Assessment of costs of reproduction in a pelagic seabird using multistate mark-recapture models

We used a long‐term population band‐resight survey database, a parallel reproduction database, and multistate mark–recapture analysis to assess the costs of reproduction, a keystone concept of life‐history evolution, in Nazca boobies (Sula granti) from Punta Cevallos, Isla Española, Galápagos, Ecuador. We used eight years of resight and breeding data to compare models that included sex‐ and state‐specific survival probabilities and probabilities of transition between reproductive states using multistate mark–recapture models. Models that included state‐specific effects were compared with models lacking such effects to evaluate costs of reproduction. The top model, optimizing the trade‐off of model simplicity and fit to the data using the Akaike Information Criterion (AIC), showed evidence of a temporally varying survival cost of reproduction: nonbreeders showed higher annual survival than breeders did in some years. Because increasing investment among breeders showed no negative association with survival and subsequent breeding success, this evidence indicates a cost to both males and females of initiating, but not of continuing, a reproductive attempt. In some cases, breeders reaching the highest reproductive state (fledging an offspring) showed higher survival or subsequent breeding success than did failed breeders, consistent with differences in overall quality that promote both survival and reproduction. Although a male‐biased adult sex ratio was observed in this population of Nazca boobies, models of state‐ and sex‐specific survival and transition probabilities were not supported, indicating that males and females do not incur different costs of reproduction, and that the observed sex ratio bias is not due to sex‐specific adult mortality.     

Disease‐structured N‐mixture models: A practical guide to model disease dynamics using count data

Obtaining inferences on disease dynamics (e.g., host population size, pathogen prevalence, transmission rate, host survival probability) typically requires marking and tracking individuals over time. While multistate mark–recapture models can produce high‐quality inference, these techniques are difficult to employ at large spatial and long temporal scales or in small remnant host populations decimated by virulent pathogens, where low recapture rates may preclude the use of mark–recapture techniques. Recently developed N‐mixture models offer a statistical framework for estimating wildlife disease dynamics from count data. N‐mixture models are a type of state‐space model in which observation error is attributed to failing to detect some individuals when they are present (i.e., false negatives). The analysis approach uses repeated surveys of sites over a period of population closure to estimate detection probability. We review the challenges of modeling disease dynamics and describe how N‐mixture models can be used to estimate common metrics, including pathogen prevalence, transmission, and recovery rates while accounting for imperfect host and pathogen detection. We also offer a perspective on future research directions at the intersection of quantitative and disease ecology, including the estimation of false positives in pathogen presence, spatially explicit disease‐structured N‐mixture models, and the integration of other data types with count data to inform disease dynamics. Managers rely on accurate and precise estimates of disease dynamics to develop strategies to mitigate pathogen impacts on host populations. At a time when pathogens pose one of the greatest threats to biodiversity, statistical methods that lead to robust inferences on host populations are critically needed for rapid, rather than incremental, assessments of the impacts of emerging infectious diseases.     

Lek fidelity and movement among leks by male Greater Sage‐grouse Centrocercus urophasianus: a capture‐mark‐recapture approach

Males in lek mating systems tend to exhibit high fidelity to breeding leks despite substantial evidence of skewed mating success among males. Although movements between leks are often reported to be rare, such movements provide a mechanism for an individual to improve lifetime fitness in response to heterogeneity in reproductive conditions. Additionally, estimates of apparent movements among leks are potentially biased due to unaccounted variation in detection probability across time and space. We monitored breeding male Greater Sage‐grouse Centrocercus urophasianus on 13 leks in eastern Nevada over a 10‐year period, and estimated movement rates among leks using capture‐mark‐recapture methods. We expected that male movement rates among leks would be low, despite predictions of low breeding success for most males, and that detection rates would be highly variable among leks and years. We used a robust design multistate analysis in Program mark to estimate probability of movements among leks, while accounting for imperfect detection of males. Male Sage‐grouse were extremely faithful to their leks; the annual probability of a male moving away from its original lek of capture was approximately 3% (se = 0.01). Detection probabilities varied substantially among leks (range = 0.21–0.95), and among years (range = 0.30–0.76), but remained relatively constant within years at each lek. These results suggest that male Sage‐grouse dispersal is either rare, or consists primarily of dispersal of sub‐adults from their natal areas prior to the breeding season. The study highlights the benefits of robust design multistate models over standard ‘live‐encounter’ analyses, as they not only permit estimation of additional parameters, such as movement rates, but also allow for more precise parameter estimates that are less sensitive to heterogeneity in detection rates. Additionally, as these data were collected using capture‐mark‐recapture methods, our approach to estimating movement rates would be beneficial in systems where radiotagging is detrimental to the study organism.     

