Summary 1.Wildlife managers are limited in the inferences they can draw about low density populations. These limits are imposed by biases in monitoring data not regularly accounted for. 2. We developed a Bayesian hierarchical model to correct biases arising from imperfect detection and spatial autocorrelation. Our analysis incorporated model selection uncertainty by treating model probabilities as parameters to be estimated in the context of model fitting. We fitted our model to count data from a monitoring programme for the mountain plover Charadrius montanus, a low density bird species in Nebraska, USA. 3. Our results demonstrated that previous accounts of the abundance and distribution of plovers in Nebraska were impacted by low detection probabilities ($5-20%). Uncorrected relative abundance estimates showed that the average number of birds per agricultural section increased over time, whereas corrected estimates showed that average abundance was stable. 4. Our method spatially interpolated relative abundance to produce distribution maps. These predictions suggested that birds were selecting some sites more frequently than others based on some habitat feature not explored in our study. Variation in mountain plover abundance appeared more heavily influenced by changes in the number of individuals occupying a few high quality sites, rather than from changes in abundance across many sites. Thus, conservation efforts may not be as efficient when focusing on low to moderate quality sites. 5. Synthesis and applications. Managers who must make decisions based on data-poor systems should adopt rigorous statistical approaches for drawing inferences. Spatial predictions provide information for deciding where to implement management, which is just as important as knowing what kind of management to apply. Our approach provides a step in the direction of making the biological signal in data-poor monitoring programmes more informative for conservation and management.

Many conservation planning frameworks rely on the assumption that one should prioritize locations for management actions based on the highest predicted conservation value (i.e., abundance, occupancy). This strategy may underperform relative to the expected outcome if one is working with a limited budget or the predicted responses are uncertain. Yet, cost and tolerance to uncertainty rarely become part of species management plans. We used field data and predictive models to simulate a decision problem involving western burrowing owls (Athene cunicularia hypugaea) using prairie dog colonies (Cynomys ludovicianus) in western Nebraska. We considered 2 species management strategies: one maximized abundance and the other maximized abundance in a cost‐efficient way. We then used heuristic decision algorithms to compare the 2 strategies in terms of how well they met a hypothetical conservation objective. Finally, we performed an info‐gap decision analysis to determine how these strategies performed under different budget constraints and uncertainty about owl response. Our results suggested that when budgets were sufficient to manage all sites, the maximizing strategy was optimal and suggested investing more in expensive actions. This pattern persisted for restricted budgets up to approximately 50% of the sufficient budget. Below this budget, the cost‐efficient strategy was optimal and suggested investing in cheaper actions. When uncertainty in the expected responses was introduced, the strategy that maximized abundance remained robust under a sufficient budget. Reducing the budget induced a slight trade‐off between expected performance and robustness, which suggested that the most robust strategy depended both on one's budget and tolerance to uncertainty. Our results suggest that wildlife managers should explicitly account for budget limitations and be realistic about their expected levels of performance. © 2013 The Wildlife Society.