Likelihood-based genetic mark-recapture estimates when genotype samples are incomplete and contain typing errors

Effective number of white shark (Carcharodon carcharias, Linnaeus) breeders is stable over four successive years in the population adjacent to eastern Australia and New Zealand

Population size is a central parameter for conservation; however, monitoring abundance is often problematic for threatened marine species. Despite substantial investment in research, many marine species remain data-poor presenting barriers to the evaluation of conservation management outcomes and the modeling of future solutions. Such is the case for the white shark (Carcharodon carcharias), a highly mobile apex predator for whom recent and substantial population declines have been recorded in many globally distributed populations. Here, we estimate the effective number of breeders that successfully contribute offspring in one reproductive cycle (Nb) to provide a snapshot of recent reproductive effort in an east Australian-New Zealand population of white shark. Nb was estimated over four consecutive age cohorts (2010, 2011, 2012, and 2013) using two genetic estimators (linkage disequilibrium; LD and sibship assignment; SA) based on genetic data derived from two types of genetic markers (single nucleotide polymorphisms; SNPs and microsatellite loci). While estimates of Nb using different marker types produced comparable estimates, microsatellite loci were the least precise. The LD and SA estimates of Nb within cohorts using SNPs were comparable; for example, the 2013 age cohort Nb(SA) was 289 (95% CI 200-461) and Nb(LD) was 208.5 (95% CI 116.4-712.7). We show that over the time period studied, Nb was stable and ranged between 206.1 (SD ± 45.9) and 252.0 (SD ± 46.7) per year using a combined estimate of Nb(LD+SA) from SNP loci. In addition, a simulation approach showed that in this population the effective population size (Ne) per generation can be expected to be larger than Nb per reproductive cycle. This study demonstrates how breeding population size can be monitored over time to provide insight into the effectiveness of recovery and conservation measures for the white shark, where the methods described here may be applicable to other data-poor species of conservation concern.

Spatial proximity moderates genotype uncertainty in genetic tagging studies

Accelerating declines of an increasing number of animal populations worldwide necessitate methods to reliably and efficiently estimate demographic parameters such as population density and trajectory. Standard methods for estimating demographic parameters from noninvasive genetic samples are inefficient because lower-quality samples cannot be used, and they assume individuals are identified without error. We introduce the genotype spatial partial identity model (gSPIM), which integrates a genetic classification model with a spatial population model to combine both spatial and genetic information, thus reducing genotype uncertainty and increasing the precision of demographic parameter estimates. We apply this model to data from a study of fishers (Pekania pennanti) in which 37% of hair samples were originally discarded because of uncertainty in individual identity. The gSPIM density estimate using all collected samples was 25% more precise than the original density estimate, and the model identified and corrected three errors in the original individual identity assignments. A simulation study demonstrated that our model increased the accuracy and precision of density estimates 63 and 42%, respectively, using three replicated assignments (e.g., PCRs for microsatellites) per genetic sample. Further, the simulations showed that the gSPIM model parameters are identifiable with only one replicated assignment per sample and that accuracy and precision are relatively insensitive to the number of replicated assignments for high-quality samples. Current genotyping protocols devote the majority of resources to replicating and confirming high-quality samples, but when using the gSPIM, genotyping protocols could be more efficient by devoting more resources to low-quality samples.

A Review of Capture-recapture Methods and Its Possibilities in Ophthalmology and Vision Sciences

Epidemiological information is expected to be used to develop key aspects of eye care such as to control and minimise the impact of diseases, to allocate resources, to monitor public health actions, to determine the best treatment options and to forecast the consequence of diseases in populations. Epidemiological studies are expected to provide information about the prevalence and/or incidence of eye diseases or conditions. To determine prevalence is necessary to perform a cross-sectional screening of the population at risk to ascertain the number of cases. The aim of this review is to describe and evaluate capture-recapture methods (or models) to ascertaining the number of individuals with a disease (e.g. diabetic retinopathy) or condition (e.g. vision impairment) in the population. The review covers the fundamental aspects of capture-recapture methods that would enable non-experts in epidemiology to use it in ophthalmic studies. The review provides information about theoretical aspects of the method with examples of studies in ophthalmology in which it has been used. We also provide a problem/solution approach for limitations arising from the lists obtained from registers or other reliable sources. We concluded that capture-recapture models can be considered reliable to estimate the total number of cases with eye conditions using incomplete information from registers. Accordingly, the method may be used to maintain updated epidemiological information about eye conditions helping to tackle the lack of surveillance information in many regions of the globe.

Can estimates of genetic effective population size contribute to fisheries stock assessments?

