. According to this, mutations at pvmdr1 might not be associated to CQ resistance. Therefore, PvMDR1 976F and CQ resistance in vitro seemed to be a counterfeit association [pvmdr1 e.g. 976F and others adjacent are not associated with CQ resistance [pvmdr1 mutations participating in the development of CQ resistance. Schousboe et al. [pvmdr1 mutations 958M, 976F and 1076L arose independently and several times at different geographic sites. In Mesoamerica, the selective force of natural selection on pvmdr1 differed spatially, with lack of statistical significance. Besides, recent genomic studies have not revealed signature of selection on pvmdr1 in parasites from different geographic sites [The Mesoamerican region has ecological and demographic conditions that favor malaria transmission. Chloroquine (CQ) and primaquine (PQ) have been the only drugs used in this region to treat patients infected with lciparum , 20, 30 ociation . In factsistance , 14, 28,e et al.  suggesteic sites –33, inclic sites .Malaria has been historically hypo endemic in Mesoamerica. From 2000 to 2010 a reduction in more than 75% was recorded in several countries including Nicaragua and Mexico. Contrarily from 2012 to 2014, Nicaragua was the only country in Mesoamerica that reported a significant increase in malaria cases [pvmdr1 haplotypes in all sites , may be attributed to ancient parasite flow across the Mesoamerican region. It was notorious a predominance of blood infections with a single pvmdr1 haplotype, presumably monoclonal infections. A clonal transmission of P. vivax might be occurring in the region. It is supported by the lack of mixed genotype infections for pvmsp142 a gene fragment, of moderate diversity, reported in NIC-NE/NW [The presence of some IC-NE/NW . In SMX,IC-NE/NW , 36.pvmdr1 haplotypes were detected. Similarly, for the blood stage antigen pvmsp142 a divergent haplotype was exclusive in Chinandega municipality (NIC-NW) [pvmdr1 haplotypes in the region, so were at the municipality level. A similar spatial pattern of haplotypes was reported for pvmsp142 [P. falciparum exposed that in the mining region there was a genetic population distinct from that detected in the Miskito area [P. falciparum, P. vivax cause relapse episodes of long latency, an additional risk of parasite dispersion [pvmdr1 mutations might have appeared at different sites in Mesoamerica as formerly suggested for other geographic sites [In Mesoamerica, parasite flow might be controlled by different factors, including separation by distance, geographic barriers, cultural and language barriers, poor road infrastructure, the effectiveness of the malaria control interventions. There is a volcanic arc in between NIC-NE and NIC-NW. At either side of the arc exclusive (NIC-NW) . In NIC-vmsp142 . Yet, thito area . The Misspersion , and micspersion . It is pic sites .pvmdr1 haplotype frequencies in Mesoamerica. The Sal I type was only detected in SMX and at low frequency. In Honduras, parasites isolated from 2004 to 2009 exhibited haplotypes with PvMDR1 976F and/or 1076L changes at a frequency of <30% [The results from this study and the epidemiological and demographic scenario suggest that a strong genetic drift might have influenced the spatial differences of the  of <30% ; while i of <30% , 31.Plasmodium vivax arriving at the American continent faced adaptive processes triggered by different local factors e.g. the specificity of the Anopheline species, the host immune pressure, the anti-malarial drugs employed, and the ecological conditions to mention some reasons [P. vivax exhibits low diversity and high differentiation in Latin America [FST values between South America and Central America, the last ones corresponded to ten strains obtained in the 1970s [pvmdr1 gene also supports a high genetic differentiation between old and new world parasites [FST value using the pvmdr1codons925–1083 matched partially the previous results, also suggesting a strong differentiation globally but low-moderate differentiation at regional level. Excepting parasites from NIC-NE/NW, these were highly differentiated from all other parasites including those from SMX and Honduras, possibly given by the recent expansion of pvmdr1 divergent haplotypes. reasons –42. Geno America . Analysihe 1970s . Evidencarasites . In the P. vivax has been at some extent circulating in the American continent. Southern Mexico is a transit area for human migrations coming from South and Central America [P. vivax [P. vivax in the Mesoamerican region comparing to other sites of America and worldwide. Furthermore, molecular monitoring of P. vivax mdr1 at different sites in Mesoamerica would help to understand transmission dynamics. Because of its variability and spatial patterns, pvmdr1 is an important marker for epidemiological surveillance to support the current initiatives to control and eliminate malaria from Mesoamerica.Regardless of its origin,  America , and stiP. vivax , 47. Thepvmdr1which likely occurred at a regional level, natural selection varied spatially and was mostly in a weakly to-mild manner. In this hypo-endemic region, a strong genetic drift might have caused the great differences in pvmdr1 haplotype frequency among the studied sites, and hence the high differentiation of parasites between NIC-NW/NIC-NE and SMX or other sites. These data are relevant to set a baseline level for epidemiological surveillance in the region.These results suggest a recent diversification of


Platycladus orientalis, a widespread conifer with long lifespan and significant adaptability. It is much used in reforestation in north China and commonly planted in central Asia. With the increasing demand for plantation forest in central to north China, breeding programs are progressively established for this species. Efficient use of breeding resources requires good understanding of the genetic value of the founder breeding materials. This study investigated the distribution of genetic variation in 192 elite trees collected for the breeding program for the central range of the species. We developed first set of 27 polymorphic EST-derived SSR loci for the species from transcriptome/genome data. After examination of amplification quality, 10 loci were used to evaluate the genetic variation in the breeding population. We found moderate genetic diversity (average He = 0.348) and low population differentiation (Fst = 0.011). Extensive admixture and no significant geographic population structure characterized this set of collections. Our analyses of the diversity and population structure are important steps toward a long-term sustainable deployment of the species and provide valuable genetic information for conservation and breeding applications. Platycladus orientalis (L.) Franco, a member of the family, Cupressaceae, is one of the dominant forest species in China. It is native to northwestern China, Korea, and the Russian Far East, with a range from the south of Mongolia to the south-central China, with also a limited suitable area in Tibetan Plateau12, Cl2) and heavy metal pollutants  in soilP. orientalis were established through provenance testing, phenotypic selection, and seed orchard establishment. Breeding populations were established based on extensive collection and testing of elite trees (plus trees) from range wide. The selected elite trees were collected into breeding archives and used to establish seed orchards for producing genetically improved seeds for plantation forests. Genetic basis of the breeding population decides the genetic quality and long-term potential of the breeding program and products. However, population genetic investigations in P. orientalis are thus very limited. Only one study based on allozyme markersP. orientalis provenance trails and populations. These studies all revealed lower genetic diversity among the provenances of P. orientalis, and genetic diversity of the central populations is less than that of the south and the north regions of the distribution range. Whether this lower diversity in the central region would cause a problem for long-term breeding remains an important unanswered question. Understanding the genetic diversity and population structure is the necessary step in forming up strategies of genetic improvement and conservation, and is urgently needed for evaluating and adjusting the breeding program for P. orientalis in China.Started in the 1980 s, tree improvement and breeding activities of P. orientalis and related species, which