Known unknowns in an imperfect world: incorporating uncertainty in recruitment estimates using multi‐event capture–recapture models

Studying the demography of wild animals remains challenging as several of the critical parts of their life history may be difficult to observe in the field. In particular, determining with certainty when an individual breeds for the first time is not always obvious. This can be problematic because uncertainty about the transition from a prebreeder to a breeder state – recruitment – leads to uncertainty in vital rate estimates and in turn in population projection models. To avoid this issue, the common practice is to discard imperfect data from the analyses. However, this practice can generate a bias in vital rate estimates if uncertainty is related to a specific component of the population and reduces the sample size of the dataset and consequently the statistical power to detect effects of biological interest. Here, we compared the demographic parameters assessed from a standard multistate capture–recapture approach to the estimates obtained from the newly developed multi‐event framework that specifically accounts for uncertainty in state assessment. Using a comprehensive longitudinal dataset on southern elephant seals, we demonstrated that the multi‐event model enabled us to use all the data collected (6639 capture–recapture histories vs. 4179 with the multistate model) by accounting for uncertainty in breeding states, thereby increasing the precision and accuracy of the demographic parameter estimates. The multi‐event model allowed us to incorporate imperfect data into demographic analyses. The gain in precision obtained has important implications in the conservation and management of species because limiting uncertainty around vital rates will permit predicting population viability with greater accuracy.     

Assessment of individual and conspecific reproductive success as determinants of breeding dispersal of female tree swallows: A capture–recapture approach

Breeding dispersal is a key process of population structure and dynamics and is often triggered by an individual's breeding failure. In both colonial and territorial birds, reproductive success of conspecifics (RSc) can also lead individuals to change breeding sites after a failure on a site. Yet, few studies have simultaneously investigated the independent contribution of individual reproductive success (RSi) and of RSc on dispersal decision. Here, we develop a modeling framework to disentangle the effects of RSi and RSc on demographic parameters, while accounting for imperfect individual detection and other confounding factors such as age or dispersal behavior in the previous year. Using a 10‐year capture–recapture dataset composed of 1,595 banded tree swallows, we assessed the effects of nonmanipulated RSi and RSc on female breeding dispersal in this semicolonial passerine. Dispersal was strongly driven by RSi, but not by RSc. Unsuccessful females were 9.5–2.5 times more likely to disperse than successful ones, depending if they had dispersed or not in the previous year, respectively. Unsuccessful females were also three times less likely to be detected than successful ones. Contrary to theoretical and empirical studies, RSc did not drive the decision to disperse but influenced the selection of the following breeding site once dispersal had been initiated. Because detection of individuals was driven by RSi, which was positively correlated to RSc, assuming a perfect detection as in previous studies may have lead us to conclude that RSc affected dispersal patterns, yet our approach corrected for this bias. Overall, our results suggest that the value and use of RSc as public information to guide dispersal decisions are likely dictated by multiple ecological determinants, such as landscape structure and extent, if this cue is indeed used.     

Survival estimation of a cryptic antelope via photographic capture–recapture

Adult survival is a primary determinant of abundance and dynamics of large herbivore populations. For species that are inconspicuous, however, accurate survival estimation depends on accommodating low detection probability. For species with individually recognizable markings, photographic capture–recapture (CR) provides an approach to estimate population parameters while accounting for imperfect detection. I investigated the use of photographic CR for a cryptic large herbivore, the nyala, in a region of Hluhluwe–iMfolozi Park, South Africa. I conducted photographic sampling based on the closed robust design, with 5–6 daily sampling occasions nested within three week‐long sampling periods, which delineated one dry and one wet season. Detection differed between sexes: encounter probability of female adults depended on whether individuals fell into high‐encounter (seasonal range: 0.61–0.71) or low‐encounter (seasonal range: 0.29–0.40) groups, whereas male adults had a constant encounter probability of 0.39 per day. For both sexes, monthly survival probability was ≥0.93 and did not differ appreciably between seasons or sexes. Given the role of survival in population dynamics, photographic CR has the potential to provide survival estimates for cryptic large herbivores that lack such information.     

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.

     

A local evaluation of the individual state‐space to scale up Bayesian spatial capture–recapture

Spatial capture–recapture models (SCR) are used to estimate animal density and to investigate a range of problems in spatial ecology that cannot be addressed with traditional nonspatial methods. Bayesian approaches in particular offer tremendous flexibility for SCR modeling. Increasingly, SCR data are being collected over very large spatial extents making analysis computational intensive, sometimes prohibitively so. To mitigate the computational burden of large‐scale SCR models, we developed an improved formulation of the Bayesian SCR model that uses local evaluation of the individual state‐space (LESS). Based on prior knowledge about a species’ home range size, we created square evaluation windows that restrict the spatial domain in which an individual's detection probability (detector window) and activity center location (AC window) are estimated. We used simulations and empirical data analyses to assess the performance and bias of SCR with LESS. LESS produced unbiased estimates of SCR parameters when the AC window width was ≥5σ (σ: the scale parameter of the half‐normal detection function), and when the detector window extended beyond the edge of the AC window by 2σ. Importantly, LESS considerably decreased the computation time needed for fitting SCR models. In our simulations, LESS increased the computation speed of SCR models up to 57‐fold. We demonstrate the power of this new approach by mapping the density of an elusive large carnivore—the wolverine (Gulo gulo)—with an unprecedented resolution and across the species’ entire range in Norway (> 200,000 km²). Our approach helps overcome a major computational obstacle to population and landscape‐level SCR analyses. The LESS implementation in a Bayesian framework makes the customization and fitting of SCR accessible for practitioners working at scales that are relevant for conservation and management.     

multimark: an R package for analysis of capture–recapture data consisting of multiple “noninvasive” marks

I describe an open‐source R package, multimark , for estimation of survival and abundance from capture–mark–recapture data consisting of multiple “noninvasive” marks. Noninvasive marks include natural pelt or skin patterns, scars, and genetic markers that enable individual identification in lieu of physical capture. multimark provides a means for combining and jointly analyzing encounter histories from multiple noninvasive sources that otherwise cannot be reliably matched (e.g., left‐ and right‐sided photographs of bilaterally asymmetrical individuals). The package is currently capable of fitting open population Cormack–Jolly–Seber (CJS) and closed population abundance models with up to two mark types using Bayesian Markov chain Monte Carlo (MCMC) methods. multimark can also be used for Bayesian analyses of conventional capture–recapture data consisting of a single‐mark type. Some package features include (1) general model specification using formulas already familiar to most R users, (2) ability to include temporal, behavioral, age, cohort, and individual heterogeneity effects in detection and survival probabilities, (3) improved MCMC algorithm that is computationally faster and more efficient than previously proposed methods, (4) Bayesian multimodel inference using reversible jump MCMC, and (5) data simulation capabilities for power analyses and assessing model performance. I demonstrate use of multimark using left‐ and right‐sided encounter histories for bobcats (Lynx rufus) collected from remote single‐camera stations in southern California. In this example, there is evidence of a behavioral effect (i.e., trap “happy” response) that is otherwise indiscernible using conventional single‐sided analyses. The package will be most useful to ecologists seeking stronger inferences by combining different sources of mark–recapture data that are difficult (or impossible) to reliably reconcile, particularly with the sparse datasets typical of rare or elusive species for which noninvasive sampling techniques are most commonly employed. Addressing deficiencies in currently available software, multimark also provides a user‐friendly interface for performing Bayesian multimodel inference using capture–recapture data consisting of a single conventional mark or multiple noninvasive marks.     

Applications of hidden hybrid Markov/semi‐Markov models: from stopover duration to breeding success dynamics

Usually in capture–recapture, a model parameter is time or time since first capture dependent. However, the case where the probability of staying in one state depends on the time spent in that particular state is not rare. Hidden Markov models are not appropriate to manage these situations. A more convenient approach would be to consider models that incorporate semi‐Markovian states which explicitly define the waiting time distribution and have been used in previous biologic studies as a convenient framework for modeling the time spent in a given physiological state. Here, we propose hidden Markovian models that combine several nonhomogeneous Markovian states with one semi‐Markovian state and which (i) are well adapted to imperfect and variable detection and (ii) allow us to consider time, time since first capture, and time spent in one state effects. Implementation details depending on the number of semi‐Markovian states are discussed. From a user's perspective, the present approach enhances the toolbox for analyzing capture–recapture data. We then show the potential of this framework by means of two ecological examples: (i) stopover duration and (ii) breeding success dynamics.     

