Management strategy evaluation of a multi-indicator adaptive framework for data-limited fisheries management

Abstract

In data-limited fisheries, making informed management decisions based on scientific advice is challenging. Here, we evaluate a multi-indicator adaptive management framework (AMF) that allows dynamic responses to changing environmental, socioeconomic, and fishing conditions. Using stakeholder-defined goals as a foundation for specifying performance metrics, we employ management strategy evaluation (MSE) to explore the performance of the AMF relative to prescriptive alternatives that are sometimes used in data-limited situations. We conduct simulations involving the two most economically-important fisheries in Belize, spiny lobster, Panulirus argus (Latreille, 1804), and queen conch, Strombus gigas (Linnaeus, 1758). Spiny lobster fishery simulations demonstrate that when relatively stable catches have historically persisted, an AMF can help to ensure that stable catches continue to persist into the foreseeable future when faced with factors such as increased entry to the fishery or environmentally-induced recruitment fluctuations. The queen conch fishery simulations demonstrate that optimizing economic performance is complicated without stock status indicators and depends greatly upon the current, yet typically unknown, state of the resource. Since our indicator-based approach could not provide direct information about resource status in relation to management reference points such as maximum sustainable yield, economic objectives could not be achieved. Nevertheless, implementing the AMF served as a beneficial control against stock collapse and could function well as an interim fishery policy during which sufficient fishery data could be collected to inform population modeling and quantitative stock assessment.