标题： 通过分层和贝叶斯优化连接农业经济学与景观生态学以实现全球可持续性 显示英文标题

标题： Bridging Farm Economics and Landscape Ecology for Global Sustainability through Hierarchical and Bayesian Optimization

摘要： 农业景观面临着在维持粮食生产的同时逆转生物多样性丧失的双重挑战。 农业环境政策往往无法实现如景观连通性等生态功能，部分原因是农场层面的经济决策与景观尺度的空间规划之间存在持续的脱节。 我们引入了一个新的分层优化框架，以弥合这一差距。 首先，一个生态强化（EI）模型确定了在单个农场层面，将土地分配给边缘和生境干预措施的经济最优方式。 然后，这些特定于农场的干预水平被传递给一个生态连通性（EC）模型，该模型在景观上空间安排它们，以最大化连通性同时保持农场层面的盈利能力。 最后，我们引入了一种贝叶斯优化（BO）方法，使用非空间的、农场层面的政策参数，将这些空间结果转化为简单、成本效益高且可扩展的政策工具，如补贴和生态溢价。 将该框架应用于加拿大的农业景观，我们展示了它如何在现实世界的经济约束下增强连通性。 我们的方法提供了一个全球适用的工具，用于将农场激励措施与生物多样性目标相一致，推动开发经济可行且生态有效的农业环境政策。 显示英文摘要

摘要： Agricultural landscapes face the dual challenge of sustaining food production while reversing biodiversity loss. Agri-environmental policies often fall short of delivering ecological functions such as landscape connectivity, in part due to a persistent disconnect between farm-level economic decisions and landscape-scale spatial planning. We introduce a novel hierarchical optimization framework that bridges this gap. First, an Ecological Intensification (EI) model determines the economically optimal allocation of land to margin and habitat interventions at the individual farm level. These farm-specific intervention levels are then passed to an Ecological Connectivity (EC) model, which spatially arranges them across the landscape to maximize connectivity while preserving farm-level profitability. Finally, we introduce a Bayesian Optimization (BO) approach that translates these spatial outcomes into simple, cost effective, and scalable policy instruments, such as subsidies and eco-premiums, using non-spatial, farm-level policy parameters. Applying the framework to a Canadian agricultural landscape, we demonstrate how it enhances connectivity under real-world economic constraints. Our approach provides a globally relevant tool for aligning farm incentives with biodiversity goals, advancing the development of agri-environmental policies that are economically viable and ecologically effective.