LAUSR

The prediction of species distribution is emerging as an important tool for conservation and rehabilitation planning of economically important plant species including medicinal and aromatic plants. Paris polyphylla, an economically and high valued medicinal plant of Indian Himalayan Region (IHR) is reported to lose its habitat area due to over-exploitation. In this study, attempts were made to understand vegetation pattern and find the potential distribution of Paris polyphylla, using MaxEnt in Pindar Valley (2000–4000 m ASL), Bageshwar district of Uttarakhand state, India. MaxEnt is a machine learning data mining technique that estimates a species distribution by finding the distribution of maximum entropy, based on existing knowledge. Phytosociological studies revealed the varying density of target species ranging from 0.50 ind/m2 (P1; 2000 m asl) to 2.00 ind/m2 (P6; 2500 m asl). Among the different geographical provinces of Uttarakhand, MaxEnt predicted wider potential distribution of P. polyphylla in temperate forest with an overall estimated area of 790.85 km2 as suitable. It was found that Precipitation, Elevation and Vegetation type were the most important variables for predicting the habitat suitability. Dwali (ca. 2500 m ASL) site was predicted as a potential distribution area of P. polyphylla with ground-based validation and further suggested this area as a potential reintroduction site. The results of this study can be used to plan protection and re-introduction of P. polyphylla in the region. MaxEnt approach is thus a very promising tool in species conservation planning and in predicting the potential distribution of other high valued medicinal plants of IHR.