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.     

Bayesian latent class models for capture–recapture in the presence of missing data

We propose a method for estimating the size of a population in a multiple record system in the presence of missing data. The method is based on a latent class model where the parameters and the latent structure are estimated using a Gibbs sampler. The proposed approach is illustrated through the analysis of a data set already known in the literature, which consists of five registrations of neural tube defects.     

Monitoring tiger populations using intensive search in a capture–recapture framework

Tigers (Panthera tigris) today face multiple threats to their survival in the form of habitat loss, poaching, depletion of wild prey through illegal hunting and loss of connectivity between populations. Monitoring of tigers is crucial to evaluate their status and react adaptively to management problems. Though camera traps are becoming increasingly popular with researchers enumerating cryptic and elusive animals, they have not been embedded in the regular management activities of tiger reserves. Tiger monitoring, though an important part of the management, is usually implemented using the unreliable pugmark approach. Camera trap-based studies are few, usually of short duration, and are generally conducted by individual scientists and organizations. In this study, we integrate photographic mark–recapture with the routine activity of searching and locating tigers for tourist viewing by the park management in meadows of Kanha Tiger Reserve which form a part of the tourism zone. We validate the density estimates from “tiger search approach” against those obtained from camera trapping and radio-telemetry conducted in conjunction in the same area. Tiger density (( hat{D} ) (SE [( hat{D} )]) per 100 km² for camera traps and tiger search, respectively, was estimated at 12.0 (1.95) and 12.0 (1.76) when effective trapping area was estimated using the half mean maximum distance moved (½ MMDM), 7.6 (1.94) and 7.5 (1.97) using the home range radius, 7.3 (1.49) and 7.5 (1.97) with the full MMDM, and 8.0 (3.0) and 6.88 (2.39) with the spatial likelihood method in Program DENSITY 4.1. Camera trapping, however, was five times more expensive than the tiger search method. Our study suggests that “tiger search approach” can be used as a regular monitoring tool in the tourism zones of tiger reserves, where often most of the source populations are located.     

Density estimation in a wolverine population using spatial capture–recapture models

Classical closed-population capture–recapture models do not accommodate the spatial information inherent in encounter history data obtained from camera-trapping studies. As a result, individual heterogeneity in encounter probability is induced, and it is not possible to estimate density objectively because trap arrays do not have a well-defined sample area. We applied newly-developed, capture–recapture models that accommodate the spatial attribute inherent in capture–recapture data to a population of wolverines (Gulo gulo) in Southeast Alaska in 2008. We used camera-trapping data collected from 37 cameras in a 2,140-km² area of forested and open habitats largely enclosed by ocean and glacial icefields. We detected 21 unique individuals 115 times. Wolverines exhibited a strong positive trap response, with an increased tendency to revisit previously visited traps. Under the trap-response model, we estimated wolverine density at 9.7 individuals/1,000 km² (95% Bayesian CI: 5.9–15.0). Our model provides a formal statistical framework for estimating density from wolverine camera-trapping studies that accounts for a behavioral response due to baited traps. Further, our model-based estimator does not have strict requirements about the spatial configuration of traps or length of trapping sessions, providing considerable operational flexibility in the development of field studies. © 2011 The Wildlife Society.     

Estimating demographic parameters for loggerhead sea turtles using mark–recapture data and a multistate model

The survival for adult loggerhead sea turtles from a saturation tagging study on Bald Head Island, NC, USA, was estimated using a multistate model with unobservable states to relax assumptions that are violated when survival is estimated from multistate models and produce more accurate estimates of survival, recapture, and breeding transition probabilities. The influence of time, trap dependence, and low site fidelity to the study nesting beach on survival and recapture were examined. The best model given the data included an imprecise site-fidelity effect on survival, constrained the reproductive cycle to 4 years, and contained a time effect on recapture rates. The estimate of annual survival for adult females was of 0.85, producing the highest estimate in the literature for loggerhead sea turtles. Multistate models should be applied to other nesting beach data for sea turtles to improve survival estimates and in turn the ability to model and manage populations.     

Estimating wild boar (Sus scrofa) abundance and density using capture–resights in Canton of Geneva, Switzerland

We estimated wild boar abundance and density using capture–resight methods in the western part of the Canton of Geneva (Switzerland) in the early summer from 2004 to 2006. Ear-tag numbers and transmitter frequencies enabled us to identify individuals during each of the counting sessions. We used resights generated by self-triggered camera traps as recaptures. Program Noremark provided Minta–Mangel and Bowden’s estimators to assess the size of the marked population. The minimum numbers of wild boars belonging to the unmarked population (juveniles and/or piglets) were added to the respective estimates to assess total population size. Over the 3 years, both estimators showed a stable population with a slight diminishing tendency. We used mean home range size determined by telemetry to assess the sampled areas and densities. Mean wild boar population densities calculated were 10.6 individuals/km² ± 0.8 standard deviation (SD) and 10.0 ind/km² ± 0.6 SD with both estimators, respectively, and are among the highest reported from Western Europe. Because of the low proportion of marked animals and, to a lesser extent, of technical failures, our estimates showed poor precision, although they displayed similar population trends compared to the culling bag statistics. Reported densities were consistent with the ecological conditions of the study area.     

Estimating multiple years,tributary-specific,and overall Atlantic salmon smolt abundance in a large Canadian catchment using capture–mark–recapture experiments

Population monitoring of Atlantic salmon (Salmo salar L.) abundance is an essential element to understand annual stock variability and inform fisheries management processes. Smolts are the life stage marking the transition from the freshwater to the marine phase of anadromous Atlantic salmon. Estimating smolt abundance allows for subsequent inferences on freshwater and marine survival rates. Annual abundances of out-migrating Atlantic salmon smolts were estimated using Bayesian models and an 18-year capture–mark–recapture time series from two to five trapping locations within the Restigouche River (Canada) catchment. Some of the trapping locations were at the outlet of large upstream tributaries, and these sampled a portion of the total out-migrating population of smolts for the watershed, whereas others were located just above the head of tide of the Restigouche River and sampled the entire run of salmon smolts. Due to logistic and environmental conditions, not all trapping locations were operational each year. Additionally, recapture rates were relatively low (<5%), and the absolute number of recaptures was relatively few (most often a few dozen), leading to incoherent and highly uncertain estimates of tributary-specific and whole catchment abundance estimates when the data were modeled independently among trapping locations and years. Several models of increasing complexity were tested using simulated data, and the best-performing model in terms of bias and precision incorporated a hierarchical structure among years on the catchability parameters and included an explicit spatial structure to account for the annual variations in the number of sampled locations within the watershed. When the best model was applied to the Restigouche River catchment dataset, the annual smolt abundance estimates varied from 250,000 to 1 million smolts, and the subbasin estimates of abundance were consistent with the spatial structure of the monitoring programme. Ultimately, increasing the probabilities of capture and the absolute number of recaptures at the different traps will be required to improve the precision and reduce the bias of the estimates of smolt abundance for the entire basin and within subbasins of the watershed. The model and approach provide a significant improvement in the models used to date based on independent estimates of abundance by trapping location and year. Total abundance and relative production in discrete spawning, nesting, or rearing areas provide critical information to appropriately understand and manage the threats to species that can occur at subpopulation spatial scales.     

Dealing with many correlated covariates in capture–recapture models

Capture–recapture models for estimating demographic parameters allow covariates to be incorporated to better understand population dynamics. However, high-dimensionality and multicollinearity can hamper estimation and inference. Principal component analysis is incorporated within capture–recapture models and used to reduce the number of predictors into uncorrelated synthetic new variables. Principal components are selected by sequentially assessing their statistical significance. We provide an example on seabird survival to illustrate our approach. Our method requires standard statistical tools, which permits an efficient and easy implementation using standard software.     

