Population size estimates based on the frequency of genetically assigned parent–offspring pairs within a subsample

Estimating population density as precise as possible is a key premise for managing wild animal species. This can be a challenging task if the species in question is elusive or, due to high quantities, hard to count. We present a new, mathematically derived estimator for population size, where the estimation is based solely on the frequency of genetically assigned parent–offspring pairs within a subsample of an ungulate population. By use of molecular markers like microsatellites, the number of these parent–offspring pairs can be determined. The study's aim was to clarify whether a classical capture–mark–recapture (CMR) method can be adapted or extended by this genetic element to a genetic‐based capture–mark–recapture (g‐CMR). We numerically validate the presented estimator (and corresponding variance estimates) and provide the R‐code for the computation of estimates of population size including confidence intervals. The presented method provides a new framework to precisely estimate population size based on the genetic analysis of a one‐time subsample. This is especially of value where traditional CMR methods or other DNA‐based (fecal or hair) capture–recapture methods fail or are too difficult to apply. The DNA source used is basically irrelevant, but in the present case the sampling of an annual hunting bag is to serve as data basis. In addition to the high quality of muscle tissue samples, hunting bags provide additional and essential information for wildlife management practices, such as age, weight, or sex. In cases where a g‐CMR method is ecologically and hunting‐wise appropriate, it enables a wide applicability, also through its species‐independent use.     

Capture–recapture models with heterogeneity to study survival senescence in the wild

Detecting senescence in wild populations and estimating its strength raise three challenges. First, in the presence of individual heterogeneity in survival probability, the proportion of high‐survival individuals increases with age. This increase can mask a senescence‐related decrease in survival probability when the probability is estimated at the population level. To accommodate individual heterogeneity we use a mixture model structure (discrete classes of individuals). Second, the study individuals can elude the observers in the field, and their detection rate can be heterogeneous. To account for detectability issues we use capture–mark–recapture (CMR) methodology, mixture models and data that provide information on individuals’ detectability. Last, emigration to non‐monitored sites can bias survival estimates, because it can occur at the end of the individuals’ histories and mimic earlier death. To model emigration we use Markovian transitions to and from an unobservable state. These different model structures are merged together using hidden Markov chain CMR models, or multievent models. Simulation studies illustrate that reliable evidence for survival senescence can be obtained using highly heterogeneous data from non site‐faithful individuals. We then design a tailored application for a dataset from a colony of black‐headed gull Chroicocephalus ridibundus. Survival probabilities do not appear individually variable, but evidence for survival senescence becomes significant only when accounting for other sources of heterogeneity. This result suggests that not accounting for heterogeneity leads to flawed inference and/or that emigration heterogeneity mimics survival heterogeneity and biases senescence estimates.     

Noninvasive Hair Sampling and Genetic Tagging of Co-Distributed Fishers and American Martens

ABSTRACT Estimation of abundance is important for assessing population responses to management actions. Accurate abundance estimates are particularly critical for monitoring temporal variation following reintroductions when the management goal is to attain population sizes capable of sustaining harvest. Numerous reintroductions have taken place in the Great Lakes region of North America, including efforts to restore extirpated fishers (Martes pennanti) and American martens (M. americana). We used a DNA-based noninvasive hair-snaring method based on one trap design and trapping -grid configuration, and evaluated capture—mark—recapture (CMR) analytical approaches to simultaneously estimate population size for co-distributed fishers and American martens in a 671-km² area of the Ottawa National Forest in the western Upper Peninsula of Michigan, USA. We included harvest as a final recapture period to increase probability of recapture and to evaluate potential violations of geographic closure assumptions. We used microsatellite markers to identify target species, eliminate congener species, and provide individual identity for estimation of abundance. Population estimates for fishers and martens on the study area ranged from 35 to 60 and 8 to 28, respectively. Estimators incorporating harvest data resulted in up to a 40% increase in abundance estimates relative to estimators without harvest. We considered population estimates not including harvest data the most appropriate for the study due to timing of sampling and environmental factors, but inclusion of harvested individuals was shown to be useful as a means to detect violations of the assumption of geographic closure. We suggest improvements on future CMR sampling designs for larger landscape scales of relevance to management through incorporation of habitat or historical harvest data. Noninvasive genetic methods that simultaneously estimate the numerical abundance of co-distributed species can greatly decrease assessment costs relative to traditional methods, and increase resulting demographic and ecological information.     

