Bayesian Averaging of Generalized Linear Models for Passive Integrated Transponder Tag Recoveries from Salmonids in the Snake River

Abstract

Abstract Bayesian methods provide a means of explicitly accounting for uncertainty in the choice of model used to interpret fisheries data. The probability of a given model being the correct model conditional on the data, the posterior probability, is a measure of the degree of belief and strength of evidence for the model. Bayesian model averaging uses these posterior probabilities to make weighted inferences, thus providing a solution to the problem of selecting a single model from a group of models that seem nearly equivalent by conventional statistical criteria. The approach is applied to a generalized linear model analysis of survival for juvenile and mature adult spring chinook salmon Oncorhynchus tshawytscha and steelhead Oncorhynchus mykiss from the Snake River. The fish, tagged as juveniles with passive integrated transponders (PIT), outmigrated from freshwater habitat to the ocean during 1989–1991, and include some of the first PIT tag recoveries of adult fish. Covariates used to model survival ...