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a b s t r a c tThere is growing demand for information regarding the impacts of decisions on ecosystem services and human benefits. Despite the large and growing quantity of published ecosystem services research, there remains a substantial gap between this research and the information required to support decisions.Research often provides models and tools that do not fully link social and ecological systems; are too complex, specialized, and costly to use; and are targeted to outcomes that differ from those needed by decision makers. Decision makers require cost-effective, straightforward, transferable, scalable, meaningful, and defensible methods that can be readily understood. We provide illustrative examples of these gaps between research and practice and describe how researchers can make their work relevant to decision makers by using Benefit Relevant Indicators (BRIs) and choosing models appropriate for particular decision contexts. We use examples primarily from the United States, including cases that illustrate varying degrees of success in closing these gaps. We include a discussion of the challenges and opportunities researchers face in adapting their work to meet the needs of practitioners.

Sustainability challenges for nature and people are complex and interconnected, such that effective solutions require approaches and a common theory of change that bridge disparate disciplines and sectors. Causal chains offer promising approaches to achieving an integrated understanding of how actions affect ecosystems, the goods and services they provide, and ultimately, human well-being. Although causal chains and their variants are common tools across disciplines, their use remains highly inconsistent, limiting their ability to support and create a shared evidence base for joint actions. In this article, we present the foundational concepts and guidance of causal chains linking disciplines and sectors that do not often intersect to elucidate the effects of actions on ecosystems and society. We further discuss considerations for establishing and implementing causal chains, including nonlinearity, trade-offs and synergies, heterogeneity, scale, and confounding factors. Finally, we highlight the science, practice, and policy implications of causal chains to address real-world linked human–nature challenges.