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.     

Parameter Redundancy in Multistate Capture‐Recapture Models

Multistate capture‐recapture models are a powerful tool to address a variety of biological questions concerning dispersal and/or individual variability in wild animal populations. However, biologically meaningful models are often over‐parameterized and consequently some parameters cannot be estimated separately. Identifying which quantities are separately estimable is crucial for proper model selection based upon likelihood tests or information criteria and for the interpretation of the estimates obtained. We show how to investigate parameter redundancy in multistate capture‐recapture models, based on formal methods initially proposed by Catchpole and his associates for exponential family distributions (Catchpole, Freeman and Morgan, 1996. Journal of the Royal Statistical Society Series B 58, 763–774). We apply their approach to three models of increasing complexity.     

An integrated population model for a long‐lived ungulate: more efficient data use with Bayesian methods

We develop an integrated population model for Svalbard reindeer Rangifer tarandus platyrhynchus, and demonstrate how this type of model can be used to extract more information from the data and separate different sources of variability in population estimates. Our model combines individual mark–recapture data with population counts and harvesting data within a Bayesian model framework, and accounts for observation error, environmental and demographic stochasticity, and age structure. From this model we obtain annual estimates of age‐specific population size, survival and fecundity. The model provides estimates of age structure at a finer scale than that found in the census data, and enables us to estimate survival for the period before calves are first caught and marked, i.e. before they enter the individual mark–recapture data. The modeling framework provides an improved approach to studying age‐structured populations that are imperfectly censused and where the demography of only a sample of individuals is known. We use data from independent censuses of the same population to evaluate population estimates obtained from the model, and show that it is successful at correcting for different types of observation error. Based on our model results, we suggest that allocating resources to the collection of supplementary mark–recapture data could improve the reliability of population projections more than making regular population censuses as exhaustive as possible. Our work demonstrates how integrated Bayesian population modeling can be used to increase the amount of information extracted from collections of data, identifying and disentangling sources of variation in individual performance and population size. This represents an important step towards increasing the predictive ability of population growth models for long‐lived species experiencing changes in environmental conditions and harvesting regimes.     

Laying date,body mass and tick infestation of nestling tropical Roseate Terns Sterna dougallii predict fledging success,first‐year survival and age at first return to the natal colony

Both intrinsic and extrinsic factors recorded at individual nests can predict offspring fitness and survival but few studies have examined these effects in the tropics. We recorded nestling survival, post‐fledging survival and age at first return of Roseate Terns breeding at Aride Island, Seychelles, over a 12‐year period (1998–2009). Nest data recorded at the egg, nestling and fledging stages were collected during six breeding seasons (1998, 2001–2005) and a capture‐mark‐recapture dataset of six cohorts of fledglings was obtained from 2001–2009. Logistic regression models were used to assess the predictive effect of reproductive variables on fledging success, while multistate capture‐mark‐recapture models were used to estimate post‐fledging survival and return–recruitment probabilities to the natal site. Nestling survival probability increased with earliness of laying and was negatively affected by tick infestation during the growth period (0–23 days). Fledging probability was also positively related to chick body condition, whereas other pre‐fledging reproductive parameters such as clutch size and egg size were not influential. A multistate modelling of age‐specific survival and return–recruitment (transition) rates found that first‐year survival differed between cohorts and was also negatively affected by tick infestation. Annual survival stabilized from age 2 onwards at 0.83 ± 0.02. Transition rates were positively related to body condition at fledging, with heavier individuals returning for the first time to the natal colony at a younger age compared with lighter individuals. These results highlight the importance of local conditions encountered by tropical seabirds during the breeding season in shaping demographic parameters.     

