Data from: Optimizing passive acoustic monitoring (PAM) for Biodiversity Studies: using species-area relationship (SAR) to predict species richness

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 three 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 minutes 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 500–1000 m apart in Campos
y Malezales, while 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.