Transience in the humpback whale population of New Caledonia and implications for abundance estimation

A phenomenon of transience in the humpback whale population breeding in New Caledonia has been highlighted in recent analyses. We used these data to illustrate the risk of flawed inference when transience is not properly accounted for in abundance estimation of resident populations. Transients are commonly defined as individuals that pass through the sampling area once, i.e., have a null probability of being caught again, and therefore induce heterogeneity in the detection process. The presence of transients can lead to severe bias in the estimation of abundance and we demonstrate how to correct for this feature when estimating abundance of resident populations. In New Caledonia, very different conclusions about the number of resident whales in the southern lagoon between 1999 and 2005 are obtained when the abundance estimate accounts for the transient whales. Without correction, the estimates of the abundance were up to twice as high across all years compared to the estimates of the resident population when a correction for transients had been incorporated. Having reliable population estimates when assessing the status of endangered species is essential in documenting recovery and monitoring of population trends. Therefore, we encourage researchers to account for transients when reporting abundances of resident populations.     

Validation of mark‐recapture population estimates for invasive common carp,Cyprinus carpio,in Lake Crescent,Tasmania

A mark‐recapture study based on the Petersen method was implemented in 1998 to estimate the abundance of the invasive common carp, Cyprinus carpio L., in Lake Crescent, Tasmania. Multiple gear types were employed to minimise capture bias, with multiple capture and recapture events providing an opportunity to compute and compare Petersen and Schnabel estimates. A single Petersen estimate on recapture data and two Schnabel estimates – one each on mark (forward‐Schnabel estimate) and recapture (reverse‐Schnabel estimate) data – were conducted. An independent long‐term double tag study facilitated estimation of the annual natural mortality. Subsequent fish‐down of the population suggests that, in all likelihood, the carp have been eradicated from the lake, providing an unprecedented opportunity to verify the forward population estimates carried out in 1998. Results suggest that all three estimates were close to the true population size, with the reverse‐Schnabel estimate being the most accurate and within 1% of the true population in this relatively large lake (～2365 ha). Greater accuracy of the reverse‐Schnabel approach can be attributed to either minimised fish behavioural (i.e. gear susceptibility or avoidance) or computational bias associated with the forward‐Schnabel and Petersen approaches, respectively. While the original estimates served as a guide in eradication of carp from the lake, the ultimate validation provides a reliable framework for abundance estimation of this invasive fish in relatively large water bodies elsewhere.     

Genetic mark–recapture population estimation in black bears and issues of scale

Abundance estimates for black bears (Ursus americanus) are important for effective management. Recently, DNA technology has resulted in widespread use of noninvasive, genetic capture–mark–recapture (CMR) approaches to estimate populations. Few studies have compared the genetic CMR methods to other estimation methods. We used genetic CMR to estimate the bear population at 2 study sites in northern New Hampshire (Pittsburg and Milan) in 2 consecutive years. We compared these estimates to those derived from traditional methods used by the New Hampshire Fish and Game Department (NHFG) using hunter harvest and mortality data. Density estimates produced with genetic CMR methods were similar both years and were comparable to those derived from traditional methods. In 2006, the estimated number of bears in Pittsburg was 79 (95% CI = 60–98) corresponding to a density of 15–24 (95% CI) bears/100 km²; the 2007 estimate was 83 (95% CI = 67–99; density = 16–24 bears/100 km²). In 2006, the estimated number of bears in Milan was 95 (95% CI = 74–117; density = 16–25 bears/100 km²); the 2007 estimate was 96 (95% CI = 77–114; density = 17–25 bears/100 km²). We found that genetic CMR methods were able to identify demographic variation at a local scale, including a strongly skewed sex ratio (2 M:1 F) in the Milan population. Genetic CMR is a useful tool for wildlife managers to monitor populations of local concern, where abundance or demographic characteristics may deviate from regional estimates. Future monitoring of the Milan population with genetic CMR is recommended to determine if the sex ratio bias continues, possibly warranting a change in local harvest regimes. © 2011 The Wildlife Society.     

