A combination of budget reductions, reduced staff, and an increasing number of listed species in the United States Fish and Wildlife Service’s (US FWS) Southeast Region resulted in a large backlog of Recovery Permit Applications as of Fiscal Year 2014. Permits are issued pursuant to Section 10(a)(1)(A) of the Endangered Species Act to authorize recovery activities involving listed species; thus, a backlog of permit applications can have important implications to species recovery. In addition to the backlog, the only performance criteria being used to assess the effectiveness of the Recovery Permit Program was a measure of quantity (i.e., the number of permits processed), while there was no means in place to assess the relative conservation value of permitted activities. Finally, data produced by permitted activities was often not collected in such a way as to allow direct assessment of FWS recovery objectives. Using a decision theoretic approach, US FWS Regional Office (RO) and Field Office (FO) biologists in the SE Region worked to improve program efficiency and effectiveness by strategically redesigning the recovery permit application & reporting process.
Results/Conclusions
While framing the problem, it was recognized that the permit application backlog needed to be cleared as a means to address the more ambitious goal of developing a more effective program in terms of species recovery. To this end, a number of efficiency measures that minimized processing time were identified and subsequently adopted by the Region. Once these alternatives were identified, alternatives for revising were developed to meet objectives including maximizing species recovery, maximizing public service, and minimizing cost. Delegation alternatives, involving revisions to how processing was delegated, and expert system alternatives, involving the development of a system that would automate much of application processing and permittee reporting, were considered along with a status quo alternative. A Bayesian Network modeling approach was used to identify the expected value for each alternative in terms of objectives. The status quo alternative was clearly sub-optimal to other alternatives, while the delegation and automated system alternatives had similar expected values. Sensitivity analysis revealed that the decision was sensitive to the relative utility weights associated with recovery and cost objectives, but the decision did not change over the range of weights associated with the public service objective. The expected value of the decision was most sensitive to initial and annualized costs associated with alternative implementation.