Multistate recapture models: Modelling incomplete individual histories

Multistate capture-recapture models are a natural generalization of the usual one-site recapture models. Similarly, individuals are sampled on discrete occasions, at which they may be captured or not. However, contrary to the one-site case, the individuals can move within a finite set of states between occasions. The growing interest in spatial aspects of population dynamics presently contributes to making multistate models a very promising tool for population biology. We review first the interest and the potential of multistate models, in particular when they are used with individual states as well as geographical sites. Multistate models indeed constitute canonical capture-recapture models for individual categorical covariates changing over time, and can be linked to longitudinal studies with missing data and models such as hidden Markov chains. Multistate models also provide a promising tool for handling heterogeneity of capture, provided states related to capturability can be defined and used. Such an approach could be relevant for population size estimation in closed populations. Multistate models also constitute a natural framework for mixtures of information in individual history data. Presently, most models can be fit using program MARK. As an example, we present a canonical model for multisite accession to reproduction, which fully generalizes a classical one-site model. In the generalization proposed, one can estimate simultaneously age-dependent rates of accession to reproduction, natal and breeding dispersal. Finally, we discuss further generalizations - such as a multistate generalization of growth rate models and models for data where the state in which an individual is detected is known with uncertainty - and prospects for software development.     

Assessing Type I error and power of multistate Markov models for panel data—A simulation study

Ordinal outcomes collected at multiple follow-up visits are common in clinical trials. Sometimes, one visit is chosen for the primary analysis and the scale is dichotomized amounting to loss of information. Multistate Markov models describe how a process moves between states over time. Here, simulation studies are performed to investigate the Type I error and power characteristics of multistate Markov models for panel data with limited non-adjacent state transitions. The results suggest that the multistate Markov models preserve the Type I error and adequate power is achieved with modest sample sizes for panel data with limited non-adjacent state transitions.     

The use of multi-state capture-recapture models to address questions in evolutionary ecology

Multi-state capture-recapture models can be used to estimate survival rates in populations that are stratified by location or by state variables associated with individual animals. In populations stratified by location, movement probabilities can be estimated and used to test hypotheses relevant to population genetics and evolutionary ecology. When the interest is in state variables, these models permit estimation and testing of hypotheses about state-specific survival probabilities. If the state variable of interest is reproductive activity or success, then the multi-state modeling approach can be used to test hypotheses about life history trade-offs and a possible cost of reproduction.     

A case-study in the clinical epidemiology of psoriatic arthritis: multistate models and causal arguments

In psoriatic arthritis, permanent joint damage characterizes disease progression and represents a major debilitating aspect of the disease. Understanding the process of joint damage will assist in the treatment and disease management of patients. Multistate models provide a means to examine patterns of disease, such as symmetric joint damage. Additionally, the link between damage and the dynamic course of disease activity (represented by joint swelling and stress pain) at both the individual joint level and otherwise can be represented within a correlated multistate model framework. Correlation is reflected through the use of random effects for progressive models and robust variance estimation for non-progressive models. Such analyses, undertaken with data from a large psoriatic arthritis cohort, are discussed and the extent to which they permit causal reasoning is considered. For this, emphasis is given to the use of the Bradford Hill criteria for causation in observational studies and the concept of local (in)dependence to capture the dynamic nature of the relationships.     

Analysis of band‐recovery data in a multistate capture‐recapture framework

Dead recoveries of marked animals are commonly used to estimate survival probabilities. Band‐recovery models can be parameterized either by r (the probability of recovering a band conditional on death of the animal) or by f (the probability that an animal will be killed, retrieved, and have its band reported). The T parametrization can be implemented in a capture‐recapture framework with two states (alive and newly dead), mortality being the transition probability between the two states. The authors show here that the f parametrization can also be implemented in a multistate framework by imposing simple constraints on some parameters. They illustrate it using data on the mallard and the snow goose. However, they mention that because it does not entirely separate the individual survival and encounter processes, the f parametrization must be used with care on reduced models, or in the presence of estimates at the boundary of the parameter space. As they show, a multistate framework allows the use of powerful software for model fitting or testing the goodness‐of‐fit of models; it also affords the implementation of complex models such as those based on mixture of information or uncertain states     

Costs of reproduction: Assessing responses to brood size manipulation on life-history and behavioural traits using multi-state capture-recapture models

Costs of reproduction are fundamental trade-offs shaping the evolution of life histories. There has been much interest, discussion and controversy about the nature and type of reproductive costs. The manipulation of reproductive effort (e.g. brood size manipulation) may alter not only life-history traits such as future adult survival rate and future reproductive effort, but also behavioural decisions affecting recapture/resighting and dispersal probabilities. We argue that many previous studies of the costs of reproduction may have erroneously concluded the existence or non-existence of such costs because of their use of local return rates to assess survival. In this paper, we take advantage of the modern multistate capture-recapture methods to highlight how the accurate assessment of the costs of reproduction requires incorporating not only recapture probability, but also behavioural 'state' variables, for example dispersal status and current reproductive investment. The inclusion of state-dependent decisions can radically alter the conclusions drawn regarding the costs of reproduction on future survival or reproductive investment. We illustrate this point by re-analysing data collected to address the question of the costs of reproduction in the collared flycatcher and the great tit. We discuss in some detail the methodological issues and implications of the analytical techniques.     

Prior distributions for stratified capture-recapture models

We consider the Arnason-Schwarz model, usually used to estimate survival and movement probabilities from capture-recapture data. A missing data structure of this model is constructed which allows a clear separation of information relative to capture and relative to movement. Extensions of the Arnason-Schwarz model are considered. For example, we consider a model that takes into account both the individual migration history and the individual reproduction history. Biological assumptions of these extensions are summarized via a directed graph. Owing to missing data, the posterior distribution of parameters is numerically intractable. To overcome those computational difficulties we advocate a Gibbs sampling algorithm that takes advantage of the missing data structure inherent in capture-recapture models. Prior information on survival, capture and movement probabilities typically consists of a prior mean and of a prior 95% credible confidence interval. Dirichlet distributions are used to incorporate some prior information on capture, survival probabilities, and movement probabilities. Finally, the influence of the prior on the Bayesian estimates of movement probabilities is examined.     

Prior distributions for stratified capture-recapture models

We consider the Arnason-Schwarz model, usually used to estimate survival and movement probabilities from capture-recapture data. A missing data structure of this model is constructed which allows a clear separation of information relative to capture and relative to movement. Extensions of the Arnason-Schwarz model are considered. For example, we consider a model that takes into account both the individual migration history and the individual reproduction history. Biological assumptions of these extensions are summarized via a directed graph. Owing to missing data, the posterior distribution of parameters is numerically intractable. To overcome those computational difficulties we advocate a Gibbs sampling algorithm that takes advantage of the missing data structure inherent in capture-recapture models. Prior information on survival, capture and movement probabilities typically consists of a prior mean and of a prior 95% credible confidence interval. Dirichlet distributions are used to incorporate some prior information on capture, survival probabilities, and movement probabilities. Finally, the influence of the prior on the Bayesian estimates of movement probabilities is examined.     

Separation of survival and movement rates in multi-state tag-return and capture-recapture models

There has been growing interest in the estimation of transition probabilities among stages (Hestbeck et al. , 1991; Brownie et al. , 1993; Schwarz et al. , 1993) in tag-return and capture-recapture models. This has been driven by the increasing interest in meta-population models in ecology and the need for parameter estimates to use in these models. These transition probabilities are composed of survival and movement rates, which can only be estimated separately when an additional assumption is made (Brownie et al. , 1993). Brownie et al. (1993) assumed that movement occurs at the end of the interval between time i and i + 1. We generalize this work to allow different movement patterns in the interval for multiple tag-recovery and capture-recapture experiments. The time of movement is a random variable with a known distribution. The model formulations can be viewed as matrix extensions to the model formulations of single open population capturerecapture and tag-recovery experiments (Jolly, 1965; Seber, 1965; Brownie et al. , 1985). We also present the results of a small simulation study for the tag-return model when movement time follows a beta distribution, and later another simulation study for the capture-recapture model when movement time follows a uniform distribution. The simulation studies use a modified program SURVIV (White, 1983). The Relative Standard Errors (RSEs) of estimates according to high and low movement rates are presented. We show there are strong correlations between movement and survival estimates in the case that the movement rate is high. We also show that estimators of movement rates to different areas and estimators of survival rates in different areas have substantial correlations.     