Combining capture–recapture data and pedigree information to assess heritability of demographic parameters in the wild

Quantitative genetic analyses have been increasingly used to estimate the genetic basis of life‐history traits in natural populations. Imperfect detection of individuals is inherent to studies that monitor populations in the wild, yet it is seldom accounted for by quantitative genetic studies, perhaps leading to flawed inference. To facilitate the inclusion of imperfect detection of individuals in such studies, we develop a method to estimate additive genetic variance and assess heritability for binary traits such as survival, using capture–recapture (CR) data. Our approach combines mixed‐effects CR models with a threshold model to incorporate discrete data in a standard ‘animal model’ approach. We employ Markov chain Monte Carlo sampling in a Bayesian framework to estimate model parameters. We illustrate our approach using data from a wild population of blue tits (Cyanistes caeruleus) and present the first estimate of heritability of adult survival in the wild. In agreement with the prediction that selection should deplete additive genetic variance in fitness, we found that survival had low heritability. Because the detection process is incorporated, capture–recapture animal models (CRAM) provide unbiased quantitative genetics analyses of longitudinal data collected in the wild.     

Modelling life history strategies with capture–recapture data: Evolutionary demography of the water skink Eulamprus tympanum

Matrix population models, elasticity analysis and loop analysis can potentially provide powerful techniques for the analysis of life histories. Data from a capture–recapture study on a population of southern highland water skinks (Eulamprus tympanum) were used to construct a matrix population model. Errors in elasticities were calculated by using the parametric bootstrap technique. Elasticity and loop analyses were then conducted to identify the life history stages most important to fitness. The same techniques were used to investigate the relative importance of fast versus slow growth, and rapid versus delayed reproduction. Mature water skinks were long‐lived, but there was high immature mortality. The most sensitive life history stage was the subadult stage. It is suggested that life history evolution in E. tympanum may be strongly affected by predation, particularly by birds. Because our population declined over the study, slow growth and delayed reproduction were the optimal life history strategies over this period. Although the techniques of evolutionary demography provide a powerful approach for the analysis of life histories, there are formidable logistical obstacles in gathering enough high‐quality data for robust estimates of the critical parameters.     

Joint estimation of survival and breeding probability in female dolphins and calves with uncertainty in state assignment

While the population growth rate in long‐lived species is highly sensitive to adult survival, reproduction can also significantly drive population dynamics. Reproductive parameters can be challenging to estimate as breeders and nonbreeders may vary in resighting probability and reproductive status may be difficult to assess. We extended capture–recapture (CR) models previously fitted for data on other long‐lived marine mammals to estimate demographic parameters while accounting for detection heterogeneity between individuals and state uncertainty regarding reproductive status. We applied this model to data on 106 adult female bottlenose dolphins observed over 13 years. The detection probability differed depending on breeding status. Concerning state uncertainty, offspring were not always sighted with their mother, and older calves were easier to detect than young‐of‐the‐year (YOY), respectively, 0.79 (95% CI 0.59–0.90) and 0.58 (95% CI 0.46–0.68). This possibly led to inaccurate reproductive status assignment of females. Adult female survival probability was high (0.97 CI 95% 0.96–0.98) and did not differ according to breeding status. Young‐of‐the‐year and 1‐year‐old calves had a significantly higher survival rate than 2‐year‐old (respectively, 0.66 CI 95% 0.50–0.78 and 0.45 CI 95% 0.29–0.61). This reduced survival is probably related to weaning, a period during which young are exposed to more risks since they lose protection and feeding from the mother. The probability of having a new YOY was high for breeding females that had raised a calf to the age of 3 or lost a 2‐year‐old calf (0.71, CI 95% 0.45–0.88). Yet, this probability was much lower for nonbreeding females and breeding females that had lost a YOY or a 1‐year‐old calf (0.33, 95% CI 0.26–0.42). The multievent CR framework we used is highly flexible and could be easily modified for other study questions or taxa (marine or terrestrial) aimed at modeling reproductive parameters.     

