Designing and Modeling Capture–Recapture Experiments
In designing a capture–recapture experiment, a substantial check list of questions that need answers is provided, as well as references to other aspects of design (e.g., designs using radio telemetry). Test methods for model fitting are briefly described before moving into a section on various measures of model comparison such as AIC, TIC, incorporating over-dispersion using QAIC (and a modification), and BIC. Various aspects of these measures are discussed. Parameter redundancy, described as either intrinsic or extrinsic, is a continual headache in capture–recapture, particularly as the models get more complicated such as with multistate models. Various methods are described for determining the existence of parameter redundancy (due to less than full rank of a certain matrix) and for finding out which of the parameters are redundant (nonidentifiable). These include using symbolic rank packages using the analytical computation of a certain derivative matrix, profile likelihood plots, Hessian matrix methods, and simulation (analytical-numerical method). The chapter ends with introducing the so-called “mother of all models” which is obtained by starting with a simple model for a closed population and then progressively adding new components and ending with an all-purpose model.