State-space modeling of animal movement and mortality with application to salmon

A stochastic model for the movement and eventual mortality of an individual animal is formulated as a combination of three components: initial spatial location, survival status at any point in time, and spatial translation be tween points in time. Alternative theories about survival and migration can be expressed in terms of different models for any of the three components. The model can be extended to groups of animals, spatially and/or temporall y aggregated, by appropriate integration. When information about animal cou nts is partial or inexact, as from mark-recapture or harvest data, state-sp ace models are a natural framework for estimating both unknown parameters a nd animal abundance. As an example, a multivariate, linear normal state-spa ce model that explicitly incorporates each of the three individual animal c omponents is formulated for the migration and harvest of Pacific coho salmo n (Oncorhynchus kisutch). Using recoveries of tagged coho salmon caught in ocean fisheries and associated measures of fishing effort, the Kalman filte r and maximum likelihood are used to estimate parameters of the processes, and the Kalman smooth is used to estimate abundances. Given estimated param eters and current harvest and effort data, real-time management of exploite d populations could be improved by using the Kalman prediction algorithm.