The land-blending strategy: Contribution of metapopulation theory to the land sparing-sharing debate

Two land management strategies have been proposed to preserve biodiversity while maintaining sufficient agricultural production: land sparing and land sharing. Debate on their efficiency continues, although a third hybrid strategy has emerged. The balance between these strategies is context-dependent, limiting generalizations. We addressed this challenge using a metapopulation-based model to simulate species persistence in agricultural landscapes under different management strategies. Our model captures the influence of contextual factors, such as landscape composition, connectivity, and pest incidence, allowing us to evaluate how landscape management strategies influence biodiversity and ecosystem services such as pest regulation. Our results highlight key factors for designing effective landscape management strategies. First, maintaining intermediate quality habitats (e.g., agroforests) within the landscape is essential to support pest controllers and thus, the provision of ecosystem services. Second, although agroforestry expansion can reduce economic returns compared to conventional agriculture, biodiversity offsets these costs when pest pressure is high and biological control is effective. These findings emphasize the importance of an integrated approach to implement effective landscape management strategies, optimizing both productivity and biodiversity conservation. This study reveals the potential of a hybrid ‘land blending’ strategy, able to outperform traditional land sparing-sharing approaches, while offering greater flexibility for change and uncertainty. To our knowledge, this study represents the first theoretical modelling approach to assess the effectiveness of conservation strategies without considering specific contextual influences. Our findings enhance our understanding of the impact of context on optimal strategies, enriching the debate and suggesting new perspectives beyond its false dichotomy.