Predictive distribution modeling for rare Himalayan medicinal plant Berberis aristata DC

Rajasri Ray, K. V. Gururaja, T. V. Ramchandra

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45 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)725-730
Number of pages6
JournalJournal of Environmental Biology
Issue number6
Publication statusPublished - 11-2011

All Science Journal Classification (ASJC) codes

  • Environmental Engineering
  • Toxicology
  • Health, Toxicology and Mutagenesis


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