Optimising biodiversity protection through artificial intelligence

Abstract

Over a million species face extinction, carrying with them untold options for food, medicine, fibre, shelter, ecological resilience, aesthetic and cultural values. There is therefore an urgent need to design conservation policies that maximise the protection of biodiversity and its contributions to people, within the constraints of limited budgets. Here we propose a novel framework for spatial conservation prioritisation that combines simulation models, reinforcement learning and ground validation to identify optimal policies. Our model quantifies the trade-off between the costs and benefits of area and biodiversity protection, allowing the exploration of multiple biodiversity metrics. Under a fixed budget, our model protects substantially more species from extinction than the random or naively targeted protection of areas. We find that regular biodiversity monitoring, even if simple and with a degree of inaccuracy, substantially improves biodiversity outcomes. Given the complexity of people-nature interactions and wealth of associated data, artificial intelligence holds great promise for improving the conservation of biological and ecosystem values in a rapidly changing and resource-limited world.

The UK’s 25 Year Plan for the Environment and the Convention on Biological Diversity: Gaps, Synergies and Opportunities

“…However, it is important to recognise not only that most of the land in the UK is privately owned and either intensively farmed or extensively developed industrially or residentially [39], but that the same government department has responsibility both for farming and fishing the landscape and seascape and for the natural condition of its biodiversity and ecosystems. This notwithstanding, clearly there is considerable scope for the 25YEP to apply indicators adopted for monitoring landscapescale targets of the CBD 2030 Framework [40], especially those to help meet the 30-by-30 commitment [41] within a UK context (Figure 2).…”