Research Projects

The Shola Sky Islands (SSI) of the Western Ghats is an exceptional biodiversity hotspot because this montane forest is home to many endemic and endangered avian species (EAS) including, Nilgiri Blue Robin (Sholicola major) and White bellied Robin (Sholicola albiventris). The existence of these ecologically significant avian communities is jeopardised by extensive deforestation and habitat disruption by invasive species and unregulated tourism. Holistic monitoring of EAS populations remains a major challenge due to the secluded nature of the region and elusive behaviour of these species. Though SSI falls within the protected areas, their isolated nature and limited connectivity pose a challenge to traditional conservation efforts. Consequently, several studies in the SSI have adopted passive acoustic monitoring (PAM) to understand avian acoustic dynamics, especially the species detection framework for the identification of Jerdon's courser. These studies have helped document the distribution and acoustic behaviour of threatened avian species and assess landscape level restoration. However, the current approaches rely on manual identification of species, which require expert knowledge and makes it hard to scale up and labour intensive for long-term monitoring. Recent advancements in PAM and Artificial intelligence (AI) present an opportunity to overcome these limitations by paving the way for accelerated data analysis, and real-time alerts. This project intends to develop an automated acoustic detection and monitoring system for EAS and its habitats to provide critical insights into species presence, behaviour and habitat use through bioacoustic indicators utilising the potential of low-cost acoustic recorders and machine learning (ML) algorithms such as Convolutional Neural Network (CNN). The distinct, temporarily structured acoustic behaviours of EAS can be reliably identified using PAM and ML techniques with high precision. The potential study sites are selected ecological zones across the SSI covering a gradient from the west to the east of the landscape - from Kodanad to Sispara and Silent Valley. Resultant data on bird seasonality across this vast landscape is a feeder for the creation of baselines on the phenology of birds and will serve as early warning for climate change in SSI. This research will generate an open-access tool and data repository to support ecological insights, guide conservation planning, inform policy, and raise community awareness in the SSI. The application of AI-ML technology in avian ecology can provide innovative and sustainable conservation solutions that can be applied to other landscapes and species.