Systematic conservation planning has a substantial theoretical underpinning that allows optimization of tradeoffs between biodiversity conservation and other socioeconomic goals. However, thistheory assumes perfect spatial information about the locations of biodiversity features (e.g., species distributions). In practice, planners represent well-known taxa and other biodiversity ''surrogates'' inprotected area systems, hoping that unmapped species will also be conserved. However, empirical research finds that surrogates predict species presence imperfectly, and sometimes rather poorly, at scales relevant toplanning, and existing theory provides no further guidance. We developed new theory, explicitly incorporating aspects of spatial scale, for the representation problem when the locations of speciesdistributions are unknown. Using probability theory and simulated and real species distributions, we found that the probability of adequately representing an unmapped species in a protected area system willbe low unless the total fraction of the region being protected is larger than the species representation target. Furthermore, successful conservation depended critically on the relative sizes of the species distributionand of the individual protected areas; fewer, larger protected areas allowed the entire species distribution to fall into an unprotected gap. This scale-dependence varied with the configuration of the protected areasystem, with the conservation objective most likely to be attained if the individual protected areas were hyperdispersed (evenly spaced across the planning region). Using these results, we developed three designprinciples for representing unmapped species in protected areas: (1) The fraction of the region placed in protected areas should be substantially larger than the species-level representation target; (2) Individualprotected areas must be at least one to two orders of magnitude smaller than the unmapped species' distribution; and (3) Protected areas should be evenly dispersed over geographic space. We also performedpreliminary investigations of the effects of surrogates and socio-economic cost data on the probability of adequately representing unmapped species, finding that the primary effect of surrogates may simply be topromote hyperdispersion of protected areas across the planning region, and that seeking to minimize opportunity costs gives poorer conservation results than random protected area placement.

There are many barriers to using science to inform conservation policy and practice. Conservation scientists wishing to produce management-relevant science must balance this goal with the imperative of demonstrating novelty and rigor in their science. Decision makers seeking to make evidence-based decisions must balance a desire for knowledge with the need to act despite uncertainty. Generating science that will effectively inform management decisions requires that the production of information (the components of knowledge) be salient (relevant and timely), credible (authoritative, believable, and trusted), and legitimate (developed via a process that considers the values and perspectives of all relevant actors) in the eyes of both researchers and decision makers. We perceive 3 key challenges for those hoping to generate conservation science that achieves all 3 of these information characteristics. First, scientific and management audiences can have contrasting perceptions about the salience of research. Second, the pursuit of scientific credibility can come at the cost of salience and legitimacy in the eyes of decision makers, and, third, different actors can have conflicting views about what constitutes legitimate information. We highlight 4 institutional frameworks that can facilitate science that will inform management: boundary organizations (environmental organizations that span the boundary between science and management), research scientists embedded in resource management agencies, formal links between decision makers and scientists at research-focused institutions, and training programs for conservation professionals. Although these are not the only approaches to generating boundary-spanning science, nor are they mutually exclusive, they provide mechanisms for promoting communication, translation, and mediation across the knowledge-action boundary. We believe that despite the challenges, conservation science should strive to be a boundary science, which both advances scientific understanding and contributes to decision making.

Migratory animals are threatened by human-induced global change. However, little is known about how stopover habitat, essential for refuelling during migration, affects the population dynamics of migratory species. Using 20 years of continent-wide citizen science data, we assess population trends of ten shorebird taxa that refuel on Yellow Sea tidal mudflats, a threatened ecosystem that has shrunk by >65% in recent decades. Seven of the taxa declined at rates of up to 8% per year. Taxa with the greatest reliance on the Yellow Sea as a stopover site showed the greatest declines, whereas those that stop primarily in other regions had slowly declining or stable populations. Decline rate was unaffected by shared evolutionary history among taxa and was not predicted by migration distance, breeding range size, non-breeding location, generation time or body size. These results suggest that changes in stopover habitat can severely limit migratory populations.

Species distribution models (SDMs) are increasingly proposed to support conservation decision making. However, evidence of SDMs supporting solutions for on-ground conservation problems is still scarce in the scientific literature. Here, we show that successful examples exist but are still largely hidden in the grey literature, and thus less accessible for analysis and learning. Furthermore, the decision framework within which SDMs are used is rarely made explicit. Using case studies from biological invasions, identification of critical habitats, reserve selection and translocation of endangered species, we propose that SDMs may be tailored to suit a range of decision-making contexts when used within a structured and transparent decision-making process. To construct appropriate SDMs to more effectively guide conservation actions, modellers need to better understand the decision process, and decision makers need to provide feedback to modellers regarding the actual use of SDMs to support conservation decisions. This could be facilitated by individuals or institutions playing the role of 'translators' between modellers and decision makers. We encourage species distribution modellers to get involved in real decision-making processes that will benefit from their technical input; this strategy has the potential to better bridge theory and practice, and contribute to improve both scientific knowledge and conservation outcomes.

Synthesis research in ecology and environmental science improves understanding, advances theory, identifies research priorities, and supports management strategies by linking data, ideas, and tools. Accelerating environmental challenges increases the need to focus synthesis science on the most pressing questions. To leverage input from the broader research community, we convened a virtual workshop with participants from many countries and disciplines to examine how and where synthesis can address key questions and themes in ecology and environmental science in the coming decade. Seven priority research topics emerged: (1) diversity, equity, inclusion, and justice (DEIJ), (2) human and natural systems, (3) actionable and use-inspired science, (4) scale, (5) generality, (6) complexity and resilience, and (7) predictability. Additionally, two issues regarding the general practice of synthesis emerged: the need for increased participant diversity and inclusive research practices; and increased and improved data flow, access, and skill-building. These topics and practices provide a strategic vision for future synthesis in ecology and environmental science.