Modeling time-varying natural mortality in size-structured assessment models

Abstract

Temporal variation in natural mortality (M) has been recognized for having a degrading effect on the performance of stock assessment models when it is not accounted for in the model. However, time-invariant M has remained widely practiced in stock assessments due to the difficulties in estimation. Therefore, in this study we conducted simulation-estimation experiments to (1) evaluate the ability of a size-structured assessment model to reliably estimate time-varying M as well as other population quantities, and (2) quantify the consequences of mis-specifying M in a size-structured assessment model. We modified an existing size-structured assessment model to include three different approaches to estimate time-varying M within the model. A wide range of scenarios regarding the underlying M patterns (variation over time and length) was considered in this study. The performance of estimation methods largely depends upon whether the assumption of length-dependent/-independent M was correctly specified in the assessment model. Among the three methods, estimating time-varying M as a mean (representing an average process) and time-specific deviations using auxiliary information performed best. Estimating time-varying M as a random walk process also performed well except when underlying time-varying M had large year-to-year variation. A temporal pattern in M affected the ability of the assessment model to provide reliable estimates of M, spawning stock biomass and recruitment. Assuming a constant M (i.e., fixing M at the correct value) was quite robust over a range of actual temporal variation in underlying M.