Estimation of spatial demographic maps from polymorphism data using a neural network

A fundamental goal in population genetics is to understand how variation is arrayed over natural landscapes. From first principles we know that common features such as heterogeneous population densities and barriers to dispersal should shape genetic variation over space, however there are few tools currently available that can deal with these ubiquitous complexities. Geographically referenced single nucleotide polymorphism (SNP) data are increasingly accessible, presenting an opportunity to study genetic variation across geographic space in myriad species. We present a new inference method that uses geo-referenced SNPs and a deep neural network to estimate spatially heterogeneous maps of population density and dispersal rate. Our neural network trains on simulated input and output pairings, where the input consists of genotypes and sampling locations generated from a continuous space population genetic simulator, and the output is a map of the true demographic parameters. We benchmark our tool against existing methods and discuss qualitative differences between the different approaches; in particular, our program is unique because it infers the magnitude of both dispersal and density as well as their variation over the landscape, and it does so using SNP data. Similar methods are constrained to estimating relative migration rates, or require identity by descent blocks as input. We applied our tool to empirical data from North American grey wolves, for which it estimated mostly reasonable demographic parameters, but was affected by incomplete spatial sampling. Genetic based methods like ours complement other, direct methods for estimating past and present demography, and we believe will serve as valuable tools for applications in conservation, ecology, and evolutionary biology. An open source software package implementing our method is available from https://github.com/kr-colab/mapNN .

Age is not just a number: How incorrect ageing impacts close-kin mark-recapture estimates of population size

Population size is a key parameter for the conservation of animal species. Close-kin mark-recapture (CKMR) relies on the observed frequency and type of kinship among individuals sampled from the population to estimate population size. Knowledge of the age of the individuals, or a surrogate thereof, is essential for inference with acceptable precision. One common approach, particularly in fish studies, is to measure animal length and infer age using an assumed age-length relationship (a 'growth curve'). We used simulation to test the effect of misspecifying the length measurement error and the growth curve on population size estimation. Simulated populations represented two fictional shark species, one with a relatively simple life history and the other with a more complex life history based on the grey reef shark (Carcharhinus amblyrhynchos). We estimated sex-specific adult abundance, which we assumed to be constant in time. We observed small median biases in these estimates ranging from 1.35% to 2.79% when specifying the correct measurement error standard deviation and growth curve. CI coverage was adequate whenever the growth curve was correctly specified. Introducing error via misspecified growth curves resulted in changes in the magnitude of the estimated adult population, where underestimating age negatively biased the abundance estimates. Over- and underestimating the standard deviation of length measurement error did not introduce a bias and had negligible effect on the variance in the estimates. Our findings show that assuming an incorrect standard deviation of length measurement error has little effect on estimation, but having an accurate growth curve is crucial for CKMR whenever ageing is based on length measurements. If ageing could be biased, researchers should be cautious when interpreting CKMR results and consider the potential biases arising from inaccurate age inference.

Geographical distribution of close kin in southern right whales on feeding grounds

This study investigated the close kinship structure of southern right whales on feeding grounds during austral summer seasons. The study was based on biopsy samples of 171 individual whales, which were genotyped with 14 microsatellite DNA loci. Kinship was investigated by using the LOD (Log Odds) score, a relatedness index for a pair of genotypes. Based on a cut-off point of LODPO > 6, which was chosen to balance false positives and negatives, a total of 28 dyads were inferred. Among these, 25 were classified as parent-offspring pairs. Additional genetic (mitochondrial DNA haplotypes) and biological (estimated body length, sex) data were used to provide additional information on the inferred close kin pairs. The elapsed time between sampling varied from 0 (close kin detected in the same austral summer season) to 17 years. All the kin pairs occurred within the Antarctic Indo sector (85°-135°E) and no pair occurred between whales within and outside of this sector. Six pairs were between individuals in high (Antarctic) and lower latitudes. Results of the present analysis on kinship are consistent with the views that whales in the Indo sector of the Antarctic are related with the breeding ground in Southwest Australia, and that whales from this population can occupy different feeding grounds. The present study has the potential to contribute to the conservation of the southern right whales through the monitoring of important population parameters such as population sizes and growth rate, in addition to assist the interpretation of stock structure derived from standard population genetic analyses.

Sources of bias in applying close-kin mark-recapture to terrestrial game species with different life histories

Close-kin mark-recapture (CKMR) is a method analogous to traditional mark-recapture but without requiring recapture of individuals. Instead, multilocus genotypes (genetic marks) are used to identify related individuals in one or more sampling occasions, which enables the opportunistic use of samples from harvested wildlife. To apply the method accurately, it is important to build appropriate CKMR models that do not violate assumptions linked to the species' and population's biology and sampling methods. In this study, we evaluated the implications of fitting overly simplistic CKMR models to populations with complex reproductive success dynamics or selective sampling. We used forward-in-time, individual-based simulations to evaluate the accuracy and precision of CKMR abundance and survival estimates in species with different longevities, mating systems, and sampling strategies. Simulated populations approximated a range of life histories among game species of North America with lethal sampling to evaluate the potential of using harvested samples to estimate population size. Our simulations show that CKMR can yield nontrivial biases in both survival and abundance estimates, unless influential life history traits and selective sampling are explicitly accounted for in the modeling framework. The number of kin pairs observed in the sample, in combination with the type of kinship used in the model (parent-offspring pairs and/or half-sibling pairs), can affect the precision and/or accuracy of the estimates. CKMR is a promising method that will likely see an increasing number of applications in the field as costs of genetic analysis continue to decline. Our work highlights the importance of applying population-specific CKMR models that consider relevant demographic parameters, individual covariates, and the protocol through which individuals were sampled.

Accounting for unobserved population dynamics and aging error in close-kin mark-recapture assessments

Obtaining robust estimates of population abundance is a central challenge hindering the conservation and management of many threatened and exploited species. Close-kin mark-recapture (CKMR) is a genetics-based approach that has strong potential to improve the monitoring of data-limited species by enabling estimates of abundance, survival, and other parameters for populations that are challenging to assess. However, CKMR models have received limited sensitivity testing under realistic population dynamics and sampling scenarios, impeding the application of the method in population monitoring programs and stock assessments. Here, we use individual-based simulation to examine how unmodeled population dynamics and aging uncertainty affect the accuracy and precision of CKMR parameter estimates under different sampling strategies. We then present adapted models that correct the biases that arise from model misspecification. Our results demonstrate that a simple base-case CKMR model produces robust estimates of population abundance with stable populations that breed annually; however, if a population trend or non-annual breeding dynamics are present, or if year-specific estimates of abundance are desired, a more complex CKMR model must be constructed. In addition, we show that CKMR can generate reliable abundance estimates for adults from a variety of sampling strategies, including juvenile-focused sampling where adults are never directly observed (and aging error is minimal). Finally, we apply a CKMR model that has been adapted for population growth and intermittent breeding to two decades of genetic data from juvenile lemon sharks (Negaprion brevirostris) in Bimini, Bahamas, to demonstrate how application of CKMR to samples drawn solely from juveniles can contribute to monitoring efforts for highly mobile populations. Overall, this study expands our understanding of the biological factors and sampling decisions that cause bias in CKMR models, identifies key areas for future inquiry, and provides recommendations that can aid biologists in planning and implementing an effective CKMR study, particularly for long-lived data-limited species.

