Data from: Optimizing passive acoustic monitoring (PAM) for Biodiversity Studies: using species-area relationship (SAR) to predict species richness
Data files
Sep 16, 2025 version files 258.24 KB
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data_10_Campos_y_Malezales.csv
255.96 KB
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README.md
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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 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.
Dataset DOI: 10.5061/dryad.0rxwdbsdd
Description of the data and file structure
This dataset compiles avian occurrence records obtained through passive acoustic monitoring within the Campos and Malezales ecoregion of Argentina. The analysis is based on 1,152 one-minute audio recordings collected using autonomous recording units (ARUs). Each audio file was manually inspected to identify all vocally detected species. These data form part of a standardized series of acoustic matrices for Neotropical biomes, ensuring compatibility and comparability across studies. Their principal value lies in providing organized and reusable information on avian richness and occurrence at a per-minute recording scale. This granularity facilitates subsequent analyses on diversity, acoustic patterns, and the optimization of passive acoustic monitoring (PAM) protocols in Neotropical landscapes through the application of species accumulation curves (SARs).
Data set description
This dataset compiles bird occurrence data obtained from the manual inspection of 1,152 one-minute audio recordings collected in the Southern Cone Mesopotamian Savanna (Campos y Malezales ecoregion, Argentina). Species presence was annotated through direct listening of each file.
The dataset is provided as a comma-separated values (CSV) file, consistent with the structure used in Rosa et al. (2024). In this series, the dataset corresponds to reference number #10 (data_10_Campos_y_Malezales.csv). The table includes a first column “Species” listing all identified species, and the subsequent columns, each representing an individual one-minute recording. The species detections are encoded as 1 (present) or 0 (absent).
These data complement similar surveys from Amazonia and Caatinga published by Rosa et al. (2024), allowing comparative analyses of acoustic biodiversity patterns across Neotropical biomes.
Access information
Other publicly accessible locations of the data:
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Data was derived from the following sources: