Chapter 3 Modeling Individual Animal Histories with Multistate Capture–Recapture Models

Abstract

Summary Many fields of science begin with a phase of exploration and description, followed by investigations of the processes that account for observed patterns. The science of ecology is no exception, and recent decades have seen a focus on understanding key processes underlying the dynamics of ecological systems. In population ecology, emphasis has shifted from the state variable of population size to the demographic processes responsible for changes in this state variable: birth, death, immigration, and emigration. In evolutionary ecology, some of these same demographic processes, rates of birth and death, are also the determinants of fitness. In animal population ecology, the estimation of state variables and their associated vital rates is especially problematic because of the difficulties in sampling such populations and detecting individual animals. Indeed, early capture–recapture models were developed for the purpose of estimating population size, given the reality that all animals are not caught or detected at any sampling occasion. More recently, capture–recapture models for open populations were developed to draw inferences about survival in the face of these same sampling problems. The focus of this paper is on multi‐state mark–recapture models (MSMR), which first appeared in the 1970s but have undergone substantial development in the last 15 years. These models were developed to deal explicitly with biological variation, in that animals in different “states” (classes defined by location, physiology, behavior, reproductive status, etc.) may have different probabilities of survival and detection. Animal transitions between states are also stochastic and themselves of interest. These general models have proven to be extremely useful and provide a way of thinking about a remarkably wide range of important ecological processes. These methods are now at a stage of refinement and sophistication where they can readily be used by biologists to tackle a wide range of important issues in ecology. In this paper, we draw together information on the state of the art in multistate mark–recapture methods, explaining the models and illustrating their use. We provide a modeling philosophy and a series of general principles on how to conduct analyses. We cover key issues and features, and we anticipate the ways in which we expect the models to develop in the years ahead. In particular: – MSMR can now be used in a straightforward fashion by population biologists, thanks to the development of sound goodness‐of‐fit procedures, reliable parameter identifiability diagnostics, and robust user‐friendly computer software.Constrained models and model selection procedures can be used in the ANOVA‐like philosophy commonly used over the last 15 years for survival models, to answer a variety of biological questions. We develop as an example a treatment of meadow vole Microtus pennsylvanicus data. – As in survival models, random effects should be an integral part of this philosophy. Some simple approaches to random effects are illustrated. – States can be defined in a very general way, for example, by combining several criteria, such as sites and reproductive states, and can include nonobservable states. We develop as an example a multisite recruitment model of roseate terns Sterna dougallii . – MSMR models appear as a natural framework for combining different sources of information, viewed as different events that can be organized into mutually exclusive alternatives. – With the available developments, MSMR models are becoming a standard tool in population biology, as shown by a rapid growth of their use in the literature. In particular, given the ease with which a variety of constrained models can now be developed, MSMR models appear as less data hungry than was often feared. – MSMR models make it also possible to unify a large array of methodology, and, as such, are both a step towards further unification in a “mother of all” model, and a sound basis for further generalizations. – Future developments concern a variety of generalizations such as the reverse time approach and population size estimation. “Multievent” models, accounting for uncertainty in state determination, and integrated state–space models are key generalizations already in full development.