Finite mixtures in capture-recapture surveys for modeling residency patterns in marine wildlife populations

This work aims to show how prior knowledge about the structure of a heterogeneous animal population can be leveraged to improve the abundance estimation from capture-recapture survey data. We combine the Open Jolly-Seber model with finite mixtures and propose a parsimonious specification tailored to the residency patterns of the common bottlenose dolphin. We employ a Bayesian framework for our inference, discussing the appropriate choice of priors to mitigate label-switching and nonidentifiability issues, commonly associated with finite mixture models. We conduct a series of simulation experiments to illustrate the competitive advantage of our proposal over less specific alternatives. The proposed approach is applied to data collected on the common bottlenose dolphin population inhabiting the Tiber River estuary (Mediterranean Sea). Our results provide novel insights into this population's size and structure, shedding light on some of the ecological processes governing its dynamics.

Advances in wildlife abundance estimation using pedigree reconstruction

The conservation and management of wildlife populations, particularly for threatened and endangered species are greatly aided with abundance, growth rate, and density measures. Traditional methods of estimating abundance and related metrics represent trade-offs in effort and precision of estimates. Pedigree reconstruction is an emerging, attractive alternate approach because its use of one-time, noninvasive sampling of individuals to infer the existence of unsampled individuals. However, advances in pedigree reconstruction could improve its utility, including forming a measure of precision for the method, establishing required spatial sampling effort for accurate estimates, ascertaining the spatial extent of abundance estimates derived from pedigree reconstruction, and assessing how population density affects the estimator's performance. Using established relationships for a stochastic, spatially explicit simulated moose (Alces americanus) population, pedigree reconstruction provided accurate estimates of the adult moose population size and trend. Novel bootstrapped confidence intervals performed as expected with intensive sampling but underperformed with moderate sampling efforts that could produce abundance estimates with low bias. Adult population estimates more closely reflected the total number of adults in the extant population, rather than number of adults inhabiting the area where sampling occurred. Increasing sampling effort, measured as the proportion of individuals sampled and as the proportion of a hypothetical study area, yielded similar asymptotic patterns over time. Simulations indicated a positive relationship between animal density and sampling effort required for unbiased estimates. These results indicate that pedigree reconstruction can produce accurate abundance estimates and may be particularly valuable for surveying smaller areas and low-density populations.

Close-kin mark-recapture informs critically endangered terrestrial mammal status

Reliable information on population size is fundamental to the management of threatened species. For wild species, mark-recapture methods are a cornerstone of abundance estimation. Here, we show the first application of the close-kin mark-recapture (CKMR) method to a terrestrial species of high conservation value; the Christmas Island flying-fox (CIFF). The CIFF is the island's last remaining native terrestrial mammal and was recently listed as critically endangered. CKMR is a powerful tool for estimating the demographic parameters central to CIFF management and circumvents the complications arising from the species' cryptic nature, mobility, and difficult-to-survey habitat. To this end, we used genetic data from 450 CIFFs captured between 2015 and 2019 to detect kin pairs. We implemented a novel CKMR model that estimates sex-specific abundance, trend, and mortality and accommodates observations from the kin-pair distribution of male reproductive skew and mate persistence. CKMR estimated CIFF total adult female abundance to be approximately 2050 individuals (95% CI (950, 4300)). We showed that on average only 23% of the adult male population contributed to annual reproduction and strong evidence for between-year mate fidelity, an observation not previously quantified for a Pteropus species in the wild. Critically, our population estimates provide the most robust understanding of the status of this critically endangered population, informing immediate and future conservation initiatives.

Calibrating epigenetic clocks with training data error

Animal age data are valuable for management of wildlife populations. Yet, for most species, there is no practical method for determining the age of unknown individuals. However, epigenetic clocks, a molecular-based method, are capable of age prediction by sampling specific tissue types and measuring DNA methylation levels at specific loci. Developing an epigenetic clock requires a large number of samples from animals of known ages. For most species, there are no individuals whose exact ages are known, making epigenetic clock calibration inaccurate or impossible. For many epigenetic clocks, calibration samples with inaccurate age estimates introduce a degree of error to epigenetic clock calibration. In this study, we investigated how much error in the training data set of an epigenetic clock can be tolerated before it resulted in an unacceptable increase in error for age prediction. Using four publicly available data sets, we artificially increased the training data age error by iterations of 1% and then tested the model against an independent set of known ages. A small effect size increase (Cohen's d >0.2) was detected when the error in age was higher than 22%. The effect size increased linearly with age error. This threshold was independent of sample size. Downstream applications for age data may have a more important role in deciding how much error can be tolerated for age prediction. If highly precise age estimates are required, then it may be futile to embark on the development of an epigenetic clock when there is no accurately aged calibration population to work with. However, for other problems, such as determining the relative age order of pairs of individuals, a lower-quality calibration data set may be adequate.

Wildlife Population Assessment: Changing Priorities Driven by Technological Advances

Advances in technology are having a large effect on the priorities for innovation in statistical ecology. Collaborations between statisticians and ecologists have always been important in driving methodological development, but increasingly, expertise from computer scientists and engineers is also needed. We discuss changes that are occurring and that may occur in the future in surveys for estimating animal abundance. As technology advances, we expect classical distance sampling and capture-recapture to decrease in importance, as camera (still and video) survey, acoustic survey, spatial capture-recapture and genetic methods continue to develop and find new applications. We explore how these changes are impacting the work of the statistical ecologist.

Implications of past and present genetic connectivity for management of the saltwater crocodile ( Crocodylus porosus )

Effective management of protected species requires information on appropriate evolutionary and geographic population boundaries and knowledge of how the physical environment and life-history traits combine to shape the population structure and connectivity. Saltwater crocodiles (Crocodylus porosus) are the largest and most widely distributed of living crocodilians, extending from Sri Lanka to Southeast Asia and down to northern Australia. Given the long-distance movement capabilities reported for C. porosus, management units are hypothesised to be highly connected by migration. However, the magnitude, scale, and consistency of connection across managed populations are not fully understood. Here we used an efficient genotyping method that combines DArTseq and sequence capture to survey ≈ 3000 high-quality genome-wide single nucleotide polymorphisms from 1176 C. porosus sampled across nearly the entire range of the species in Queensland, Australia. We investigated historical and present-day connectivity patterns using fixation and diversity indices coupled with clustering methods and the spatial distribution of kin pairs. We inferred kinship using forward simulation coupled with a kinship estimation method that is robust to unspecified population structure. The results demonstrated that the C. porosus population has substantial genetic structure with six broad populations correlated with geographical location. The rate of gene flow was highly correlated with spatial distance, with greater differentiation along the east coast compared to the west. Kinship analyses revealed evidence of reproductive philopatry and limited dispersal, with approximately 90% of reported first and second-degree relatives showing a pairwise distance of <50 km between sampling locations. Given the limited dispersal, lack of suitable habitat, low densities of crocodiles and the high proportion of immature animals in the population, future management and conservation interventions should be considered at regional and state-wide scales.

