A central paradigm in conservation biology is that population bottlenecks reduce genetic diversity and population viability. In an era of biodiversity loss and climate change, understanding the determinants and consequences of bottlenecks is therefore an important challenge. However, as most studies focus on single species, the multitude of potential drivers and the consequences of bottlenecks remain elusive. Here, we combined genetic data from over 11,000 individuals of 30 pinniped species with demographic, ecological and life history data to evaluate the consequences of commercial exploitation by 18th and 19th century sealers. We show that around one third of these species exhibit strong signatures of recent population declines. Bottleneck strength is associated with breeding habitat and mating system variation, and together with global abundance explains much of the variation in genetic diversity across species. Overall, bottleneck intensity is unrelated to IUCN status, although the three most heavily bottlenecked species are endangered. Our study reveals an unforeseen interplay between human exploitation, animal biology, demographic declines and genetic diversity. Historical hunting has caused documented declines in pinnipeds, but the extent to which hunting caused genetic bottlenecks among species was unknown. Here, the authors show evidence of severe bottlenecks in several pinniped species, particularly those that breed on land. Of particular interest are sharp reductions in the effective population size (Ne) known as population bottlenecks4, which may negatively impact the viability and adaptive evolutionary potential of species through a variety of stochastic demographic processes and the loss of genetic diversity8. Specifically, small bottlenecked populations have elevated levels of inbreeding and genetic drift, which decrease genetic variability and can lead to the fixation of mildly deleterious alleles and ultimately drive a vortex of extinction10. Hence, investigating the bottleneck histories of wild populations and their determinants and consequences is more critical than ever before, as we live in an era where global anthropogenic alteration and destruction of natural habitats are driving species declines on an unprecedented scale12.Unravelling the demographic histories of species is a fundamental goal of population biology and has tremendous implications for understanding the genetic variability observed today14. A versatile solution for inferring population bottlenecks from a single sample of individuals is to compare levels of observed and expected genetic diversity, the latter of which can be simulated under virtually any demographic scenario based on the coalescent17. A variety of approaches based on this principle have been developed, one of the most widely used being the heterozygosity-excess test, which compares the heterozygosity of a panel of neutral genetic markers to the expectation in a stable population under mutation-drift equilibrium18. Although theoretically well grounded, these methods are highly sensitive to the assumed mutation model, which is seldom known19. A more sophisticated framework for inferring demographic histories is coalescent-based approximate Bayesian computation (ABC)20. ABC has the compelling advantages of making it possible to (i) compare virtually any demographic scenario as long as it can be simulated, (ii) estimate key parameters of the model such as the bottleneck effective population size and (iii) incorporate uncertainty in the specification of models by defining priors. Due to this flexibility, ABC has become a state of the art approach for inferring population bottlenecks as well as demographic histories in general29.Unfortunately, detailed information about past population declines across species is sparse because historical population size estimates are often either non-existent or highly uncertain30 and might also experience stronger demographic declines as only a fraction of individuals contribute towards the genetic makeup of subsequent generations. Consequently, to disentangle the forces shaping population bottlenecks, we need comparative studies incorporating genetic, ecological and life-history data from multiple closely related species within a consistent analytical framework.Although the widespread availability of neutral molecular markers such as microsatellites has facilitated numerous genetic studies of bottlenecks in wild populations, the vast majority of studies focused exclusively on single species and were confined to testing for the presence or absence of bottlenecks. We therefore know very little about the intensity of demographic declines and how these are influenced by anthropogenic impacts as well as by factors intrinsic to a given species. For example, species occupying breeding habitats that are more accessible to humans would be expected to be at higher risk of declines, while species with highly skewed mating systems tend to have lower effective population sizes34, others have reported unexpectedly high genetic variation after supposedly strong population declines38. Hence, it is not yet clear how population size changes contribute towards one of the most fundamental questions in evolutionary genetics—how and why genetic diversity varies across species41. To tackle this question, we need to compare closely related species because deeply divergent taxa vary so profoundly in their genetic diversity due to differences in their life-history strategies that any effects caused by variation in Ne will be hard to detect and decipher41.Another question that remains elusive due to a lack of comparative studies is to what extent recent bottlenecks have impacted the genetic diversity of wild populations. While a number of influential studies of heavily bottlenecked species have indeed found very low levels of genetic variability7, there is now growing evidence that low genetic diversity increases extinction risk42 and on a broader scale that threatened species tend to show reduced diversity7. Nevertheless, due to a lack of studies measuring bottlenecks consistently across species, it remains an open question as to how the loss of genetic diversity caused by demographic declines ultimately translates into a species' extinction risk, which can be assessed by its International Union for Conservation of Nature (IUCN) status.Finally, the relative contributions of genetic diversity and demographic factors towards extinction risk remain unclear. While historically there has been a debate about the immediate importance of genetic factors towards species viability43. Pinnipeds include some of the most extreme examples of commercial exploitation known to man, with several species including the northern elephant seal having been driven to the brink of extinction for their fur and blubber by 18th to early 20th century sealers13. By contrast, other pinniped species inhabiting pristine environments such as Antarctica have probably had very little contact with humans13. Hence, pinnipeds show large differences in their demographic histories within the highly constrained time window of commercial sealing and thereby represent a unique natural experiment for exploring the causes and consequences of recent bottlenecks.An outstanding opportunity to address these questions is provided by the pinnipeds, a clade of marine carnivores inhabiting nearly all marine environments ranging from the poles to the tropics and showing remarkable variation in their ecological and life-history adaptationsHere, we conducted a broad-scale comparative analysis of population bottlenecks using a combination of genetic, ecological and life-history data for 30 pinniped species. We inferred the strength of historical declines across species from the genetic data using two complimentary coalescent-based approaches, heterozygosity-excess and ABC. Heterozygosity-excess was used as a measure of the relative strength of recent population declines, while a consistent ABC framework was used to evaluate the probability of each species having experienced a severe bottleneck during the known timeframe of commercial exploitation, as well as to estimate relevant model parameters. Finally, we used Bayesian phylogenetic mixed models to investigate the potential causes and consequences of past bottlenecks while controlling for phylogenetic relatedness among species. We hypothesised that (i) extreme variation in the extent to which species were exploited by man should be reflected in their genetic bottleneck signatures; (ii) ecological and life-history traits could have an impact on the strength of bottleneck signatures across species; (iii) past bottlenecks should reduce contemporary genetic diversity; and (iv) heavily bottlenecked species with reduced genetic diversity will be more likely to be of conservation concern.We report striking variation in genetic bottleneck signatures across pinnipeds, with 11 species exhibiting strong genetic signatures of population declines and estimated bottleneck effective population sizes reflecting just a few tens of surviving individuals in the most extreme cases. Despite being caused by human exploitation, these genetic bottlenecks are mediated by both breeding habitat and mating system variation, implying that species ecology and life-history contribute towards responses to anthropogenic exploitation. Furthermore, up to five-fold variation in genetic diversity across species is explained by a combination of bottleneck history, global abundance and breeding habitat. Finally, exploring the consequences of historical bottlenecks for conservation, we show that genetic bottleneck signatures are unrelated to IUCN status across all species, although three of the four most heavily bottlenecked species are currently endangered. We conclude that the genetic consequences of anthropogenic exploitation depend heavily on a species’ biology, while quantifying demographic histories can substantially contribute to understanding patterns of genetic diversity across species.Ho) and allelic richness (Ar) varying by over two and almost five-fold respectively across species (Supplementary Table r = 0.92) and tended to be higher in ice breeding seals, intermediate in fur seals and sea lions, and substantially lower in a handful of species including northern elephant seals and monk seals  for each species, which was highly repeatable across a range of mutation models  and one  whereby 0.5 is the expectation for a stable population. Considerable heterogeneity was found across species, with northern and southern elephant seals, grey seals, Guadalupe fur seals and Antarctic fur seals showing the strongest bottlenecks signals. By contrast, the majority of ice-breeding seals exhibited heterozygosity-deficiency, consistent with historical population expansions.We used two different coalescent-based approaches to infer the extent of recent population bottlenecks. First, the amount of heterozygosity-excess at selectively neutral loci such as microsatellites is an indicator of recent bottlenecks because during a population decline the number of alleles decreases faster than heterozygosityNe to vary from pre-bottleneck to post-bottleneck in both models within realistic priors (see Methods for details) while the bottleneck model also included a severe decrease in Ne to below 500 during the time of peak sealing. Therefore, both models incorporate longer-term declines or expansions within realistic bounds for all species but only the bottleneck model captures a recent and severe decrease in Ne due to anthropogenic exploitation. ABC was clearly able to distinguish between the two models, with simulations under the bottleneck model being correctly classified 85% of the time and simulations under the non-bottleneck model being correctly classified 89% of the time  varied substantially across species and was strongly but imperfectly correlated with prophet-exc  also had a prediction error below one in both models, visual inspection of the cross-validation results revealed high variation in the estimates and a systematic underestimation of larger Nehist values, so this parameter was