Comparing the efficiencies of individual-scoring and complementarity-based algorithms in prioritizing existing protected areas

Abstract

Evaluation of conservation priorities in existing protected areas (PAs) is critical for effective allocation of limited conservation resources and optimization of PA structure. Traditional approaches of prioritizing within existing PAs are based on individual-scoring and complementarity-based algorithms, and are often guided by individual natural features rather than complementarity among PAs. However, the efficiencies of synthesizing individual-scoring and complementarity-based algorithms in prioritizing PAs are still unknown. Here, we implemented three algorithms for prioritizing 42 Nature Reserves (NRs) in Yunnan Province, China, including an individual-scoring algorithm (ISA), species-number complementarity algorithm (SNCA), and a new species-value complementarity algorithm (SVCA) which synthesized the former two algorithms. Subsequently, the species number and species conservation value evaluated the efficiencies of three algorithms and explored their implications in conservation planning. We found that the spatial distribution of priority levels of NRs varied in different biogeographical regions, and that most of the national NRs received higher conservation priorities than provincial NRs in all three algorithms. Additionally, the average species number conservation efficiency in SNCA was 3.45 and 1.61% higher than in ISA and SVCA, respectively, while the average species conservation value efficiency in SVCA was 1.83 and 3.37% higher than in SNCA and ISA. These results indicated that SNCA could protect more species at a minimum cost if conservation planning was aimed only at protecting the number of species. However, when species conservation value is the focus of conservation planning, SVCA was more cost-effective. Our analysis supports efforts at efficient allocation of conservation resources in individual PAs and entire PA network at different biogeographical scales, and informs decision-makers on leveraging cost-effective conservation efforts in the future.