Complete tag loss in capture–recapture studies affects abundance estimates: An elephant seal case study

1. In capture–recapture studies, recycled individuals occur when individuals lose all of their tags and are recaptured as though they were new individuals. Typically, the effect of these recycled individuals is assumed negligible.
2. Through a simulation‐based study of double‐tagging experiments, we examined the effect of recycled individuals on parameter estimates in the Jolly–Seber model with tag loss (Cowen & Schwarz, 2006). We validated the simulation framework using long‐term census data of elephant seals.
3. Including recycled individuals did not affect estimates of capture, survival, and tag‐retention probabilities. However, with low tag‐retention rates, high capture rates, and high survival rates, recycled individuals produced overestimates of population size. For the elephant seal case study, we found population size estimates to be between 8% and 53% larger when recycled individuals were ignored.
4. Ignoring the effects of recycled individuals can cause large biases in population size estimates. These results are particularly noticeable in longer studies.

     

Hidden population size estimation and diagnostics using two respondent-driven samples with applications in Armenia

Estimating the size of hidden populations is essential to understand the magnitude of social and healthcare needs, risk behaviors, and disease burden. However, due to the hidden nature of these populations, they are difficult to survey, and there are no gold standard size estimation methods. Many different methods and variations exist, and diagnostic tools are needed to help researchers assess method-specific assumptions as well as compare between methods. Further, because many necessary mathematical assumptions are unrealistic for real survey implementation, assessment of how robust methods are to deviations from the stated assumptions is essential. We describe diagnostics and assess the performance of a new population size estimation method, capture–recapture with successive sampling population size estimation (CR-SS-PSE), which we apply to data from 3 years of studies from three cities and three hidden populations in Armenia. CR-SS-PSE relies on data from two sequential respondent-driven sampling surveys and extends the successive sampling population size estimation (SS-PSE) framework by using the number of individuals in the overlap between the two surveys and a model for the successive sampling process to estimate population size. We demonstrate that CR-SS-PSE is more robust to violations of successive sampling assumptions than SS-PSE. Further, we compare the CR-SS-PSE estimates to population size estimations using other common methods, including unique object and service multipliers, wisdom of the crowd, and two-source capture–recapture to illustrate volatility across estimation methods.     

Capture–recapture analysis with a latent class model allowing for local dependence and observed heterogeneity

In epidemiology, capture–recapture models are commonly used to estimate the size of an unknown population based on several incomplete lists of individuals. The method operates under two main assumptions: independence between the lists (local independence) and homogeneity of capture probabilities of individuals. In practice, these assumptions are rarely satisfied. We introduce a multinomial latent class model that can account for both list dependence and heterogeneity. Parameter estimation is performed by maximizing the conditional likelihood function with the use of the EM algorithm. In addition, a new approach for evaluating the standard errors of the parameter estimates is discussed, which considerably reduces the computational burden associated with the evaluation of the variance of the population size estimate.     

Using pedigree reconstruction to estimate population size: genotypes are more than individually unique marks

Estimates of population size are critical for conservation and management, but accurate estimates are difficult to obtain for many species. Noninvasive genetic methods are increasingly used to estimate population size, particularly in elusive species such as large carnivores, which are difficult to count by most other methods. In most such studies, genotypes are treated simply as unique individual identifiers. Here, we develop a new estimator of population size based on pedigree reconstruction. The estimator accounts for individuals that were directly sampled, individuals that were not sampled but whose genotype could be inferred by pedigree reconstruction, and individuals that were not detected by either of these methods. Monte Carlo simulations show that the population estimate is unbiased and precise if sampling is of sufficient intensity and duration. Simulations also identified sampling conditions that can cause the method to overestimate or underestimate true population size; we present and discuss methods to correct these potential biases. The method detected 2–21% more individuals than were directly sampled across a broad range of simulated sampling schemes. Genotypes are more than unique identifiers, and the information about relationships in a set of genotypes can improve estimates of population size.     