not considered further. Figure Nebot  in seven species including both phocids and otariids, while even smaller Nebot values  were estimated for four phocids comprising the landlocked Saimaa ringed seal, both monk seal species and the northern elephant seal. Mutation rate estimates were remarkably consistent across species, with modes of the posterior distributions typically varying around 1 × 10−4  across all datasets (see Supplementary Table K was more than one (n = 12), we recalculated genetic summary statistics and repeated the bottleneck analyses based on individuals comprising the largest cluster. Using the largest genetic clusters did not appreciably affect our results, with repeatabilities for the genetic summary statistics and bottleneck signatures all being greater than 0.9 . Overall, 6% of loci were found to deviate from HWE in both 52. We therefore tested whether small population sizes during the LGM followed by expansions could result in similar genetic patterns across pinnipeds to recent bottlenecks caused by anthropogenic exploitation  could have reduced habitat availability and consequently population sizeshet-exc and pbot, with land-breeders on average showing stronger bottleneck signatures  an important life-history variable as species with a high SSD aggregate in denser breeding colonies, making them more valuable to hunters, and polygyny reduces effective population size. Third, the length of the breeding season may have impacted the vulnerability of a given species to exploitation and finally, generation time could potentially mediate population recovery. We found clear differences between ice-breeding and land-breeding seals in both propres Fig. . In addiSSD Fig.  but not het-exc and pbot as response variables respectively and breeding habitat, SSD, breeding season length and generation time fitted as predictors (see Methods for details). Both models explained an appreciable amount of variation  and structure coefficients  with log transformed global abundance, breeding habitat and SSD fitted as predictor variables together with the two bottleneck measures prophet-exc and pbot . A substantial 75% of the total variation in Ar was explained . Specifically, Ar decreased nearly five-fold from the species with the lowest pbot to the species with the highest pbot . Specites Fig.  can be hdel Fig. . Howeverdel Fig.  also revAr. Based on data from the IUCN red list , we classified species into two categories; the first of these, which we termed ‘low concern’ comprised species listed as ‘least concern’ and ‘near threatened’, while the second combined species listed as ‘vulnerable’ or ‘endangered’ into a ‘high concern’ category. Using a phylogenetic mixed model, we did not find any clear differences in either heterozygosity-excess or pbot with respect to conservation status  that a species’ observed genetic diversity is similar to the diversity of a population that experienced a severe reduction in Ne below 500, and therefore provides an absolute bottleneck measure. By contrast, heterozygosity excess (prophet-exc) theoretically captures sudden recent reductions in Ne even in fairly large populations18 and therefore provides a relative bottleneck measure. Concretely, given the average sample size of individuals and loci used in this study, we would expect to detect an excess of heterozygosity at the majority of loci  when a 100– to 1000–fold reduction in Ne occurred, regardless of the magnitude of Ne (see simulations in ref. 18).To explore the interplay between historical demography, ecological and life-history variation, genetic diversity and conservation status, we used a comparative approach based on genetic data from over 80% of all extant pinniped species. To model bottleneck strength, we used two approaches that capture different but complementary facets of genetic diversity resulting from population bottlenecks. Using ABC, we contrasted a bottleneck model incorporating a severe decrease in 55.We specifically focused on two simple ABC models reflecting only recent demographic histories to test a clear hypothesis—large scale commercial exploitation caused severe bottlenecks and reduced the genetic diversity of many pinnipeds. This focus on a short time-frame and well known sealing history allowed us to clearly define our models around reasonable priors. Furthermore, although the genetic diversity simulated based on models of recent demographic history could in principle also be generated by more ancient bottlenecks, these are unlikely to be detected reliably using microsatellite data when a subsequent recovery occurredNebot < 50) were inferred for the northern elephant seal, a textbook example of a species that bounced back from the brink of extinction56, as well as for the two monk seals and the Saimaa ringed seal, species with very small geographic ranges and a long history of anthropogenic interaction13. Slightly weaker bottlenecks were estimated for seven further species including Antarctic and Guadalupe fur seals, both of which share a known history of commercial exploitation for their fur13. At the other end of the continuum, several Antarctic species that have not been commercially hunted such as crabeater and Weddell seals showed unequivocal support for the non-bottleneck model in line with expectations. Surprisingly, several otariid species known to have been hunted in the hundreds of thousands  to millions  did not show support for a bottleneck as strong as simulated in our analyses. This suggests that sufficiently large numbers of individuals must have survived despite extensive sealing, possibly on inaccessible shores or remote islands57.ABC analysis supported the bottleneck model for more than a third of the species. The strongest bottlenecks . This is probably because many species reached economic extinction well above this threshold, when populations became too small to sustain the sealing industry.We hypothesised that not all pinniped species were equally affected by commercial exploitation partly due to intrinsic differences relating to a species’ ecology and life-history. In line with this, we found a strong influence of breeding habitat on bottleneck signatures, with both prop12 we still lack a clear understanding of how recent declines in Ne affect contemporary genetic diversity in wild populations40. Here, we explained a large proportion of the five-fold variation in allelic richness (Ar) observed from the most to the least diverse pinniped species. First, Ar was strongly associated with pbot but not with prophet-exc, in agreement with the theoretical expectation that populations have to decline to a very small Ne, as was simulated in our ABC analysis, to lose a substantial proportion of their diversity3. Second, we showed that global abundance across species was tightly linked to Ar despite the likely impact of bottlenecks and the limited time-window for the recovery of genetic diversity. As differences in genetic diversity across species are largely determined by long-term Ne,2 this implies that contemporary population sizes across pinnipeds must to some extent resemble patterns of historical abundance, and hence that many bottlenecked species have to a large extent rebounded to occupy their original niches. Third, Ar was higher in ice-breeding relative to land-breeding seals. However, a low unique R2 of breeding habitat in our model suggests that this probably reflects the more intense bottleneck histories of land-breeding seals rather than a true ecological effect.Although vast numbers of species are declining globally at unprecedented ratesAr was on average around 21% lower in species within the ‘high concern’ category, consistent with previous evidence from a broad range of species7. While three out of the four pinniped species with the strongest estimated bottlenecks are currently listed as endangered, species from both categories did not overall differ in their bottleneck signatures. Our comparative study of population bottlenecks is therefore encouraging: population bottlenecks do not necessarily result in reduced genetic diversity and population viability. As shown here, global bans on commercial sealing at the beginning of the 20th century allowed many surviving pinniped populations to recover in abundance. Those that have not sufficiently rebounded illustrate the two fundamental conservation challenges, especially as biodiversity loss and climate change continue at unprecedented rates: halting population declines and promoting population recovery.Finally, we compared genetic diversity and bottleneck strength between species that are currently classified by the IUCN as being of conservation concern versus those that are not. We found that We obtained microsatellite data for a total of 30 pinniped species including three subspecies of ringed seal .Phylogenetic data were downloaded from the 10k trees website47 to infer the most likely number of genetic clusters (K) across all datasets. For computational and practical reasons, we used the ParallelStructure package in R63 to run these analyses on a computer cluster. For all of the species for which the best supported value of K was more than one, we recalculated genetic summary statistics and repeated the bottleneck analyses based on individuals comprising the largest cluster and calculated repeatabilities including 95% confidence intervals (CIs) for all variables using the rptGaussian function in the rptR package64. We also tested all loci from each dataset for deviations from HWE using χ2 and exact tests implemented in pegas65 and applied Bonferroni correction to the resulting p-values.In order to maximise data quality, we checked all datasets by eye and generated summary statistics and tables of allele counts to identify potentially erroneous genotypes including typographical or formatting errors. In ambiguous cases, we contacted the authors to verify the correct genotypes. As several of the datasets included samples from more than one geographical location, we used a Bayesian approach implemented in STRUCTURE version 2.3.4Ho) and allelic richness (Ar) with strataG66 as well as the proportion of low frequency alleles (LFA), defined as alleles with a frequency of <5%, using self-written code. For maximal comparability across species with different sample sizes, we randomly sampled ten individuals from each dataset 1000 times with replacement and calculated the corresponding mean and 95% CI for each summary statistic. We did not attempt to standardise our genetic diversity measures by the number of microsatellites, as differences in the number of loci are not expected to systematically bias the mean of any summary statistic across loci.In order to examine patterns of genetic diversity across species, we calculated observed heterozygosity , but occasional larger jump mutations of several repeat units also occur69. Consequently, BOTTLENECK allows the user to specify a range of mutation models, from the strict SMM through two phase models (TPMs) with varying proportions of multi-step mutations to the infinite alleles model (IAM) where every new mutation is novel. We therefore evaluated the SMM plus three TPMs with 70%, 80% and 90% single-step mutations respectively and the default variance of the geometric distribution (0.30). For each of the mutational models, the heterozygosity of each locus expected under mutation-drift equilibrium given the observed number of alleles (Heq) was determined using 10,000 coalescent simulations. The proportion of loci for which He was greater than Heq (prophet-exc) was then quantified for all of the mutation models. To quantify consistency of the measure across mutation models, we calculated the repeatability of prophet-exc using the rptR package64 in R with 1000 bootstraps while adjusting for the mutation model as a fixed effect. Although the relative pattern across species was very consistent across mutation models , absolute values of prophet-exc within species decreased with lower proportions of multistep mutations . Given our posterior estimates  effective population size Nehist were drawn from a log-normal distribution with Ne ~ lognorm and Nehist ~ lognorm. This