Burmese pythons in Florida: A synthesis of biology, impacts, and management tools

Burmese pythons (Python molurus bivittatus) are native to southeastern Asia, however, there is an established invasive population inhabiting much of southern Florida throughout the Greater Everglades Ecosystem. Pythons have severely impacted native species and ecosystems in Florida and represent one of the most intractable invasive-species management issues across the globe. The difficulty stems from a unique combination of inaccessible habitat and the cryptic and resilient nature of pythons that thrive in the subtropical environment of southern Florida, rendering them extremely challenging to detect. Here we provide a comprehensive review and synthesis of the science relevant to managing invasive Burmese pythons. We describe existing control tools and review challenges to productive research, identifying key knowledge gaps that would improve future research and decision making for python control.

Measurably recombining malaria parasites

Genomic epidemiology has guided research and policy for various viral pathogens and there has been a parallel effort towards using genomic epidemiology to combat diseases that are caused by eukaryotic pathogens, such as the malaria parasite. However, the central concept of viral genomic epidemiology, namely that of measurably mutating pathogens, does not apply easily to sexually recombining parasites. Here we introduce the related but different concept of measurably recombining malaria parasites to promote convergence around a unifying theoretical framework for malaria genomic epidemiology. Akin to viral phylodynamics, we anticipate that an inferential framework developed around recombination will help guide practical research and thus realize the full public health potential of genomic epidemiology for malaria parasites and other sexually recombining pathogens.

A simulation-based evaluation of methods for estimating census population size of terrestrial game species from genetically-identified parent-offspring pairs

Estimates of wildlife population size are critical for conservation and management, but accurate estimates are difficult to obtain for many species. Several methods have recently been developed that estimate abundance using kinship relationships observed in genetic samples, particularly parent-offspring pairs. While these methods are similar to traditional Capture-Mark-Recapture, they do not need physical recapture, as individuals are considered recaptured if a sample contains one or more close relatives. This makes methods based on genetically-identified parent-offspring pairs particularly interesting for species for which releasing marked animals back into the population is not desirable or not possible (e.g., harvested fish or game species). However, while these methods have successfully been applied in commercially important fish species, in the absence of life-history data, they are making several assumptions unlikely to be met for harvested terrestrial species. They assume that a sample contains only one generation of parents and one generation of juveniles of the year, while more than two generations can coexist in the hunting bags of long-lived species, or that the sampling probability is the same for each individual, an assumption that is violated when fecundity and/or survival depend on sex or other individual traits. In order to assess the usefulness of kin-based methods to estimate population sizes of terrestrial game species, we simulated population pedigrees of two different species with contrasting demographic strategies (wild boar and red deer), applied four different methods and compared the accuracy and precision of their estimates. We also performed a sensitivity analysis, simulating population pedigrees with varying fecundity characteristics and various levels of harvesting to identify optimal conditions of applicability of each method. We showed that all these methods reached the required levels of accuracy and precision to be effective in wildlife management under simulated circumstances (i.e., for species within a given range of fecundity and for a given range of sampling intensity), while being robust to fecundity variation. Despite the potential usefulness of the methods for terrestrial game species, care is needed as several biases linked to hunting practices still need to be investigated (e.g., when hunting bags are biased toward a particular group of individuals).

Close Kin Dyads Indicate Intergenerational Dispersal and Barriers

AbstractThe movement of individuals through continuous space is typically constrained by dispersal ability and dispersal barriers. A range of approaches have been developed to investigate these. Kindisperse is a new approach that infers recent intergenerational dispersal (σ) from close kin dyads and appears particularly useful for investigating taxa that are difficult to observe individually. This study, focusing on the mosquito Aedes aegypti, shows how the same close kin data can also be used for barrier detection. We empirically demonstrate this new extension of the method using genome-wide sequence data from 266 Ae. aegypti. First, we use the spatial distribution of full-sib dyads collected within one generation to infer past movements of ovipositing female mosquitoes. These dyads indicated the relative barrier strengths of two roads and performed favorably against alternative genetic methods for detecting barriers. We then use Kindisperse to quantify recent intergenerational dispersal (σ=81.5-197.1 m generation^-1/2) from the difference in variance between the sib and the first cousin spatial distributions and, from this, estimate effective population density (ρ=833-4,864 km^-2). Dispersal estimates showed general agreement with those from mark-release-recapture studies. Barriers, σ, ρ, and neighborhood size (331-526) can inform forthcoming releases of dengue-suppressing Wolbachia bacteria into this mosquito population.

Rapid assessment of adult abundance and demographic connectivity from juvenile kin pairs in a critically endangered species

The viability of spatially structured populations depends on the abundance and connectivity between subpopulations of breeding adults. Yet, for many species, both are extremely difficult to assess. The speartooth shark is a critically endangered elasmobranch inhabiting tropical rivers with only three adults ever recorded in Australia. Close-kin mark-recapture models, informed by sibling pairs among 226 juveniles, were developed to estimate adult abundance and connectivity in two Australian river systems. Sixty-eight sibling pairs were found, and adult abundance was estimated at 892 for the Adelaide River and 1128 for the Alligator Rivers. We found strong evidence for female philopatry, with most females returning to the same river to pup. Adelaide River males appear largely philopatric, whereas Alligator Rivers males are highly connected to the Adelaide River. From only 4 years of sampling, our results demonstrate that juvenile-only kin pairs can inform simultaneous estimates of abundance and connectivity in a rare and threatened species.

A new GTSeq resource to facilitate multijurisdictional research and management of walleye Sander vitreus

Conservation and management professionals often work across jurisdictional boundaries to identify broad ecological patterns. These collaborations help to protect populations whose distributions span political borders. One common limitation to multijurisdictional collaboration is consistency in data recording and reporting. This limitation can impact genetic research, which relies on data about specific markers in an organism's genome. Incomplete overlap of markers between separate studies can prevent direct comparisons of results. Standardized marker panels can reduce the impact of this issue and provide a common starting place for new research. Genotyping-in-thousands (GTSeq) is one approach used to create standardized marker panels for nonmodel organisms. Here, we describe the development, optimization, and early assessments of a new GTSeq panel for use with walleye (Sander vitreus) from the Great Lakes region of North America. High genome-coverage sequencing conducted using RAD capture provided genotypes for thousands of single nucleotide polymorphisms (SNPs). From these markers, SNP and microhaplotype markers were chosen, which were informative for genetic stock identification (GSI) and kinship analysis. The final GTSeq panel contained 500 markers, including 197 microhaplotypes and 303 SNPs. Leave-one-out GSI simulations indicated that GSI accuracy should be greater than 80% in most jurisdictions. The false-positive rates of parent-offspring and full-sibling kinship identification were found to be low. Finally, genotypes could be consistently scored among separate sequencing runs >94% of the time. Results indicate that the GTSeq panel that we developed should perform well for multijurisdictional walleye research throughout the Great Lakes region.

