Using Artificial Intelligence to Optimize Ecological Restoration for Climate and Biodiversity

The restoration of degraded ecosystems is critical for mitigating climate change and reversing biodiversity loss. Depending on the primary objective-such as maximizing carbon sequestration or protecting threatened species-and within the boundaries of budget limitations, different spatial priorities have been identified at global and regional scales. Funding mechanisms to support such work comprise public sources, philanthropy, and the private sector, including the sale of carbon and biodiversity credits. However, effectively exploring trade-offs among restoration objectives and estimating the price of biodiversity and carbon credits to design financially viable projects remains scientifically challenging. Here we harness the power of artificial intelligence implemented in the software CAPTAIN to identify spatial priorities for ecological restoration that maximize multiple objectives at once and to allow a robust evaluation of biodiversity and climate outcomes. We find through a series of simulations that even low-to-moderate consideration of biodiversity in the design of restoration projects leads to the selection of restored areas that disproportionately improve the conservation of threatened species, while the carbon captured is only slightly reduced. We propose the bundling of biodiversity and carbon credits to support restoration efforts even in areas previously excluded for economic reasons. Applying our methodology to plant diversity in the Atlantic Forest of eastern South America, one of the most biodiverse and threatened ecosystems globally, we demonstrate its potential to guide restoration and to quantify trade-offs between multiple optimization scenarios. Our study provides a robust, scalable method to maximize the joint benefits of ecological restoration for climate and nature.