Using multiple data types and integrated population models to improve our knowledge of apex predator population dynamics

Current management of large carnivores is informed using a variety of parameters, methods, and metrics; however, these data are typically considered independently. Sharing information among data types based on the underlying ecological, and recognizing observation biases, can improve estimation of individual and global parameters. We present a general integrated population model (IPM), specifically designed for brown bears (Ursus arctos), using three common data types for bear (U. spp.) populations: repeated counts, capture–mark–recapture, and litter size. We considered factors affecting ecological and observation processes for these data. We assessed the practicality of this approach on a simulated population and compared estimates from our model to values used for simulation and results from count data only. We then present a practical application of this general approach adapted to the constraints of a case study using historical data available for brown bears on Kodiak Island, Alaska, USA. The IPM provided more accurate and precise estimates than models accounting for repeated count data only, with credible intervals including the true population 94% and 5% of the time, respectively. For the Kodiak population, we estimated annual average litter size (within one year after birth) to vary between 0.45 [95% credible interval: 0.43; 0.55] and 1.59 [1.55; 1.82]. We detected a positive relationship between salmon availability and adult survival, with survival probabilities greater for females than males. Survival probabilities increased from cubs to yearlings to dependent young ≥2 years old and decreased with litter size. Linking multiple information sources based on ecological and observation mechanisms can provide more accurate and precise estimates, to better inform management. IPMs can also reduce data collection efforts by sharing information among agencies and management units. Our approach responds to an increasing need in bear populations’ management and can be readily adapted to other large carnivores.     

Patterns and processes in shorebird survival rates: a global review

Changes in demographic rates underpin changes in population size, and understanding demographic rates can greatly aid the design and development of strategies to maintain populations in the face of environmental changes. However, acquiring estimates of demographic parameters at relevant spatial scales is difficult. Measures of annual survival rates can be particularly challenging to obtain because large‐scale, long‐term tracking of individuals is difficult and the resulting data contain many inherent biases. In recent years, advances in both tracking and analytical techniques have meant that, for some taxonomic groups, sufficient numbers of survival estimates are available to allow variation within and among species to be explored. Here we review published estimates of annual adult survival rates in shorebird species across the globe, and construct models to explore the phylogenetic, geographical, seasonal and sex‐based variation in survival rates. Models of 295 survival estimates from 56 species show that survival rates calculated from recoveries of dead individuals or from return rates of marked individuals are significantly lower than estimates from mark–recapture models. Survival rates also vary across flyways, largely as a consequence of differences in the genera that have been studied and the analytical methods used, with published estimates from the Americas and from smaller shorebirds (Actitis, Calidris and Charadrius spp.) tending to be underestimated. By incorporating the analytical method used to generate each estimate within a mixed model framework, we provide method‐corrected species‐specific and genus‐specific adult annual survival estimates for 52 species of 15 genera.     

Potential for parasite-induced biases in aquatic invertebrate population studies

Recent studies highlight the need to include estimates of detection/capture probability in population studies. This need is particularly important in studies where detection and/or capture probability is influenced by parasite-induced behavioral alterations. We assessed potential biases associated with sampling a population of the amphipod Gammarus lacustris in the presence of Polymorphus spp. acanthocephalan parasites shown to increase positive phototaxis in their amphipod hosts. We trapped G. lacustris at two water depths (benthic and surface) and compared number of captures and number of parasitized individuals at each depth. While we captured the greatest number of G. lacustris individuals in benthic traps, parasitized individuals were captured most often in surface traps. These results reflect the phototaxic movement of infected individuals from benthic locations to sunlit surface waters. We then explored the influence of varying infection rates on a simulated population held at a constant level of abundance. Simulations resulted in increasingly biased abundance estimates as infection rates increased. Our results highlight the need to consider parasite-induced biases when quantifying detection and/or capture probability in studies of aquatic invertebrate populations.     

