title: Predictive distribution modeling for rare Himalayan medicinal plant Berberis aristata DC.
abstract: Predictive distribution modelling of Berberis aristata DC, a rare threatened plant with high medicinal values has been done with an aim to understand its potential distribution zones in Indian Himalayan region. Bioclimatic and topographic variables were used to develop the distribution model with the help of three different algorithms viz. Genetic Algorithm for Rule-set Production (GARP), Bioclim and Maximum entropy (MaxEnt). Maximum entropy has predicted wider potential distribution (10.36%) compared to GARP (4.63%) and Bioclim (2.44%). Validation confirms that these outputs are comparable to the present distribution pattern of the B. aristata. This exercise highlights that this species favours Western Himalaya. However, GARP and MaxEnt's prediction of Eastern Himalayan states (i. e. Arunachal Pradesh, Nagaland and Manipur) are also identified as potential occurrence places require further exploration.
authors.affiliation: Energy and Wetland Research Group, Centre for Ecological Sciences, Indian Institute of Science, Bangalore, 560 012, India.
authors.forename: Rajasri
authors.initials: R
authors.lastname: Ray
authors.forename: K V
authors.initials: KV
authors.lastname: Gururaja
authors.forename: T V
authors.initials: TV
authors.lastname: Ramchandra
mesh_terms: D028142:Berberis / Q000502:physiology*; D003710:Demography; D007194:India; D008954:Models, Biological; D010946:Plants, Medicinal