When should we use non-stationary adaptive management? A value of information analysis

Making informed conservation decisions under climate change is a challenging task for practitioners, since decisions depend on changing environmental conditions and uncertain ecosystem responses to climate change. Given such uncertainties, the best practice to manage natural systems is adaptive management, where decisions dynamically adapt to the response of the ecosystem to previous conservation actions. Although adaptive approaches are optimal, they are also difficult to implement, have high computational costs, and recommend strategies that can be complex to interpret. These factors can hinder their on-ground application. On the other hand, simpler but suboptimal decision models can result in more interpretable recommendations, and might still yield good outcomes for ecosystems. Exploring trade-offs between complex optimal solutions and simpler sub-optimal solutions is essential for maximising conservation impact. In this manuscript, we use value of information theory to help managers simplify their decision-models, while balancing optimality of strategies. Our approach provides modelling recommendations by determining the benefits of modelling non-stationary ecosystem dynamics and the uncertain ecosystem response to climate change. We illustrate our approach on four scenarios inspired from the management of the Great Barrier Reef, Australia, under different climate change trajectories. We find that the two main drivers of the recommended reduction in model complexity are the strength of non-stationarity (e.g. climate change trajectory) and the degree of uncertainty in ecosystem responses to climate change (e.g. uncertainty in the thermal resistance of a coral reef). When non-stationarity is weak, the decision problem can be reduced from a non-stationary to a stationary formulation. Similarly, when uncertainty in the response to climate change is low, this uncertainty can be safely ignored in the decision-making process. Conversely, when non-stationarity is strong and/or uncertainty is high, our approach justifies the need to account for these complexities when making decisions, as simpler approaches would yield poor outcomes. This manuscript guides managers in simplifying a modelling approach to manage ecosystems in the face of climate change. Our protocol can help simplify complex decision problems, allowing to reduce computational costs and enhance interpretability. By finding the balance between simplicity and optimality of models, this work contributes to bridging the gap between complex modelling and on-ground applications.