How to monitor elusive lizards: comparison of capture–recapture methods on giant day geckos (Gekkonidae, Phelsuma madagascariensis grandis) in the Masoala rainforest exhibit, Zurich Zoo

Rapid and reliable estimation of population size is needed for the efficient monitoring of animal populations of conservation concern. Unfortunately, technical advances in this area have not been paralleled in uptake in conservation, which may be due to difficulties in implementation or the lack of general guidelines for application. Here we tested five different methods used to estimate population size [capture–mark–recapture (CMR), finite-mixture models, model averaging of finite-mixture models, accumulation curve methods (ACM), and the line transect method (LT)] using extensive capture–recapture data of the giant day gecko (Gekkonidae, Phelsuma madagascariensis grandis, Gray 1870) at the Masoala rainforest exhibit, Zurich Zoo. When the complete data were analyzed [30 sessions (and 27 sessions for the LT)], all methods except the LT produced similar estimates of population size. The simple ACM gave a small coefficient of variation (CV), but did not cover the most likely value of population size at moderate sampling effort. Nevertheless, the ACM was the only method that showed a reasonable convergence when subsets of data were used. CMR and Pledger models included the reference value in their confidence intervals (CI) after 25 and 30 sessions, respectively. Although model averaging did slightly improve the estimate, the CV was still high for the full dataset. Our method of using subsets of data to test the robustness of estimates is simple to apply and could be adopted more widely in such analyzes to evaluate sensitivity to method of evaluation. In conclusion, simple accumulation methods showed similar efficiency to more complex statistical models, and are likely to be sufficiently precise for most conservation monitoring purposes. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Combining capture–recapture data and known ages allows estimation of age‐dependent survival rates

In many animal populations, demographic parameters such as survival and recruitment vary markedly with age, as do parameters related to sampling, such as capture probability. Failing to account for such variation can result in biased estimates of population‐level rates. However, estimating age‐dependent survival rates can be challenging because ages of individuals are rarely known unless tagging is done at birth. For many species, it is possible to infer age based on size. In capture–recapture studies of such species, it is possible to use a growth model to infer the age at first capture of individuals. We show how to build estimates of age‐dependent survival into a capture–mark–recapture model based on data obtained in a capture–recapture study. We first show how estimates of age based on length increments closely match those based on definitive aging methods. In simulated analyses, we show that both individual ages and age‐dependent survival rates estimated from simulated data closely match true values. With our approach, we are able to estimate the age‐specific apparent survival rates of Murray and trout cod in the Murray River, Australia. Our model structure provides a flexible framework within which to investigate various aspects of how survival varies with age and will have extensions within a wide range of ecological studies of animals where age can be estimated based on size.     

Population dynamics of the non-native freshwater gastropod,Cipangopaludina chinensis (Viviparidae): a capture-mark-recapture study

Capture-mark-recapture (CMR) is commonly used in conservation biology, but rarely used to study non-native species in freshwater habitats. The power of CMR lies in the ability to go beyond simple density estimates and to quantify invasion dynamics and vital population parameters. I applied CMR to a population of the non-native Chinese mystery snail (Cipangopaludina chinensis, Viviparidae) in a 1.46 ha pond on Long Island, NY to estimate population size and survival probability in the waterbody and to uncover potential mechanisms for enormous differences in introduction success within and between waterbodies (observed densities range <1–40 individuals m^?2). The C. chinensis population increased from approximately 150 to nearly 970 individuals from 2010 to 2012. Daily capture probabilities were low (<0.2) for snails of all sizes. Daily survival probabilities were size-dependent (almost 1.0 for snails larger than 30 mm shell length, and decreasing below that threshold), suggesting size-dependent mortality. This study highlights the ease of applying CMR to C. chinensis and its potential for other non-native species. Traditional survey methods such as density estimates with transects or quadrats cannot document increasing population sizes or size-specific mortality factors, which are essential for understanding introduction success and dynamics.     

Estimating local population size of the European Nightjar Caprimulgus europaeus using territory capture–recapture models

Capsule The capture–recapture model M(o) is an efficient way to estimate local population size.

