Incorporating management action suitability in conservation plans.
Abstract
Conservation decision makers must negotiate social and technical complexities to achieve desired biodiversity outcomes. Quantitative models can inform decision making, by evaluating and predicting management outcomes, so that comparisons can be made between alternative courses of action. However, whether a proposed action is appropriate for implementation, regardless of its contribution to management outcomes, also requires consideration. Existing quantitative models have yet to fully incorporate the suitability of proposed management actions, which hinders their ability to inform decision making. We used gradient boosted decision trees - a machine-learning technique - to determine the suitability of alternative management actions available to a biodiversity conservation programme. We demonstrate our approach using the Predator Free 2050 programme - a large and complex conservation initiative that seeks to eradicate selected invasive vertebrates from the entirety of New Zealand by 2050. We created a nationally contiguous network of management tools to suppress populations of invasive species across the entire country. We then used our suitability predictions to explore three scenarios for selecting invasive species management tools, based on maximising (a) implementation probability, (b) humaneness and (c) cost-savings. Our models highlighted that an interplay of factors influence where management tools can potentially be implemented. Our management scenarios revealed what different contiguous management networks could look like for New Zealand over the next 10-15 years as an interim step to achieving Predator Free 2050. Each scenario differed in the tools selected for implementation in different places and in the overall economic costs associated with creating a contiguous management network. Some locations were identified as unsuitable for any existing management tools, indicating that future transformative technologies may be required to create a contiguous network. Synthesis and applications. Conservation decision making must not only consider biodiversity outcomes but also whether selected management actions are appropriate in the first place. Here, we used machine-learning techniques to determine the suitability of competing managements actions that are proposed to meet biodiversity objectives. Our approach provides an objective, transparent and reproducible framework to determine the suitability of actions at sites across large spatial extents, under complex social and technical constraints.