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1

A simulation study of impacts of error structure on modelling stock-recruitment data using generalized linear models

Abstract Stock-recruitment (S-R) models are commonly fitted to S-R data with a least-squares method. Errors in modeling are usually assumed to be normal or lognormal, regardless of whether such an assumption is realistic. A Monte Carlo simulation approach was used to evaluate the impact of the assumption of error structure on S-R modeling. The generalized […]

2

Incorporating uncertainty into area under the curve and peak count salmon escapement estimation

Abstract Uncertainty can be incorporated into area-under-the-curve (AUC) and peak count estimates of salmon escapement by conducting replicate fish counts and developing independent escapement estimates over several years. We describe a bootstrap procedure that follows the trapezoidal AUC method and incorporates the uncertainty associated with fish counts, the shape of the spawner curve, observer efficiency, […]