presents a handicap for potential genetic management and conservation for this valued conifer tree.Microsatellites, or simple sequence repeat (SSR) polymorphisms, are popular molecular markers for population genetic investigations. Carefully selected SSRs can behave as co-dominant genetic marker, and show high polymorphism. Despite growing competition from new markers such as SNP (single nucleotide polymorphism), these versatile and cost-effective SSRs will remain as a convenient tool in population and pedigree investigations of many plant systems. SSR development requires sequence data. The source of sequence data could be size-fractionated genomic DNA and EST (expressed sequence tag) libraryP. orientalis. We wanted to understand the genetic property of this breeding stock and whether they can support the long-term breeding program for this species in central China.In this study, we first aimed to establish a set of promising SSRs for genetic diversity and mating system investigations. We then applied these markers to characterize the distribution of genetic variation in one important breeding population from the central range of P. orientalisJuniperus communisWe searched the transcriptome assembly of He) ranged from 0.032 at locus Po108 to 0.573 at locus Po75, averaging 0.348 over all loci. Observed heterozygosity (Ho) ranged from 0.033 at locus Po108 to 0.484 at locus Po75, averaging 0.280 over all loci. The estimated null allele frequencies (Pn) ranged from 0.000 at Po107, Po108 and Po180 to 0.149 at Po173; 8 of the 10 analyzed loci had low null allele frequency, but two loci (Po145 and Po173) had Pn values close to 15%. Compared to genomic sequence derived SSRs in conifers (e.g. Norway sprucePID = 0.010). Thus not every tree had a unique multi-locus genotype; 14 genotypes each was assigned to 2–3 individual trees and a total of 29 trees had shared genotypes.A total of 44 alleles were detected at the 10 SSRs loci in the 192 sampled elite trees (origins shown in Np) were found at 7 loci distributed in the three populations (Nt) revealed in each population ranged from 26 (the east population) to 40 (the north population). The effective number of alleles (Nea) and allelic richness (AR) estimated for each population ranged from 1.59 (the north population) to 1.83 (the east population) and from 2.47 (the north population) to 2.66 (the southwest population), respectively. The highest He estimate (0.393) occurred in the east population, whereas the lowest He of 0.333 was identified for the north population. The Ho in these populations followed the same pattern with the lowest value (0.258) found in the north population and the highest in the east (0.366), and all Ho values were lower than He.Based on their origins, the 192 trees were roughly divided into three populations, north, east and southwest. At population level, 14 private alleles  departure in 16 out of 30 cases, distributed over all 10 loci. All the departures were due to homozygote excess. The average value of inbreeding coefficient (F) over loci and populations was 0.197 and ranged from 0.070 in the east population to 0.225 in the north population  decreased to 0.126 (The single-locus tests for Hardy-Weinberg Equilibrium (HWE) in the three populations showed significant (pulation . The F vhe locus . We remoto 0.126 .Fst = 0.011) (Nm = 22.48 migrants per generation). Pairwise population Fst values were very similar (0.011 to 0.012), and all the population pairs had significant gene flow (The estimation of genetic differentiation among the populations across the 10 SSR loci was very low (= 0.011)  translatene flow . AMOVA aene flow .The neighbour network based on genetic distances among the 192 trees formed a big mixed network with no clear geographic structure . The NJ P. orientalis individual and failed to reveal any meaningful genetic structure and geographical grouping for the three populations   indicateP. orientalis is a widespread coniferous tree species with important economic and ecological values in China and other parts of Asia. The species’ wide adaptability and strong resistance increased its utilization, thus making it a suitable choice for both restoration and breeding activities. To establish a sound strategy for breeding and deployment, a good understanding of the distribution of genetic resources in the species range and the breeding population is a necessary first step towards a long-term sustainable goal. Up to now, genetic studies in P. orientalis are very limited, and available tools for genetic and breeding applications are under developed. For these reasons, we performed extensive data mining from the transcriptome and genome assembly of P. orientalis and a closely related species J. communis  for SSR markers that can be used for genetic diversity and breeding applications.P. orientalis. Based on the transcriptome and genomic data, we successfully amplified 87 EST-SSR loci in P. orientalis, but only 27 loci showed polymorphism in our 192 trees. Of these 27 loci, 20 were developed from P. orientalis transcriptome data and 7 from the genomic data of J. communis. Juniperus and Platycladus are sister clades in the Cupressaceae family19SSRs are widely used in many fields of genetic and breeding studies, such as assessing the distribution of genetic variability, mating systems, and parentage analyses in breeding and production populations1516SSR fingerprinting technique provides an effective way in confirming genetic identities of accessions, parents, and their putative offspring. Mislabeling in achieves occurs often seed orchards2122P. orientalis breeding program in China. We investigated the levels and partitioning of genetic variation in the breeding stock for the central range of the species and found reasonably high variability and very low geographic differentiation in this collection.This study represents the first investigation into the genetic composition of the founder populations of the He = 0.349) in the 192 trees from three populations. Higher diversity is a desired property in germplasm resources to support a long-term effective breeding program.An earlier study on this species using AFLPs detected low levels of genetic variability (the mean Nei’s gene diversity was 0.124) in 18 stands collected throughout the countryFst and AMOVA showed that only about 1% of observed total differentiations were partitioned among populations. Additionally, STRUCTURE, PCA and Neighbornet analyses all showed little geographic boundaries in the collection, and all trees had a mixed ancestry. The low divergence in the upland and the plain of central China could be due to several factors such as the relatively small geographic scale of our survey, continuous distribution, homogenous landscape, extensive gene flow, the population history and past anthropogenic activities. Gene flow promotes higher levels of genetic variation within populations and lower differentiation among populations which counteracts negative effects of genetic drift or directional selection on differentiationP. orientalis is wind pollinated with high pollen production, both pollen and seed dispersal can be substantial. The studied trees were collected within a 230 km radius of Henan province where landscape is homogenous. The relatively small geographic range and plain landscape would see more frequent gene exchange and result in little differentiation between groups. However, pollination dynamics and mating system of the species remain to be poorly investigated, and should be a topic of research effort in the following studies to better understand the distribution of genetic variation.We found very low population differentiation in the central range of the species; both P. orientalis is a favorite temple tree of Taoists, Buddhist and Confucian priests and in many areas inside and outside China it has escaped from cultivation and established spontaneous standsPast anthropogenic activities can insert significant influence on the present population structure and patterns of genetic diversity in forest treesKnowledge of the genetic structure of founder populations and patterns of gene flow is essential for establishing efficient breeding and seed orchard programs of conifer treesP. orientalis. In the present study, leaves of one ramet from each of the 192 elite clones with outstanding seed output potential were sampled for DNA extraction. The information about the 3 sampled populations  is shown in P. orientalis and cover the northern to