Abstract

Healthy ecosystems are essential for conserving biodiversity and supporting human well-being, yet human activities impose significant pressures, risking ecological features such as species, habitats and ecosystem services. While Systematic Conservation Planning (SCP) has been widely used to address these issues, traditional approaches often result in risk-averse solutions, focusing management efforts on low-impact sites and potentially leading to suboptimal conservation outcomes. To address this gap, we propose a mixed-integer programming (MIP) approach designed to explicitly reduce ecosystem risk by prioritizing cost-effective actions and sites to manage multiple stressors, while accounting for spatial needs such as connectivity and socio-political boundaries. Our framework integrates a state-of-the-art ecological risk assessment tool (InVEST-Habitat Risk Assessment) with an SCP-based mathematical programming tool (prioriactions R package) for guiding risk management. We applied this framework to Chilean Patagonia, using giant kelp (Macrocystis pyrifera) forests as a proxy for coastal ecosystems under pressure from stressors such as aquaculture, vessel activities, and ocean temperatures. The framework was assessed across five planning scenarios, each with different spatial needs, and was compared with traditional approaches. The results demonstrated that our approach consistently outperforms traditional ones, ensuring risk reduction in all scenarios. In key comparisons, the traditional approaches failed to exceed 47% of the risk reduction target. In addition to meeting higher risk reductions, our approach successfully overcame spatial needs in all scenarios. However, the inclusion of these constraints increased computational difficulty by 36 to 150 times and solution costs by up to 71%. These findings highlight the flexibility of the framework, but also emphasize the need for coordinated planning. This work aims to bridge the gap between risk assessment and risk management in conservation planning by explicitly incorporating risk as a primary objective instead of a secondary outcome. Our framework is applicable to any multi-stressor context, providing a flexible tool for designing cost-effective management strategies. By focusing on risk reduction and incorporating spatial needs, this approach enhances long-term ecosystem resilience.