Aims To test if a single capture–recapture modelling approach, combined with a simple survey method, can produce estimates of local population size from a dataset involving large‐scale multi‐observer surveys

Methods We sampled the presence of Nightjars in three separate sessions at three forests. Territory numbers were estimated using conventional territory‐mapping criteria. We ran different capture–recapture models to analyse the detection histories of territories obtained across the three sampling sessions and in the three different forests, using either only registrations of churring birds or all contacts.

Results The capture–recapture model M(o), assuming a constant detection probability, was the most efficient one to produce estimates of local population size. Using only two of the three sampling sessions gave less precise, though quite similar, estimates of the number of territories, with standard deviations representing 5–10% of the estimate values. However, this was reduced to 0.7–3.5%, i.e. three to seven times lower, when using the three sessions.

Conclusion Repeated sampling sessions to map territories can be efficiently used within the capture–recapture model M(o) to estimate detection probability and produce precise estimates of local population size.     

Inference from single occasion capture experiments using genetic markers

Accurate estimation of the size of animal populations is an important task in ecological science. Recent advances in the field of molecular genetics researches allow the use of genetic data to estimate the size of a population from a single capture occasion rather than repeated occasions as in the usual capture–recapture experiments. Estimating the population size using genetic data also has sometimes led to estimates that differ markedly from each other and also from classical capture–recapture estimates. Here, we develop a closed form estimator that uses genetic information to estimate the size of a population consisting of mothers and daughters, focusing on estimating the number of mothers, using data from a single sample. We demonstrate the estimator is consistent and propose a parametric bootstrap to estimate the standard errors. The estimator is evaluated in a simulation study and applied to real data. We also consider maximum likelihood in this setting and discover problems that preclude its general use.     

Estimation of capture probabilities using generalized estimating equations and mixed effects approaches

Modeling individual heterogeneity in capture probabilities has been one of the most challenging tasks in capture–recapture studies. Heterogeneity in capture probabilities can be modeled as a function of individual covariates, but correlation structure among capture occasions should be taking into account. A proposed generalized estimating equations (GEE) and generalized linear mixed modeling (GLMM) approaches can be used to estimate capture probabilities and population size for capture–recapture closed population models. An example is used for an illustrative application and for comparison with currently used methodology. A simulation study is also conducted to show the performance of the estimation procedures. Our simulation results show that the proposed quasi‐likelihood based on GEE approach provides lower SE than partial likelihood based on either generalized linear models (GLM) or GLMM approaches for estimating population size in a closed capture–recapture experiment. Estimator performance is good if a large proportion of individuals are captured. For cases where only a small proportion of individuals are captured, the estimates become unstable, but the GEE approach outperforms the other methods.     

Edge Effect on Density Estimates of a Radiotracked Population of Yellow-Necked Mice

ABSTRACT Density estimates for small-mammal populations from capture-mark-recapture (CMR) data have played an important role in many studies of theoretical and applied ecology. Defining effective trapping area (ETA) is one of the main issues affecting accuracy of density estimates. Our objective was to assess sensitivity of CMR density estimates to correctors based on movement parameters calculated from trapping and radiotelemetry data. From May to November 2005, we conducted monthly CMR trapping in a beech (Fagus sylvaticus) forest of the province of Trento, northern Italy. In conjunction with CMR, we radio-marked 32 yellow-necked mice (Apodemus flavicollis) captured from July to October and located them daily using radiotelemetry. We estimated population size (N) by model averaging with Program MARK. We calculated ETA using several definitions of the boundary strip, including full and half mean maximum distance moved (MMDM) from capture-recapture and telemetry data and mean radius of mean monthly home ranges. The boundary strip (W) increased with the amount of behavioral information embodied in the estimates. The largest W and lowest density values were based on radius of mean home ranges followed by MMDM calculated from telemetry data. The ETA based on movement distances increased more than proportionally when N decreased, suggesting that low population density combined with scarce resources results in rodents moving more in search of food, thus leading to overestimated ETA and underestimated density values. Although robust behavioral information would certainly improve density estimates, we suggest caution in relating ranging movements to capture probability and hence in using correctors based on movement distances to infer density values.