Evaluating the potential biases in carnivore capture–recapture studies associated with the use of lure and varying density estimation techniques using photographic-sampling data of the Malagasy civet

Estimating density of elusive carnivores with capture–recapture analyses is increasingly common. However, providing unbiased and precise estimates is still a challenge due to uncertainties arising from the use of (1) bait or lure to attract animals to the detection device and (2) ad hoc boundary-strip methods to compensate for edge effects in area estimation. We used photographic-sampling data of the Malagasy civet Fossa fossana collected with and without lure to assess the effects of lure and to compare the use of four density estimators which varied in methods of area estimation. The use of lure did not affect permanent immigration or emigration, abundance and density estimation, maximum movement distances, or temporal activity patterns of Malagasy civets, but did provide more precise population estimates by increasing the number of recaptures. The spatially-explicit capture–recapture (SECR) model density estimates ±SE were the least precise as they incorporate spatial variation, but consistent with each other (Maximum likelihood-SECR = 1.38 ± 0.18, Bayesian-SECR = 1.24 ± 0.17 civets/km²), whereas estimates relying on boundary-strip methods to estimate effective trapping area did not incorporate spatial variation, varied greatly and were generally larger than SECR model estimates. Estimating carnivore density with ad hoc boundary-strip methods can lead to overestimation and/or increased uncertainty as they do not incorporate spatial variation. This may lead to inaction or poor management decisions which may jeopardize at-risk populations. In contrast, SECR models free researchers from making subjective decisions associated with boundary-strip methods and they estimate density directly, providing more comparable and valuable population estimates.     

A nonparametric method of estimation of the population size in capture–recapture experiments

A recent method for estimating a lower bound of the population size in capture–recapture samples is studied. Specifically, some asymptotic properties, such as strong consistency and asymptotic normality, are provided. The introduced estimator is based on the empirical probability generating function (pgf) of the observed data, and it is consistent for count distributions having a log-convex pgf (-class). This is a large family that includes mixed and compound Poisson distributions, and their independent sums and finite mixtures as well. The finite-sample performance of the lower bound estimator is assessed via simulation showing a better behavior than some close competitors. Several examples of application are also analyzed and discussed.     

Does your species have memory? Analyzing capture–recapture data with memory models

We examine memory models for multisite capture–recapture data. This is an important topic, as animals may exhibit behavior that is more complex than simple first‐order Markov movement between sites, when it is necessary to devise and fit appropriate models to data. We consider the Arnason–Schwarz model for multisite capture–recapture data, which incorporates just first‐order Markov movement, and also two alternative models that allow for memory, the Brownie model and the Pradel model. We use simulation to compare two alternative tests which may be undertaken to determine whether models for multisite capture–recapture data need to incorporate memory. Increasing the complexity of models runs the risk of introducing parameters that cannot be estimated, irrespective of how much data are collected, a feature which is known as parameter redundancy. Rouan et al. (JABES, 2009, pp 338–355) suggest a constraint that may be applied to overcome parameter redundancy when it is present in multisite memory models. For this case, we apply symbolic methods to derive a simpler constraint, which allows more parameters to be estimated, and give general results not limited to a particular configuration. We also consider the effect sparse data can have on parameter redundancy and recommend minimum sample sizes. Memory models for multisite capture–recapture data can be highly complex and difficult to fit to data. We emphasize the importance of a structured approach to modeling such data, by considering a priori which parameters can be estimated, which constraints are needed in order for estimation to take place, and how much data need to be collected. We also give guidance on the amount of data needed to use two alternative families of tests for whether models for multisite capture–recapture data need to incorporate memory.     