Close-kin mark-recapture methods to estimate demographic parameters of mosquitoes

Close-kin mark-recapture (CKMR) methods have recently been used to infer demographic parameters such as census population size and survival for fish of interest to fisheries and conservation. These methods have advantages over traditional mark-recapture methods as the mark is genetic, removing the need for physical marking and recapturing that may interfere with parameter estimation. For mosquitoes, the spatial distribution of close-kin pairs has been used to estimate mean dispersal distance, of relevance to vector-borne disease transmission and novel biocontrol strategies. Here, we extend CKMR methods to the life history of mosquitoes and comparable insects. We derive kinship probabilities for mother-offspring, father-offspring, full-sibling and half-sibling pairs, where an individual in each pair may be a larva, pupa or adult. A pseudo-likelihood approach is used to combine the marginal probabilities of all kinship pairs. To test the effectiveness of this approach at estimating mosquito demographic parameters, we develop an individual-based model of mosquito life history incorporating egg, larva, pupa and adult life stages. The simulation labels each individual with a unique identification number, enabling close-kin relationships to be inferred for sampled individuals. Using the dengue vector Aedes aegypti as a case study, we find the CKMR approach provides unbiased estimates of adult census population size, adult and larval mortality rates, and larval life stage duration for logistically feasible sampling schemes. Considering a simulated population of 3,000 adult mosquitoes, estimation of adult parameters is accurate when ca. 40 adult females are sampled biweekly over a three month period. Estimation of larval parameters is accurate when adult sampling is supplemented with ca. 120 larvae sampled biweekly over the same period. The methods are also effective at detecting intervention-induced increases in adult mortality and decreases in population size. As the cost of genome sequencing declines, CKMR holds great promise for characterizing the demography of mosquitoes and comparable insects of epidemiological and agricultural significance.

Low levels of sibship encourage use of larvae in western Atlantic bluefin tuna abundance estimation by close-kin mark-recapture

Globally, tunas are among the most valuable fish stocks, but are also inherently difficult to monitor and assess. Samples of larvae of Western Atlantic bluefin tuna Thunnus thynnus (Linnaeus, 1758) from standardized annual surveys in the northern Gulf of Mexico provide a potential source of "offspring" for close-kin mark-recapture (CKMR) estimates of abundance. However, the spatial patchiness and highly skewed numbers of larvae per tow suggest sampled larvae may come from a small number of parents, compromising the precision of CKMR. We used high throughput genomic profiling to study sibship within and among larval tows from the 2016 standardized Gulf-wide survey compared to targeted sampling carried out in 2017. Full- and half-siblings were found within both years, with 12% of 156 samples in 2016 and 56% of 317 samples in 2017 having at least one sibling. There were also two pairs of cross cohort half-siblings. Targeted sampling increased the number of larvae collected per sampling event but resulted in a higher proportion of siblings. The combined effective sample size across both years was about 75% of the nominal size, indicating that Gulf of Mexico larval collections could be a suitable source of juveniles for CKMR in Western Atlantic bluefin tuna.

How continuing mortality affects recovery potential for prohibited sharks: The case of white sharks in South Africa

It can be difficult to determine whether a prohibition to exploitation ensures effective conservation or recovery for species that remain exposed to fishing effort and other sources of mortality throughout their range. Here we used simulation modeling of four life history scenarios (different productivity and population size) to contextualize potential population response to multiple levels of mortality, using white sharks (Carcharodon carcharias) in South Africa as a case study. The species has been protected since 1991, yet substantial uncertainty about population dynamics persists and recent declines at two aggregation sites have renewed conservation concern. All scenarios indicated that annual removals in the 10s of individuals would substantially limit the potential for and magnitude of any abundance increase following prohibition. Because average known removals from the KwaZulu-Natal Sharks Board’s Bather Protection Program have typically remained higher than these thresholds, they likely eliminated much of the conservation benefit derived from prohibition. The only life history scenario to achieve appreciable increase when simulated removals were similar to published averages assumed maturation occurred at a much younger age than currently understood. Our results demonstrate why general application of life history-based simulations can provide a useful mechanism to evaluate the biological plausibility of life history information and abundance trends, and to explore the scope for population response to recovery actions. For South Africa, our results suggest that even known levels of white shark removals, which likely underestimate total removals within their range, may be sufficient to drive abundance decline and new mitigation measures may be required to ensure population recovery.

Estimating contemporary migration numbers of adults based on kinship relationships in iteroparous species

This paper describes the development of estimators for the contemporary migration number and rate of adults between two populations in iteroparous species. The proposed estimators are based on known half-sibling (HS) and/or parent-offspring (PO) relationships observed between populations across breeding seasons. The rationale is that HS and PO pairs exhibit information about the occurrence frequency of parental movements during the breeding interval. The proposed method allows for variance in the average number of offspring per parent within and between populations. In addition, coupled with the PO pairs found within the population, the estimators can be obtained using only genetic data. Generally, a sample size representing the square root of the population size is required to obtain meaningful migration information. We describe a detailed evaluation of the performance of the proposed estimators by running an individual-based model, and the results provide guidance regarding sample sizes to ensure the required accuracy and precision. In addition, given that there are few effective methods to estimate adult movement (especially when populations cannot be genetically distinct), we discuss the usefulness of the proposed kinship assignment method in terms of conservation biology and wildlife management.

Evaluating sources of bias in pedigree-based estimates of breeding population size

Applications of genetic-based estimates of population size are expanding, especially for species for which traditional demographic estimation methods are intractable due to the rarity of adult encounters. Estimates of breeding population size (N_S ) are particularly amenable to genetic-based approaches as the parameter can be estimated using pedigrees reconstructed from genetic data gathered from discrete juvenile cohorts, therefore eliminating the need to sample adults in the population. However, a critical evaluation of how genotyping and sampling effort influence bias in pedigree reconstruction, and how these biases subsequently influence estimates of N_S , is needed to evaluate the efficacy of the approach under a range of scenarios. We simulated a model system to understand the interactive effects of genotyping and sampling effort on error in genetic pedigrees reconstructed from the program COLONY. We then evaluated how errors in pedigree reconstruction influenced bias and precision in estimates of N_S using three different rarefaction estimators. Results indicated that pedigree error can be minimal when adequate genetic data are available, such as when juvenile sample sizes are large and/or individuals are genotyped at many informative loci. However, even in cases for which data are limited, using results of the simulation analysis to understand the magnitude and sources of bias in reconstructed pedigrees can still be informative when estimating N_S . We applied results of the simulation analysis to evaluate N ̂ $$ \hat{N} $$ _S for a population of federally endangered Atlantic sturgeon (Acipenser oxyrinchus oxyrinchus) in the Delaware River, USA. Our results indicated that N_S is likely to be three orders of magnitude lower compared with historic breeding population sizes, which is a considerable advancement in our understanding of the population status of Atlantic sturgeon in the Delaware River. Our analyses are broadly applicable in the design and interpretation of studies seeking to estimate N_S and can help to guide conservation decisions when ecological uncertainty is high. The utility of these results is expected to grow as rapid advances in genetic technologies increase the popularity of genetic population monitoring and estimation.

