About the Project
Project Name: Integrating Wildlife Conservation & Technology Through Acoustic Monitoring of Wild Ruffed Lemurs
Grantee: Carly Batist
Discipline: Biological Anthropology
Funding Cycle: 2018-2019
Summary
With the development of complex equipment, technology, and software over the past decade, wildlife conservation practices have begun to utilize these novel methods to monitor protected areas and endangered species within them. Madagascar is a biodiversity hotspot, but only 10% of the original forest remains. Given that, it is shocking that conservation technology is not used more readily, especially with regard to the island’s infamous lemurs. Black-and-white ruffed lemurs (Varecia variegata) are diurnal, arboreal lemurs native to Madagascar’s eastern montane rainforests. They are a critically-endangered species due to habitat destruction, hunting, and climate change. V. variegata vocalizations have never been studied in the wild before, and we know next to nothing about the acoustic structure or function of calls despite their complex vocal repertoire and deafening roar-shriek choruses. Additionally, because they are so vocal, they are an ideal species to use acoustic monitoring technology with to detect the presence of species in different areas and estimate abundance and ranging patterns. For this project, I studied a 40-individual community of V. variegata at Mangevo, Ranomafana National Park, Madagascar. My team and I conducted full-day behavioral observations to understand what happens right before and right after specific vocalizations. We also used direct acoustic monitoring to record vocalizations during these behavioral observations. Additionally, this study was the first ever to use passive acoustic monitoring (PAM) on a lemur species. PAM uses remotely-activated, sound-triggered recording devices that are placed throughout a study area to detect the vocalizations of species of interest. Because PAM generates so much data, I will be running a machine learning algorithm that uses recordings from the direct monitoring as training data to extract Varecia calls from the passive monitoring data.