Use of posterior predictive checks as an inferential tool for investigating individual heterogeneity in animal population vital rates

The investigation of individual heterogeneity in vital rates has recently received growing attention among population ecologists. Individual heterogeneity in wild animal populations has been accounted for and quantified by including individually varying effects in models for mark–recapture data, but the real need for underlying individual effects to account for observed levels of individual variation has recently been questioned by the work of Tuljapurkar et al. (Ecology Letters, 12, 93, 2009) on dynamic heterogeneity. Model‐selection approaches based on information criteria or Bayes factors have been used to address this question. Here, we suggest that, in addition to model‐selection, model‐checking methods can provide additional important insights to tackle this issue, as they allow one to evaluate a model's misfit in terms of ecologically meaningful measures. Specifically, we propose the use of posterior predictive checks to explicitly assess discrepancies between a model and the data, and we explain how to incorporate model checking into the inferential process used to assess the practical implications of ignoring individual heterogeneity. Posterior predictive checking is a straightforward and flexible approach for performing model checks in a Bayesian framework that is based on comparisons of observed data to model‐generated replications of the data, where parameter uncertainty is incorporated through use of the posterior distribution. If discrepancy measures are chosen carefully and are relevant to the scientific context, posterior predictive checks can provide important information allowing for more efficient model refinement. We illustrate this approach using analyses of vital rates with long‐term mark–recapture data for Weddell seals and emphasize its utility for identifying shortfalls or successes of a model at representing a biological process or pattern of interest.     

Integrating stable isotopes,parasite, and ring‐reencounter data to quantify migratory connectivity—A case study with Barn Swallows breeding in Switzerland,Germany, Sweden,and Finland

Ecosystems around the world are connected by seasonal migration. The migrant animals themselves are influenced by migratory connectivity through effects on the individual and the population level. Measuring migratory connectivity is notoriously difficult due to the simple requirement of data conveying information about the nonbreeding distribution of many individuals from several breeding populations. Explicit integration of data derived from different methods increases the precision and the reliability of parameter estimates. We combine ring‐reencounter, stable isotope, and blood parasite data of Barn Swallows Hirundo rustica in a single integrated model to estimate migratory connectivity for three large scale breeding populations across a latitudinal gradient from Central Europe to Scandinavia. To this end, we integrated a non‐Markovian multistate mark‐recovery model for the ring‐reencounter data with normal and binomial mixture models for the stable isotope and parasite data. The integration of different data sources within a mark‐recapture modeling framework enables the most precise quantification of migratory connectivity on the given broad spatial scale. The results show that northern‐breeding populations and Southern Africa as well as southern‐breeding populations and Western–Central Africa are more strongly connected through Barn Swallow migration than central European breeding populations with any of the African wintering areas. The nonbreeding distribution of Barn Swallows from central European breeding populations seems to be a mixture of those populations breeding further north and south, indicating a migratory divide.     

