Data from: Strategies for sustainable management of renewable resources during environmental change

Management strategies that can deal with unexpected change in resource dynamics are becoming increasingly important as a consequence of global environmental change. Here we undertake a novel approach to studying resource growth problems using a computational form of adaptive management to find optimal strategies for prevalent natural resource management dilemmas. We scrutinize adaptive management, or learning-by-doing, to understand better how to simultaneously manage and learn about a system when its dynamics are unknown. We study important trade-offs such as valuing present versus future outcomes, exploration versus business as usual, and, updating versus retaining knowledge, to investigate decision-making with respect to optimal actions (harvest efforts) for sustainable management during change. To operationalize these dynamics we use an artificially intelligent model and analyze how different trends and fluctuations in resource growth rates affect different management strategies. We find those resources with decreasing trends in growth rate demand higher adaptation rates and more exploration compared to resources with increasing trends in growth rate for optimal efficiency. However, to achieve both high efficiency and high robustness to endogenous and exogenous disturbances, management strategies should strive for: high learning rates to new knowledge, high valuation of future outcomes, and modest exploration around what is perceived as the optimal management strategy.