Estimating abundance for a savanna elephant population using mark–resight methods: a case study for the Tembe Elephant Park, South Africa

Elephants living in dense woodlands are difficult to count. Many elephant populations in Africa occur in such conditions. Estimates of these populations based on total counts, aerial counts and dung counts often lack information on precision and accuracy. We use standard mark–recapture field methods to obtain estimates of population size with associated confidence limits. We apply this approach to a closed elephant population in the Tembe Elephant Park (300 km²), South Africa. A registration count completed in 4 months gives a known population size. We evaluate mark–recapture models against the known population size. Individual identification profiles obtained for elephants during the registration count and mark–recapture events indicate that at least 167 elephants live in the park. We consider this value as an estimate of the minimum number alive. We include 189 sightings of bulls and 37 sightings of breeding herds in the mark–recapture modelling. Of the models we test (Petersen, Schnabel, Schumacher, Jolly–Seber, Bowden's, Poisson and negative binomial), Bowden's gives an estimate closest to the registration count. Assumptions of the model are not violated. For all models except one (negative binomial), our estimates improve with increased sampling intensity. Confidence intervals do not improve with increased effort except for the Schnabel model. Mark–recapture methods should be considered as reliable estimators of population size for elephants occurring in dense woodlands and forests when other methods cannot be relied on.     

Recommendations for photo‐identification methods used in capture‐recapture models with cetaceans

Capture‐recapture methods are frequently employed to estimate abundance of cetaceans using photographic techniques and a variety of statistical models. However, there are many unresolved issues regarding the selection and manipulation of images that can potentially impose bias on resulting estimates. To examine the potential impact of these issues we circulated a test data set of dorsal fin images from bottlenose dolphins to several independent research groups. Photo‐identification methods were generally similar, but the selection, scoring, and matching of images varied greatly amongst groups. Based on these results we make the following recommendations. Researchers should: (1) determine the degree of marking, or level of distinctiveness, and use images of sufficient quality to recognize animals of that level of distinctiveness; (2) ensure that markings are sufficiently distinct to eliminate the potential for “twins” to occur; (3) stratify data sets by distinctiveness and generate a series of abundance estimates to investigate the influence of including animals of varying degrees of markings; and (4) strive to examine and incorporate variability among analysts into capture‐recapture estimation. In this paper we summarize these potential sources of bias and provide recommendations for best practices for using natural markings in a capture‐recapture framework.     

Assessing the abundance of Bristol Bay belugas with genetic mark‐recapture methods

The Bristol Bay stock of beluga whales (Delphinapterus leucas) is genetically distinct and resides in Bristol Bay year‐round. We estimated the abundance of this population using genetic mark‐recapture, whereby genetic markers from skin biopsies, collected between 2002 and 2011, were used to identify individuals. We identified 516 individual belugas in two inner bays, 468 from Kvichak Bay and 48 from Nushagak Bay, and recaptured 75 belugas in separate years. Using a POPAN Jolly‐Seber model, abundance was estimated at 1,928 belugas (95% CI = 1,611–2,337), not including calves, which were not sampled. Most belugas were sampled in Kvichak Bay at a time when belugas are also known to occur in Nushagak Bay. The pattern of genetic recaptures and data from belugas with satellite transmitters suggested that belugas in the two bays regularly mix. Hence, the estimate of abundance likely applies to all belugas within Bristol Bay. Simulations suggested that POPAN estimates of abundance are robust to most forms of emigration, but that emigration causes negative bias in both capture and survival probabilities. Because it is likely that some belugas do not enter the sampling area during sampling, our estimate of abundance is best considered a minimum population size.     

State uncertainty models and mark‐resight models for understanding non‐breeding site use by Piping Plovers

Conservation of beach‐nesting medium‐distance migrants has focused on breeding areas because protection of nests is more tractable than protection of non‐breeding habitat. As breeding ground management has encountered diminishing returns, interest in understanding threats in non‐breeding areas has increased. However, robust estimates of non‐breeding demographic rates and abundance are generally lacking, hindering the study of limiting factors. Estimating such rates is made more difficult by complex population dynamics at non‐breeding sites. In South Carolina, endangered Piping Plovers Charadrius melodus start arriving in July and some depart prior to December (the autumn‐only population) while others remain through at least March (the wintering population). State uncertainty capture‐mark‐recapture models provide a means for estimating vital rates for such co‐occurring populations. We estimated the proportion of the population entering the study area per survey (entry probability) and proportion remaining per survey (persistence rate) for both populations during autumn, and abundance of the wintering population, at four sites in South Carolina in 2006/7 and 2007/8, taking advantage of birds previously colour‐ringed on the breeding grounds. We made fairly precise estimates of entry and persistence rates with small sample sizes. Cumulative entry probability was ~50% by the end of July and reached 95% for both populations by October. Estimated stopover duration for birds in the autumn‐only population was 35 days in year 1 and 42 days in year 2. We estimated a wintering super‐population size of 71 ± 16 se birds in the first year and 75 ± 16 in the second. If ringing programmes on the breeding grounds continue, standardized resighting surveys in the non‐breeding period and mark‐recapture models can provide robust estimates of entry and persistence rates and abundance. Habitat protection intended to benefit non‐breeding Piping Plovers at our coastal sites should be in effect by late summer, as many birds are resident from July to the end of winter.     