Extreme longevity in freshwater mussels revisited: sources of bias in age estimates derived from mark–recapture experiments

1. There may be bias associated with mark–recapture experiments used to estimate age and growth of freshwater mussels. Using subsets of a mark–recapture dataset for Quadrula pustulosa, I examined how age and growth parameter estimates are affected by (i) the range and skew of the data and (ii) growth reduction due to handling. I compared predictions from von Bertalanffy growth models based on mark–recapture data with direct observation of mussel age and growth inferred from validated shell rings. 2. Growth models based on a dataset that included observations from a wide range of length classes (spanning ≥ the upper 50% of the population length range) produced only slightly biased age estimates for small and medium‐sized individuals (overestimated by 1–2 years relative to estimates from validated shell rings) but estimates became increasingly biased for larger individuals. Growth models using data that included only observations of larger animals (< the upper 50% of length range) overestimated age for all length classes, and estimated maximum age was two to six times greater than the maximum age observed in the population (47 years). Similarly, growth models using a left‐skewed dataset overestimated age. 3. Reductions of growth due to repeated handling also resulted in overestimates of age. The estimated age of mussels that were handled in two consecutive years was as much as twice that of mussels that were handled only once over the same period. Assuming a constant reduction in the annual rate of growth, handling an individual for five consecutive years could result in an estimated age that is five times too high. 4. These findings show that mark–recapture methods have serious limitations for estimating mussel age and growth. A previous paper (Freshwater Biology, 46, 2001, 1349) presented longevity estimates for three mussel species that were an order of magnitude higher than estimates inferred from shell rings. Because those estimates of extreme longevity were based on mark–recapture methods and subject to multiple, additive sources of bias, they cannot be considered accurate representations of life span and cannot be used to conclude that traditional methods of bivalve ageing by interpretation of shell rings are flawed.     

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.     

Inferences about population dynamics from count data using multistate models: a comparison to capture–recapture approaches

Wildlife populations consist of individuals that contribute disproportionately to growth and viability. Understanding a population's spatial and temporal dynamics requires estimates of abundance and demographic rates that account for this heterogeneity. Estimating these quantities can be difficult, requiring years of intensive data collection. Often, this is accomplished through the capture and recapture of individual animals, which is generally only feasible at a limited number of locations. In contrast, N‐mixture models allow for the estimation of abundance, and spatial variation in abundance, from count data alone. We extend recently developed multistate, open population N‐mixture models, which can additionally estimate demographic rates based on an organism's life history characteristics. In our extension, we develop an approach to account for the case where not all individuals can be assigned to a state during sampling. Using only state‐specific count data, we show how our model can be used to estimate local population abundance, as well as density‐dependent recruitment rates and state‐specific survival. We apply our model to a population of black‐throated blue warblers (Setophaga caerulescens) that have been surveyed for 25 years on their breeding grounds at the Hubbard Brook Experimental Forest in New Hampshire, USA. The intensive data collection efforts allow us to compare our estimates to estimates derived from capture–recapture data. Our model performed well in estimating population abundance and density‐dependent rates of annual recruitment/immigration. Estimates of local carrying capacity and per capita recruitment of yearlings were consistent with those published in other studies. However, our model moderately underestimated annual survival probability of yearling and adult females and severely underestimates survival probabilities for both of these male stages. The most accurate and precise estimates will necessarily require some amount of intensive data collection efforts (such as capture–recapture). Integrated population models that combine data from both intensive and extensive sources are likely to be the most efficient approach for estimating demographic rates at large spatial and temporal scales.     

Using dynamic N‐mixture models to test cavity limitation on northern flying squirrel demographic parameters using experimental nest box supplementation