concentrated sampling within plausible ranges that fitted most species  while also occasionally drawing samples in the hundreds of thousands to fit the few species with very large populations. The bottleneck effective population size Nebot was drawn from a uniform distribution between 1 and 500  while the bottleneck start and end times tbotstart and tbotend were drawn from uniform distributions ranging between ten and 70  and one and 30  generations ago respectively. Hence, the bottleneck time priors encompassed the last four centuries for all species, as their estimated generation times vary between approximately 7 and 19 years . All population size changes were therfore modeled as instantaneous changes at times tbotstart, tbotend or thist.Genetic data under both models were simulated from broad enough prior distributions to fit all 30 species while keeping the priors as tightly bound as possible around plausible values. The bottleneck model was defined with seven different parameters Fig. . The curers Fig. . Ne, Neh7 datasets of 40 individuals and ten microsatellite loci each under the two demographic scenarios using the fastsimcoal function in strataG66 as an R interface to fastsimcoal273, a continuous-time coalescent simulator. For both the simulated and empirical data, we used five different summary statistics for the ABC inference, all calculated as the mean across loci. Allelic richness , allelic size range, expected heterozygosity , the M-ratio53 and the proportion of low frequency alleles (LFA) . The summary statistics for the empirical datasets were computed by repeatedly re-sampling 40 individuals with replacement from the full datasets and calculating the mean across 1000 subsamples . As a small number of loci in the empirical data exhibited slight deviations from constant repeat patterns , we calculated the M-ratio as an approximation using the most common repeat pattern of a locus to calculate the range of the allele size r and subsequently the M-ratio with M = k/(r + 1) where k is the number of alleles. All statistics were calculated using a combination of functions from the strataG package and self-written code. For the ABC analysis, we used a tolerance threshold of 5 × 10−4, thereby retaining 5000 simulations with summary statistics closest to those of each empirical dataset. For estimating the posterior probability for each scenario and each species, we used the multinomial regression method75 as implemented in the function postpr in the abc package25 where the model indicator is the response variable of a polychotomous regression and the accepted summary statistics are the predictors. To construct posterior distributions from the accepted summary statistics for the model parameters, we used a local linear regression approach20 implemented in the abc function of the abc package.We simulated a total of 2 × 107 simulations were considered as pseudo-observed data and classified into either the bottleneck or the non-bottleneck model using all of the remaining simulations. If the summary statistics are able to discriminate between the models, a large posterior probability should be assigned to the model that generated the pseudo-observed dataset. This was repeated 100 times and the resulting posterior probabilities for a given model were averaged to derive the rate of misclassification. We furthermore used a hypothesis test based on the prior predictive distribution45 implemented in the gfit function in the abc package to check for each species that the preferred model provided a good fit to the observed data. Specifically, we used the median distance between the accepted and observed summary statistics as a test statistic, whereby the null distribution was generated using summary statistics from the pseudo-observed datasets. Hence, a non-significant p-value indicates that the distance between the observed summary statistics and the accepted summary statistics is not larger than the expectation based on pseudo-observed data sets, i.e., the assigned model provides a good fit to the observed data.We evaluated whether ABC could distinguish between the two models by performing a leave-one-out cross validation implemented by the cv4postpr function of the abc package. Here, the summary statistics of one of the existing 2 × 1025.In order to determine which parameters  could be reliably estimated, we used leave-one-out cross validation implemented in the cv4abc function from the abc package to determine the accuracy of our ABC parameter estimates. For a randomly selected pseudo-observed dataset, parameters were estimated via ABC based on the remaining simulations using the rejection algorithm and a prediction error was calculated. This is possible because we know the true parameter values from which a given pseudo-observed dataset was simulated. This procedure was repeated 1000 times and a mean prediction error ranging between 0 and 1 was calculated, where 0 reflects perfect estimation and 1 means that the posterior estimate does not contain any information about the true parameter value46 for each species. First, we estimated the posterior distribution of each parameter using ABC. Second, we sampled 1000 multivariate parameters from their respective posterior distributions and used those to simulate summary statistics a posteriori based on the preferred model. Last, we plotted those summary statistics as histograms and superimposed the observed summary statistics across all species21.To further confirm the fit of the preferred models, we conducted posterior predictive checks76 to evaluate the ecological and life-history variables affecting bottleneck strength and genetic diversity, and to test whether bottleneck history and genetic diversity are predictive of contemporary conservation status. Details of all the models are given in Supplementary Table 78. In models without binary fixed effects, the predictor variables were standardised by one standard deviation. For all models, we report the marginal R2 as in ref. 79. Some of the predictors in our models were correlated and multicollinearity might lead to suppression effects and make the interpretation of regression coefficients difficult54. We therefore reported standardized β estimates, structure coefficients, R2 values for all variables in all models. The structure coefficients represent the correlation between a predictor and the fitted response of a model independent of the other predictors, and therefore reflect the direct contribution of a variable to that model. On the other hand, the unique R2 is the difference between the marginal R2 of a model including and a model excluding a predictor, which will be small when another predictor explains much of the same variation in the response54. All model estimates were presented as the posterior median and 95% credible intervals (CIs). We used uninformative priors with a belief (shape) parameter v = 1 for the variance-covariance matrices of the random effects and inverse-Wishart priors with v = 0.002 for residual variances. For each model, three independent MCMC chains were run for 110,000 iterations, with a burn-in of 10,000 iterations and a thinning interval of 100 iterations. Convergence was checked visually and by applying the Gelman–Rubin criterion to three independent chains. All of the upper 95% confidence limits of the potential scale inflation factors were below 1.05.Finally, we used Bayesian phylogenetic mixed models in MCMCglmm67 were implemented in R80. The complete documented analysis pipeline can be downloaded from our GitHub repository https://github.com/mastoffel/pinniped_bottlenecks.All data wrangling steps and statistical analyses except for the heterozygosity-excess testsSupplementary InformationPeer Review File


Didemnum vexillum on artificial substrates. Colonies sampled in summer were shown to form a metapopulation, with high levels of admixture, extreme outcrossing, and some substructure. These patterns were consistent within the subsampled winter colonies and with the inclusion of chimeric data. However, allelic richness and diversity were significantly different between winter and summer samples, altering interpretations relating to population connectivity and pelagic larval duration. This study demonstrates the importance of including seasonal sampling and imperative life history traits in genetic studies for clear interpretations and the successful management of introduced species.Disentangling pathways by which nonindigenous species expand and spread regionally remains challenging. Molecular ecology tools are often employed to determine the origins and spread of introduced species, but the complexities of some organisms may be reducing the efficacy of these tools. Some colonial species exhibit complexities by way of chimerism and winter colony regression, which may alter the genetic diversity of populations and mask the connectivity occurring among them. This study uses nuclear microsatellite data and simple GIS‐based modeling to investigate the influence of chimerism and winter regression on the genetic diversity and patterns of genetic population connectivity among colonies of  To this end, numerous studies have employed genetic markers to identify the extent of introductory pathways, the role of natural and anthropogenic vectors, and the rate of postborder spread  individuals as two individuals, thereby artificially inflating the population size, whereas Smith (Didemnum vexillum (Kott 2002), from New Zealand and Japan, but treated these as a separate experimental study due to the infancy of investigations of chimerism for this species. The main issue posed by chimerism for pathway identification is the potential for misinterpretation of genetic diversity and downstream population structure analyses.Chimerism is particularly problematic for genetic studies, but is yet to be comprehensively investigated and incorporated into analyses and interpretations. In a chimeric colony, two copies of a gene can occur in a single zooid, resulting in multiple copies within a small section of the colony tissue. Multiple studies have now identified chimerism in NIS, such as the colonial Botryllid and Didemnid ascidians  compare the observed genetic connectivity with predictions of dispersal generated from mathematical modeling; (c) assess the influence of chimerism on the genetic diversity and observed genetic connectivity; and (d) assess the influence of seasonal regression on the genetic structure of a subset of D. vexillum colonies.The primary goal of this region‐specific study was to investigate standard population genetic measures of an invader with a relatively well‐mapped pathway of invasion, but complex life history and genomic arrangement. Microsatellite markers were employed for fine‐scale spatial and temporal resolution. The four objectives were to: (a) investigate the genetic structure of 22.1D. vexillum were collected from artificial structures  between January–March 2013 , Pelorus Sound (N = 9 sites in summer and N = 6 sites in winter), and Queen Charlotte Sound (N = 5 sites in summer). The Port Nelson site represented a younger incursion, outside of the Marlborough Sounds region, sampled in summer to provide a more complete broad‐scale resolution for assignment tests. It was not used in the connectivity or seasonal comparisons. Samples were collected from colonies or colony fragments situated ≥2 m apart to minimize the chance of pseudoreplication due to sampling from clonally related colonies  amplification was extracted using a lithium chloride/chloroform extraction protocol . Loci were pooled for multiplexing , as per Smith , the corrected p‐value (CPV) was obtained, where α = 0.05; k = the number of tests performed, and i = the ith observation  were assessed for deviations from Hardy–Weinberg equilibrium (HWE) and linkage disequilibrium (LD) using ARLEQUIN v.3.5.1.3  and secondary introductions (adjacent populations in Pelorus Sound), likelihood assignments were conducted in GenALEx v.6.5. Isolation by distance (IBD) among populations within Queen Charlotte Sound and Pelorus Sound was assessed using MANTEL tests in GenALEx v.6.5  with a geographic