An integrated passive acoustic monitoring and deep learning pipeline for black-and-white ruffed lemurs (Varecia variegata) in Ranomafana National Park, Madagascar.

The urgent need for effective wildlife monitoring solutions in the face of global biodiversity loss has resulted in the emergence of conservation technologies such as passive acoustic monitoring (PAM). While PAM has been extensively used for marine mammals, birds, and bats, its application to primates is limited. Black-and-white ruffed lemurs (Varecia variegata) are a promising species to test PAM with due to their distinctive and loud roar-shrieks. Furthermore, these lemurs are challenging to monitor via traditional methods due to their fragmented and often unpredictable distribution in Madagascar's dense eastern rainforests. Our goal in this study was to develop a machine learning pipeline for automated call detection from PAM data, compare the effectiveness of PAM versus in-person observations, and investigate diel patterns in lemur vocal behavior. We did this study at Mangevo, Ranomafana National Park by concurrently conducting focal follows and deploying autonomous recorders in May-July 2019. We used transfer learning to build a convolutional neural network (optimized for recall) that automated the detection of lemur calls (57-h runtime; recall = 0.94, F1 = 0.70). We found that PAM outperformed in-person observations, saving time, money, and labor while also providing re-analyzable data. Using PAM yielded novel insights into V. variegata diel vocal patterns; we present the first published evidence of nocturnal calling. We developed a graphic user interface and open-sourced data and code, to serve as a resource for primatologists interested in implementing PAM and machine learning. By leveraging the potential of this pipeline, we can address the urgent need for effective primate population surveys to inform conservation strategies.

Medicinal plants used by women from Agnalazaha littoral forest (Southeastern Madagascar).

BACKGROUND: The country of Madagascar is renowned for its high level of biodiversity and endemism, as well as the overwhelming pressures and threats placed on the natural resources by a growing population and climate change. Traditional medicine plays an important role in the daily lives of the Malagasy for various reasons including limited access to healthcare, limited markets and traditional values. The objective of this study was to assess the modern utitilization of the Agnalazaha Forest by the local population in Mahabo-Mananivo, Madagascar, for medicinal plants used by women, and to establish a list of medicinal plants used by women sourced from Agnalazaha Forest. METHODS: Ethnobotanical studies were conducted over a period of five months in 2010 to determine the diversity of medicinal plants used by women in the commune of Mahabo-Mananivo. In all, 498 people were interviewed, both male and female ranging age from 15 to over 60 years old. RESULTS: 152 medicinal plants used by local people were collected during the ethnobotanical studies. Among the recorded species, eight native species are widely used by women. These species are known for their therapeutic properties in treating placental apposition and complications during childbirth as well as tropical illnesses such as malaria, filariasis, and sexual diseases like gonorrhea and syphilis. CONCLUSIONS: Littoral forests are rare ecosystems that are highly threatened on the island nation of Madagascar. Our investigation into the use of medicinal plants sourced from and around the Agnalazaha Forest by the women of Mahabo-Mananivo reinforces the need for this natural resource as a first line of health care for rural families.