Dynamic N‐mixture models have been recently developed to estimate demographic parameters of unmarked individuals while accounting for imperfect detection. We propose an application of the Dail and Madsen ( 2011 : Biometrics, 67 , 577–587) dynamic N‐mixture model in a manipulative experiment using a before‐after control‐impact design (BACI). Specifically, we tested the hypothesis of cavity limitation of a cavity specialist species, the northern flying squirrel, using nest box supplementation on half of 56 trapping sites. Our main purpose was to evaluate the impact of an increase in cavity availability on flying squirrel population dynamics in deciduous stands in northwestern Québec with the dynamic N‐mixture model. We compared abundance estimates from this recent approach with those from classic capture–mark–recapture models and generalized linear models. We compared apparent survival estimates with those from Cormack–Jolly–Seber (CJS) models. Average recruitment rate was 6 individuals per site after 4 years. Nevertheless, we found no effect of cavity supplementation on apparent survival and recruitment rates of flying squirrels. Contrary to our expectations, initial abundance was not affected by conifer basal area (food availability) and was negatively affected by snag basal area (cavity availability). Northern flying squirrel population dynamics are not influenced by cavity availability at our deciduous sites. Consequently, we suggest that this species should not be considered an indicator of old forest attributes in our study area, especially in view of apparent wide population fluctuations across years. Abundance estimates from N‐mixture models were similar to those from capture–mark–recapture models, although the latter had greater precision. Generalized linear mixed models produced lower abundance estimates, but revealed the same relationship between abundance and snag basal area. Apparent survival estimates from N‐mixture models were higher and less precise than those from CJS models. However, N‐mixture models can be particularly useful to evaluate management effects on animal populations, especially for species that are difficult to detect in situations where individuals cannot be uniquely identified. They also allow investigating the effects of covariates at the site level, when low recapture rates would require restricting classic CMR analyses to a subset of sites with the most captures.     

ESTIMATING AND VISUALIZING FITNESS SURFACES USING MARK–RECAPTURE DATA

Understanding how selection operates on a set of phenotypic traits is central to evolutionary biology. Often, it requires estimating survival (or other fitness‐related life‐history traits) which can be difficult to obtain for natural populations because individuals cannot be exhaustively followed. To cope with this issue of imperfect detection, we advocate the use of mark‐recapture data and we provide a general framework for both the estimation of linear and nonlinear selection gradients and the visualization of fitness surfaces. To quantify the strength of selection, the standard second‐order polynomial regression method is integrated in mark‐recapture models. To visualize the form of selection, we use splines to display selection acting on multivariate phenotypes in the most flexible way. We employ Markov chain Monte Carlo sampling in a Bayesian framework to estimate model parameters, assessing traits relevance and calculating the optimal amount of smoothing. We illustrate our approach using data from a wild population of Common blackbirds (Turdus merula) to investigate survival in relation to morphological traits, and provide evidence for correlational selection using the new methodology. Overall, the framework we propose will help in exploring the full potential of mark‐recapture data to study natural selection.     

Improved methods for estimating abundance and related demographic parameters from mark‐resight data

Over the past decade, there has been much methodological development for the estimation of abundance and related demographic parameters using mark‐resight data. Often viewed as a less‐invasive and less‐expensive alternative to conventional mark recapture, mark‐resight methods jointly model marked individual encounters and counts of unmarked individuals, and recent extensions accommodate common challenges associated with imperfect detection. When these challenges include both individual detection heterogeneity and an unknown marked sample size, we demonstrate several deficiencies associated with the most widely used mark‐resight models currently implemented in the popular capture‐recapture freeware Program MARK. We propose a composite likelihood solution based on a zero‐inflated Poisson log‐normal model and find the performance of this new estimator to be superior in terms of bias and confidence interval coverage. Under Pollock's robust design, we also extend the models to accommodate individual‐level random effects across sampling occasions as a potentially more realistic alternative to models that assume independence. As a motivating example, we revisit a previous analysis of mark‐resight data for the New Zealand Robin (Petroica australis) and compare inferences from the proposed estimators. For the all‐too‐common situation where encounter rates are low, individual detection heterogeneity is non‐negligible, and the number of marked individuals is unknown, we recommend practitioners use the zero‐inflated Poisson log‐normal mark‐resight estimator as now implemented in Program MARK.     