Euclidean distance matrix (kilometers). The Port Nelson population was excluded from the IBD assessment, as spatial disharmony with sites would generate structural information, not pattern, in the results.NA), private allelic richness (APN), and expected and observed heterozygosity (EH/OH) for loci and all populations were made using GenALEx v.6.5. The population‐specific inbreeding coefficient (FIS) was obtained using ARLEQUIN v.3.5.1.3. Rarefaction, based upon the lowest sample size (N = 19), was used to evaluate differences in allelic diversity  among putative source and recipient sites , and among populations within sites, with single‐factor analysis of variance tests (ANOVAs) computed in R v.3.0.2  performed in ARLEQUIN v.3.5.1.3. No more than two loci were missing per individual, and 10% missing data per loci were selected as an acceptable missing data rate . Two of the PLD models (12‐ and 24‐hr) were tested with separate AMOVAs to assess genetic differentiation among sampled locations within and among resulting clusters. The clusters formed in the 24‐hr PLD were almost identical to the sampled farms within the 36‐hr PLD model (generated by the matrix), both models were represented by the 24‐hr model for further analysis. An AMOVA was not done on the 2‐hr PLD model as 273 clusters were formed, with each population sampled representing unique clusters.FST  computed in R v.3.0.2 was used to test the significance of any differences.To assess the influence of chimerism, the raw allele data were rescored so that additional allele peaks detected by the ABI 3100 DNA analyzer within 50% of the height of the two main allele peaks (the diploid dataset) were incorporated into one dataset. These were then treated as polyploid data as it was not possible to isolate the different individuals or genotypes in chimeric results. POLYSAT in R v.3.0.2  in GENALEX v.6.5 for both diploid and polyploid datasets derived from the summer sampling period.D. vexillum sites sampled in Pelorus Sound during summer were resampled in winter (August 2014) to assess the role of dieback on genetic diversity and patterns of genetic population connectivity , with 3 Dvex01) to 13 (Dvex30 and Dvex33) alleles observed per locus . Therefore, the full set of loci was used for all final analyses.All populations, across seasons, showed significant deviations from Hardy–Weinberg equilibrium (HWE) for some of the loci; this was primarily due to homozygote deficits. MICRO‐CHECKER suggested no evidence of stutter or genotyping errors across all sampled populations, but detected the potential presence of null alleles for four markers, though this was not consistent across all populations within each locus3.1.1D. vexillum genome.Of the of 420 linkage disequilibrium tests, 11 (2.6%) showed evidence for loci pairs with linkage disequilibrium after FDR correction (CPV = 0.008). The test for linkage disequilibrium in ARLEQUIN assumes Hardy–Weinberg proportions of genotypes and therefore the significance of these tests may be a result of departures from HWE . Overall, genetic diversity for the three primary locations sampled  was consistently high, with average observed and expected heterozygosity ranging from 0.57 ± 0.12 to 0.73 ± 0.05 and 0.52 ± 0.09 to 0.64 ± 0.05, respectively . Queen Charlotte Sound populations exhibited the lowest levels of observed and expected heterozygosity on average  in allelic diversity , when compared across sites  and across populations within Pelorus Sound and Queen Charlotte Sound.There was a total range of 4 Dvex01) to 13 (Dvex30 and Dvex33) alleles for the eight polymorphic loci (Supporting information Table  to 13 (Dp = 0.260). Similarly, there was no evidence or trend for isolation by distance among the Pelorus Sound populations and among Queen Charlotte Sound populations.There was no evidence for isolation by distance among summer populations in Pelorus Sound and Queen Charlotte Sound , indicating little to moderate genetic differentiation, as defined by Wright  and among populations within sites, although this explained only a small proportion of the genetic variation  and distance measures (Jost's D), which showed that each cluster group  had at least one population present in another cluster group , from which it was not significantly differentiated , ensuring both estimators provided comparable results. Genetic differentiation was therefore not reflective of PLD and current movements alone.AMOVA results revealed no significant genetic differentiation among clusters for the 24‐ and 12‐hr PLD models , providing some confidence in the program. However, for chimeric colonies, the frequency of more common alleles was reduced, less common allele frequencies were increased, and rare alleles were introduced . Of the 168 linkage disequilibrium tests completed for the winter diploid dataset, four (2.4%) showed evidence for loci pairs with linkage disequilibrium after FDR correction (CPV = 0.009), spread across populations sampled in Pelorus Sound. Overall, genetic diversity was consistently higher in winter than summer, with a wider range of average observed and expected heterozygosity and a slightly higher observed heterozygosity, ranging from 0.46 (±0.10) to 0.76 (±0.05) and 0.49 (±0.08) to 0.66 (±0.03), respectively  and expected heterozygosity  for winter and summer populations. All winter populations exhibited homozygote deficiency, with no evidence of significant inbreeding  to 9 (Dvex03) alleles for the eight polymorphic loci  within winter sites or between summer and winter sites.Separate ANOVA tests showed no significant differences in allelic diversity , and among populations within sites , although this only explained a small proportion of the genetic variation (Table FCT) and 5.54% (FSC), respectively). Significant variation was also detected within the summer and winter groups, which explained 91.8% of the variation in the data , indicating little to moderate genetic differentiation.AMOVA results revealed significant genetic differentiation between the summer and winter populations  and distance measures (Jost's D) , and significant differences in the overall allele diversity and frequency were also not found.The proportion of chimeric colonies identified in winter (17%–48%) was similar to summer (19%–47%). Overall, no significant differentiation was found between summer and winter populations when incorporating chimeric data  is a common feature of microsatellite loci, and very rare alleles are often uninformative for population‐based analyses, as their presence may be due to reoccurring mutations rather than historical association or contemporary gene flow (Hale, Burg, & Steeves, Styela plicata before and after a summer dieback event, which had occurred for the previous 20 years since the establishment of the population. Utilizing the COI gene, they could show weak but significant differences among time periods. Specifically, the die‐off event caused an increase in genetic diversity, less evidence of inbreeding, and a net gain of microsatellite alleles. The interpretation of the full dataset suggested neighboring populations were quick to occupy the cleared space with novel alleles maintaining a viable and genetically stable population over time. In contrast to our study, Pineda, Turon, et al. (Temporal sampling, while integral to ecological studies, is not always possible or is deemed too costly for genetic studies of nonindigenous species, despite multiple studies highlighting the deeper understanding of the invasion obtained from temporal sampling (Goldstien, Inglis, Schiel, & Gemmell, , et al.  consisteConnectivity of populations is often inferred from genetic structure of sampled populations, and no study to our knowledge has specifically modeled and aligned dispersal clusters to genetic structure of populations within and among the clusters. Consistent with the genetic structure of sampled populations, our data were influenced by temporal sampling, whereby the winter samples better aligned to modeled clusters for an assumed 24‐hr pelagic larval duration.This study has highlighted subtle, but important shifts in the downstream analysis of population genetic data when chimeras and seasonal dieback are considered. These shifts become more important when assessing the potential influence on management options. For instance, the assignment of source populations informs authorities of specific vector potential from an area, therefore spurious results due to sampling error may identify false introduction points and pathways of local spread, as well as inflating or understating the number of potential incursions into a newly invaded region. Similarly, the alignment of genetic structure to natural dispersal potential may divert management tools away from vector management to node clearance, yet temporal sampling indicates that this result cannot be used as conclusive if sampling is restricted to a snapshot moment.D. vexillum reproduce for a longer period than normally considered, for 9 months of the year, but not through the winter months. They also suggest that periods of increased recruitment and growth as seen in D. vexillum will influence the level of connectivity and potential spread of established populations, with higher biomass leading to increased propagule pressure. Furthermore, our study suggests that the winter dieback period provides an opportunity for rare alleles to propagate through the region when growth increases again the following season, thereby suggesting that a winter removal program presents an opportunity to further reduce biomass and potentially the genetic diversity of the spreading population. While further study would be required to ensure removal does not encourage supercolonies by further reducing the genetic diversity and thereby promoting allorecognition and enhanced fusion (Smith, Fletcher, Forrest, Atalah, et al.  showed tn Smith, , the redD. vexillum could exceed 1 km under high current flow regimes. Using Bayesian analyses based on these predictions and other parameters, we tested the potential for genetic connection over these distances with distinct dispersal clusters. We observed conflicting results between our summer and winter samples. The summer samples suggest genetic connectivity over 12 and 24 hr of modeled dispersal, but the winter samples were differentiated with a 24‐hr modeled period, which alone would suggest limited dispersal potential. However, in both datasets, the genetic structure was consistent with this previous study in suggesting a greater potential for long‐distance dispersal and connectivity than previously considered possible for this species (Fletcher, Forrest, & Bell, Recent work by Fletcher, Forrest, and Bell  indicateStyela clava in New Zealand (Goldstien et al., Numerous studies have shown that in addition to natural dispersal, human activities facilitate the transport and introduction of species outside of their natural ranges (Bulleri, Balata, Bertocci, Tamburello, & Benedetti‐Cecchi, Genetic data as a tool to advise management are limited by the resolution at which the organisms and their populations are sampled. Here, we have highlighted two important components of sampling and processing genetic data for colonial nonindigenous species that can alter the interpretation of genetic structure and of dispersal pathways; two primary aspects of invasion ecology that assist with the management of global and regional spread. Therefore, we would argue that seasonal sampling and the inclusion of important life history traits, such as chimerism, in genetic studies, are worth the effort for refinement of interpretations and for the management of NIS.None Declared.Ashleigh Watts conducted the work as a component of her Master's thesis under the supervision of Dr Sharyn Goldstien and Dr Grant Hopkins. All authors contributed to the design of the project. Ashleigh Watts collected and processed all genetics samples, under the guidance of Dr Sharyn Goldstien. Ashleigh Watts analyzed the data under the guidance of Dr Sharyn Goldstien and Dr Grant Hopkins. Assistance with modeling was provided by Cawthron staff. Click here for additional data file.