Evolving spatial conservation prioritization with intraspecific genetic data

Spatial conservation prioritization (SCP) is a planning framework used to identify new conservation areas on the basis of the spatial distribution of species, ecosystems, and their services to human societies. The ongoing accumulation of intraspecific genetic data on a variety of species offers a way to gain knowledge of intraspecific genetic diversity and to estimate several population characteristics useful in conservation, such as dispersal and population size. Here, we review how intraspecific genetic data have been integrated into SCP and highlight their potential for identifying conservation area networks that represent intraspecific genetic diversity comprehensively and that ensure the long-term persistence of biodiversity in the face of global change.

Pedigree analysis and estimates of effective breeding size characterize sea lamprey reproductive biology

The sea lamprey (Petromyzon marinus) is an invasive species in the Great Lakes and the focus of a large control and assessment program. Current assessment methods provide information on the census size of spawning adult sea lamprey in a small number of streams, but information characterizing reproductive success of spawning adults is rarely available. We used RAD-capture sequencing to genotype single nucleotide polymorphism (SNP) loci for ~1600 sea lamprey larvae collected from three streams in northern Michigan (Black Mallard, Pigeon, and Ocqueoc Rivers). Larval genotypes were used to reconstruct family pedigrees, which were combined with Gaussian mixture analyses to identify larval age classes for estimation of spawning population size. Two complementary estimates of effective breeding size (N b), as well as the extrapolated minimum number of spawners (N s), were also generated for each cohort. Reconstructed pedigrees highlighted inaccuracies of cohort assignments from traditionally used mixture analyses. However, combining genotype-based pedigree information with length-at-age assignment of cohort membership greatly improved cohort identification accuracy. Population estimates across all three streams sampled in this study indicate a small number of successfully spawning adults when barriers were in operation, implying that barriers limited adult spawning numbers but were not completely effective at blocking access to spawning habitats. Thus, the large numbers of larvae present in sampled systems were a poor indicator of spawning adult abundance. Overall, pedigree-based N b and N s estimates provide a promising and rapid assessment tool for sea lamprey and other species.

Population and seascape genomics of a critically endangered benthic elasmobranch, the blue skate Dipturus batis

The blue skate (Dipturus batis) has a patchy distribution across the North-East Atlantic Ocean, largely restricted to occidental seas around the British Isles following fisheries-induced population declines and extirpations. The viability of remnant populations remains uncertain and could be impacted by continued fishing and by-catch pressure, and the projected impacts of climate change. We genotyped 503 samples of D. batis, obtained opportunistically from the widest available geographic range, across 6 350 single nucleotide polymorphisms (SNPs) using a reduced-representation sequencing approach. Genotypes were used to assess the species' contemporary population structure, estimate effective population sizes and identify putative signals of selection in relation to environmental variables using a seascape genomics approach. We identified genetic discontinuities between inshore (British Isles) and offshore (Rockall and Faroe Island) populations, with differentiation most pronounced across the deep waters of the Rockall Trough. Effective population sizes were largest in the Celtic Sea and Rockall, but low enough to be of potential conservation concern among Scottish and Faroese sites. Among the 21 candidate SNPs under positive selection was one significantly correlated with environmental variables predicted to be affected by climate change, including bottom temperature, salinity and pH. The paucity of well-annotated elasmobranch genomes precluded us from identifying a putative function for this SNP. Nevertheless, our findings suggest that climate change could inflict a strong selective force upon remnant populations of D. batis, further constraining its already-restricted habitat. Furthermore, the results provide fundamental insights on the distribution, behaviour and evolutionary biology of D. batis in the North-East Atlantic that will be useful for the establishment of conservation actions for this and other critically endangered elasmobranchs.

Genome-wide analysis reveals the genetic stock structure of hoki (Macruronus novaezelandiae)

The assessment of the genetic structuring of biodiversity is crucial for management and conservation. This is particularly critical for widely distributed and highly mobile deep-water teleosts, such as hoki (Macruronus novaezelandiae). This species is significant to Māori people and supports the largest commercial fishery in New Zealand, but uncertainty about its stock structure presents a challenge for management. Here, we apply a comprehensive genomic analysis to shed light on the demographic structure of this species by (1) assembling the genome, (2) generating a catalogue of genome-wide SNPs to infer the stock structure and (3) identifying regions of the genome under selection. The final genome assembly used short and long reads and is near complete, representing 93.8% of BUSCO genes, and consisting of 566 contigs totalling 501 Mb. Whole-genome re-sequencing of 510 hoki sampled from 14 locations around New Zealand and Australia, at a read depth greater than 10×, produced 227,490 filtered SNPs. Analyses of these SNPs were able to resolve the stock structure of hoki into two genetically and geographically distinct clusters, one including the Australian and the other one all New Zealand locations, indicating genetic exchange between these regions is limited. Location differences within New Zealand samples were much more subtle (global F ST  = 0.0006), and while small and significant differences could be detected, they did not conclusively identify additional substructures. Ten putative adaptive SNPs were detected within the New Zealand samples, but these also did not geographically partition the dataset further. Contemporary and historical N e estimation suggest the current New Zealand population of hoki is large yet declining. Overall, our study provides the first genomic resources for hoki and provides detailed insights into the fine-scale population genetic structure to inform the management of this species.

High bycatch rates of manta and devil rays in the "small-scale" artisanal fisheries of Sri Lanka

Background

Expanding fisheries in developing nations like Sri Lanka have a significant impact on threatened marine species such as elasmobranchs. Manta and devil (mobulid) rays have some of the most conservative life history strategies of any elasmobranch, and even low to moderate levels of bycatch from gillnet fisheries may lead to significant population declines. A lack of information on life history, demographics, population trends, and fisheries impacts hinders effective management measures for these species.

Method

We report on mobulid fishery landings over nine years between 2011 and 2020 across 38 landing sites in Sri Lanka. We collected data on catch numbers, body sizes, sex, and maturity status for five mobulid species. We used a Bayesian state-space model to estimate monthly country-wide catch rates and total annual landings of mobulid rays. We used catch curve analyses to estimate total mortality for Mobula mobular, and evaluated trends in recorded body sizes across the study period for M. mobular, M. birostris, M. tarapacana and M. thurstoni.