A practical introduction to sequentially Markovian coalescent methods for estimating demographic history from genomic data

A common goal of population genomics and molecular ecology is to reconstruct the demographic history of a species of interest. A pair of powerful tools based on the sequentially Markovian coalescent have been developed to infer past population sizes using genome sequences. These methods are most useful when sequences are available for only a limited number of genomes and when the aim is to study ancient demographic events. The results of these analyses can be difficult to interpret accurately, because doing so requires some understanding of their theoretical basis and of their sensitivity to confounding factors. In this practical review, we explain some of the key concepts underpinning the pairwise and multiple sequentially Markovian coalescent methods (PSMC and MSMC, respectively). We relate these concepts to the use and interpretation of these methods, and we explain how the choice of different parameter values by the user can affect the accuracy and precision of the inferences. Based on our survey of 100 PSMC studies and 30 MSMC studies, we describe how the two methods are used in practice. Readers of this article will become familiar with the principles, practice, and interpretation of the sequentially Markovian coalescent for inferring demographic history.     

Assessing population parameters and trends of Guiana dolphins (Sotalia guianensis): An eight‐year mark‐recapture study

This study represents the first attempt to study the population dynamics of Guiana dolphins (Sotalia guianensis), by evaluating a set of demographic parameters. The population of the Caravelas River estuary, eastern Brazil, was systematically monitored through a long‐term mark‐recapture experiment (2002–2009). Abundance estimates revealed a small population (57–124 dolphins), comprised of resident dolphins and individuals that temporarily leave or pass through the study area. Temporary emigration from the estuary to adjacencies (γ″= 0.33 ± 0.07 SE) and return rate (1 ?γ′= 0 .67) were moderate and constant, indicating that some dolphins use larger areas. Survival rate (?= 0.88 ± 0.07 SE) and abundance were constant throughout the study period. Power analysis showed that the current monitoring effort has high probability of detecting abrupt population declines (1 ?β= 0.9). Although the monitoring is not yet sensitive to subtle population trends, sufficient time to identify them is feasible (additional 3 yr). Despite such apparent stability, this population, as many others, inhabits waters exposed to multiple human‐related threats. Open and closed population modeling applied to photo‐identification data provide a robust baseline for estimating several demographic parameters and can be applied to other populations to allow further comparisons. Such synergistic efforts will allow a reliable definition of conservation status of this species.     

When are genetic methods useful for estimating contemporary abundance and detecting population trends?

The utility of microsatellite markers for inferring population size and trend has not been rigorously examined, even though these markers are commonly used to monitor the demography of natural populations. We assessed the ability of a linkage disequilibrium estimator of effective population size (N_e) and a simple capture-recapture estimator of abundance (N) to quantify the size and trend of stable or declining populations (true N = 100–10,000), using simulated Wright–Fisher populations. Neither method accurately or precisely estimated abundance at sample sizes of S = 30 individuals, regardless of true N. However, if larger samples of S = 60 or 120 individuals were collected, these methods provided useful insights into abundance and trends for populations of N = 100–500. At small population sizes (N = 100 or 250), precision of the N_e estimates was improved slightly more by a doubling of loci sampled than by a doubling of individuals sampled. In general, monitoring N_e proved a more robust means of identifying stable and declining populations than monitoring N over most of the parameter space we explored, and performance of the N_e estimator is further enhanced if the N_e/N ratio is low. However, at the largest population size (N = 10,000), N estimation outperformed N_e. Both methods generally required ≥ 5 generations to pass between sampling events to correctly identify population trend.     

Capwire: a R package for estimating population census size from non‐invasive genetic sampling

Non‐invasive genetic sampling is an increasingly popular approach for investigating the demographics of natural populations. This has also become a useful tool for managers and conservation biologists, especially for those species for which traditional mark–recapture studies are not practical. However, the consequence of collecting DNA indirectly is that an individual may be sampled multiple times per sampling session. This requires alternative statistical approaches to those used in traditional mark–recapture studies. Here we present the R package capwire , an implementation of the population size estimators of Miller et al. (Molecular Ecology 2005; 14 : 1991), which were designed to deal specifically with this type of sampling. The aim of this project is to enable users across platforms to easily manipulate their data and interact with existing R packages. We have also provided functions to simulate data under a variety of scenarios to allow for rigorous testing of the robustness of the method and to facilitate further development of this approach.     

