Biodiversity conservation decisions are difficult, especially when they involve differing values, complex multidimensional objectives, scarce resources, urgency, and considerable uncertainty. Decision science embodies a theory about how to make difficult decisions and an extensive array of frameworks and tools that make that theory practical. We sought to improve conceptual clarity and practical application of decision science to help decision makers apply decision science to conservation problems. We addressed barriers to the uptake of decision science, including a lack of training and awareness of decision science; confusion over common terminology and which tools and frameworks to apply; and the mistaken impression that applying decision science must be time consuming, expensive, and complex. To aid in navigating the extensive and disparate decision science literature, we clarify meaning of common terms: decision science, decision theory, decision analysis, structured decision-making, and decision-support tools. Applying decision science does not have to be complex or time consuming; rather, it begins with knowing how to think through the components of a decision utilizing decision analysis (i.e., define the problem, elicit objectives, develop alternatives, estimate consequences, and perform trade-offs). This is best achieved by applying a rapid-prototyping approach. At each step, decision-support tools can provide additional insight and clarity, whereas decision-support frameworks (e.g., priority threat management and systematic conservation planning) can aid navigation of multiple steps of a decision analysis for particular contexts. We summarize key decision-support frameworks and tools and describe to which step of a decision analysis, and to which contexts, each is most useful to apply. Our introduction to decision science will aid in contextualizing current approaches and new developments, and help decision makers begin to apply decision science to conservation problems.

Pacific sand lance (Ammodytes personatus) support marine food webs in the Salish Sea, yet our knowledge of intertidal spawning habitat for this species is limited. Increasing participation in community science surveys for intertidal sand lance spawning has resulted in the detection of eggs on >90 beaches in the Canadian Salish Sea since 2001. Using this data, we developed a MaxEnt habitat suitability model using 6 environmental variables from a suite of 9. We estimate that only 5.4% of the intertidal zone of the Canadian Salish Sea has a moderate to high likelihood of providing suitable sand lance spawning habitat. This rare habitat was best predicted by its proximity to estuaries, shoreline slope, distance to predicted subtidal sand lance burying habitat, seabed substrate and aspect. Our model could be used as the basis for a Pacific coast-wide model in areas with less available information. Identifying intertidal spawning habitat of sand lance will support conservation efforts intended to maintain forage fish species.

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