Prioritizing populations based on recovery potential

For wide-ranging species, it is often too expensive or politically challenging to effectively implement conservation action across their range. In these cases, conservation actions may be vigorously applied where the situation appears most dire, but inadvertently at the expense of where success is more probable. Consequently, it is prudent to use a prioritization approach that highlights areas of probable success. Using Southern Mountain Caribou as a target species, we develop a simple algorithm that integrates scaled habitat quality measures and population characteristics known to affect the demographics of caribou and weights them according to their relative importance as defined by expert opinion. The algorithm ranks subpopulations by their relative conservation status and, as a result, how likely they are to respond to additional conservation efforts and contribute to long-term species persistence. Sensitivity analyses are then used to measure the implications of variance among key criteria and the potential variance in expert weighting. The transparent method quickly allows for real, or potential changes in criteria values, scaling, or their relative weighting, thus providing a baseline metric for conservation discussion, subpopulation comparisons, and adaptive management action. A web-based application of the algorithm can be used directly or adapted for other species. This transparent framework can be used by conservation scientists and managers for prioritizing populations for receiving recovery actions to maximize long-term conservation impact.