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Nature conservation is underresourced, requiring managers to prioritize where, when, and how to spend limited funds. Prioritization methods identify the subset of actions that provide the most benefit to an actor's objective. However, spending decisions by conservation actors are often misaligned with their objectives. Although this misalignment is frequently attributed to poor choices by the actors, we argue that it can also be a byproduct of working alongside other organizations. Using strategic analyses of multi-actor systems in conservation, we show how interactions among multiple conservation actors can create misalignment between the spending and objectives of individual actors and why current uncoordinated prioritizations lead to fewer conservation objectives achieved for individual actors. We draw three conclusions from our results. First, that misalignment is an unsuitable metric for evaluating spending, because it may be necessary to achieve actors’ objectives. Second, that current prioritization methods cannot identify optimal decisions (as they purport to do), because they do not incorporate other actors’ decisions. Third, that practical steps can be taken to move actors in the direction of coordination and thereby better achieve their conservation objectives.

Dispersal emerges as a consequence of how an individual's phenotype interacts with the environment. Not all dispersing individuals have the same phenotype, and variation among individuals can generate complex variation in the distribution of dispersal distances and directions. While active locomotion performance is an obvious candidate for a dispersal phenotype, its effects on dispersal are difficult to measure or predict, especially in small organisms dispersing in wind or currents. Therefore, we analyzed the effects of larval swimming on dispersal and settlement of coral-reef fish larvae using a high-resolution biophysical model. The model is, to date, the only biophysical model of marine larval dispersal that has been statistically validated against genetic parentage estimates of larval origin and destination, and incorporates empirically-estimated larval behaviors and their ontogeny. Larval swimming, in combination with depth, orientation and navigation behaviors, actually reduced dispersal distances compared to those of passive larvae. Swimming had no consistent effects on long distance dispersal, but increased the spread of settlement locations. Swimming speed, in contrast, did not consistently affect median dispersal distances, but faster swimming larvae had greater mean and maximum dispersal distances than slower swimming larvae. Finally, faster larval swimming speeds consistently increased the probability of settlement. Our analysis shows how larval swimming differentially affects multiple properties of dispersal kernels. In doing so, it indicates how selection could favor faster larval swimming to increase settlement and local retention, which may actually result in longer dispersal distances as a by-product of larvae trying to locate habitat rather than to disperse greater distances.

Data hungry, complex ecosystem models are often used to predict the consequences of threatened species management, including perverse outcomes. Unfortunately, this approach is impractical in many systems, which have insufficient data to parameterize ecosystem interactions or reliably calibrate or validate such models. Here we demonstrate a different approach, using a minimum realistic model to guide decisions in data- and resource-scarce systems. We illustrate our approach with a case-study in an invaded ecosystem from Christmas Island, Australia, where there are concerns that cat eradication to protect native species, including the red-tailed tropicbird, could release meso-predation by invasive rats. We use biophysical constraints (metabolic demand) and observable parameters (e.g. prey preferences) to assess the combined cat and rat abundances which would threaten the tropicbird population. We find that the population of tropicbirds cannot be sustained if predated by 1607 rats (95% credible interval (CI) [103, 5910]) in the absence of cats, or 21 cats (95% CI [2, 82]) in the absence of rats. For every cat removed from the island, the bird's net population growth rate improves, provided that the rats do not increase by more than 77 individuals (95% CI [30, 174]). Thus, in this context, one cat is equivalent to 30–174 rats. Our methods are especially useful for on-the-ground predator control in the absence of knowledge of predator-predator interactions, to assess whether 1) the current abundance of predators threatens the prey population of interest, 2) managing one predator species alone is sufficient to protect the prey species given potential release of another predator, and 3) control of multiple predator species is needed to meet the conservation goal. Our approach demonstrates how to use limited information for maximum value in data-poor systems, by shifting the focus from predicting future trajectories, to identifying conditions which threaten the conservation