The potential for passive acoustic monitoring and automated detection to improve conservation efforts of tarsiers in Sulawesi, Indonesia

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Abstract

Tarsiers are small, haplorrhine primates that occur in Southeast Asia. Tarsiers on the island of Sulawesi range from Vulnerable to Critically Endangered, and many are data deficient, which means there is a great need for improved monitoring approaches. Sulawesi tarsiers are pair-living, territorial, and engage in duets within human hearing range, which makes them ideal candidates for passive acoustic monitoring (PAM), an approach that relies on autonomous acoustic recording units. Here, we provide a proof-of-concept workflow that combines PAM, automated detection, and a simple occupancy modeling example to monitor Gursky's spectral tarsier (Tarsius spectrumgurskyae) in Tangkoko National Park, North Sulawesi, Indonesia. We used a custom trained BirdNET model deployed over ~520 hours of PAM data and manually verified all detections. Similar to previous work, we found that the majority of tarsier duet vocalizations occurred around sunrise, with a few vocalizations emitted in a non-duet context other times during the night. Using the true positive detections, we were able to create a detection history for occupancy modeling. Our simple occupancy modeling yielded occupancy and detection estimates consistent with expectations for the well-studied population at this site. We advocate that future work includes occupancy modeling across land use gradients, different forest types, and under different management regimes, to improve tarsier conservation efforts across Sulawesi. We provide the labeled training data and the trained model to facilitate future work.
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This is a Preprint and has not been peer reviewed. This is version 2 of this Preprint. You must log in to post a comment. There are no comments or no comments have been made public for this article. This is a Preprint and has not been peer reviewed. This is version 2 of this Preprint. Add a Comment You must log in to post a comment. Comments There are no comments or no comments have been made public for this article. Tarsiers are small, haplorrhine primates that occur in Southeast Asia. Tarsiers on the island of Sulawesi range from Vulnerable to Critically Endangered, and many are data deficient, which means there is a great need for improved monitoring approaches. Sulawesi tarsiers are pair- living, territorial, and engage in duets within human hearing range, which makes them ideal candidates for passive acoustic monitoring (PAM), an approach that relies on autonomous acoustic recording units. Here, we provide a proof-of-concept workflow that combines PAM and automated detection to monitor Gursky's spectral tarsier (Tarsius spectrumgurskyae) in Tangkoko National Park, North Sulawesi, Indonesia. We used a custom trained BirdNET model deployed over ~520 hours of PAM data and manually verified all detections. Similar to previous work, we found that the majority of tarsier duet vocalizations occurred around sunrise, with a few vocalizations emitted in a non-duet context other times during the night. To our knowledge, this is one of the first applications of deep learning-based automated detection for the study of tarsiers. We advocate for future PAM studies across land use gradients, different forest types, and under different management regimes, to improve tarsier conservation efforts across Sulawesi. We provide the labeled training data and the trained model to facilitate future work. https://doi.org/10.32942/X2DT0M Life Sciences transfer learning, autonomous recording units, nonhuman primates, bioacoustics, autonomous recording units, nonhuman primates Published: 2026-02-09 15:07 Last Updated: 2026-05-09 04:36 CC-BY Attribution-NonCommercial 4.0 International Conflict of interest statement: None Data and Code Availability Statement: All scripts needed to recreate the analyses are openly available on GitHub (https://github.com/DenaJGibbon/tarsier-automated-detection). Training data, manually verified true and false positive tarsier detections, and the trained BirdNET model are available on Zenodo (https://doi.org/10.5281/zenodo.18483496). Language: English

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