Optimizing passive acoustic monitoring (PAM) for biodiversity studies: Using species–area relationship (SAR) to predict species richness

Abstract Passive acoustic monitoring (PAM) using autonomous recording units (ARUs) has become a key tool in long-term, low-cost ecological studies. However, one of the main challenges lies in storing and analyzing the large volume of data it generates, which requires significant processing effort and species annotation. In this context, it is crucial to establish a sampling and acoustic data analysis protocol that maximizes the efficiency of ecological information retrieval. This study proposes the application of the species–area relationship (SAR) mathematical model to optimize the use of ARUs and reduce the effort required for acoustic data analysis, aiming to predict the number of detected species in 3 Neotropical ecoregions: Amazonia, Caatinga, and Campos y Malezales. Our results suggest that increasing the number of ARUs (12 in this study) while reducing the post-recording listening effort (12 min per ARU) enhances sampling efficiency, allowing for a more accurate representation of biodiversity in the study sites. The SAR model was first applied to estimate both alpha and beta diversity in relation to sampling effort. In addition, beta diversity increased by 20% between ARUs spaced 500–1,000 m apart in Campos y Malezales; whereas in Amazonia and Caatinga, where distances between recorders were shorter (200–250 m), the increase was much smaller (0.8–5%). This highlights the importance of spatial configuration among recorders when interpreting species turnover patterns. Our findings support the design of sampling strategies adapted to different ecological contexts and levels of sampling effort. These insights provide a solid foundation for improving the management and optimization of biodiversity monitoring protocols in Neotropical environments. Similarly, they may be applied in other ecoregions using PAM, contributing to the development of more efficient methodologies for large-scale assessment of biological communities.