Spatial modelling of critical dugong habitats (CDHs) in India

Abstract

In India, there is a scarcity of comprehensive data on dugong distribution and their habitat suitability, complicating the development of effective conservation strategies. Baseline data on priority areas for dugong conservation was provided by the Wildlife Institute of India back in 2012. Therefore, a temporal data gap exists for a re-evaluation study of CDHs through collating recent dugong occurrences and fishing pressure collected through primary surveys. Further, this study could fill the data gaps by employing advanced spatial modelling techniques and integrating primary and secondary data collected through multi-stakeholder involvement, thereby providing a robust understanding of recent and more accurate CDHs. The study focused on four key dugong ranges along the Indian coast: the Andaman and Nicobar Islands (ANI), Palk Bay and the Gulf of Mannar (PB-GoM) in Tamil Nadu, and the Gulf of Kutch (GoK) in Gujarat. These regions are recognised for their rich marine biodiversity and extensive seagrass meadows, which are critical for dugong survival. The ANI features relatively pristine marine environments, while PB-GoM and GoK are heavily impacted by fishing activities and coastal development, making them significant areas for conservation focus. The primary objectives of this study are: 1) To understand the distribution status of seagrass meadows in selected dugong habitats using in situ and remote sensing data, 2) Mapping the interface between dugong distribution and fisheries in selected dugong habitats, 3) Mapping the environmental governing factors that determine dugong distribution, and 4) Integrating environmental and habitat parameters in GIS platform to classify Critical Dugong Habitats (CDHs). The study employed a combination of in-situ surveys and remote sensing techniques to map seagrass meadows. Intertidal and subtidal surveys were conducted using the line-intercept transect method, and satellite imagery from Sentinel-2A and 2B was utilised for classification in the Google Earth Engine platform. The study highlights the challenges of mapping in turbid waters and identifies suitable classification algorithms curated for different water conditions for better mapping accuracy.