Assessing trade-offs in developing a landscape-scale nest monitoring programme for a threatened shorebird

Abstract

Effective monitoring of wildlife species requires thorough planning and development of survey programmes that can address management and conservation objectives. Decisions about monitoring programmes include where to survey, survey design and how much effort to allocate at survey sites are typically predicated on limited budgets and available resources. When the scope of inference requires monitoring on a broad spatial scale, predictions of habitat distribution or suitability may be useful for identifying potential survey sites. We focused on a threatened but widely distributed shorebird, the piping plover ( Charadrius melodus ), which is actively monitored across some, but not all of its range. Our objective was to use piping plover habitat distribution maps, which vary annually, to assess the effectiveness of multiple monitoring programme scenarios and their associated costs. In the breeding range, efforts to improve productivity for species of conservation concern often focus on improving probabilities of nest survival. Consequently, collecting adequate nesting data is crucial for obtaining accurate and precise estimates of nest survival and for evaluating the effectiveness of management actions. By simulating the nest monitoring process, we evaluated how much area, where and how often to survey each site when estimating nest survival and detecting effects of potential management actions. As expected, precision increased and bias decreased around nest survival estimates with greater survey coverage and nest visit frequency. We also identified monitoring programmes with negative net values where survey costs outweighed statistical benefits. Although we applied our simulation framework to evaluate nest monitoring designs for piping plovers, it could be extended to assess whether different monitoring programmes can detect changes in the distribution of other species or occupancy of habitats over time.