Optimality in prioritizing conservation projects

The resources available to safeguard biodiversity are limited, and so funding must be allocated cost-effectively. To achieve this, conservation projects-such as threatened species recovery projects, or pest management projects-are often prioritized using algorithms. Conventionally, prioritizations have been generated by ranking projects according to their cost-effectiveness and selecting the top projects within a budget, or using backwards heuristic algorithms which iteratively remove projects until a budget is met. Yet such algorithms may not deliver optimal solutions. We investigated the performance of exact algorithms, a class of algorithms that guarantee optimality, compared with conventional algorithms for project prioritization. Specifically, we conducted a simulation study, involving 40 conservation projects, 50 species, and 80 management actions, and a case study involving recovery projects for 62 of New Zealand's threatened bird species. In each of these studies, we generated prioritizations by (i) exact algorithms, (ii) ranking projects using cost-effectiveness, (iii) a backwards heuristic algorithm, and (iv) randomly funding projects. After generating the prioritizations, we evaluated their performance. We found that exact algorithms outperform conventional algorithms for project prioritization. In the simulation study, both the ranking and backwards heuristic algorithms returned solutions that were highly suboptimal when compared with solutions by exact algorithms. In the case study, both conventional algorithms returned solutions that would be expected to result in the needless loss of millions of years of avian evolutionary history due to poor planning. Furthermore, conventional algorithms returned solutions with large amounts of unallocated funding-providing little guidance for decision makers. Despite concerns that exact algorithm solvers require an inordinate amount of time, the longest run in either study took less than three minutes. Our results suggest that conservation agencies could benefit enormously from exact algorithms. To help make exact algorithms more accessible, we developed the oppr R package () which can use open-source and commercial exact algorithm solvers to identify optimal solutions for a range of objectives and constraints. Our findings suggest that conservation plans could be substantially improved using exact algorithms, which could potentially save millions of dollars and lead to more species being saved from extinction.