Sustainable exploitation of fisheries populations is challenging to achieve when the size of the population prior to exploitation and the actual numbers removed over time and across fishing zones are not clearly known. Quantitative fisheries' modeling is able to address this problem, but accurate and reliable model outcomes depend on high quality input data. Much of this information is obtained through the operation of the fishery under consideration, but while this seems appropriate, biases may occur. For example, poorly quantified changes in fishing methods that increase catch rates can erroneously suggest that the overall population size is increasing. Hence, the incorporation of estimates of abundance derived from independent data sources is preferable. We review and evaluate a fisheries-independent method of indexing population size; inferring adult abundance from estimates of the genetic effective size of a population (N_e ). Recent studies of elasmobranch species have shown correspondence between N_e and ecologically determined estimates of the population size (N). Simulation studies have flagged the possibility that the range of N_e /N ratios across species may be more restricted than previously thought, and also show that declines in N_e track declines in the abundance of model fisheries species. These key developments bring this new technology closer to implementation in fisheries science, particularly for data-poor fisheries or species of conservation interest.

Estimating population density and connectivity of American mink using spatial capture-recapture

Estimating the abundance or density of populations is fundamental to the conservation and management of species, and as landscapes become more fragmented, maintaining landscape connectivity has become one of the most important challenges for biodiversity conservation. Yet these two issues have never been formally integrated together in a model that simultaneously models abundance while accounting for connectivity of a landscape. We demonstrate an application of using capture-recapture to develop a model of animal density using a least-cost path model for individual encounter probability that accounts for non-Euclidean connectivity in a highly structured network. We utilized scat detection dogs (Canis lupus familiaris) as a means of collecting non-invasive genetic samples of American mink (Neovison vison) individuals and used spatial capture-recapture models (SCR) to gain inferences about mink population density and connectivity. Density of mink was not constant across the landscape, but rather increased with increasing distance from city, town, or village centers, and mink activity was associated with water. The SCR model allowed us to estimate the density and spatial distribution of individuals across a 388 km² area. The model was used to investigate patterns of space usage and to evaluate covariate effects on encounter probabilities, including differences between sexes. This study provides an application of capture-recapture models based on ecological distance, allowing us to directly estimate landscape connectivity. This approach should be widely applicable to provide simultaneous direct estimates of density, space usage, and landscape connectivity for many species.

Guidelines for MSAT and SNP panels that lead to high-quality data for genetic mark–recapture studies

Molecular markers with inadequate power to discriminate among individuals can lead to false recaptures (shadows), and inaccurate genotyping can lead to missed recaptures (ghosts), potentially biasing genetic mark–recapture estimates. We used simulations to examine the impact of microsatellite (MSAT) and single nucleotide polymorphism (SNP) marker-set size, allelic frequency, multitubes approaches, and sample matching protocols on shadow and ghost events in genetic mark–recapture studies, presenting guidance on the specifications for MSAT and SNP marker panels, and sample matching protocols necessary to produce high-quality data. Shadow events are controllable by increasing the number of markers or by selecting markers with high discriminatory power; reasonably sized marker sets (e.g., ≥9 MSATs or ≥32 SNPs) of moderate allelic diversity lead to low probabilities of shadow errors. Ghost events are more challenging to control and low allelic dropout or false-allele error rates produced high rates of erroneous mismatches in mark–recapture sampling. Fortunately, error-tolerant matching protocols, which use information from positively matching loci between comparisons of samples, and multitubes protocols to achieve consensus genotypes are effective at eliminating ghost events. We present a case study on Pacific walrus, Odobenus rosmarus divergens (Illiger, 1815), using simulation results to inform genetic marker choices.

Linkage Disequilibrium Estimation of Effective Population Size with Immigrants from Divergent Populations: A Case Study on Spanish Mackerel (Scomberomorus commerson)

Estimates of genetic effective population size (Ne) using molecular markers are a potentially useful tool for the management of endangered through to commercial species. However, pitfalls are predicted when the effective size is large because estimates require large numbers of samples from wild populations for statistical validity. Our simulations showed that linkage disequilibrium estimates of Ne up to 10,000 with finite confidence limits can be achieved with sample sizes of approximately 5000. This number was deduced from empirical allele frequencies of seven polymorphic microsatellite loci in a commercially harvested fisheries species, the narrow-barred Spanish mackerel (Scomberomorus commerson). As expected, the smallest SD of Ne estimates occurred when low-frequency alleles were excluded. Additional simulations indicated that the linkage disequilibrium method was sensitive to small numbers of genotypes from cryptic species or conspecific immigrants. A correspondence analysis algorithm was developed to detect and remove outlier genotypes that could possibly be inadvertently sampled from cryptic species or nonbreeding immigrants from genetically separate populations. Simulations demonstrated the value of this approach in Spanish mackerel data. When putative immigrants were removed from the empirical data, 95% of the Ne estimates from jacknife resampling were greater than 24,000.