Results

We find that catch rates have declined an order of magnitude for all species across the study period, and that total annual captures of mobulid rays by the Sri Lankan artisanal fishing fleet exceed the estimated annual captures of mobulids in all global, industrial purse seine fisheries combined. Catch curve analyses suggest that M. mobular is being fished at rates far above the species' intrinsic population growth rate, and the average sizes of all mobulids in the fishery except for M. birostris are declining. Collectively, these findings suggest overfishing of mobulid ray populations in the northern Indian Ocean by Sri Lankan artisanal fisheries. We recommend strengthening the management of these species through improved implementation of CITES, CMS, and regional fisheries management actions. In addition, we report on the demographic characteristics of mobulids landed in Sri Lanka and provide the first record of M. eregoodoo in the country.

Estimating dispersal using close kin dyads: The kindisperse R package

Investigating dispersal in animal populations can be difficult, particularly for taxa that are hard to directly observe such as those that are small or rare. A promising solution may come from new approaches that use genome-wide sequence data to detect close kin dyads and estimate dispersal parameters from the distribution of these dyads. These methods have so far only been applied to mosquito populations. However, they should have broad applicability to a range of taxa, although no assessment has yet been made on their performance under different dispersal conditions and study designs. Here we develop an R package and shiny app, kindisperse, that can be used to estimate dispersal parameters from the spatial distribution of close kin. kindisperse can handle study designs that target different life stages and allows for a range of dispersal kernel shapes and organismal life histories; we provide implementation examples for a vertebrate (Antechinus) and an invertebrate (Aedes). We use simulations run in kindisperse to compare the performance of two published close kin methodologies, showing that one method produces unbiased estimates whereas the other produces downward-biased estimates. We also use kindisperse simulations to investigate how study design affects dispersal estimates, and we provide guidelines for the size and shape of sample sites as well as the number of close kin needed for accurate estimates. kindisperse is easily adaptable for application to a variety of research contexts ranging from invasive pests to threatened species where noninvasive DNA sampling can be used to detect close kin.

CKMRpop: Forward-in-time simulation and tabulation of pairwise kin relationships in age-structured populations

In the last five years, interest in close-kin mark-recapture (CKMR), a variant of mark-recapture that uses genetically inferred kin as 'recaptures', has grown dramatically. However, understanding the basis of CKMR, and properly implementing it, remains challenging. This paper describes an R package, CKMRpop, for simulating age-structured populations with user-specified demography, overdispersed variance in reproductive success (allowing for different ratios of effective to census size) and random sampling of individuals. Using compiled code for the simulation makes it feasible to simulate populations of millions of individuals. From the simulation output, pairs of sampled individuals related within a user-specified number of generations are found. Such pairs form the foundation for CKMR inference, and simulating them provides insight for understanding the statistical basis for CKMR and for assessing the feasibility of CKMR in different scenarios. We predict that CKMRpop will serve as an important tool for researchers contemplating CKMR estimation of population size. Furthermore, the methods presented here for identifying and categorizing relationships beyond half-siblings allow a more complete picture of the wide variety of kin pairs encountered in populations. This identifies the fraction of kin pairs that may not be the target of a CKMR experiment, but may be inadvertently mistaken for a more closely related 'target' kin pair. Additionally, as more distant kin categories will likely be accurately inferred from increasingly available and inexpensive whole genome resequencing, understanding the distributions of more distant relationships in populations is a first step towards broadening the scope of CKMR to include them.

Advances and opportunities in malaria population genomics

Almost 20 years have passed since the first reference genome assemblies were published for Plasmodium falciparum, the deadliest malaria parasite, and Anopheles gambiae, the most important mosquito vector of malaria in sub-Saharan Africa. Reference genomes now exist for all human malaria parasites and nearly half of the ~40 important vectors around the world. As a foundation for genetic diversity studies, these reference genomes have helped advance our understanding of basic disease biology and drug and insecticide resistance, and have informed vaccine development efforts. Population genomic data are increasingly being used to guide our understanding of malaria epidemiology, for example by assessing connectivity between populations and the efficacy of parasite and vector interventions. The potential value of these applications to malaria control strategies, together with the increasing diversity of genomic data types and contexts in which data are being generated, raise both opportunities and challenges in the field. This Review discusses advances in malaria genomics and explores how population genomic data could be harnessed to further support global disease control efforts.

Optimal sample size for calibrating DNA methylation age estimators

Age is a fundamental parameter in wildlife management as it is used to determine the risk of extinction, manage invasive species, and regulate sustainable harvest. In a broad variety of vertebrates species, age can be determined by measuring DNA methylation. Animals with known ages are initially required during development, calibration, and validation of these epigenetic clocks. However, wild animals with known ages are frequently difficult to obtain. Here, we perform Monte‐Carlo simulations to determine the optimal sample size required to create an accurate calibration model for age estimation by elastic net regression modelling of cytosine‐phosphate‐guanine methylation data. Our results suggest a minimum calibration population size of 70, but ideally 134 individuals or more for accurate and precise models. We also provide estimates to the extent a model can be extrapolated beyond a distribution of ages that was used during calibration. The findings can assist researchers to better design age estimation models and decide if their model is adequate for determining key population attributes.

Considering sampling bias in close-kin mark-recapture abundance estimates of Atlantic salmon

Genetic methods for the estimation of population size can be powerful alternatives to conventional methods. Close-kin mark-recapture (CKMR) is based on the principles of conventional mark-recapture, but instead of being physically marked, individuals are marked through their close kin. The aim of this study was to evaluate the potential of CKMR for the estimation of spawner abundance in Atlantic salmon and how age, sex, spatial, and temporal sampling bias may affect CKMR estimates. Spawner abundance in a wild population was estimated from genetic samples of adults returning in 2018 and of their potential offspring collected in 2019. Adult samples were obtained in two ways. First, adults were sampled and released alive in the breeding habitat during spawning surveys. Second, genetic samples were collected from out-migrating smolts PIT-tagged in 2017 and registered when returning as adults in 2018. CKMR estimates based on adult samples collected during spawning surveys were somewhat higher than conventional counts. Uncertainty was small (CV < 0.15), due to the detection of a high number of parent-offspring pairs. Sampling of adults was age- and size-biased and correction for those biases resulted in moderate changes in the CKMR estimate. Juvenile dispersal was limited, but spatially balanced sampling of adults rendered CKMR estimates robust to spatially biased sampling of juveniles. CKMR estimates based on returning PIT-tagged adults were approximately twice as high as estimates based on samples collected during spawning surveys. We suggest that estimates based on PIT-tagged fish reflect the total abundance of adults entering the river, while estimates based on samples collected during spawning surveys reflect the abundance of adults present in the breeding habitat at the time of spawning. Our study showed that CKMR can be used to estimate spawner abundance in Atlantic salmon, with a moderate sampling effort, but a carefully designed sampling regime is required.