Sources of variability in spotted owl population growth rate: testing predictions using long-term mark–recapture data

For long-lived iteroparous vertebrates that annually produce few young, life history theory predicts that reproductive output (R) and juvenile survival should influence temporal variation in population growth rate (λ) more than adult survival does. We examined this general prediction using 15 years of mark–recapture data from a population of California spotted owls (Strix occidentalis occidentalis). We found that survival of individuals ≥1 year old (ϕ) exhibited much less temporal variability , where CV is coefficient of variation, than R and that R was strongly influenced by environmental stochasticity. Although λ was most sensitive ( ; log-transformed sensitivity) to ϕ and much less sensitive to either R or juvenile survival (survival rate of owls from fledging to 1 year old; ), we estimated that R contributed as much as ϕ to the observed annual variability in λ. The contribution of juvenile survival to variability in λ was proportional to its These results are consistent with the hypothesis that natural selection may have favored the evolution of longevity in spotted owls as a strategy to increase the probability of experiencing favorable years for reproduction. Our finding that annual weather patterns that most affected R (temperature and precipitation during incubation) and ϕ (conditions during winter related to the Southern Oscillation Index) were equally good at explaining temporal variability in λ supports the conclusion that R and ϕ were equally responsible for variability in λ. Although currently accepted conservation measures for spotted owl populations attempt to enhance survival, our results indicated that conservation measures that target R may be as successful, as long as actions do not reduce ϕ.     

Spatial capture–recapture models allowing Markovian transience or dispersal

Spatial capture–recapture (SCR) models are a relatively recent development in quantitative ecology, and they are becoming widely used to model density in studies of animal populations using camera traps, DNA sampling and other methods which produce spatially explicit individual encounter information. One of the core assumptions of SCR models is that individuals possess home ranges that are spatially stationary during the sampling period. For many species, this assumption is unlikely to be met and, even for species that are typically territorial, individuals may disperse or exhibit transience at some life stages. In this paper we first conduct a simulation study to evaluate the robustness of estimators of density under ordinary SCR models when dispersal or transience is present in the population. Then, using both simulated and real data, we demonstrate that such models can easily be described in the BUGS language providing a practical framework for their analysis, which allows us to evaluate movement dynamics of species using capture–recapture data. We find that while estimators of density are extremely robust, even to pathological levels of movement (e.g., complete transience), the estimator of the spatial scale parameter of the encounter probability model is confounded with the dispersal/transience scale parameter. Thus, use of ordinary SCR models to make inferences about density is feasible, but interpretation of SCR model parameters in relation to movement should be avoided. Instead, when movement dynamics are of interest, such dynamics should be parameterized explicitly in the model.     

An improved procedure to estimate wolf abundance using non-invasive genetic sampling and capture–recapture mixture models

Non-invasive genetic sampling (NGS) is increasingly used to estimate the abundance of rare or elusive species such as the wolf (Canis lupus), which cannot be directly counted in forested mountain habitats. Wolf individual and familial home ranges are wide, potentially connected by long-range dispersers, and their populations are intrinsically open. Appropriate demographic estimators are needed, because the assumptions of homogeneous detection probability and demographic closeness are violated. We compiled the capture–recapture record of 418 individual wolf genotypes identified from ca. 4,900 non-invasive samples, collected in the northern Italian Apennines from January 2002 to June 2009. We analysed this dataset using novel capture–recapture multievent models for open populations that explicitly account for individual detection heterogeneity (IDH). Overall, the detection probability of the weakly detectable individuals, probably pups, juveniles and migrants (P = 0.08), was ca. six times lower than that of the highly detectable wolves (P = 0.44), probably adults and dominants. The apparent annual survival rate of weakly detectable individuals was lower (Φ = 0.66) than those of highly detectable wolves (Φ = 0.75). The population mean annual finite rate of increase was λ = 1.05 ± 0.11, and the mean annual size ranged from N = 117 wolves in 2003 to N = 233 wolves in 2007. This procedure, combining large-scale NGS and multievent IDH demographic models, provides the first estimates of abundance, multi-annual trend and survival rates for an open large wolf population in the Apennines. These results contribute to deepen our understanding of wolf population ecology and dynamics, and provide new information to implement sound long-term conservation plans.