southern Henan province with latitudinal range from 33.33° (N) at east to 35.42° (N) at north, and the longitude range from 112.17° (E) at southwest to 113.51° (E) at north. Yellow river geographically divides the sampled populations into two groups, one in the north (the northern population) and the other group in the southwest (the southwestern population) and east (the eastern population). And the north population is located in the southeast foot of Taihang mountains, the important mountain range and geographical boundary for eastern China. Each population was represented by 21 to 136 individuals.In the mid of 1980s’, seed orchard  was constructed by cloning 268 elite trees selected from 3 provenances  in Henan province which is in the central range of −1 for PCR amplification.The genomic DNA was extracted by using the method of cetyltrimethylammonium bromide (CTAB)P. orientalisJ. communis and then 96 loci were selected for further evaluation. In total, primers for 292 SSR loci were synthesized by Sangon Biotech (China). Polymorphism of the SSR loci was evaluated with a sample set of one individual from each of eight natural populations: Lingshou (Hebei Province of China), Mianyang (Sichuan), Feicheng (Anhui), Beizhen (Liaoning), Baoji (Shanxi), Bameng (Inner Mongolia), Heshui (Gansu), Nanping (Fujian).Our SSR markers were developed from two sources. For the first resource, a total of 1,684 loci with simple sequence repeats were retrieved from the high-throughput RNA sequencing based transcriptome assembly of QDD version 3.1The synthesized primer pairs were validated by polymerase chain reactions (PCR) using M13-tail technique, where a 5′ M13-tailed forward primer combined with a fluorescent label Ne), observed heterozygosity (Ho), expected heterozygosity (He) for each SSR loci were generated in GenALEx version 6.5Pn) for each microsatellite locus and each population were estimated using FreeNANt), the average number of alleles per locus (Na), allelic richness (AR), the effective number of alleles (Nea), the number of private alleles by population (Np), the observed (Ho) and expected (He) heterozygosity, and inbreeding coefficient (F) were estimated by SPAGeDiHWE) based on Markov chain iterations and analysis of molecular variance (AMOVA) partitioning the total genetic variation among and within population was tested with ARLEQUINFst) between populations was determined using multi-locus comparisons in GenALEx based on 999 permutations. The Fst value was also calculated by containing or excluding null allele (ENA) in FreeNA to examine the genetic divergence among populations and the potential effect of null alleles on genetic differentiation. As the Fst statistic is an indirect measure of gene flow, inversely related to the effective migration rate, it was used in the following formula Nm = 0.25 (1 − Fst)/Fst to estimate the number of migrants per generation between populations.Allelic frequencies including the number of alleles, number of effective alleles  was applied to calculate the genetic distances among individual trees with distance of Dm, and Dm is considered the minimum genetic distancePOPULATIONS software version 1.2.34  using the “adegenet” packageet al.In population structure analysis, the genetic structure of the 3 populations was analyzed using STRUCTURE version 2.3How to cite this article: Jin, Y. et al. Genetic evaluation of the breeding population of a valuable reforestation conifer Platycladus orientalis (Cupressaceae). Sci. Rep.6, 34821; doi: 10.1038/srep34821 (2016).


Platycercus elegans), we show that both heterozygosity and genotype rarity of individual hosts predicted infection, but in contrasting ways. Heterozygosity was negatively associated with probability of infection, but not with infection load. In contrast, increased host genotype rarity was associated with lower viral load in infected individuals, but did not predict infection probability. These effects were largely consistent across subspecies, but were not evident at the population level. Subspecies and age were also strongly associated with infection. Our study provides novel insights into infection dynamics by quantifying rarity and diversity simultaneously. We elucidate roles that host genetic diversity can play in infection dynamics, with implications for understanding population divergence, intraspecific diversity and conservation.Genetic diversity at community, population and individual levels is thought to influence the spread of infectious disease. At the individual level, inbreeding and heterozygosity are associated with increased risk of infection and disease severity. Host genotype rarity may also reduce infection risk if pathogens are co-adapted to common or local hosts, but to date, no studies have investigated the relative importance of genotype rarity and heterozygosity for infection in a wild, sexually reproducing vertebrate. With beak and feather disease virus (BFDV) infection in a wild parrot ( Therefore, pathogens have long been considered a strong selective force against susceptible host individuals4. Genetic diversity is hypothesised to influence the susceptibility of hosts to infection directly, and it is generally accepted that lower genetic diversity increases susceptibility1. In natural populations, individuals that are more homozygous tend to exhibit higher infection probabilities9 and pathogen loads11. A possible mechanism explaining this phenomenon could relate to the reduced capability of individuals with reduced allelic diversity at loci associated with immunity (e.g. major histocompatibility complex or Toll-like receptors) to defend themselves against pathogens14, or indirectly through negative effects associated with inbreeding 16. In addition, individual susceptibility may also be related to the co-adaptation of the pathogen with common genotypes, which gives rise to a selective advantage for rare genotypes19. Whilst selection against homozygotes results in greater overall population heterozygosity2, rare genotype advantage due to selection against common genotypes, results in negative frequency-dependent selection on genotypes21. Hence pathogens have considerable potential to contribute to the maintenance of genetic diversity and subsequently hinder or promote host divergence between wild populations23. Although studies have separately investigated roles of heterozygote advantage and negative-frequency dependent selection in pathogen susceptibility, studies are currently lacking which assess these aspects concurrently in the same system. Furthermore, while there is evidence for pathogen selection against common host species at a community level24 , no study has investigated the effect of multilocus genotype rarity in a wild, sexually reproducing vertebrate. The use of highly variable species, in species complexes, such as the crimson rosella (Platycercus elegans), offers a unique opportunity to investigate host-pathogen interactions and determine how genetic diversity influences pathogen infection22.Pathogens by definition have negative health consequences for their host and often reduce host likelihood of survival or reproductionP. elegans, we tested whether genetic diversity influences susceptibility to a highly prevalent pathogen . P. elegans is a common parrot which occupies diverse habitats in south eastern Australia26. Its distribution encircles unsuitable habitat and includes several subspecies and putative hybrid populations based primarily on plumage colouration , the capsid (CAP), and the replication-associated ORF (REP)29. Associated signs of disease vary between species, but typically include feather malformation and/or loss, beak and claw deformity and immune suppression30. The latter often results in death, thereby explaining the high mortality rates and the status of BFDV as a global conservation concern33. Recently, we found that the normally highly pathogenic BFDV varied in both prevalence and infection load between the P. elegans subspecies22. These findings are consistent with the interpretation that the subspecies differ in their susceptibility to the disease and/or infection, and have implications for the evolution of population divergence in this system22. BFDV prevalence and load were notably lower in phenotypically intermediate forms (P. e. adelaidae and Western Slopes (WS)), than the two most phenotypically distinct subspecies (P. e. elegans and P. e. flaveolus)22; WS is a putative hybrid population where P. e. elegans and