Length‐Based Reference Points for Data‐Limited Situations: Applications and Restrictions

Abstract

Current fisheries management policies generally require an assessment of stock status, which is a difficult task when population and fisheries data are limited. Three simple metrics based on catch length compositions (i.e., that reflect exclusive take of mature individuals, P mat ; that consist primarily of fish of optimal size, the size at which the highest yield from a cohort occurs, P opt ; and that demonstrate the conservation of large, mature individuals, P mega ) can be used to monitor population status relative to exploitation. The metrics (collectively referred to as P x ) were intended to avoid growth and recruitment overfishing, but there was no quantitative linkage to stock status and calculation of future sustainable catches. We attempt to make this connection by exploring the relationship of P x measures to fishing mortality and spawning biomass (SB). The relationships are compared specifically to the current target reference point (0.4 times the virgin, or unfished, SB [SB 0 ]) and limit reference point (0.25SB 0 ) used for the U.S. West Coast groundfish fishery by using simulations based on a deterministic age‐structured population dynamics model. Sensitivity to fishery selectivity, life history traits, and recruitment compensation (steepness) is explored. Each P x measure showed a wide range of possible values depending on fishery selectivity, steepness, and the ratio of the length at maturity ( L mat ) to the optimal fishing length ( L opt ). Although the values of P x may be compatible with sustainable fishing, these values are not always sufficient to ensure stock protection from overfishing. Moreover, values for P x cannot be interpreted adequately without knowledge of the selectivity pattern. A new measure, P obj (the sum of P mat , P opt , and P mega ), is introduced to distinguish selectivity patterns and construct a decision tree for development of stock status indicators. Heuristic indicator values are presented to demonstrate the utility of this approach. Although several caveats remain, this approach builds on the recommendations of previous literature by giving further guidance related to interpreting catch length composition data under variable fishery conditions without collecting additional information. It also provides a link to developing harvest control rules that inform proactive fisheries management under data‐limited conditions.