Drawing the lines: resolving fishery management units with simple fisheries data

Abstract

The task of assessing marine resources should begin with defining management units. Often this step is overlooked or defined at temporal scales irrelevant to management needs. Additionally, traditional methods to define stock structure can be data intensive and (or) cost prohibitive and thus not available for emerging or data-limited fisheries. We present an approach that uses commonly available fisheries data (catch and effort) to delineate management units for dynamically independent populations. Spatially explicit standardized indices of abundance are grouped using a two-step partitioning cluster analysis that includes abundance index uncertainty. This “management unit estimator” is tested via simulation and found generally to recover the true number of management units across data of different temporal length, sample size, and quality. Management units are then determined for four species with varying ecologies, fishery histories, and data issues that exemplify the challenges of applying this method to messy data sets. Defining management units via relative abundance incorporates changes in population connectivity in relation to current removals and environmental conditions and creates consistency of index use within assessments. The two-step clustering approach is simple and widely applicable to situations wherein the clustering metric contains uncertainty.