Assessment of mark–recapture models to estimate the abundance of a humpback whale feeding aggregation in Southeast Alaska

Aim The aim of this study was to use photographs of the unique pattern on the ventral surface of the flukes to estimate the abundance of humpback whales (Megaptera novaeangliae) in a discrete feeding aggregation in northern Southeast Alaska. Location The study was located in northern Southeast Alaska, USA, in the eastern North Pacific Ocean. Methods This study evaluated mark–recapture models, ranging from the simpler models (pooled and stratified, closed Petersen estimators) to more complex multi‐strata models (closed Darroch and open Hilborn). The Akaike Information Criterion, corrected (AICc) was used as a model comparison statistic. Results Our best estimate of whale abundance in northern Southeast Alaska in 2000 is 961 whales [95% confidence interval (657, 1076)]. This estimate comes from the Hilborn open, multi‐strata approach with constant migration over time, time‐dependent capture probabilities by area, and a fixed survival rate of 0.98. The simpler models were problematic owing to several aspects of whale behaviour, including that (1) the whales did not mix randomly throughout the study area, (2) some whales emigrated temporarily outside the study area and were not available for capture, and (3) whales were not equally identifiable because they did not behave in the same way when they showed their flukes upon diving. This led to heterogeneity in capture probability and a bias in the estimates. The more complex models stratified by area, and using migration movements among areas, compensated for some of these issues when estimating population size. Main conclusions We believe that the Hilborn open, multi‐strata model produced the best estimate because: (1) it incorporated the best information about survival, (2) it used detailed information about the various release groups, (3) the analysis provided an integrated environment in which parameters such as migration and capture probabilities are shared, (4) the three strata encompassed a large portion of the areas used by whales, and (5) the Hilborn model selected was superior in terms of model selection criteria and biological realism. These data provide valuable insights into the numbers and movements of humpback whales in three areas of Southeast Alaska.     

Use of PIT tags to assess individual heterogeneity of laboratory‐reared juveniles of the endangered Cumberlandian combshell (Epioblasma brevidens) in a mark–recapture study

The federally endangered Cumberlandian combshell (Epioblasma brevidens) was propagated and reared to taggable size (5–10 mm), and released to the Powell River, Tennessee, to augment a relict population. Methodology using passive integrated transponder (PIT) tags on these mussels greatly facilitated the detection process. The overall mean detection probability and survival rate of released individuals reached 97.8 to 98.4% and 99.7 to 99.9% (per month), respectively, during nine successive recapture occasions in the 2‐year study period, regardless of seasonality. Nonhierarchical models and hierarchical models incorporating individual and seasonal variations through a Bayesian approach were compared and resulted in similar performance of prediction for detection probability and survival rate of mussels. This is the first study to apply the mark–recapture method to laboratory‐reared mussels using PIT tags and stochastic models. Quantitative analyses for individual heterogeneity allowed examination of demographic variance and effects of heterogeneity on population dynamics, although the individual and seasonal variations were small in this study. Our results provide useful information in implementing conservation strategies of this faunal group and a framework for other species or similar studies.     

Application of a reverse dot blot DNA–DNA hydridization method to quantify host‐feeding tendencies of two sibling species in the Anopheles gambiae complex

A DNA–DNA hybridization method, reverse dot blot analysis (RDBA), was used to identify Anopheles gambiae s.s. and Anopheles arabiensis (Diptera: Culicidae) hosts. Of 299 blood‐fed and semi‐gravid An. gambiae s.l. collected from Kisian, Kenya, 244 individuals were identifiable to species; of these, 69.5% were An. arabiensis and 29.5% were An. gambiae s.s. Host identifications with RDBA were comparable with those of conventional polymerase chain reaction (PCR) followed by direct sequencing of amplicons of the vertebrate mitochondrial cytochrome b gene. Of the 174 amplicon‐producing samples used to compare these two methods, 147 were identifiable by direct sequencing and 139 of these were identifiable by RDBA. Anopheles arabiensis bloodmeals were mostly (94.6%) bovine in origin, whereas An. gambiae s.s. fed upon humans more than 91.8% of the time. Tests by RDBA detected that two of 112 An. arabiensis contained blood from more than one host species, whereas PCR and direct sequencing did not. Recent use of insecticide‐treated bednets in Kisian is likely to have caused the shift in the dominant vector species from An. gambiae s.s. to An. arabiensis. Reverse dot blot analysis provides an opportunity to study changes in host‐feeding by members of the An. gambiae complex in response to the broadening distribution of vector control measures targeting host‐selection behaviours.