Smart city movements are growing all over the world. The undertaking is expected to solve a plethora of problems arising from urbanization. Indonesia is one of the countries who march toward the development of sustainable smart cities. However, before the government can start a smart city project, they need to assess the readiness of each target city. Data in this article illustrate the readiness of six major cities in Indonesia, which are Semarang, Makassar, Jakarta, Samarinda, Medan, and Surabaya. They represent the four biggest islands in Indonesia. The readiness assessment was based on three main elements and six Smart City Pillars taken from Smart City Master Plan Preparation Guidance Book prepared by Ministry of Communication and Information Technology of the Republic of Indonesia. Those elements serve as a checklist to determine the readiness of the cities. Data for qualitative analysis were gathered through interviews and triangulated through secondary sources, such as publication from Statistics Indonesia and the assessment reports. The dataset contains information on the readiness assessment is presented in this article. The indices of the six region's readiness assessment are presented in percentages. The group comprises of twenty-four cities that have been selected in the first phase and fifty cities in the second phase. They were a part of the Indonesia 100 Smart Cities Movement initiated by Ministry of Communication and Information Technology of the Republic of Indonesia in 2017 and 2018, and Jakarta as the capital city of Indonesia. The urban features of seventy-five smart cities are presented in The readiness assessment datasets on six major smart cities are presented in this article. Data measurements were conducted based on three main elements and six smart city pillars. 1.11.Structures including human capital, financial capital, and governance capital.2.Infrastructures including physical, digital, and social.3.Superstructures including regional regulation (statute), institutional, and implementation development based on smart-city aspects.The essence of the Smart City concept is the city and all its components can manage existing resources to support and maintain the continuity of the ecosystems. Two steps were used to assess regional readiness. First step, the regions were assessed on three main elements based on the Smart City Master Plan Preparation Guidance Book 1.Smart Governance including Public Service, Bureaucracy, and Public Policy2.Smart Branding including Tourism Branding, Business Branding, and City Appearance Branding3.Smart Economy including Competitive Industry, Welfare, and Transaction4.Smart Living including Harmonization of Regional Layout, Health Facilities, Mobility Access5.Smart Society including Community Interaction, Learning Ecosystem, and Safety & Security6.Smart Environment including Environmental Protection, Waste Management, and Energy ResponsibilitySecond step, the cities were further assessed based on the dimensions of six Smart City Pillars. The measures used are:22.1The dataset in this article relates to the concept of Indonesia Smart Cities Platform Ecosystems which discussed the effort of the Indonesian government to implement the Smart City concept in all national development aspects This dataset has six major cities (i.e. samples) that represent Indonesia's main islands, which are Medan, Jakarta, Semarang, Surabaya, Samarinda, and Makassar. The Ministry did not include Jakarta in their 100 Smart City Program because the city is a Special Region which does not belong to West Java, Central Java, or East Java province. However, Jakarta was selected as sample because this city, along with Bandung and Surabaya, is a pioneer in the development of smart cities in Indonesia and its effort towards sustainable smart cities has not ceased.The six major cities were also chosen because of the availability of regional logistical support facilities and infrastructure, such as airports, seaports, container terminals, warehousing, and access to the main road. The availability of seaport became one of the most important considerations because seaport has roles that cannot be replaced by other modes, such as airports, highways, and trains 2.2The qualitative data were gathered through a series of interviews. To ensure the validity of the data, the interviewers questioned members of the central governments and academics. Each interview was transcribed. Content analysis was done to interpret and code textual material . The data in the form of interview quotes were then categorized based on the pre-determined theme and theoretical constructs . The data were further corroborated using secondary data, such as publications from Statistics Indonesia, the assessment results, end-of-year performance reports form the central and local governments, and New Urban Agenda from Ministry of Public Works and Housing.In accordance to Yin's recommendation After gathering data for all sample cities, the readiness of each city was calculated by using an equation (see Eq.1) based on works of Atmojo et al. Eq.


Gymnocarpos przewalskii as a model to address the evolutionary history and current population genetic structure of species in northwestern China. We employed two chloroplast DNA fragments (rps16 and psbB‐psbI), one nuclear DNA fragment (ITS), and simple sequence repeat (SSRs) to investigate the spatial genetic pattern of G. przewalskii. High genetic diversity  was identified in almost all populations, and most of the population have private haplotypes. Moreover, multimodal mismatch distributions were observed and estimates of Tajima's D and Fu's FS tests did not identify significantly departures from neutrality, indicating that recent expansion of G. przewalskii was rejected. Thus, we inferred that G. przewalskii survived generally in northwestern China during the Pleistocene. All data together support the genotypes of G. przewalskii into three groups, consistent with their respective geographical distributions in the western regions—Tarim Basin, the central regions—Hami Basin and Hexi Corridor, and the eastern regions—Alxa Desert and Wulate Prairie. Divergence among most lineages of G. przewalskii occurred in the Pleistocene, and the range of potential distributions is associated with glacial cycles. We concluded that climate oscillation during Pleistocene significantly affected the distribution of the species.Northwestern China has a wealth of endemic species, which has been hypothesized to be affected by the complex paleoclimatic and paleogeographic history during Quaternary. In this paper, we used  Many geRibes meyeri  to investigate the spatial genetic pattern of 22.1G. przewalskii were collected during field surveys from June to September 2013. A total of 194 individuals from 21 sites were sampled, representing nearly all known populations across most of the G. przewalskii range , 0.1 μL forward primer (10 μmol/L), 0.4 μl reverse primer (10 μmol/L) and fluorescently labeled (FAM) M13 primer (10 μmol/L), and 0.2 U Taq polymerase. PCR cycling conditions were as follows: 5 min at 95°C, followed by 30 cycles of 30 s at 95°C, 45 s at 47–64°C, and 45 s at 72°C, an additional 8 cycles of 30 s at 95°C, 45 s at 53°C, and 45 s at 72°C, and a final 12‐min extension step at 72°C  was determined using DnaSP v5 , haplotype diversity (h), Tajima's D  of populations that are maximally differentiated from each other without a priori assumptions about population groupings. The analysis was run for K = 2–20, starting from 100 random initial conditions for each run. FCT values were given for every calculation. Finally, we obtained two independent K groupings with maximized FCT values , hT , GST , and NST (coefficient of genetic variation between haplotypes). Phylogeographical structure is present if NST is significantly higher than GST  values and the matrix of geographical distances. The analyses were carried out in the Alleles In Space software , which test the correlation between the matrix of pairwise genetic distance  and Silene atocioides ; outgroups for ITS were Silene noctiflora (FN821141), Paronychia canariensis (AJ310959), and Gymnocarpos decandrus (KF850591). Outgroup sequences were downloaded from NCBI (http://www.ncbi.nlm.nih.gov/). The haplotypes from unphased ITS genotypes of Silene noctiflora were obtained using methods similar to those with G. przewalskii. Optimal models of DNA evolution for the data were inferred using the MrModelTest 2.3 program  and Bayesian inference (BI) methods. Outgroups for the cpDNA analysis were G. przewalskii, we employed a Bayesian approach in the software BEAST version 1.8.1. An uncorrelated lognormal relaxed clock was used for clock models, and a constant size process was used for modeling speciation. The MCMC search was run for 70,000,000 and 80,000,000 generations for ITS and cpDNA, respectively, and sampled every 1,000 generations. Four independent Markov chains were used in this process. TRACER 1.5    implemented in ARLEQUIN 3.1 , the number of effective alleles (NE), observed heterozygosity (HO), expected heterozygosity (HE), allelic fixation index (F), gene flow (Nm), and Hardy–Weinberg equilibrium (HWE) by POPGENE 1.32  was estimated by HP‐RARE 1.1 . For each K value, ten independent runs were conducted with a burn‐in period of 10,000 and 10,000 Markov Chain Monte Carlo (MCMC) generations.To indicate the genetic relationship among populations, an UPGMA tree based on pairwise Nei's genetic distances was constructed by POPGENE 1.32  was carried using Arlequin 3.1. In total, 27 points were obtained to be used for SDM analyses (Supporting information Table www.worldclim.org). The nineteen bioclimatic variables were strongly correlated with each other, which could result in model overfitting  to reconstruct the paleo‐ and current distributions for  Graham, . To avoi Graham, . We projThe data set for the LGM climate was downloaded from the WorldClim database at a resolution of 2.5 min and projected using the species’ current bioclimatic niche onto past climate layers. To construct the LGM potential distributions, we projected the model in both general circulation models: the Community Climate System Model . Lack of recombination allowed direct analysis of the ITS sequences.Recombination was not detected in the ITS alignment by the pairwise homoplasy index . For ITS data, NST (0.873) was significantly larger than GST (0.474) (p = 0.005 < 0.01), indicating the presence of significant phylogeographic structure across the species’ range. In contrast, we did not observe NST (0.700) significantly larger than GST (0.619) in the cpDNA data.Estimates of average within population diversity and total diversity for both cpDNA and ITS were very high in FCT reached the maximum value (FCT = 0.89225). The fixation index FST by AMOVA and pairwise difference (Tables FST = 89.22%) can be explained by among‐group variation. The SAMOVA analyses using cpDNA indicated that a grouping structure was not present, because the K values got higher as FCT got higher.For SAMOVA results of ITS, populations were divided into four groups  as r = 0.260; p = 0.005 < 0.01; ITS: r = 0.505; p = 0.001 < 0.01), suggesting strong isolation by distance. The MJ Network illustrates the complex genealogical relationships among haplotypes found for cpDNA and ITS  in all population of G. przewalskii. The geographical isolation and genetic distance were also significantly correlated with the populations of G. przewalskii in the Tarim Basin .In analysis of SSRs, according to the Nei's genetic similarity coefficients and genetic distance, the relationship between populations SB, LY, CM, ZW, and AZQ is closest  can be explained by among‐group variation. The MP ITS tree is comprised of two major clusters (Tarim Basin + Hami Basin + Hexi Corridor and Alxa Desert + Wulate Prairie).The MP tree and Bayesian tree are highly similar, only differing in minor aspects, so we here employed the MP tree Figure  as an exe Tables , 7 of cpG. przewalskii occurred in the Pleistocene , indicating that the SDM developed under the current climate conditions accurately predicted the distribution of the species. The averaged distributions for the LGM and current conditions predicted by this model are shown in Figure For G. przewalskii predicted by MAXENT is basically consistent with the actually observed geographical distribution of the species. The illustration shows that the most suitable climatic conditions are concentrated in the northern edges of the Tarim Basin, Hami Basin, Hexi Corridor, and Alxa Desert.The present potential distribution of high support (0.6–1) for The LGM potential distribution of the species was predicted by CCSM and MIROC models. Compared with the present potential distribution, the LGM distribution showed a general range contraction. Both CCSM and MIROC models showed that all these suitable areas were isolated from each other. Overall, the range of potential distributions is associated with glacial cycles; the distribution range contracted under glacial conditions and expanded during interglacials.44.1G. przewalskii (hT = 0.866) in northwestern China was found to be high compared to the average value (hT = 0.670) for other plants  was found to be high compared to several other plants occurring in adjacent regions , pairwise difference  within most population  compared to the average value  for other plants , because of high levels of genetic variation, unique haplotypes, and ancestral haplotypes in these four regions (Ma et al., Biological refugia are relatively stable areas where a species is predicted to have had persistent survival throughout climatic fluctuations during the Pleistocene. According to Petit et al. , plant pG. przewalskii, the Tarim Basin, the Hami Basin, the Hexi Corridor, the Alxa Desert, and the Wulate Prairie can be deduced as glacial refugia for species and localized range expansion from these refugia during the interglacial.In present study, combining all data sets, we observed that almost all populations have high levels of genetic diversity, and 14 populations distributed in each group have unique haplotypes Tables , 3. ThusG.przewalskii have a wealth of mountains that have complicated topology. The species in the Tarim Basin and the Hami Basin are mainly distributed along the southern Tianshan Mountains. The species in Hexi Corridor is mainly distributed along the northern Qilian Mountains. The species in Wulate Prairie is mainly distributed along the Yin Mountain. Populations of the species are distributed in scattered patchiness and mainly grow along foothills. Areas of complicated topography might provide suitable microenvironment in relative stability and survived there during severe climatic oscillations (Rull, G. przewalskii so that the species can survive during climatic oscillations (Rull, Schubert, & Aravena, The distribution areas of ns Rull, . In addiNone declared.M. Z. conceived and designed the experiments. S. J. collected samples, performed the experiments, and analyzed the data. M. Z. and S. J. wrote, edited, and reviewed the MS. Click here for additional data file. Click here for additional data file.


