An indicator-based adaptive management framework and its development for data-limited fisheries in Belize

Decisions regarding the selection and implementation of management strategies that constrain fishing pressure can be among the most difficult choices that fisheries managers and stakeholders must make. These types of decisions often need to be confronted in a data-limited context, where few if any management measures are currently in place or fisheries are managed independent of adequate scientific advice. This situation can sometimes create a high risk of overfishing and potential loss of economic and social benefits. To address this situation, simple model-free indicator-based frameworks have the potential to be effective decision-making platforms for fisheries where quantitative estimates of biomass and fishing mortality based reference points are lacking. In this paper, a multi-indicator framework is developed that enables decision-makers to proceed with management decisions in data-limited situations. Model-free indicators are calculated using trends in observed data, rather than stock assessment derived estimates of biomass and fishing mortality. The framework developed is adaptive so that adjustments to catch or effort are recursive and can respond to changing environments, socioeconomic conditions, and fishing practices. Using stakeholder-defined objectives as a foundation, indicators and reference points of fishery performance are chosen that can be evaluated easily by undertaking analyses of available data. Indicators from multiple data streams are used so that uncertainty in one indicator can be hedged through careful interpretation and corroboration of information from alternative indicators. During the adaptive management cycle, managers and stakeholders evaluate each indicator against the associated reference points to determine performance measures, interpret the results using scientific and local knowledge, and adjust fishery management tactics accordingly using pre-defined harvest control rules. The framework facilitates the interpretation of situations in which performance measures suggest divergent stock abundance or productivity levels. A case study is presented on this framework's development for conch and lobster fisheries of Belize.