Investigating the planning efficiency of species richness- and complementarity-based algorithms in data deficient areas

Abstract

Efficient conservation planning is a necessary approach for protecting endangered species with deficient distribution data and reducing biodiversity loss. Richness- and complementarity-based algorithms can improve conservation planning efficiency to different degrees, but differences in planning efficiency caused by data availability and algorithms are often disregarded and require clarification. Here, we classified endangered, endemic, and national key protected plant species based on their occurrence data availability for species distribution modeling. We implemented species richness- (SRA), species number complementarity- (SNCA) and species value complementarity-based algorithms (SVCA) to identify priority conservation areas (PCAs) in the Southeast Himalaya Biodiversity Priority Conservation Area. We established six scenarios and compared their planning efficiencies. The spatial distribution of PCAs and their conservation efficiency varied depending on data availability and optimization algorithms. The 17%, 30%, and 45% PCAs identified in the six scenarios differed in geographical pattern, whereas differences in species occurrences had less effect on their conservation efficiency. Specifically, the PCAs identified by using species with sufficient data as surrogates captured species with high conservation value. Additionally, SNCA was better suited for capturing species number, and SVCA was more cost-effective for protecting species area and species of high conservation value, but SRA distribution pattern presented less fragmented and well-connected. Nevertheless, 50.11% area of the optimized PCAs remained uncovered by existing protected areas. Our analysis reveals the influence of species occurrence data availability, and algorithms on planning efficiency, and provides a cost-effective solution for improving planning efficiency and optimizing protected area network in data deficient areas.