Estimating catch within integrated stock assessments: Distinguishing truth from error in sparse-spike data

Abstract

Collecting accurate information on catch levels from marine recreational fishing can be challenging and often results in highly uncertain estimates of landings and discards. Sparse spikes are a common characteristic of these data, with isolated values much higher (e.g., 2–4 times) the surrounding values in a time series of annual estimates. In many cases, it is unclear whether these spikes represent observation error or true, even if anomalous, values. Utilizing multiple data sources simultaneously, integrated stock assessment models may be able to distinguish truth from error in sparse-spike catch estimates, however this supposition has not been well tested. Here, we developed a simulation study to elucidate conditions under which an integrated stock assessment model might perform well (or poorly) when confronted with sparse-spike catch estimates. We found that unconstrained estimation could distinguish truth from error in anomalous values, but only under specific conditions of supporting data. If the anomaly is known to be biased high, estimated catches can get closer to the true value. If the error mechanism is unknown, our results suggest that the true underlying value of a sparse spike is only recoverable when other input data are highly accurate, primarily a reliable index of abundance, but also age composition data in the same year.