Comprehensive conservation planning based on multiple biodiversity metrics

On-the-ground conservation efforts require managers to balance various and
sometimes conflicting conservation goals. For instance, areas important
for conserving threatened and endangered species may have little spatial
agreement with high functional redundancy. Using prioritization tools can
further complicate conservation prioritizations if conflicting diversity
metrics identify different high-priority areas. We compared five
community-level diversity metrics for birds across the conterminous US to
identify how much agreement existed between each before and after using a
prioritization framework. We examined spatial agreement among
metrics before (a priori) and after (a posteriori) prioritization using
integer linear programming. We compared a posteriori outputs for 10% and
30% conservation goals. We also assessed data layer correlation and
agreement (i.e., overlap) a prior and a posterior. As expected, the a
priori diversity metrics were poorly to moderately correlated (median =
0.31, range = 0.11 to 0.71), but all a posteriori solutions had areas of
agreement. Accordingly, our a posteriori metrics identified different
areas as high-priority for conservation, none aligning well with current
protected areas (mean = 13-15% agreement). However, the a posteriori
approach allowed us to include a continuity constraint (identify adjacent
important pixels) and easily find areas of high-priority
agreement.  Metric agreement depended on a priori or a posteriori
evaluation, highlighting managers' challenges when deciding where and
how to enact conservation. Given these challenges, a posteriori solutions
best support multiple-objective, complex, and large conservation planning
conservation problems. Importantly, all of our a posteriori maps agreed in
areas, suggesting aggregates of several metrics could instill certainty in
decision-making if prioritization solutions were obtained at different
times. Overall, our results underscore the critical importance of
generating maps and metrics useful for on-the-ground management, carefully
selecting biodiversity metrics that best reflect conservation goals, and
employing prioritization software for generating conservation solutions.