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 between 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 temporally aggregated, by appropriate integration. When information about animal counts is partial or inexact, as from mark-recapture or harvest data, state-space models are a natural framework for estimating both unknown parameters and animal abundance. As an example, a multivariate, linear normal state-space model that explicitly incorporates each of the three individual animal components is formulated for the migration and harvest of Pacific coho salmon (Oncorhynchus kisutch). Using recoveries of tagged coho salmon caught in ocean fisheries and associated measures of fishing effort, the Kalman filter and maximum likelihood are used to estimate parameters of the processes, and the Kalman smooth is used to estimate abundances. Given estimated parameters and current harvest and effort data, real-time management of exploited populations could be improved by using the Kalman prediction algorithm.