Climate and density influence annual survival and movement in a migratory songbird

Assessing the drivers of survival across the annual cycle is important for understanding when and how population limitation occurs in migratory animals. Density‐dependent population regulation can occur during breeding and nonbreeding periods, and large‐scale climate cycles can also affect survival throughout the annual cycle via their effects on local weather and vegetation productivity. Most studies of survival use mark–recapture techniques to estimate apparent survival, but true survival rates remain obscured due to unknown rates of permanent emigration. This is especially problematic when assessing annual survival of migratory birds, whose movement between breeding attempts, or breeding dispersal, can be substantial. We used a multistate approach to examine drivers of annual survival and one component of breeding dispersal (habitat‐specific movements) in a population of American redstarts (Setophaga ruticilla) over 11 years in two adjacent habitat types. Annual survival displayed a curvilinear relation to the Southern Oscillation Index, with lower survival during La Niña and El Niño conditions. Although redstart density had no impact on survival, habitat‐specific density influenced local movements between habitat types, with redstarts being less likely to disperse from their previous year's breeding habitat as density within that habitat increased. This finding was strongest in males and may be explained by conspecific attraction influencing settlement decisions. Survival was lowest in young males, but movement was highest in this group, indicating that apparent survival rates were likely biased low due to permanent emigration. Our findings demonstrate the utility of examining breeding dispersal in mark–recapture studies and complement recent work using spatially explicit models of dispersal probability to obtain greater accuracy in survival estimates.     

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.     

Making use of multiple surveys: Estimating breeding probability using a multievent‐robust design capture–recapture model

Increased environmental stochasticity due to climate change will intensify temporal variance in the life‐history traits, and especially breeding probabilities, of long‐lived iteroparous species. These changes may decrease individual fitness and population viability and is therefore important to monitor. In wild animal populations with imperfect individual detection, breeding probabilities are best estimated using capture–recapture methods. However, in many vertebrate species (e.g., amphibians, turtles, seabirds), nonbreeders are unobservable because they are not tied to a territory or breeding location. Although unobservable states can be used to model temporary emigration of nonbreeders, there are disadvantages to having unobservable states in capture–recapture models. The best solution to deal with unobservable life‐history states is therefore to eliminate them altogether. Here, we achieve this objective by fitting novel multievent‐robust design models which utilize information obtained from multiple surveys conducted throughout the year. We use this approach to estimate annual breeding probabilities of capital breeding female elephant seals (Mirounga leonina). Conceptually, our approach parallels a multistate version of the Barker/robust design in that it combines robust design capture data collected during discrete breeding seasons with observations made at other times of the year. A substantial advantage of our approach is that the nonbreeder state became “observable” when multiple data sources were analyzed together. This allowed us to test for the existence of state‐dependent survival (with some support found for lower survival in breeders compared to nonbreeders), and to estimate annual breeding transitions to and from the nonbreeder state with greater precision (where current breeders tended to have higher future breeding probabilities than nonbreeders). We used program E‐SURGE (2.1.2) to fit the multievent‐robust design models, with uncertainty in breeding state assignment (breeder, nonbreeder) being incorporated via a hidden Markov process. This flexible modeling approach can easily be adapted to suit sampling designs from numerous species which may be encountered during and outside of discrete breeding seasons.     

Effect of haemosporidian infections on host survival and recapture rate in the blue tit

Parasites are ubiquitous in the wild and by imposing fitness costs on their hosts they constitute an important selection factor. One of the most common parasites of wild birds are Plasmodium and Haemoproteus, protozoans inhabiting the blood, which cause avian malaria and malaria‐like disease, respectively. Although they are expected to cause negative effects in infected individuals, in many cases studies in natural populations failed to detect such effect. Using data from seven breeding seasons (2008–2014), we applied a multistate capture–mark–recapture approach to study the effect of infection with malaria and malaria‐like parasites, individual age and sex on the probability of survival and recapture rate in a small passerine, the blue tit Cyanistes caeruleus, inhabiting the island of Gotland, Sweden. We found no effect of infection on survival prospects. However, the recapture rate of infected individuals was higher than that of uninfected ones. Thus, while our data do not support the presence of infection costs in terms of host survival, it suggests that parasites from the genera Plasmodium and Haemoproteus may affect some aspects of host behaviour, which translates into biased estimation of infection frequency at the population level.     

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.