Simulation testing methods for estimating misreported catch in a state-space stock assessment model

Abstract

Abstract State-space stock assessment models have become increasingly common in recent years due to their ability to estimate unobserved variables and explicitly model multiple sources of random error. Therefore, they may be able to better estimate unobserved processes such as misreported fishery catch. We examined whether a state-space assessment model was able to estimate misreported catch in a simulated fishery. We tested three formulations of the estimation model, which exhibit increasing complexity: (i) assuming no misreporting, (ii) assuming misreporting is constant over time, and (iii) assuming misreporting follows a random walk. We tested these three estimation models against simulations using each of the three assumptions and an additional fourth assumption of uniform random misreporting over time. Overall, the worst estimation errors occurred when misreporting was ignored while it was in fact occurring, while there was a relatively small cost for estimating misreporting when it was not occurring. Estimates of population scale and fishing mortality rate were particularly sensitive to misreporting assumptions. Furthermore, in the uniform random scenario, the relatively simple model that assumed misreporting was fixed across ages and time was more accurate than the more complicated random walk model, despite the increased flexibility of the latter.