Maintaining connectivity for broad-scale ecological processes like dispersal and gene flow is essential for conserving endangered species in fragmented landscapes. However, determining which habitats should be set aside to promote connectivity has been difficult because existing models cannot incorporate effects of multiple pathways linking populations. Here, we test an ecological connectivity model that overcomes this obstacle by borrowing from electrical circuit theory. The model vastly improves gene flow predictions because it simultaneously integrates all possible pathways connecting populations. When applied to data from threatened mammal and tree species, the model consistently outperformed conventional gene flow models, revealing that barriers were less important in structuring populations than previously thought. Circuit theory now provides the best-justified method to bridge landscape and genetic data, and holds much promise in ecology, evolution, and conservation planning.

Resource managers often need scientific information to match their decisions (typically short‐term and local) to complex, long‐term, large‐scale challenges such as adaptation to climate change. In such situations, the most reliable route to actionable science is coproduction, whereby managers, policy makers, scientists, and other stakeholders first identify specific decisions to be informed by science, and then jointly define the scope and context of the problem, research questions, methods, and outputs, make scientific inferences, and develop strategies for the appropriate use of science. Here, we present seven recommended practices intended to help scientists, managers, funders and other stakeholders carry out a coproduction project, one recommended practice to ensure that partners learn from attempts at coproduction, and two practices to promote coproduction at a programmatic level. The recommended practices focus research on decisions that need to be made, give priority to processes and outcomes over stand‐alone products, and allocate resources to organizations and individuals that engage in coproduction. Although this article focuses on the coproduction of actionable science for climate change adaptation and natural resource management, the approach is relevant to other complex natural‐human systems.