P. e. flaveolus overlap on the western slopes of the Great Dividing Range28. In addition, factors such as BFDV phylogeny, geographic location, and the susceptible host community (Psittaciforme species diversity and composition) were not associated with prevalence or load. It seems possible, therefore, that differences in individual and population level genetic diversity among these populations may explain this intraspecific variation in BFDV infection22.In wild ion Fig. 27,28. BFP. elegans using microsatellite markers to investigate the effects of host genetic diversity on BFDV prevalence and viral load, using two distinct indices, overall genetic diversity  and a novel index of genotype rarity . Our aim was to assess the roles of genome-wide heterozygosity and genotype rarity of hosts, as opposed to functional and adaptive interactions between specific host genes and the pathogen. This approach provides information on the effect of genome-wide processes on infection such as inbreeding, introgression and population dynamics34. To test our hypotheses that an individual’s overall heterozygosity or rarity within a population predicts infection probability or severity, we modelled whether HL or APR were associated with the likelihood of BFDV infection, or with viral load in infected hosts. Our analyses controlled for subspecies, age class and sex, which are other host traits that have previously been associated with BFDV infection22, and sought to confirm whether HL and APR operate independently. In addition, we investigated genetic local effects using single microsatellite loci, specifically whether pairwise host genetic distance  was correlated with BFDV genetic distance to test if common host genotypes share similar BFDV strains, as would be predicted in an antagonistically co-evolving host-pathogen relationship35, and whether genetic diversity differed across subspecies.In the study reported here, we genotyped c = 0.59) and the best model was 2.55 × 1019 times more likely than the null model (intercept only). All four plausible models contained the predictors subspecies and age class. HL (homozygosity-by-loci) was present in the two most highly ranked models, indicating that it was also an important predictor of infection status , while the 95% CI of parameter estimates for HL remained non-overlapping zero . This showed that whilst controlling for APR, HL remains an important predictor of BFDV infection. We found no evidence to suggest that local genetic effects explained the effect of HL on BFDV infection probability .Four plausible models were found to predict infection status, with a combined likelihood of 90.8% Table . The twous Table . Accordius Table . Subspecon Table . Additio10 times more likely than the null model (intercept only). As with infection status, all of these models included the predictors subspecies and age class. In contrast to infection status, in models predicting viral load APR appeared in the top-ranked model as well as the fourth ranked model, while HL only appeared in the third ranked model , while the 95% CI of parameter estimates for APR remained non-overlapping zero . This showed that when controlling for HL, APR remained an important predictor of BFDV viral load. We found no evidence to suggest that local genetic effects explained the effect of HL on BFDV viral load probability .Four plausible models were found to predict viral load, with a combined likelihood of 92.1%  compared to all other subspecies, whilst P. e. adelaidae was more heterozygous than WS .  WS Fig. . APR waswer Fig. . GeneticP = 0.85; REP: Mantel r = 0.003, n = 26, P = 0.94; CAP: Mantel r = −0.09, n = 31, P = 0.05). Removing sequences that showed evidence of recombination did not alter these results .When comparing host and virus genetic distances between individuals, whilst controlling for geographic distance, we found no correlation between BFDV genetic distance and host genetic distance  and genotype rarity (APR) predicted infection status and viral load of beak and feather disease virus (BFDV) in P. e. elegans and P. e. adelaidae . A positive relationship between individual host heterozygosity and infection status has been found in studies on other host-pathogen systems, often using a moderately sized panel of microsatellite loci (compared to genome-wide sequencing studies) as we did in this study16. Similarly, our effect sizes are within a similar range to previous work on host heterozygosity and infection16. Taken together, our study in concert with previous findings, suggest that host heterozygosity offers a modest but significant advantage in terms of reducing susceptibility to infection in general. The specific mechanisms underlying this relationship remain to be determined, but might relate to the neutral markers reflecting multi-locus genetic diversity across the whole genome, and therefore, potentially across immune effector loci, or loci with epistatic effects on resistance/tolerance. Individuals that have higher levels of heterozygosity may be able to resist a wider range of pathogens, or in this instance a greater range of BFDV variants. Although a single individual can host several different genetic strains, BFDV is relatively conserved anti-genetically29. Alternatively, higher levels of heterozygosity may reflect an immunogenic advantage due to overdominance or due to rare allele advantage. In contrast, less heterozygous individuals are more likely to display deleterious recessive alleles as a result of inbreeding depression, resulting in increased pathogen susceptibility15.A key finding of our study is that individuals with lower heterozygosity (higher HL) were more likely to be infected with BFDV Fig. . This padae Fig. ; however19. This would give rare host genotypes within a population a selective advantage, potentially leading to a negative frequency-dependent scenario (i.e. ‘Red Queen’ dynamics)18. However, when comparing pairwise host and pairwise virus genetic distances between individuals whilst controlling for geographic distance, we found no significant correlations between BFDV genetic distance and host genetic distance. That suggests that BFDV may not be strongly co-adapted to the most common host genotype, as we might have expected given BFDV viral load was found to be higher in common host genotypes. However, differences in evolutionary rates between the host and pathogen, or alternatively, sampling at too high spatial and/or temporal scale are plausible explanations for this negative finding35. Furthermore, BFDV genetic distance and host genetic distance are measured at the individual level, which may not reflect pathogen adaptation to common genotypes at the population level. Future research is needed to explore coevolution across multiple generations to test for “Red Queen” dynamics. The finding that individual birds with more common genotypes had a higher BFDV load could also provide an important explanation for the evolution of host dispersal; individuals that possess a common genotype would be under more intense pressure to disperse in order to escape co-adapted pathogens9. Our results could provide support for a disease escape-by-dispersal hypothesis, because it is likely that our average pairwise relatedness measure is correlated with dispersal between populations36.We also found that multilocus genotype rarity, unlike heterozygosity, was an important predictor of BFDV load among the 106 BFDV infected hosts. APR showed a positive association with viral load, a pattern which was evident in all subspecies and the WS population Fig. . Thus, o2, or alternatively, more homozygous populations could be more susceptible and therefore display a higher prevalence. However, when there are multiple comparisons across different populations it is difficult to determine causality in heterozygosity-fitness correlations37. Although we found that genetic diversity varied between subspecies  indicated that the markers we used do reflect heterozygosity across the genome and the number of markers we have used is within a similar range to other studies detecting heterozygosity-fitness correlations40. Increasing the number of markers used in this analysis is likely to improve the estimates as demonstrated in Hoffman et al.11. However, it may also be useful to examine specific functional regions such as the major-histocompatibility complex13, Toll-like receptors14 or cytokine regions41, which may improve our understanding of the mechanism through which heterozygosity