Effects of unequal capture probability on stock assessment abundance and mortality estimates: an example using the US Atlantic sea scallop fishery

Abstract

Most stock assessment models assume that the probability of capture for all individuals of the same size or age in the stock area is equal. However, this assumption is rarely, if ever, satisfied. We used spatially referenced simulations, based on the US Atlantic sea scallop (Placopecten magellanicus) fishery, to generate catch, survey index, fishing effort, and size structure data that we input into a (nonspatial) catch-at-size stock assessment model to estimate abundance and mortality rates. We show that spatial patterns in fishing mortality degrade model performance for sessile stocks. Fishing mortality tended to be overestimated and abundance underestimated because trends in fishing mortality were exaggerated and the model misestimated the numbers of larger individuals due to spatial fishing patterns. These results are particularly relevant to sedentary species such as scallops, but are applicable wherever strong spatial patterns exist in fishing mortality.

Calculation of population-level fishing mortality for single- versus multi-area models: application to models with spatial structure

“…The base assumption of homogeneous fish density between areas was made to follow the approach of Beverton and Holt (1957) and Maury et al (1997) and to maintain tractability. We also acknowledge there are other ways of allocating effort or abundance that we did not explore here, for example based on distance from a fishing port (Truesdell et al 2017).…”

Introduction to “Space Oddity: Recent Advances Incorporating Spatial Processes in the Fishery Stock Assessment and Management Interface”

“…Simulation testing has seen broad application for addressing the robustness of spatial stock assessments to connectivity assumptions, particularly in comparison with spatially implicit (e.g., separating estimates of fishing mortality and selectivity by area) or spatially aggregated models that are unable to directly confront spatial processes. Assessment performance has been examined using simulations across a variety of spatial and nonspatial modeling assumptions, including homogeneously distributed fishing effort (Truesdell et al 2017), environmentally driven recruitment (Denson et al 2017), unmodeled processes (e.g., age-based movement (Lee et al 2017) or larval dispersal , and complex population structures (e.g., natal homing; Vincent et al 2017).…”