DOI: 10.5061/dryad.7sqv9s4wx
title: Comprehensive conservation planning based on multiple biodiversity metrics

description: 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.
description: # Comprehensive conservation planning based on multiple biodiversity
 metrics
 [https://doi.org/10.5061/dryad.7sqv9s4wx](https://doi.org/10.5061/dryad.7sqv9s4wx) ## Description of the data and file structure DataLayers folder subfolders: apriori = the a priori layer generated (five different TIFF files) aposteriori = the results from 10% and 30% solutions from PrioritizR software for all five data layers plus them combined comparisons = map layers showing the differences between the a priori and a posteriori layers. For all layers th or thr = Threatened and endangered richness dec = decreasing population species richness all or overall = all species richness FR or frc = functional redundancy RQ or rqc = Rao's quadratic entropy ## Sharing/Access information See the Appendix for additional details.  ## Code/Software priorityAreas_connectivity_dryadCode2.R = code that runs prioritizR in R to determine priority areas with a continuity constraint Raster_and_RF_2p5km_draydCode1.R = code that takes raster layers and runs RandomForest analyses to generate a priori layers Comparison_APriori_draydCode0.R = code that compares a priori results