Abstract
Habitat suitability models are powerful tools in predicting species distributions and assessing the potential impacts of environmental changes. In this article, a habitat suitability model was developed for Paradoxornis flavirostris, a threatened (Vulnerable) bird species found in the northeastern part of India, using remote sensing data and machine learning techniques. The occurrence records for P. flavirostris were considered from primary as well as multiple secondary sources like GBIF & eBird, and bioclimatic variables such as temperature, precipitation, and humidity were collected from www.worldclim.org. Then, MaxEnt algorithm was used to model the habitat suitability of P. flavirostris based on the collected data. Additionally, the model was also run to project the future range of P. flavirostris under different climate change scenarios. The model also predicts potentially suitable habitats for P. flavirostris outside of its current range, suggesting areas where the species may expand or contract its distribution in the future. This research provides valuable insights into the habitat suitability and potential range dynamics of P. flavirostris, and can inform conservation planning and management efforts for this threatened bird species.