affects fitness.For studies examining heterozygosity-fitness correlations, it is important to utilise a set of neutral markers that reflect genome-wide heterozygosity. Our analyses . However, HL and APR were only weakly correlated and the distribution of APR in groups of high and low HL was similar  predicts pathogen load across subspecies. Our findings also support the hypothesis that individual heterozygosity is negatively associated with infection probability (i.e. greater heterozygosity associated with lower probability of infection), in line with previous similar studies in a range of host-pathogen systemsP. elegans (n = 224) were sampled throughout the range of the species in south-eastern Australia, encompassing the three mainland subspecies  and a putative hybrid population  which occurs where P. e. elegans and P. e. flaveolus overlap on the western slopes of the Great Dividing Range , as previous microsatellite and mitochondrial DNA analyses have shown that birds north of this location represent a genetic cluster distinct from all southern populations27. Blood or tissue samples were collected and used for genotyping, molecular sexing, and BFDV testing following Eastwood et al.42. The age class of each individual was scored based on distinct plumage characteristics, following Eastwood et al.42: green body plumage denoted sub-adult (1st year birds); yellow-red body plumage with a white underwing stripe denoted “young adults” (2nd and 3rd year birds); yellow-red body plumage and no white underwing stripe denoted “old adults” (>3 years). All work involving animals was approved by Deakin University’s Animal Ethics Committee (Project no: A33-2008 and A51-2011), and in accordance with the legal requirements of Australia and the relevant states.During 2004–2011, and Fig. 27, was n42 and sex was determined using molecular methods43. To measure BFDV prevalence and load, we first standardised DNA concentration with a Beckman DU spectrophotometer  and then used a probe based quantitative real-time PCR technique, which has previously been used22 and validated in P. elegans42. For full details of BFDV detection in this species see Eastwood et al.42. In brief, the method amplifies a 100 nucleotide region in the highly conserved replication-associated protein ORF using the following primer set: forward primer 900 nm (5′-GAC GCG GAT AAC GAG AAG TAT TG-3′), reverse primer 300 nm (5′-GCA ACA GCT CCA TCG AAA GC-3′) and probe 100 nm (5′-FAM CCG TCT CTC GCC ACA ATG CCC AGG TAMRA-3′). Quantitative PCR was performed in a Stratagene Mx3005 P  with Brilliant Multiplex qPCR master mix solution . We used the following qPCR conditions: initial denaturation of 10 min at 95 °C; followed by 40 cycles of: 30 s at 95 °C, 60 s at 60 °C and 30 s at 72 °C; followed by final extension of 5 min at 72 °C. The method is highly repeatable (inter-assay r = 0.99) and reproducible between repeated extractions (r = 85). Both blood and muscle tissue samples were used in this study which give highly similar viral load estimates42. For BFDV positive samples (n = 106), BFDV load was measured as relative gene expression using the comparative Ct method44 and relative to a control sample 42.Ammonium acetate DNA extraction was used for all samplesAgGT07, AgGT21, CP03E01, CP52A09, Ero03, Ero08, Cl2, Cl3, and Cfor2627), using the methods described in Joseph et al.27. In addition to the n = 224 samples collected, aged, sexed and tested for BFDV for this study as described above, we added a further n = 139 for which genotypes using the same microsatellite methods were already known27; this larger dataset was used for tests of null alleles, linkage disequilibrium, Hardy-Weinberg equilibrium, and population differences in genetic diversity parameters (see Supplementary Information). Sample sizes for the combined dataset were as follows: P. e. elegans: n = 100; P. e. flaveolus: n = 52; WS: n = 99; P. e. adelaidae: n = 112. Using IRmacroN3  implemented in Microsoft Excel 2010, we determined the number of alleles and the allele size ranges per locus, and estimated the proportion of null alleles. Using Arlequin version 3.5, we found no deviations from Hardy-Weinberg equilibrium or linkage equilibrium and low FST values which indicate little population differentiation, . To test the strength of single locus effects on our g2 statistic, we performed a delete-one jackknife which revealed that the exclusion of two loci, C13 and Cfor2627, resulted in a more positive g2 statistic , while the removal of any of the other seven loci had a negative influence on our g2 statistic . Subsequently, we excluded both C13 and Cfor2627 which resulted in a g2 statistic significantly greater than zero , suggesting that this reduced panel of seven loci may better reflect genome-wide heterozygosity than the full panel of nine loci. However, there was a strong correlation between HL calculated from the full panel and from the reduced panel of seven loci , and both sets of loci gave similar conclusions regarding the effect of heterozygosity when used interchangeably in the top models . The g2 values in this study, using either nine or seven microsatellites, are similar to those reported in other studies investigating heterozygosity fitness correlations48, and a g2 significantly different from zero should not be considered a pre-requisite for testing heterozygosity fitness correlations49. For these reasons, we used all nine loci for the analyses reported elsewhere in this study.Rosella samples were genotyped using nine microsatellite loci  by weighting the allelic contribution of each locus. More homozygous individuals have an HL value closer to one, and more heterozygous individuals a value closer to zero. To estimate the rarity of an individual’s genotype within the population, we calculated average pairwise relatedness (APR) to all other individuals sampled within the population. This simple measure of genotype rarity is based on the assumption that a common genotype will have a higher average pairwise relatedness compared to a rare genotype in the same population. To calculate pairwise relatedness to all individuals, we used the method described by Queller and Goodnight51 implemented in Coancestry (Version 1.0.1.7)52. To provide some additional validation that APR is a robust, general estimate of genotype rarity we quantified the probability of belonging to a population using a genetic assignment method implemented in Geneclass253. Typically used for identifying dispersers, Geneclass2 calculates a likelihood estimate 54; using Monte Carlo re-sampling, the program simulates a random sample of expected genotypes . This calculates the probability that an individual’s genotype is found within a given population54, hereafter the probability of assignment index. APR and the probability of assignment index were highly correlated  suggesting that APR has the ability to distinguish between dispersers, common genotypes and mixed genotypes . The results indicated that there was no population sub-structure at the subspecies level (data not shown). This analysis concurred with earlier findings in P. elegans which reported four homogenous genetic clusters corresponding to the three subspecies and the WS population based on microsatellite data27.We calculated measures of genetic diversity of hosts at both the individual and population levels. At the individual level, all metrics were calculated based on allele frequencies within each subspecies. Homozygosity-by-loci (HL) was calculated using the excel macro IRmacroN3, as described inet al.49, we tested for genetic local effects on heterozygosity by analysing whether the models containing HL explained less variance than the models including single-locus heterozygosities, which were assigned as either heterozygous or homozygous at each locus separately (n = 9 loci). Single-locus heterozygosity variables or HL were included separately as fixed predictors in the top model for BFDV infection including subspecies and age, and the top model for BFDV viral load including subspecies, age and sex. For each comparison, BFDV infection and BFDV viral load, the residual deviances from both models (Single-locus heterozygosity variables or HL) were compared using an F-ratio test.Following Szulkin 56 using the rarefaction method57.At the population level, genetic diversity was estimated using an additional two parameters, allelic richness  and private allele richness . Both were calculated with the program HP-rare version 