A new method for estimating the size of small populations from genetic mark-recapture data

The use of non-invasive genetic sampling to estimate population size in elusive or rare species is increasing. The data generated from this sampling differ from traditional mark-recapture data in that individuals may be captured multiple times within a session or there may only be a single sampling event. To accommodate this type of data, we develop a method, named capwire, based on a simple urn model containing individuals of two capture probabilities. The method is evaluated using simulations of an urn and of a more biologically realistic system where individuals occupy space, and display heterogeneous movement and DNA deposition patterns. We also analyse a small number of real data sets. The results indicate that when the data contain capture heterogeneity the method provides estimates with small bias and good coverage, along with high accuracy and precision. Performance is not as consistent when capture rates are homogeneous and when dealing with populations substantially larger than 100. For the few real data sets where N is approximately known, capwire's estimates are very good. We compare capwire's performance to commonly used rarefaction methods and to two heterogeneity estimators in program capture: Mh-Chao and Mh-jackknife. No method works best in all situations. While less precise, the Chao estimator is very robust. We also examine how large samples should be to achieve a given level of accuracy using capwire. We conclude that capwire provides an improved way to estimate N for some DNA-based data sets.     

Open population maximum likelihood spatial capture‐recapture

Open population capture‐recapture models are widely used to estimate population demographics and abundance over time. Bayesian methods exist to incorporate open population modeling with spatial capture‐recapture (SCR), allowing for estimation of the effective area sampled and population density. Here, open population SCR is formulated as a hidden Markov model (HMM), allowing inference by maximum likelihood for both Cormack‐Jolly‐Seber and Jolly‐Seber models, with and without activity center movement. The method is applied to a 12‐year survey of male jaguars (Panthera onca) in the Cockscomb Basin Wildlife Sanctuary, Belize, to estimate survival probability and population abundance over time. For this application, inference is shown to be biased when assuming activity centers are fixed over time, while including a model for activity center movement provides negligible bias and nominal confidence interval coverage, as demonstrated by a simulation study. The HMM approach is compared with Bayesian data augmentation and closed population models for this application. The method is substantially more computationally efficient than the Bayesian approach and provides a lower root‐mean‐square error in predicting population density compared to closed population models.     

Mark‐resight abundance and survival estimation of Indo‐Pacific bottlenose dolphins,Tursiops aduncus,in the Swatch‐of‐No‐Ground,Bangladesh

A mark‐resight analysis under Pollock's robust design was applied to Indo‐Pacific bottlenose dolphins Tursiops aduncus in the Swatch‐of‐No‐Ground (SoNG) submarine canyon, Bangladesh, during the winter seasons of 2005–2009. Information from sightings of photo‐identified individuals (1,144) and unmarked individuals generated abundance estimates of 1,701 (95% confidence interval [CI]= 1,533–1,888), 1,927 (95% CI = 1,851–2,006), 2,150 (95% CI = 1,906–2,425), and 2,239 (95% CI = 1,985–2,524) individuals for seasons 1–4, respectively. This makes the population among the largest assessed of the species. Overall apparent survival was estimated as 0.958 (95% CI = 0.802–0.992). Interseasonal probabilities of transitioning to an unobservable state were estimated as 0.045, 0.363, and 0.300 for years 1–2, 2–3, and 3–4, respectively, and the overall probability of remaining in an unobservable state was 0.688. These probabilities, together with an apparent increase in abundance during the study period, indicate that the identified dolphins are part of a larger superpopulation moving throughout a more extensive geographic area. Of the photo‐identified dolphins, 28.2% exhibited injuries related to entanglements with fishing gear. This implies a strong potential for fatal interactions that could jeopardize the conservation status of the population, which otherwise appears favorable.     

State–space mark–recapture estimates reveal a recent decline in abundance of North Atlantic right whales

