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Integrated assessment models that incorporate biodiversity and ecosystem services could be an important tool for improving our understanding of interconnected social-economic-ecological systems, and for analyzing how policy alternatives can shift future trajectories towards more sustainable development. Despite recent scientific and technological advances, key gaps remain in the scientific community’s ability to deliver information to decision-makers at the pace and scale needed to address sustainability challenges. We identify five research frontiers for integrated social-economic-ecological modeling (primarily focused on terrestrial systems) to incorporate biodiversity and ecosystem services: 1) downscaling impacts of direct and indirect drivers on ecosystems; 2) incorporating feedbacks in ecosystems; 3) linking ecological impacts to human well-being, 4) disaggregating outcomes for distributional equity considerations, and 5) incorporating dynamic feedbacks of ecosystem services on the social-economic system. We discuss progress and challenges along each of these five frontiers and the science-policy linkages needed to move new research and information into action.

Native pollinators and, particularly bees, are a critical component of agricultural systems. Unfortunately, many factors are leading to their declines, including habitat loss. Consequently, approaches have emerged that aim to restore pollinator habitat in managed landscapes. A widely adopted technique in Europe and North America is the planting of flowering shrubs and forbs along field edges. These habitats usually include a variety of species, chosen because they are attractive to pollinators and because they flower continuously over those pollinators' flight seasons. Because there are many potential plant species with different flowering times and pollinator preferences, selecting a subset is challenging. Here, we develop a tool that identifies a plant mix that optimizes some assessment criteria (e.g., pollinator visitation, richness, or phenology). We test our tool by showing that it identifies mixes that better satisfy these criteria than ones found using conventional expert-driven methods, when applied to a plant–pollinator dataset.

Native pollinators and, particularly bees, are a critical component of agricultural systems. Unfortunately, many factors are leading to their declines, including habitat loss. Consequently, approaches have emerged that aim to restore pollinator habitat in managed landscapes. A widely adopted technique in Europe and North America is the planting of flowering shrubs and forbs along field edges. These habitats usually include a variety of species, chosen because they are attractive to pollinators and because they flower continuously over those pollinators' flight seasons. Because there are many potential plant species with different flowering times and pollinator preferences, selecting a subset is challenging. Here, we develop a tool that identifies a plant mix that optimizes some assessment criteria (e.g., pollinator visitation, richness, or phenology). We test our tool by showing that it identifies mixes that better satisfy these criteria than ones found using conventional expert-driven methods, when applied to a plant–pollinator dataset.