Parasitic nematodes are highly diverse and common, infecting virtually all animal species, and the importance of their roles in natural ecosystems is increasingly becoming apparent. How genes flow within and among populations of these parasites - their population genetics - has profound implications for the epidemiology of host infection and disease, and for the response of parasite populations to selection pressures. The population genetics of nematode parasites of wild animals may have consequences for host conservation, or influence the risk of zoonotic disease. Host movement has long been recognised as an important determinant of parasitic nematode population genetic structure, and recent research has also highlighted the importance of nematode life histories, environmental conditions, and other aspects of host ecology. Commonly, factors influencing parasitic nematode population genetics have been studied in isolation, such that an integrated view of the drivers of population genetic structure of parasitic nematodes is still lacking. Here, we seek to provide a comprehensive, broad, and integrative picture of these factors in parasitic nematodes of wild animals that will be a useful resource for investigators studying non-model parasitic nematodes in natural ecosystems. Increasingly, new methods of analysing the population genetics of nematodes are becoming available, and we consider the opportunities that these afford in resolving hitherto inaccessible questions of the population genetics of these important animals. Parasitic nematode infection is ubiquitous in wild animals and can profoundly alter the physiology, behaviour and reproductive success of hosts , 2, and Population genetic structure - the distribution of genetic variation in time and space - affects how a species responds to selection pressures, and so shapes its evolution . StudyinThe population genetics of metazoan parasites, including nematodes, has been reviewed extensively , 20–25, Here, we provide a resource that collates what is known about the population genetics of parasitic nematodes in natural ecosystems, that we envisage will be useful to researchers investigating the important, but little-understood, roles that parasitic nematodes play in such ecosystems. To this end, we comprehensively review the population genetics of parasitic nematodes in wild animals, including every relevant study of which we are aware. We take wild animals to be ones that establish populations without direct human management, even if they live commensally in human settlements (such as cities), or if their habitat is undergoing anthropogenic changes, since these host populations may nonetheless influence the population genetics of their parasitic nematodes in ways that hosts whose populations are managed by humans cannot. We consider the parasitic nematodes of terrestrial vertebrates, marine vertebrates, and arthropods, and draw together the evidence to present a synthesis on the factors determining the population genetics of parasitic nematodes in wild animals. We examine the population genetics of parasitic nematodes in wild animals that have undergone recent habitat change, asking how parasite populations respond to anthropogenic influences on natural systems. Finally, we assess future prospects in the study of parasitic nematode population genetics, discussing opportunities provided by high-throughput DNA-sequencing-based methods, and highlighting the importance of including extra-host stages in population genetics studies.Globocephaloides trifidospicularis [Hypodontus macropi complex [Rugopharynx australis from Macropus robustus and M. rufus [Capillaria species [H. macropi from subspecies of Macropus robustus, H. macropi from subspecies of Macropus rufogriseus [ Labiosimplex australis from M. rufogriseus [H. macropi from Petrogale persephone [Globocephaloides affinis from Macropus dorsalis [Cloacina species [Commonly, population genetic studies of the parasitic nematodes that infect macropod marsupials reveal that what was previously classified as a single nematode species with a broad host range is actually a complex of cryptic species, each with a narrow host range Table . This hiicularis , nor in  complex , nor in M. rufus , nor in  species , but wasogriseus  and Labiogriseus . In somersephone , Globocedorsalis , and sev species , but thiL. australis collected from Tasmania and Kangaroo Island, and between both of these populations and mainland Australia [H. macropi in M. rufogriseus showed genetic differentiation from that on mainland Australia [R. australis in M. giganteus and M. fuliginosus did not show population differentiation despite being sampled from both Kangaroo Island and mainland Australia [G. trifidospicularis sampled from multiple macropod species [Why do some of these parasite species have genetic population structuring while others do not? There was genetic differentiation between ustralia . Similarustralia . This suustralia , nor did species . This maH. macropi in Macropus dorsalis, M. agilis and Wallabia bicolor, respectively [R. australis collected from M. robustus and M. rufus [H. macropi recovered from M. robustus were found to be differentiated from those in South Australia [H. macropi in M. robustus were over 1500 km apart, compared with up to 800 km in other species. Naturally, as distances between populations increase, host-mediated transmission between the populations becomes rarer, though the exact distances over which host-mediated transmission becomes inefficient will likely vary with the vagility of host species. It should be noted that in many cases these studies involved low numbers of hosts and parasites , facilitating parasite gene flow among otherwise discontiguous populations of very mobile hosts.The apparent lack of population genetic differentiation in  mammals –43, and Trichinella spp.? Trichinella transmission stages remain in the muscle of their parents’ host and infection of a new host occurs by predation [But what factors drive the among-host differentiation and low intra-specific diversity seen in redation . This liredation , 10, 21)Trichinella spp. appears to be driven by (i) highly mobile hosts and a broad host range, promoting gene flow, and (ii) its life-cycle, promoting clumped transmission of sibling parasites and so lowering Ne.In summary, the population genetics of Heligmosomoides polygyrus, a parasite of the European woodmouse (Apodemus sylvaticus), show extensive population genetic structure across the host species’ range [H. polygyrus populations being more strongly differentiated than those of A. sylvaticus according to mitochondrial sequence analysis of both species [Heligmosomoides polygyrus has a faster mitochondrial mutation rate and generation time than its host [H. polygyrus compared with A. sylvaticus and so contributing to the comparatively stronger population genetic structure of H. polygyrus.The limited dispersal of wild rodent individuals –48 mights’ range , 49–51,  species . Heligmoits host , 52, likTrichuris muris infects rats and mice (including A. sylvaticus), while T. arvicolae infects arvicoline rodents (lemmings and voles), and both Trichuris spp. are found throughout Europe. Like H. polygyrus, T. muris and T. arvicolae both show extensive population genetic structure across their geographical range, as determined by analysis of both mitochondrial and nuclear loci [H. polygyrus and both Trichuris spp., with a delineation between eastern and western populations, and greater diversity in southern populations, compared with northern. These patterns may reflect range expansion of the rodent hosts from southern refugia during the last ice age, at least 12,000 years ago [H. polygyrus than in Trichuris spp. [H. polygyrus than in Trichuris spp., arising from a shorter generation time in the former compared with the latter  [Trichuris spp. A broader host range may allow a parasite to occupy and traverse a wider range of environments, potentially increasing gene flow rates and slowing genetic drift [ear loci –55. Indeears ago , 53–55. ris spp. , 53–55. ctively) , 57, or ic drift .Angiostrongylus cantonensis and A. malaysiensis, parasites of rodents of the family Muridae [A. cantonensis, one of which may be conspecific with individuals identified as A. malaysiensis [A. cantonensis (sensu stricto) and A. malaysiensis can be examined. In both species, population genetic structure was detected among provinces in Thailand [Angiostrongylus spp. over large distances.The population genetics of  Muridae , has bee Muridae –69. Howeysiensis , 71. It Thailand . It is lNeoheligmonella granjoni, a parasite of multimammate mice (Mastomys spp.), was sampled from M. natalensis and M. erythroleucus within a 70 km2 rural area of Senegal, and genotyped at 10 microsatellite loci [M. erythroleucus, which will promote gene flow across the study area, including among populations of the much more sedentary host, M. natalensis [2 area) was also seen in Longistriata caudabullata, a nematode that parasitises Blarina sp. shrews [Other studies have investigated parasitic nematode population genetic structure at finer geographical scales. For example, ite loci . These dtalensis , 73. A l. shrews , showingStrongyloides ratti is a parasite of brown rats, Rattus norvegicus, and shows little genetic differentiation among UK sampling sites ~20–250 km apart [R. norvegicus dispersal is sufficient to genetically homogenise the S. ratti population at these scales. While S. ratti did not show genetic differentiation among host populations, there was differentiation among infrapopulations [Strongyloides spp. are unusual because the parasitic adults reproduce clonally, so that all of a single parasite’s offspring are genetically identical [km apart . This maulations . Strongydentical , and alodentical , this maSyphacia stroma and H. polygyrus from the same A. sylvaticus host individuals showed that S. stroma has substantially lower genetic diversity and higher population differentiation than H. polygyrus [Syphacia stroma has haplodiploid sex determination, in which haploid males develop from unfertilised eggs produced by diploid females, while in H. polygyrus males and females are both produced sexually. Mating system is recognised as an important factor in parasite population genetics [H. polygyrus and S. stroma may explain their different population genetic structures. Haplodiploidy lowers Ne by reducing the number of individuals contributing to the next generation , and this may lead to greater genetic drift in S. stroma compared with H. polygyrus. Syphacia stroma and H. polygyrus