Contribution of Capture-Mark-Recapture Modeling to Studies of Evolution by Natural Selection

Abstract

Capture-Mark-Recapture (CMR) modeling is one of the most commonly used estimation methods in population ecology of wild animals. Until recently, much of the emphasis of this method was on the estimation of abundance and survival probability. Despite common interest in estimation of such demographic parameters, evolutionary ecologists have often been more critical of CMR estimation methods than wildlife biologists, mostly because the available models did not allow investigators to address what is at the heart of evolutionary ecology. Evolutionary ecology aims at explaining biological diversity: studies in this area of research necessarily involve assessment of variation in traits among individuals, including fitness components. The main limitation of early CMR models was the inability to handle states among which individuals move in a stochastic manner throughout life (e.g., breeding activity and number of offspring raised, locations, physiological states, etc.). Several important advances have enhanced ecologists' ability to address evolutionary hypotheses using CMR data; namely multistate models and models with individual covariates.

Recently, methodological advances have allowed investigators to handle random effects models. This is bringing CMR models close to modern statistical models (Generalized linear mixed models) whose use is rapidly increasing in quantitative genetics. In quantitative genetics, the animal model aims at disentangling sources of phenotypic variation to draw inferences about heritability of any type of trait (morphological, demographic, behavioral, physiological traits). The animal model partitions variation in the trait of interest using variance components. Understanding evolution by natural selection and predicting its pace and direction requires understanding of the genetic and environmental influences on a trait. Phenotypic characteristics such as morphological or life-history traits (i.e. demographic parameters such as number of offspring raised and survival probability) are likely to be influenced by a large number of genes, the genetic basis of which can be quantified via statistical inferences based on similarities among relatives in a population. The extent of evolutionary responses in a quantitative trait is assumed to be proportional to the force of natural selection and heritability of a trait. Estimating the genetic basis of quantitative traits can be tricky for wild animal populations in natural environments: environmental variation often obscures the underlying evolutionary patterns. However, this genetic basis of traits is at the heart of natural selection, and recently there has been increased interest in applying the animal model to natural populations to understand their evolutionary dynamics. Such models have been applied to estimation of heritability in life history traits, either in the rare study populations where detection probability is close to 1, or without considering the probability of detecting animals that are alive and present in the study area (recapture or resighting probability). Applications of the animal model to demographic parameters (fitness components) such as survival, breeding probability or to lifetime reproductive success in wild animal populations where detection probability is < 1 require trans-disciplinary efforts; this is necessary to address evolutionary processes in such populations.