A DNA methylation age predictor for zebrafish

Changes in DNA methylation at specific CpG sites have been used to build predictive models to estimate animal age, predominantly in mammals. Little testing for this effect has been conducted in other vertebrate groups, such as bony fish, the largest vertebrate class. The development of most age-predictive models has relied on a genome-wide sequencing method to obtain a DNA methylation level, which makes it costly to deploy as an assay to estimate age in many samples. Here, we have generated a reduced representation bisulfite sequencing data set of caudal fin tissue from a model fish species, zebrafish (Danio rerio), aged from 11.9-60.1 weeks. We identified changes in methylation at specific CpG sites that correlated strongly with increasing age. Using an optimised unique set of 26 CpG sites we developed a multiplex PCR assay that predicts age with an average median absolute error rate of 3.2 weeks in zebrafish between 10.9-78.1 weeks of age. We also demonstrate the use of multiplex PCR as an efficient quantitative approach to measure DNA methylation for the use of age estimation. This study highlights the potential further use of DNA methylation as an age estimation method in non-mammalian vertebrate species.

Population genomics for wildlife conservation and management

Biodiversity is under threat worldwide. Over the past decade, the field of population genomics has developed across nonmodel organisms, and the results of this research have begun to be applied in conservation and management of wildlife species. Genomics tools can provide precise estimates of basic features of wildlife populations, such as effective population size, inbreeding, demographic history and population structure, that are critical for conservation efforts. Moreover, population genomics studies can identify particular genetic loci and variants responsible for inbreeding depression or adaptation to changing environments, allowing for conservation efforts to estimate the capacity of populations to evolve and adapt in response to environmental change and to manage for adaptive variation. While connections from basic research to applied wildlife conservation have been slow to develop, these connections are increasingly strengthening. Here we review the primary areas in which population genomics approaches can be applied to wildlife conservation and management, highlight examples of how they have been used, and provide recommendations for building on the progress that has been made in this field.

Genome-wide SNPs detect no evidence of genetic population structure for reef manta rays (Mobula alfredi) in southern Mozambique

Little is known about the extent of genetic connectivity along continuous coastlines in manta rays, or whether site visitation is influenced by relatedness. Such information is pertinent to defining population boundaries and understanding localized dispersal patterns and behaviour. Here, we use 3057 genome-wide single-nucleotide polymorphisms (SNPs) to evaluate population genetic structure and assess the levels of relatedness at aggregation sites of reef manta rays (Mobula alfredi) in southern Mozambique (n = 114). Contrary to indications of limited dispersal along the southern Mozambican coastline inferred from photo-identification and telemetry studies, our results show no evidence of population structure (non-significant F_ST < 0.001) for M. alfredi along this coast. We also found no evidence that individuals sampled at the same site were more related than expected by chance for males, females or across both sexes, suggesting that kinship may not influence visitation patterns at these sites. We estimated the effective population size (N_e) of this population to be 375 (95% CI = 369-380). Comparison to a distant eastern Indian Ocean site (Western Australia, n = 15) revealed strong genetic differentiation between Mozambique and Western Australia (F_ST = 0.377), identifying the Indian Ocean basin as a barrier to dispersal. Our findings show that genetic connectivity in M. alfredi extends for several hundred kilometres along continuous coastlines. We therefore recommend that the population in Mozambique be considered a discrete management unit, and future conservation plans should prioritize integrated strategies along the entire southern coastline.

Nearly unbiased estimator of contemporary Ne /N based on kinship relationships

This study develops a nearly unbiased estimator of the ratio of the contemporary effective mother size to the census size in a population, as a proxy of the ratio of contemporary effective size (or effective breeding size) to census size (N_e /N or N_b /N). The proposed estimator is based on both known mother-offspring (MO) and maternal-sibling (MS) relationships observed within the same cohort, in which sampled individuals in the cohort probably share MO relationships with sampled mothers. The rationale is that the frequency of MO and MS pairs contains information regarding the contemporary effective mother size and the (mature) census size, respectively. Therefore, the estimator can be obtained only from genetic data. We also evaluate the performance of the estimator by running an individual-based model. The results of this study provide the following: (a) parameter range for satisfying the unbiasedness, and (b) guidance for sample sizes to ensure the required accuracy and precision, especially when the order of the ratio is available. Furthermore, the results demonstrate the usefulness of a sibship assignment method for genetic monitoring, providing insights for interpreting environmental and/or anthropological factors fluctuating N_e /N (or N_b /N), especially in the context of conservation biology and wildlife management.

One panel to rule them all: DArTcap genotyping for population structure, historical demography, and kinship analyses, and its application to a threatened shark

With recent advances in sequencing technology, genomic data are changing how important conservation management decisions are made. Applications such as Close-Kin Mark-Recapture demand large amounts of data to estimate population size and structure, and their full potential can only be realised through ongoing improvements in genotyping strategies. Here we introduce DArTcap, a cost-efficient method that combines DArTseq and sequence capture, and illustrate its use in a high resolution population analysis of Glyphis garricki, a rare, poorly known and threatened euryhaline shark. Clustering analyses and spatial distribution of kin pairs from four different regions across northern Australia and one in Papua New Guinea, representing its entire known range, revealed that each region hosts at least one distinct population. Further structuring is likely within Van Diemen Gulf, the region that included the most rivers sampled, suggesting additional population structuring would be found if other rivers were sampled. Coalescent analyses and spatially explicit modelling suggest that G. garricki experienced a recent range expansion during the opening of the Gulf of Carpentaria following the conclusion of the Last Glacial Maximum. The low migration rates between neighbouring populations of a species that is found only in restricted coastal and riverine habitats show the importance of managing each population separately, including careful monitoring of local and remote anthropogenic activities that may affect their environments. Overall we demonstrated how a carefully chosen SNP panel combined with DArTcap can provide highly accurate kinship inference and also support population structure and historical demography analyses, therefore maximising cost-effectiveness.

Robustness of close-kin mark-recapture estimators to dispersal limitation and spatially varying sampling probabilities

Close-kin mark-recapture (CKMR) is a method for estimating abundance and vital rates from kinship relationships observed in genetic samples. CKMR inference only requires animals to be sampled once (e.g., lethally), potentially widening the scope of population-level inference relative to traditional monitoring programs.One assumption of CKMR is that, conditional on individual covariates like age, all animals have an equal probability of being sampled. However, if genetic data are collected opportunistically (e.g., via hunters or fishers), there is potential for spatial variation in sampling probability that can bias CKMR estimators, particularly when genetically related individuals stay in close proximity.We used individual-based simulation to investigate consequences of dispersal limitation and spatially biased sampling on performance of naive (nonspatial) CKMR estimators of abundance, fecundity, and adult survival. Population dynamics approximated that of a long-lived mammal species subject to lethal sampling.Naive CKMR abundance estimators were relatively unbiased when dispersal was unconstrained (i.e., complete mixing) or when sampling was random or subject to moderate levels of spatial variation. When dispersal was limited, extreme variation in spatial sampling probabilities negatively biased abundance estimates. Reproductive schedules and survival were well estimated, except for survival when adults could emigrate out of the sampled area. Incomplete mixing was readily detected using Kolmogorov-Smirnov tests.Although CKMR appears promising for estimating abundance and vital rates with opportunistically collected genetic data, care is needed when dispersal limitation is coupled with spatially biased sampling. Fortunately, incomplete mixing is easily detected with adequate sample sizes. In principle, it is possible to devise and fit spatially explicit CKMR models to avoid bias under dispersal limitation, but development of such models necessitates additional complexity (and possibly additional data). We suggest using simulation studies to examine potential bias and precision of proposed modeling approaches prior to implementing a CKMR program.