have broadly similar generation times [Analysis of olygyrus . Syphacigenetics , 11, 23,on times  and sharIn summary, the population genetics of parasitic nematodes in wild rodents appears to be defined largely by hosts’ low dispersal ranges. However, different patterns of population genetic structure among parasite species sharing a host species suggest that parasite mating system and generation time are also influential.Ostertagia gruehneri and Marshallagia marshalli, both parasites of reindeer (Rangifer tarandus), show a lack of population genetic structuring [Teladorsagia boreoarcticus in muskoxen (Ovibos moschatus) [Mazamastrongylus odocoilei, a parasite of white-tailed deer (Odocoileus virginianus), showed genetically structured populations [M. odocoilei, compared with the internal transcribed spacer  individuals travel over much greater distances than rodents, and so may facilitate comparatively greater gene flow of their parasitic nematodes. ucturing , 81, a pschatus) . In contulations . This diigratory , and so igratory .Dictyocaulus eckerti is a parasite of several species of deer (Cervus spp. and Dama spp.). Analysis of mitochondrial sequence data found weak genetic structuring in D. eckerti [D. capreolus , had comparatively lower genetic diversity and more strongly genetically structured populations when sampled sympatrically [Dictyocaulus capreolus is susceptible to population bottlenecks if roe deer numbers fall, whereas D. eckerti can weather a crash in the population of any one host species by persisting in other host species, and thereby maintain a high census size , while Parapharyngodon cubensis is a species complex  together infecting a broad range of lizards and snakes. Study of the population genetics of these nematodes, sampled from various Caribbean Anolis spp. hosts, found that genetic diversity was partitioned both among and within islands [S. anolis populations were more strongly genetically differentiated than populations of P. cubensis A or P. cubensis B, likely because S. anolis has a narrow host species range made up of poor dispersers [P. cubensis complex each make use of a wider range of hosts, among which may be more mobile host species [Spauligodon atlanticus, parasites of Gallotia spp. lizards, all showed strong genetic structuring within and among islands of the Canary Isles, despite differing in the extent of their host range [S. atlanticus do not overlap, precluding nematode gene flow between them. islands . Howeverspersers , while t species . In contst range . This maRhabdias ranae, a parasite of the northern leopard frog (Lithobates pipiens), revealed low microsatellite heterozygosity, differentiation among infrapopulations and population genetic structure at a very fine scale, with differentiation emerging among ponds less than 1 km apart [Rhabdias ranae is a specific parasite of L. pipiens and lacks an intermediate host, so its dispersal is likely mediated almost entirely through L. pipiens movement. Hence, sibling extra-host stages are likely to remain clumped in the environment and infect a new host together, explaining infrapopulation differentiation. If L. pipiens habitually visit the same locations (e.g. show fidelity to a particular breeding pond), then they may even be re-infected with the offspring of their own parasites [R. ranae, which includes a self-fertilising hermaphroditic stage.Population genetic analysis of km apart . Rhabdiaarasites . Such a arasites . Low hetTrichostrongylus tenuis is a strongylid parasite of galliform and anseriform birds, and is particularly prevalent in red grouse (Lagopus lagopus scotica). Population genetic analysis of T. tenuis in UK red grouse revealed high microsatellite diversity, and a lack of population genetic structure among host individuals and among geographically separated populations [T. tenuis in a goose in Iceland and those in UK grouse suggests long distance T. tenuis gene flow. Some waterfowl species, such as the pink-footed goose (Anser brachyrhynchus), migrate between the UK and Iceland [T. tenuis gene flow between these countries.ulations , 93. Thiulations , presuma Iceland , presentMost nematode parasites of marine mammals and birds are trophically transmitted among intermediate hosts before reaching the definitive host . As hostAnisakis simplex is a complex of several cryptic species with different geographical and definitive host ranges [A. simplex complex , requires adult female hosts on breeding beaches to complete its life-cycle [U. sanguinis to show genetic differentiation among host breeding beaches, but in fact no population genetic structure was observed in U. sanguinis at all [Uncinaria lucasi infecting northern fur seals  [Uncinaria spp. is not fully understood and that transmission also occurs in other ways - male sea lions do move among breeding beaches [fe-cycle , and femfe-cycle . One migs at all , and a sursinus) . This ma beaches , so tranHysterothylacium aduncum is a poorly-defined nematode species complex that infects a broad range of marine fish species [H. aduncum from sprats (Sprattus sprattus) in western Europe showed two genetically distinct populations: one in the English Channel and the Bay of Biscay, and one in the Mediterranean and North Sea [H. aduncum populations in the Mediterranean and North Sea via migration, and sampling of H. aduncum from other hosts is needed to test this hypothesis. In contrast with H. aduncum, there was no genetic structure in Hysterothylacium fabri within the Mediterranean, when considering either geography or host fish species [Some fish species travel around the globe, while others spend their whole lives in a small home patch, and this diversity in movement behaviour is likely to affect the population genetics of their parasitic nematodes.  species . ITS seqorth Sea . The geoorth Sea . Potenti species .Rhabdochona lichtenfelsi, shows strong genetic differentiation among lakes, with the degree of differentiation correlating with the time since the lakes became separated [Pelteobagrus fulvidraco) parasite Procamallanus fulvidraconis in isolated lakes were not significantly genetically different from each other [Camallanus cotti parasitizes a variety of freshwater fish species, and it showed no population genetic structure among the Yangtze and Minjiang river systems , though populations from the Pearl River were distinct [Parasites of fish in discontiguous water bodies can become genetically distinct. For example, splitfin fishes  live in a series of unconnected lakes in Mexico, and their parasite, eparated . In contch other . These lch other , so therdistinct .Collectively, studies of the population genetics of parasitic nematodes in aquatic environments reveal that their population genetic structures emerge at the scale over which hosts move, with genetically unstructured populations being common. Population genetic structure can emerge in these parasites when populations are restricted to isolated water bodies, or where host movement is constrained.Heterorhabditis marelatus has a low level of mitochondrial genetic diversity and shows extreme population genetic structuring among sample sites (7 to 890 km apart) [H. marelatus, since the extreme pathogenicity of H. marelatus kills hosts before they can carry their parasites far, preventing host movement from contributing significantly to parasite dispersal [H. marelatus may also contribute to its strong population genetic structure; Heterorhabditis spp. infections are initiated by juveniles which, upon maturation into hermaphrodites, self-fertilise to produce males and further hermaphrodites that continue to reproduce on the host’s cadaver [Strelkovimermis spiculatus, sampled from mosquito larvae (Aedes spp. and Culex pipiens), with genetic differentiation observed among ponds ~7 to 14 km apart [Isomermis lairdi, a parasite of larval blackflies (Simulium spp.), when sampled from three rivers and multiple host species [I. lairdi, resulting in less structured populations compared with S. spiculatus.Virtually all invertebrate taxa are infected by parasitic nematodes, but the life histories of these nematodes are often poorly understood, and their population genetics barely explored. The insect parasite m apart) . This maispersal . The lif cadaver . This likm apart . In cont species . That thThaumamermis zealandica, a parasite of the sandhopper , showed a complete absence of genetic diversity in three mitochondrial protein-coding genes when sampled from numerous hosts along an ~580 km stretch of New Zealand coast [T. zealandica at all sampling sites as a single population; (ii) a very recent population bottleneck; (iii) an extremely low mitochondrial mutation rate; or (iv) a combination of these factors [Armadillidium vulgare) parasite Thaumamermis cosgrovei [Thaumamermis spp.nd coast . This co factors . Extremeosgrovei , suggestBlatticola blattae, a parasite of cockroaches (Blattella germanica), showed that the parasite population genetic structure closely mirrored that of the host, with both showing differentiation among buildings within cities, and among cities 900 km apart [H. marelatus, B. blattae is not markedly pathogenic and individuals form long-term associations with their hosts [RAPD . Sequence analysis of both ITS and mtDNA have shown a lack of genetic differentiation among B. schroederi from geographically isolated panda populations [B. schroederi has a much larger Ne than its host [B. schroederi populations. It has also been suggested that B. schroederi gene flow may occur among panda populations in the absence of panda movement, for example through association with a presently unknown paratenic host [ulations –127, whiulations . The likits host  and so uits host , drift mTrypanoxyuris minutus and T. atelis, parasites of the primates Alouatta spp. and Ateles geoffroyi, respectively, in Mexico. Since 1940, on-going forest fragmentation has created discontiguous host populations, among which host migration is very rare [Trypanoxyuris spp. showed that parasite populations in different forest fragments were not genetically differentiated [Trypanoxyuris gene flow among forest fragments, and/or the failure of Trypanoxyuris populations to detectably differentiate since becoming reproductively isolated. The latter explanation assumes that Trypanoxyuris populations were genetically unstructured prior to forest fragmentation, and this seems plausible as parasitic nematodes from a range of wild primates show limited population genetic structure when looking within host species [An analogous situation is seen in ery rare . Despiteentiated . There a species –139.Baylisascaris procyonis infecting raccoons (Procyon lotor) has been studied using both ribosomal and microsatellite loci in its native range [B. procyonis populations in Germany, two well-differentiated clades have been detected by both ITS and mitochondrial sequence analysis, suggesting two independent introductions of B. procyonis into Germany [B. procyonis clades showed very low genetic diversity, likely the result of population bottlenecks  in Australia [Passalurus ambiguus in invasive rabbits (Oryctolagus cuniculus) in China [The population genetics of ve range , 141, wi Germany . Both Geustralia , and in in China , and is in China .Spirocamallanus istiblenni was introduced to the Hawaiian archipelago with one of its hosts, the bluestripe snapper (Lutjanus kasmira). Population genetic analysis of S. istiblenni confirmed that the parasite originated from French Polynesia and showed that the introduced population was less genetically diverse than the native population [S. istiblenni has transmitted to native fish, shown by a lack of genetic differentiation between parasites from introduced and native hosts. Population genetic investigations into Camallanus cotti, invasive in Hawaiian stream fishes, revealed its probable invasion history, suggesting an initial introduction in O’ahu, where genetic diversity was highest, and subsequent migration to other islands in the archipelago [C. cotti in its native range [C. cotti was reduced compared with native populations, once again demonstrating the effects of population bottlenecks in introduced parasitic nematode populations.The nematode pulation . Populathipelago . Comparive range  showed tAnguillicoloides crassus and its host, the Japanese eel (Anguilla japonica), were introduced from Asia to North America and Europe, and since then A. crassus has spread rapidly in European and American eels , causing severe pathology in these naïve host species [A. crassus, suggesting multiple introduction events from different source populations [A. crassus was introduced in southern Europe and has since spread northwards [A. crassus has revealed its introduction history. Infrapopulation differentiation in A. crassus was studied in two European rivers, one of which was regularly artificially restocked with eels from a variety of sources and one in which eels had arrived by natural dispersal [A. crassus had high genetic diversity among hosts and a substantial deviation from Hardy-Weinberg equilibrium  invasive parasitic nematodes are likely to have low genetic diversity (due to population bottlenecks); and (ii) that host populations are likely to lose diversity more rapidly following habitat fragmentation than parasitic nematode populations . This latter effect may have a consequence for parasite ecology, since hosts will differ in their genetic resistance to parasites, and genetic bottlenecks of host populations may therefore lead to altered degrees of parasitism. Supporting this, ion size , 132, sua priori for non-model parasitic nematodes infecting wild animals. However, rapid advances in the sequencing and assembly of nematode genomes mean that it may often be feasible to generate a reference genome for the species in question [Each of the methods routinely used to analyse parasitic nematode population genetics detects variation in a small number of loci , and it question .Caenorhabditis elegans [C. elegans not only revealed the population genetics of this species, but also its recent evolutionary history, finding that the low genetic diversity of C. elegans likely arose from recent selective sweeps (see Table In restriction-associated DNA sequencing (RADSeq) the genome of an individual is digested with a restriction endonuclease, and the resulting fragments are size-filtered and then sequenced . Random  elegans . The stuIn whole-genome sequencing, individuals are genotyped at virtually every locus polymorphic among samples. This allows the relationships among individuals, and hence the genetic structure of populations, to be resolved at the finest possible scale , and givPlasmodium falciparum has found that genes that function in evasion of host immunity and in resistance to drugs show signs of selection [However, the difficulties of whole-genome sequencing are worth overcoming, due to the wealth of information it can provide. Of particular importance is the ability of whole-genome sequencing to identify genes that are under selection, and in the case of parasites, these genes may be relevant to pathogenicity, epidemiology and control. For example, whole-genome sequencing of the malaria parasite election . Similarelection , 166. Geelection .Wuchereria bancrofti [Strongyloides stercoralis among host individuals and populations [Heterodera glycines without the use of a reference genome [H. glycines, but also to identify genetic variants showing signatures of selection. Whether a reference assembly is used or not, whole-genome sequencing studies will improve our understanding of how parasitic nematodes respond to natural selection pressures. As the climate changes, parasitic nematodes will encounter novel selection pressures, and how they respond to these pressures may have important consequences, not only for host species but for the ecosystem as a whole.Whole-genome sequencing has already been used to study the population genetics of parasitic nematodes that infect people, demonstrating among-host differentiation in ancrofti , and detulations . Howevere genome . This ste genome , was ablParasitic nematodes typically have extra-host transmission stages in their life-cycle, and as these stages are the pool from which the next generation of parasites will arise, they contribute directly to Ne. Differences in the number, spatial distribution and temporal distribution of these stages may therefore influence the rate of genetic drift in a population, and hence the population genetic structure more widely. Despite their likely importance in both population genetics and epidemiology, very little is known about the ecology of extra-host stages, and what is known largely pertains to human or livestock pathogens , 173. ThAs with all species, the population genetics of parasitic nematodes in wild animals is ultimately determined by the (i) rate of gene flow among populations and (ii) strength of genetic drift within populations. Nematodes have very limited dispersal ability of their own, so that they are largely dependent on their hosts for dispersal. Therefore, in principle, nematode populations should ultimately come to be structured at the scale over which hosts move. Broadly speaking, this rule is largely followed - there is very limited population genetic structuring of parasitic nematodes infecting highly mobile hosts, such as ocean-going mammals , while tTrichinella spp., which shows gene flow at the continental scale [Dictyocaulus eckerti, a generalist parasite of deer [Neoheligmonella granjoni [Heterorhabditis marelatus, may show reduced gene flow if they significantly hamper their host’s movement [Nematode species that use more than one host species often have less structured populations than the movement of any one host species would predict. This has been observed in al scale , in Dict of deer , and in granjoni , 73. Themovement .Syphacia stroma likely reduce Ne in comparison with the fully diploid Heligmosomoides polygyrus, with which S. stroma shares a host [Dictyocaulus viviparus compared with Ostertagia ostertagi in domestic cattle means that the former likely has lower Ne [H. polygyrus compared with Trichuris muris may contribute to the comparatively more strongly genetically structured populations of the former when both species are analysed from the same host individuals [In the absence of gene flow all populations will ultimately diverge genetically due to genetic drift, but this process takes time. How much time depends on the strength of genetic drift, in turn dictated by (among other things) Ne, and so Ne is a major determinant in population genetic structure. Ne is rarely measured directly but can be estimated from known aspects of parasite and host life history. For example, haploid males in s a host , while tlower Ne , 178. Inividuals , 53, 54.Parasite life history traits such as host range, reproductive strategy, generation time, and the prevalence, intensity and pathogenicity of infection in the host are therefore all important in determining the population genetics of parasitic nematodes. These factors interact with host ecology, and in particular, host movement behaviour, to establish patterns of parasite population genetic structure. However, the relative importance of parasite life history traits remains poorly understood. Animals are commonly infected by more than one species of nematode, and this fact could be exploited to better understand population genetics in nematodes infecting wild animals. For example, comparison of life history traits among co-infecting parasites would allow their effects on parasite populations genetics to be separated from host-dependent effects.The abundance and spatial and temporal dynamics of extra-host stage parasitic nematodes in the environment remains almost entirely unknown. There have been some attempts to study the extra-host stages of nematodes infecting domestic livestock , but theLooking to the future, the use of high-throughput sequencing-based methods (dd)RADSeq and whole-genome sequencing) will dramatically improve the resolution and accuracy with which population structure can be detected. Furthermore, whole-genome sequencing will allow other aspects of parasitic nematode genomes, such as the size and nature of selection, and the extent of linkage disequilibrium, to be interrogated, and this knowledge will improve our understanding of parasitic nematode biology.The population genetics of parasitic nematodes in wild animals is determined by a combination of host ecology - especially host movement behaviour - and parasite life history. Studying the population genetics of parasitic nematodes of wild animals can reveal how their populations respond to selective pressures. With this information, we can assess the risk parasitic nematodes pose to natural ecosystems and to the health of humans and domestic animals, as anthropogenic activities drive environmental changes and changes in species’ geographical ranges. Our understanding of the population genetics, biology and evolutionary history of parasitic nematodes will be improved if investigators incorporate extra-host transmission stages and take advantage of high-throughput DNA-sequencing technologies in future studies.