Catch Curve and Capture Recapture Models: A Bayesian Combined Approach

Abstract

When studying animal populations, one demographic parameter of interest is the annual rate of survival. Methods for estimating survival rates of animal populations fall into two general categories: methods based on marked or non-marked animals. Catch curve analysis falls into the latter category of non-marked animal methods, and is based on strong assumptions about population dynamics. Capture-recapture methods, on the other hand, use marked animals and require assumptions about homogeneous individual capture and survival probabilities.

We focus specifically on Chapman and Robson’s catch curve analysis, the Cormack-Jolly-Seber (CJS) open population model, and Udevtiz and Ballachey’s augmentation of catch curve data with ages-at-death data, which are a random sample from the natural deaths that occur in a population between two time periods.

In Chapter 1, we develop the Bayesian approach to catch curve analysis, beginning with the simple situation of a single catch curve. After extending our method to multiple years, we relax the model assumptions to include random effects for survival across years. The proposed model is validated using predictive distributions and compared with the traditional methods. We conclude that many benefits can be obtained from the Bayesian approach to the analysis of a single or multiple year catch curve.

In Chapter 2, we augment catch curve data with capture-recapture data in a hierarchical Bayesian framework. We estimate the fidelity rate and the population growth rate. We illustrate these models with a data set and simulation study.

In Chapter 3, we develop a Bayesian method for analyzing catch curve and ages-at-death data together, based on the likelihoods developed in Udevitz and Ballachey. We utilize the Bayesian framework and relax both the assumption of a stable age-distribution and that of a known population growth rate.