122. These data were used to test for correlations between P. elegans and BFDV genetic distance while controlling for geographic distance, using partial Mantel tests . Genetic distance in P. elegans was calculated by transforming the Queller and Goodnight51 relatedness coefficient for each dyad by subtracting this value from one. Genetic distances range between zero (high similarity) and two (very distant). Pairwise BFDV genetic distance, recombination detection, and geographic distance were calculated following Eastwood et al.22. We used three different BFDV genome segments for analysis, which correspond to different coding regions that may be under different selective pressures. These included a 726 nucleotide segment of the replication associated open reading frame (Rep ORF), a 744 nucleotide segment of the capsid ORF (Cap ORF), and lastly, these regions concatenated with a non-coding region, hereby termed partial BFDV genome (1629 nucleotides). Mantel tests were repeated after excluding sequences with evidence for recombination22, with similar results obtained.For a subsample of infected samples (n = 36), BFDV sequences were available (GenBank accession numbers KJ953847 - KJ953885)10 transformed to achieve normality), we used model selection based on Akaike Information Criteria . Using generalized linear models (GLM), we compared the same set of candidate models for infection status  and viral load . The predictors were host traits that have previously been identified as important in BFDV 20 as well as the two individual genetic diversity measures hypothesised to influence infection (HL and APR); all predictors were modelled as fixed effects. The set of candidate models included all combinations of one or more predictors, except that the two genetic diversity measures were not included together in models in case their correlation biased estimates. This decision to analyse HL and APR separately was done a priori, however we additionally tested for confounding effects between HL and APR by including these terms together in the top models of both BFDV infection and viral load. The final candidate set included 23 models for each response variable, which are shown in Supplementary Table c weight, and models were considered plausible if their AICc weight was >0.05.To identify the best predictors of BFDV infection status (infected/uninfected) and viral load , parameter estimates for categorical variables  indicate the proportion of infected individuals in each group, while for continuous variables  the parameter estimates indicate the odds-ratio (i.e. increased likelihood of infection from a one unit increase in the predictor). For the continuous response variable , all estimates indicate the slope. Continuous predictors (HL and APR) were considered important (or ‘significant’) if the 95% confidence range of model averaged estimates did not overlap one (for odds-ratios) or zero (for slopes). Categorical predictors  were considered important if the model averaged 95% confidence ranges of their groups did not overlap or parameter weight was >0.95. Parameter weights were calculated by summing the weights for all models in which that parameter appears, to provide an estimate of the relative importance of each predictor.To calculate robust estimates of effect sizes, we carried out model averaging of parameter estimates and errors, following Symonds and Moussallic). Likewise, we tested whether there was an interaction between age and sex within the top models for both BFDV infection and viral load. The interaction between age and sex was non-significant in both cases (P > 0.05) and was therefore not included in the final analyses.We evaluated interactions between genetic diversity and subspecies, and between host sex and age, using a post-hoc approach. Using the top model for both BFDV infection and BFDV viral load model selection analyses, we included the interaction between subspecies and either HL or APR. However, all interactions using subspecies and a genetic measure were non-significant (P > 0.05) and no interactions led to more strongly supported top models for infection status and viral load . Means and estimates are shown with standard error unless otherwise stated.Data were examined to ensure the assumptions of each statistical analyses were met; viral gene expression was not normally distributed and this was rectified using log transformation prior to modelling. Statistical analyses were performed using the programs RSupplementary Information


Protonibea diacanthus, is the only large sciaenid in coastal waters of northern Australia where it is targeted by commercial, recreational and indigenous fishers due to its food value and predictable aggregating behaviour. Localized declines in the abundance of this species have been observed, highlighting the urgent requirement by managers for information on fine‐ and broad‐scale population connectivity. This study examined the population structure of P. diacanthus across north‐western Australia using three complementary methods: genetic variation in microsatellite markers, otolith elemental composition and parasite assemblage composition. The genetic analyses demonstrated that there were at least five genetically distinct populations across the study region, with gene flow most likely restricted by inshore biogeographic barriers such as the Dampier Peninsula. The otolith chemistry and parasite analyses also revealed strong spatial variation among locations within broad‐scale regions, suggesting fine‐scale location fidelity within the lifetimes of individual fish. The complementarity of the three techniques elucidated patterns of connectivity over a range of spatial and temporal scales. We conclude that fisheries stock assessments and management are required at fine scales (100 s of km) to account for the restricted exchange among populations (stocks) and to prevent localized extirpations of this species. Realistic management arrangements may involve the successive closure and opening of fishing areas to reduce fishing pressure.As pressure on coastal marine resources is increasing globally, the need to quantitatively assess vulnerable fish stocks is crucial in order to avoid the ecological consequences of stock depletions. Species of Sciaenidae  are important components of tropical and temperate fisheries and are especially vulnerable to exploitation. The black‐spotted croaker,  For genetic analyses, muscle tissue or fin clips were sampled and immediately placed into vials containing molecular grade 95% ethanol or 20% dimethyl sulfoxide (DMSO) solution in 5 mol/L NaCl and stored at 4°C in the field and −20°C in the laboratory. The sagittal otoliths were dissected from each fish, cleaned and rinsed thoroughly, dried, and stored in paper envelopes prior to preparation for trace element analysis. Gills and internal body organs were removed, placed into individual bags and frozen until examination for parasites. The same individuals were used for otolith microchemistry, genetic and parasite analyses.A total of 298 fish were collected from 11 sampling locations across north‐western Australia from Roebuck Bay  to the Vanderlin Islands in the Gulf of Carpentaria (Northern Territory) Figure  meant th2.2Population structure was tested in an hierarchical fashion, with genetics being deployed at the broad scale to guide population structure and ecological markers used at the finer scale. The nature of otolith microchemistry reflects fine‐scale spatial variation between environments and is not useful for broad‐scale variation. Indeed, lack of significance  on a broad spatial scale does not indicate ecological population homogeneity. Fine‐scale genetic analyses were used to integrate across the three methods for a pair of locations in NW Australia for which one presented a striking genetic heterogeneity.Prd012, Prd023, Prd044, Prd042, Prd018, Prd045, Prd046, Prd020, Prd036, Prd049, Prd024) were genotyped across all the samples as part of multiplexes as described in Taillebois et al.  following the manufacturer's instructions to produce 100 μl of eluted DNA from each sample. All the DNA extracts were quantified using the Qubit v3 (ThermoFisher) fluorometric machine. Eleven species‐specific microsatellite loci  was required to be considered significant.The potential for null alleles, large allele dropout and stuttering to interfere with scoring accuracy was checked for each microsatellite locus in each sample using MICROCHECKER version 2.2.3  as