Determination of Fish Movement Patterns from Tag Recoveries using Maximum Likelihood Estimators

Abstract

A general method for analysis of movement data from tag returns is proposed which has four major components: (1) a population dynamics and movement model that describes how the number of tagged individuals in each spatial location changes over time; (2) an observation model which describes how the tags are recovered and reported; (3) a likelihood function that specifies the likelihood of observing a specific number of recoveries in each space/time stratum as a function of the number thought to be there under a specific set of parameters of the population dynamics, movement and observation models, and (4) a nonlinear function minimization computer algorithm. This approach is applied to movements of skipjack tuna (Euthynnus pelamis). When tagging and recapture take place in each spatial stratum, reliable estimates of movement rates can be obtained. The approach described is completely general and can be used in cases where movement takes place continuously, or only once in the life history. Methods for determining confidence limits and evaluation of residuals are presented and extensions that include tagging mortality, tag shedding, and size specific vulnerability are discussed.