Restoration-mediated secondary contact leads to introgression of alewife ecotypes separated by a colonial-era dam

Secondary contact may have important implications for ecological and evolutionary processes; however, few studies have tracked the outcomes of secondary contact from its onset in natural ecosystems. We evaluated an anadromous alewife (Alosa pseudoharengus ) reintroduction project in Rogers Lake (Connecticut, USA), which contains a landlocked alewife population that was isolated as a result of colonial-era damming. After access to the ocean was restored, adult anadromous alewife were stocked into the lake. We assessed anadromous juvenile production, the magnitude and direction of introgression, and the potential for competition between ecotypes. We obtained fin clips from all adult alewife stocked into the lake during the restoration and a sample of juveniles produced in the lake two years after the stocking began. We assessed the ancestry of juveniles using categorical assignment and pedigree reconstruction with newly developed microhaplotype genetic markers. Anadromous alewives successfully spawned in the lake and hybridized with the landlocked population. Parentage assignments revealed that male and female anadromous fish contributed equally to juvenile F1 hybrids. The presence of landlocked backcrosses shows that some hybrids were produced within the first two years of secondary contact, matured in the lake, and reproduced. Therefore, introgression appears directional, from anadromous into landlocked, in the lake environment. Differences in estimated abundance of juveniles of different ecotypes in different habitats were also detected, which may reduce competition between ecotypes as the restoration continues. Our results illustrate the utility of restoration projects to study the outcomes of secondary contact in real ecosystems.

Inferring trait-specific similarity among individuals from molecular markers and phenotypes with Bayesian regression

Modeling covariance structure based on genetic similarity between pairs of relatives plays an important role in evolutionary, quantitative and statistical genetics. Historically, genetic similarity between individuals has been quantified from pedigrees via the probability that randomly chosen homologous alleles between individuals are identical by descent (IBD). At present, however, many genetic analyses rely on molecular markers, with realized measures of genomic similarity replacing IBD-based expected similarities. Animal and plant breeders, for example, now employ marker-based genomic relationship matrices between individuals in prediction models and in estimation of genome-based heritability coefficients. Phenotypes convey information about genetic similarity as well. For instance, if phenotypic values are at least partially the result of the action of quantitative trait loci, one would expect the former to inform about the latter, as in genome-wide association studies. Statistically, a non-trivial conditional distribution of unknown genetic similarities, given phenotypes, is to be expected. A Bayesian formalism is presented here that applies to whole-genome regression methods where some genetic similarity matrix, e.g., a genomic relationship matrix, can be defined. Our Bayesian approach, based on phenotypes and markers, converts prior (markers only) expected similarity into trait-specific posterior similarity. A simulation illustrates situations under which effective Bayesian learning from phenotypes occurs. Pinus and wheat data sets were used to demonstrate applicability of the concept in practice. The methodology applies to a wide class of Bayesian linear regression models, it extends to the multiple-trait domain, and can also be used to develop phenotype-guided similarity kernels in prediction problems.

Conservation and Management of Salmon in the Age of Genomics

Salmon were among the first nonmodel species for which systematic population genetic studies of natural populations were conducted, often to support management and conservation. The genomics revolution has improved our understanding of the evolutionary ecology of salmon in two major ways: (a) Large increases in the numbers of genetic markers (from dozens to 10⁴-10⁶) provide greater power for traditional analyses, such as the delineation of population structure, hybridization, and population assignment, and (b) qualitatively new insights that were not possible with traditional genetic methods can be achieved by leveraging detailed information about the structure and function of the genome. Studies of the first type have been more common to date, largely because it has taken time for the necessary tools to be developed to fully understand the complex salmon genome. We expect that the next decade will witness many new studies that take full advantage of salmonid genomic resources.

Nearly unbiased estimator of contemporary effective mother size using within-cohort maternal sibling pairs incorporating parental and nonparental reproductive variations

In this study, we developed a nearly unbiased estimator of contemporary effective mother size in a population, which is based on a known maternal half-sibling relationship found within the same cohort. Our method allows for variance of the average number of offspring per mother (i.e., parental variation, such as age-specific fecundity) and variance of the number of offspring among mothers with identical reproductive potential (i.e., nonparental variation, such as family-correlated survivorship). We also developed estimators of the variance and coefficient of variation of contemporary effective mother size and qualitatively evaluated the performance of the estimators by running an individual-based model. Our results provide guidance for (i) a sample size to ensure the required accuracy and precision when the order of effective mother size is available and (ii) a degree of uncertainty regarding the estimated effective mother size when information about the size is unavailable. To the best of our knowledge, this is the first report to demonstrate the derivation of a nearly unbiased estimator of effective population size; however, its current application is limited to effective mother size and situations, in which the sample size is not particularly small and maternal half-sibling relationships can be detected without error. The results of this study demonstrate the usefulness of a sibship assignment method for estimating effective population size; in addition, they have the potential to greatly widen the scope of genetic monitoring, especially in the situation of small sample size.

A genomic approach to inferring kinship reveals limited intergenerational dispersal in the yellow fever mosquito

Understanding past dispersal and breeding events can provide insight into ecology and evolution and can help inform strategies for conservation and the control of pest species. However, parent-offspring dispersal can be difficult to investigate in rare species and in small pest species such as mosquitoes. Here, we develop a methodology for estimating parent-offspring dispersal from the spatial distribution of close kin, using pairwise kinship estimates derived from genome-wide single nucleotide polymorphisms (SNPs). SNPs were scored in 162 Aedes aegypti (yellow fever mosquito) collected from eight close-set, high-rise apartment buildings in an area of Malaysia with high dengue incidence. We used the SNPs to reconstruct kinship groups across three orders of kinship. We transformed the geographical distances between all kin pairs within each kinship category into axial standard deviations of these distances, then decomposed these into components representing past dispersal events. From these components, we isolated the axial standard deviation of parent-offspring dispersal and estimated neighbourhood area (129 m), median parent-offspring dispersal distance (75 m) and oviposition dispersal radius within a gonotrophic cycle (36 m). We also analysed genetic structure using distance-based redundancy analysis and linear regression, finding isolation by distance both within and between buildings and estimating neighbourhood size at 268 individuals. These findings indicate the scale required to suppress local outbreaks of arboviral disease and to target releases of modified mosquitoes for mosquito and disease control. Our methodology is readily implementable for studies of other species, including pests and species of conservation significance.