implemented in ARLEQUIN. The significance of any differentiation was determined by permutation of 22,000 replicates.To test alternate FST values were calculated (i) to further investigate the population structure within the groups revealed with the clustering approaches and (ii) to test whether P. diacanthus is constrained by a pattern of isolation by distance (IBD). Fixation indices (FST) between pairs of sample localities were estimated as implemented in ARLEQUIN version 3.5.2.2  (G) versus geographic (D) distances among locations using ARLEQUIN.Pairwise sulcus acusticus to represent growth in the period prior to capture. A total of 11 trace elements  and the internal standard (43Ca) were analysed from the two ablation zones in each otolith. Details on the general methodology for the otolith preparation, the laser ablation and data transformation are presented in Appendix 43Ca ratio. These ratios were converted to molar ratios and were expressed as element:Ca molar ratios in mmol/mol or μmol/mol  was sampled adjacent to the P. diacanthus. Direct life cycle parasites generally have short‐lived larval stages that are unable to cover large distances and should reflect locality of origin. Indirect life cycle parasites, on the other hand, can travel through multiple hosts and thus have a distribution pattern that is ultimately at the mercy of a number of different factors. Actual life cycles of the majority of aquatic parasites remain unknown, and we can only generalize as to hosts within the cycle based on current knowledge. Initially, analyses were performed for external parasites (=direct) and larval stages (=indirect or permanent parasites) separately. Both analyses showed significant separation among sites. We subsequently combined the data for direct and indirect parasites for the final analyses, as this provided more robust and biologically reliable and interpretable results (see below).Parasites were also used as biological tags to investigate the fine‐scale population structure of Details on the general methodology for the parasite extraction and examination are presented in Appendix The otolith and parasite data were broken down into three geographic regions for statistical analyses (i) because these ecological markers are thought to operate over smaller spatial and temporal scales compared to the genetics; hence, they require a smaller scale of analysis and (ii) to test of the current jurisdictional management boundary between Western Australia and the Northern Territory and the regional management units across the Northern Territory. Each region included the geographically closest sample location from the adjacent region to allow inter‐region comparisons. The Western region includes all of the Western Australian locations  as well as Wa, which is the closest Northern Territory sample location Figure . The DarY + 1) transformed. Homogeneity of the covariance matrices for the grouping factor was assessed using Box's M‐test based on the chi‐square approximation with the biotools package in R . Linear discriminant function analysis (LDFA) was conducted to provide a statistical and visual indication of the similarities of either the parasite communities or the multi‐elemental otolith microchemical signatures among samples at the regional spatial scale using the vegan package in R .3P. diacanthus. The level of missing data was low (2.15%) and as such was retained and identified as missing data in the subsequent analyses . The number of alleles per locus ranged from 5 (Prd046) to 21 (Prd012)  out of the 121 tests performed for each locus against each location. There was no significant test for linkage disequilibrium between pairs of loci across all locations after Bonferroni correction . The analysis proceeded without removing loci or individuals from the data set. Heterozygosity was moderate to high for all loci across all locations  showed an initial sharp decline in values until K = 5 after which BIC values increased   Figure c. Along ) Figure c.p‐value = .000) and 2.28%  of the total genetic variation. As both scenarios explained very similar among group genetic variation, they are both likely to represent biological population structure. However, the 4‐group scenario revealed by STRUCTURE will be used in the discussion, as it is the result of unsupervised analyses, which was not the case for DAPC.AMOVA was used to compare the different scenarios supported by STRUCTURE and DAPC. The 4‐ and 5‐group scenario highlighted by STRUCTURE and DAPC, respectively, explained 2.15%  and two locations in the eastern part of the sampling area (AS and VI) were genetically distinct from almost all other locations surveyed in the study. By contrast, the seven locations  in between the eastern and western locations formed an undifferentiated group where only three of 42 pairwise FST values were significant and none of them were significant after Bonferroni correction.The pairwise r between the genetic and geographic distance matrices was equal to 0.655 (coefficient of determination r2 = .429) and was statistically significant . There was a linear relationship between FST/(1 − FST) and geographic distances . The correlation coefficient between the parasites abundance and the geographic distance between locations was low and marginally significant .To evaluate the spatial processes that drive population structure, an isolation‐by‐distance model was examined. The Mantel correlation coefficient s Figure . Thus, fP. diacanthus and information on the individuals' life history. Of the 11 elements measured, concentrations of 7Li, 25Mg, 55Mn, 60Ni, 63Cu, 66Zn, 88Sr and 138Ba in the near cores and margins of P. diacanthus otoliths were measured above the LOD in a total of 286 fish  provided further information about population structure in Appendix . A totalAppendix . Of thesp‐value <.001, Table The multi‐elemental signatures of the near core and margins and the parasite abundances differed significantly among the three broad‐scale regions  and among locations within each of the three regions . In contrast, the near core (juvenile phase) samples incorporated growth from different years because fish in each location were of variable ages and sources may vary by age and cohort. Individual mis‐assignments were compared between near core and margin, and there was no evidence of matching pattern of mis‐assignment , reflecting the fact that the margin sample incorporated growth at the sampling location immediately prior to collection  strongly suggest that P. diacanthus is generally sedentary following larval settlement.In this study, the combined application of genetic markers, otolith chemistry and parasites across the same specimens provided three independent lines of evidence and considerable power to detect the highly complex population structure of 4.1P. diacanthus based on the fine‐scale population structure, it should be noted that there was overlap in the multivariate analyses of parasite assemblage compositions and otolith elemental signatures between some locations that potentially reflect dispersal by a proportion of juvenile or adult fish. Similar to the IBD pattern observed in the genetic analyses, the correlation between geographic distance and parasite assemblage diversity showed a decline in the similarity of parasite assemblages with increasing distance. The genetic IBD pattern indicates that reproduction is more likely to occur between individuals in the same location. The positive correlation between distance and parasite assemblage dissimilarity could be interpreted in a similar manner, suggesting that parasites are shared by the dispersal of fish within and among neighbouring populations, which may be responsible for the relative similarity of neighbouring locations. Alternatively, it may be that similarity in parasite assemblages among neighbouring sites is driven by (i) dispersal of parasites independent of P. diacanthus movement ; (ii) the spatial distributions of the endemic areas of the different parasite species  within each location .As previously mentioned, the generally lower classification success in the near core compared to the margin can, at least in part, be attributed to the fact that the near core samples incorporated growth from different years , the offshore region of the AS and the VI in the Gulf of Carpentaria. The deepest genetic structure observed was between RB in Western Australia and the rest of the sampling range. The distance between RB and the nearest