Comparing conservation land acquisition strategies using agent-based models

Most unprotected biodiversity is found outside state-owned protected areas, so developing effective conservation initiatives on privately and communally-owned land is critical. Conservationists have a long history of working with these landowners and their actions can be divided into two broad categories. The first is where they agree to take over responsibility for management, either by buying the land or agreeing on long-term leases. The second is where they “rent” the land for conservation and pay people to manage their land appropriately, often through agri-environment schemes. However, we still know relatively little about the effectiveness of these two approaches. Here we use an agent-based modelling approach to investigate the biodiversity outcomes over time of different land acquisition scenarios, based on varying buying and renting budgets and different levels of landowner willingness to engage with the conservation authority. We found that buying land always led to better conservation outcomes, with biodiversity scores being 23.4 times higher when 100% of the budget was for buying compared to when 100% of the budget was for renting. This was mostly because buying land ensured it was managed in perpetuity, allowing the biodiversity value of each land parcel to increase over time. We also found that land-owner willingness to sell or rent their land had a large impact on results, with biodiversity scores varying by 28 times depending on the level of support. Our modelling system will next be used to incorporate more sophisticated measures of biodiversity value and investigate other scenarios for developing ecological networks on privately-owned land, such as longer-term rental agreements and conservation stewardship agreements. In this way we hope to guide future conservation policy to develop large-scale conservation areas in England and inform global strategies that account for biodiversity and stakeholder preferences when designing ecological networks.