Costs of reproduction in common eiders ( Somateria mollissima ): An assessment of relationships between reproductive effort and future survival and reproduction based on observational and experimental studies

The two traditional approaches to the study of costs of reproduction, correlational and experimental, have been used in parallel in a breeding colony of common eiders ( Somateria mollissima ) and were compared in this paper. The analysis of the observational data was based on a two-strata capture-recapture model, the strata being defined on the basis of the clutch size laid by individual females in a given year. The best model according to AIC C indicated substantial variation in survival, recapture and transition rates, but overall a pattern emerged: females laying large clutches have a somewhat higher survival and much higher capture rate than females laying small clutches, and transition from large to small clutch size occurs much more frequently than the reverse transition. The analysis of the experimental data (adding/removing one egg) showed that no clear effect was found on either survival or transition rates. We conclude by suggesting (1) that condition should be included in multi-strata models in addition to reproductive effort; (2) that a specific study design for estimating the proportion of non-breeding females should be implemented, and (3) that non-breeding (a non-observable state in this study) may be influenced by previous reproduction events.     

Costs of reproduction in common eiders ( Somateria mollissima ): an assessment of relationships between reproductive effort and future survival and reproduction based on observational and experimental studies

The two traditional approaches to the study of costs of reproduction, correlational and experimental, have been used in parallel in a breeding colony of common eiders ( Somateria mollissima ) and were compared in this paper. The analysis of the observational data was based on a two-strata capture-recapture model, the strata being defined on the basis of the clutch size laid by individual females in a given year. The best model according to AIC C indicated substantial variation in survival, recapture and transition rates, but overall a pattern emerged: females laying large clutches have a somewhat higher survival and much higher capture rate than females laying small clutches, and transition from large to small clutch size occurs much more frequently than the reverse transition. The analysis of the experimental data (adding/removing one egg) showed that no clear effect was found on either survival or transition rates. We conclude by suggesting (1) that condition should be included in multi-strata models in addition to reproductive effort; (2) that a specific study design for estimating the proportion of non-breeding females should be implemented, and (3) that non-breeding (a non-observable state in this study) may be influenced by previous reproduction events.     

Capture-recapture and evolutionary ecology: A difficult wedding?

Most of the questions raised by evolutionary ecologists require the estimation of demographic parameters such as survival probabilities, breeding propensity, age at maturity, etc. These parameters are usually obtained by capturing, marking and recapturing or resighting (CMR) individuals during their lives. Because exhaustivity cannot be achieved in the field, it is necessary to have an estimate of sampling intensity (capture/resighting probabilities). Statisticians and bio-statisticians have recently developed a wide variety of models devoted to the analysis of capture-recapture data. For a number of reasons, these models are not widely used by evolutionary ecologists. This paper describes the problems that can be encountered when ignoring the measure of sampling intensity. The potential for applying CMR models to current questions in evolutionary ecology is reviewed, particularly with respect to measuring the cost of reproduction and trade-offs. Some future model developments are also sketched which are needed to meet fully the requirements of evolutionary ecologists.     

A three-sample multiple-recapture approach to census population estimation with heterogeneous catchability

"A central assumption in the standard capture-recapture approach to the estimation of the size of a closed population is the homogeneity of the 'capture' probabilities. In this article we develop an approach that allows for varying susceptibility to capture through individual parameters using a variant of the Rasch model from psychological measurement situations. Our approach requires an additional recapture. In the context of census undercount estimation, this requirement amounts to the use of a second independent sample or alternative data source to be matched with census and Post-Enumeration Survey (PES) data.... We illustrate [our] models and their estimation using data from a 1988 dress-rehearsal study for the 1990 census conducted by the U.S. Bureau of the Census, which explored the use of administrative data as a supplement to the PES. The article includes a discussion of extensions and related models."     