location CS (~400 km) may partly explain this pronounced genetic break. However, similar or greater distances between other locations  did not result in such genetic discontinuity.At broader spatial scales, the genetic analyses revealed the existence of major discontinuities along the north‐western Australian coastline, resulting in several genetically discrete populations of P. diacanthus across its range. The tidal regime, the composition of the substrate and the extent of the riverine network influence the size of the mangrove communities in this area. Thus, the Kimberley and Canning bioregions separated by the Dampier Peninsula  creates contrasted marine coastal habitats, inhibiting larval dispersal and reinforcing spatial population structure. Likewise, quantitative analyses of fish assemblages have revealed two main biogeographic regions, the Kimberley and Canning coasts  also reported a strong east‐west phylogeographic break between RB and populations of Van Diemen Gulf or in the Gulf of Carpentaria  highlighted the distinctiveness between Western Australia (Pilbara region to the south of the Canning) and the Northern Territory, but again lacked intermediate sampling  were distinct from CS in the Kimberley region. The hydrodynamics of the JBG, in particular the effect of the seasonal current flow and the high bottom stress, are likely to be factors linked to the isolation of Wy from the rest of the Kimberley area Condie, . The geoS. semifasciatus, individuals from the VI were grouped with locations on the Northern Territory coast and not with the other eastern Gulf of Carpentaria locations in the Queensland jurisdiction , Wy also has larger fish  and do not present such a genetic distinctiveness. The segregation of genotypes in the RB population did not seem to depend on the year of sampling nor to be linked to the fish age or cohort, although the limited temporal window of our sampling regime did not allow statistical testing of this hypothesis.Despite the overall evidence of a strong genetic break, Bayesian analysis at the individual level showed that RB consisted of an admixed population, with some fish having genotypes similar to CS individuals Table . Ocean cUsing all three techniques to detect migrants through mis‐assignments tests for contemporary connectivity between locations. Neither the otolith or parasite data showed evidence of matching mis‐assignment to genotypes at the individual level between RB and CS locations. If the migrants arrived as juveniles, they would have the genetic signature of the source population, but as they mature in the new location, they would acquire the local parasite and otolith signature. If the migrants arrived as adults, they would have the genetic and near core signatures of the source population, and possibly intermediate parasite fauna composition. Alternatively, the genetically distinct immigrants could be the descendants of true immigrants—hence, they would be genetically distinct, but have the local otolith (near core and margin) and parasite signatures. The absence of a matching pattern between genetics and ecological methods in individual mis‐assignment reveals that the genetically mis‐assigned individuals from RB may (i) not be contemporary immigrants but rather a genetic signal of ancestral immigrants or (ii) be contemporary immigrants from sites not sampled in the study. Fine‐scale sampling and temporal series collection would be required to further investigate the processes that drive the genetic composition of fish in the RB population.4.4P. diacanthus shown by this study across northern Australia emphasizes the likely vulnerability of the species to overfishing, and importantly, guides the appropriate spatial scale for stock assessment and fisheries management. In particular, the population genetics and otolith and parasites data suggest that stock assessments and fisheries management on a spatial scale of as little as 100 s of km may be justified. Given genetic breaks among broad regions, it is unlikely that immigration of recruits from other regions would contribute to population recovery in the short term should one or more regions suffer severe population declines. At a finer scale, the parasite and otolith chemistry analyses suggest that movement of recruits among sites  is generally limited and that heavily depleted sites may be highly reliant on self‐recruitment for population maintenance and, therefore, may be slow to recover once depleted. Within the Western Australia fisheries management jurisdiction, RB, CS and Wy were genetically distinct from each other, indicating that location‐specific stock assessments and location‐specific management arrangements for P. diacanthus need to be considered by fisheries managers, or alternatively that the location‐specific “stocks” identified are considered within assessment, monitoring and management frameworks.The highly complex population structure of Protonibea diacanthus is targeted by commercial, recreational and indigenous fisheries, which makes population modelling challenging over limited spatial scales of 100 s of kms. The difficulty with modelling over this spatial scale is the logistical challenges associated with collecting matching stock‐specific data such as reproduction and spawning dynamics, growth rates, exploitation rates and natural mortality. This logistical challenge comes with a significant cost burden. These factors indicate that fine‐scale management is problematical once the additional cost of compliance, regulation and enforcement is added. Within that framework, consideration needs to be given to a range of other options that limit fishing effort, including limited entry, limiting fishing effort, consideration of targeted fisheries spatial closures, rotational harvests or a combination of these approaches with other traditional fisheries management arrangements . This approach has been used in the Northern Territory where a series of reef‐scale closure areas have been implemented which has subsequently shown to aid the recovery of P. diacanthus and Lutjanus johnii (NTG P. diacanthus based on information from this study (Thor Saunders pers. com) demonstrating that management at the scale of populations is achievable.hnii NTG . Conside5P. diacanthus. Significant spatial genetic population structure was supported by finer spatial‐scale parasite assemblage and otolith chemistry data. There is evidence of a small amount of migration—most likely in the pelagic larval phase—from adjacent populations corresponding to prevailing currents, which was inferred from integration across data from three sources.Several lines of evidence support a model of low dispersal in P. diacanthus is highly structured across northern Australia from RB to the VI has demonstrated that the combination of ecological and genetic markers provides a more holistic view of population structure when deployed together on the same set of individuals. Genetics provides the overall framework for applying ecological methods and provides also considerable information about exchange between populations. Sensitive ecological methods, such as otolith chemistry and parasites, provide resolution of the fine‐scale spatial separation within and between collection locations, but are unable to provide broader scale information over intergenerational timescales. Our results collectively demonstrate location‐specific fidelity in the juvenile and adult phases and suggest that dispersal is likely to occur primarily during the very early life history as eggs and/or pelagic larvae. Further research  would be required to directly quantify the extent of site attachment across the life history of P. diacanthus.Our finding that P. diacanthus across north‐western Australia consists of a set of spatially structured populations with low connectivity sets a framework to develop approaches for the assessment and spatial management of the species. The collapse of the North Queensland stock , it may take multiple generations to replenish depleted stocks and there is potential that some heavily depleted stocks may not recover at all.The finding that https://doi.org/10.5061/dryad.9rs5d).Data files with individual information  are available from DRYAD , the Australian Helminthological Collection (AHC) and Marine Invertebrates section of the South Australian Museum (SAM), the Parasitology Collection of the Queensland Museum (QM), and the Natural History Museum of London (BMNH). Click here for additional data file.