North Atlantic right whales (Eubalaena glacialis Müller 1776) present an interesting problem for abundance and trend estimation in marine wildlife conservation. They are long lived, individually identifiable, highly mobile, and one of the rarest of cetaceans. Individuals are annually resighted at different rates, primarily due to varying stay durations among several principal habitats within a large geographic range. To date, characterizations of abundance have been produced that use simple accounting procedures with differing assumptions about mortality. To better characterize changing abundance of North Atlantic right whales between 1990 and 2015, we adapted a state–space formulation with Jolly‐Seber assumptions about population entry (birth and immigration) to individual resighting histories and fit it using empirical Bayes methodology. This hierarchical model included accommodation for the effect of the substantial individual capture heterogeneity. Estimates from this approach were only slightly higher than published accounting procedures, except for the most recent years (when recapture rates had declined substantially). North Atlantic right whales' abundance increased at about 2.8% per annum from median point estimates of 270 individuals in 1990 to 483 in 2010, and then declined to 2015, when the final estimate was 458 individuals (95% credible intervals 444–471). The probability that the population's trajectory post‐2010 was a decline was estimated at 99.99%. Of special concern was the finding that reduced survival rates of adult females relative to adult males have produced diverging abundance trends between sexes. Despite constraints in recent years, both biological (whales' distribution changing) and logistical (fewer resources available to collect individual photo‐identifications), it is still possible to detect this relatively recent, small change in the population's trajectory. This is thanks to the massive dataset of individual North Atlantic right whale identifications accrued over the past three decades. Photo‐identification data provide biological information that allows more informed inference on the status of this species.     

Population abundance in arctic grayling using genetics and close‐kin mark‐recapture

Arctic Grayling (Thymallus arcticus) are among the most widely distributed and abundant freshwater fish in the Yukon Territory of Canada, yet little information exists regarding their broad and fine‐scale population structures or the number and size of these populations. The estimation of population abundance is fundamental for robust management and conservation, yet estimating abundance in the wild is often difficult. Here, we estimated abundance of an Arctic Grayling population using multiple genetic markers and the close‐kin mark‐recapture (CKMR) method. A total of N = 1,104 Arctic Grayling collected from two systems in Yukon were genotyped at 38 sequenced microsatellites. We first identified structure and assessed genetic diversity (effective population size, N^e). Collections from one of the systems (Lubbock River) comprised adults and young‐of‐the‐year sampled independently allowing the identification of parent–offspring pairs (POPs), and thus, the estimation of abundance using CKMR. We used COLONY and CKMRsim to identify POPs and both provided similar results leading to indistinguishable estimates (95% CI) of census size, that is, N^c(COLONY) = 1858 (1259–2457) and N^c(CKMRsim)=1812 (1229–2389). The accuracy of the population abundance estimates can in the future be improved with temporal sampling and more precise age or size‐specific fecundity estimates for Arctic Grayling. Our study demonstrates that the method can be used to inform management and conservation policy for Arctic Grayling and likely also for other fish species for which the assumption of random and independent sampling of adults and offspring can be assured.     

Upper bound estimators of the population size based on ordinal models for capture‐recapture experiments

Capture‐recapture studies have attracted a lot of attention over the past few decades, especially in applied disciplines where a direct estimate for the size of a population of interest is not available. Epidemiology, ecology, public health, and biodiversity are just a few examples. The estimation of the number of unseen units has been a challenge for theoretical statisticians, and considerable progress has been made in providing lower bound estimators for the population size. In fact, it is well known that consistent estimators for this cannot be provided in the very general case. Considering a case where capture‐recapture studies are summarized by a frequency of frequencies distribution, we derive a simple upper bound of the population size based on the cumulative distribution function. We introduce two estimators of this bound, without any specific parametric assumption on the distribution of the observed frequency counts. The behavior of the proposed estimators is investigated using several benchmark datasets and a large‐scale simulation experiment based on the scheme discussed by Pledger.     

Using incidental mark‐encounter data to improve survival estimation

1. Obtaining robust survival estimates is critical, but sample size limitations often result in imprecise estimates or the failure to obtain estimates for population subgroups. Concurrently, data are often recorded on incidental reencounters of marked individuals, but these incidental data are often unused in survival analyses.
2. We evaluated the utility of supplementing a traditional survival dataset with incidental data on marked individuals that were collected ad hoc. We used a continuous time‐to‐event exponential survival model to leverage the matching information contained in both datasets and assessed differences in survival among adult and juvenile and resident and translocated Mojave desert tortoises (Gopherus agassizii).
3. Incorporation of the incidental mark‐encounter data improved precision of all annual survival point estimates, with a 3.4%–37.5% reduction in the spread of the 95% Bayesian credible intervals. We were able to estimate annual survival for three subgroup combinations that were previously inestimable. Point estimates between the radiotelemetry and combined datasets were within |0.029| percentage points of each other, suggesting minimal to no bias induced by the incidental data.
4. Annual survival rates were high (>0.89) for resident adult and juvenile tortoises in both study sites and for translocated adults in the southern site. Annual survival rates for translocated juveniles at both sites and translocated adults in the northern site were between 0.73 and 0.76. At both sites, translocated adults and juveniles had significantly lower survival than resident adults. High mortality in the northern site was driven primarily by a single pulse in mortalities.
5. Using exponential survival models to leverage matching information across traditional survival studies and incidental data on marked individuals may serve as a useful tool to improve the precision and estimability of survival rates. This can improve the efficacy of understanding basic population ecology and population monitoring for imperiled species.

     

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.