Approaches for the direct estimation of u,and demographic contributions to u,using capture-recapture data

We first consider the estimation of the finite rate of population increase or population growth rate, u i , using capture-recapture data from open populations. We review estimation and modelling of u i under three main approaches to modelling openpopulation data: the classic approach of Jolly (1965) and Seber (1965), the superpopulation approach of Crosbie & Manly (1985) and Schwarz & Arnason (1996), and the temporal symmetry approach of Pradel (1996). Next, we consider the contributions of different demographic components to u i using a probabilistic approach based on the composition of the population at time i + 1 (Nichols et al., 2000b). The parameters of interest are identical to the seniority parameters, n i , of Pradel (1996). We review estimation of n i under the classic, superpopulation, and temporal symmetry approaches. We then compare these direct estimation approaches for u i and n i with analogues computed using projection matrix asymptotics. We also discuss various extensions of the estimation approaches to multistate applications and to joint likelihoods involving multiple data types.     

Estimation of trade-offs with capture-recapture models: A case study on the lesser snow goose

Trade-offs between traits such as fecundity or survival are fundamental to much of our understanding of the evolution of life histories. There has been much renewed interest and controversy concerning methods for estimating trade-offs, in the wild or in captivity, and with or without experimental manipulations. In this paper, we assess the general question of the utility of modern capture-recapture methods as a robust tool for estimating trade-offs in natural populations. We present results from analyses of two forms of trade-offs: the cost of present reproduction on future survival and the cost of present reproduction on the probability of breeding in the future. We apply the methods to data from a long-term study of a snow goose population, and generally discuss the advantages and potential problems with various approaches.     

Nonparametric estimation of the sojourn time distributions for a multipath model

Summary. We use a multipath (multistate) model to describe data with multiple end points. Statistical inference based on the intermediate end point is challenging because of the problems of nonidentifiability and dependent censoring. We study nonparametric estimation for the path probability and the sojourn time distributions between the states. The methodology proposed can be applied to analyse cure models which account for the competing risk of death. Asymptotic properties of the estimators proposed are derived. Simulation shows that the methods proposed have good finite sample performance. The methodology is applied to two data sets.     

捕获-再捕获模型的统计学原理

捕获-再捕获模型由国外学者首创,最初用于野生动物总体规模估计,后来经过改进逐步应用于人口普查质量评估和其他统计领域。为了正确使用该模型,采取独一无二的方法,从试验背景、组格概率和边缘概率之间的关系、组格条件概率、条件多项分布和条件似然函数等方面对其进行全面解读和研究。研究表明:使用捕获-再捕获模型必须遵守三项理论原则:即总体封闭原则、个体同质原则、独立性原则;对实际问题与理论原则之间存在的差距必须做三件事情:即发现实际问题与理论原则之间的所有分歧点、评估各个分歧点问题的严重程度、寻找解决问题的办法。     

Regression analysis for discrete event history or failure time data

The paper deals with discrete-time regression models to analyze multistate—multiepisode models for event history data or failure time data collected in follow-up studies, retrospective studies, or longitudinal panels. The models are applicable if the events are not dated exactly but only a time interval is recorded. The models include individual specific parameters to account for unobserved heterogeneity. The explantory variables may be time-varying and random with distributions depending on the observed history of the process. Different estimation procedures are considered: Estimation of structural as well as individual specific parameters by maximization of a joint likelihood function, estimation of the structural parameters by maximization of a conditional likelihood function conditioning on a set of sufficient statistics for the individual specific parameters, and estimation of the structural parameters by maximization of a marginal likelihood function assuming that the individual specific parameters follow a distribution. The advantages and limitations of the different approaches are discussed.     

Α Markov model for longitudinal studies with incomplete dichotomous outcomes

Missing outcome data constitute a serious threat to the validity and precision of inferences from randomized controlled trials. In this paper, we propose the use of a multistate Markov model for the analysis of incomplete individual patient data for a dichotomous outcome reported over a period of time. The model accounts for patients dropping out of the study and also for patients relapsing. The time of each observation is accounted for, and the model allows the estimation of time‐dependent relative treatment effects. We apply our methods to data from a study comparing the effectiveness of 2 pharmacological treatments for schizophrenia. The model jointly estimates the relative efficacy and the dropout rate and also allows for a wide range of clinically interesting inferences to be made. Assumptions about the missingness mechanism and the unobserved outcomes of patients dropping out can be incorporated into the analysis. The presented method constitutes a viable candidate for analyzing longitudinal, incomplete binary data.