nstantaneous and historical temperature effects on a-pinene
Predictive distribution modeling for
rare Himalayan
medicinal plant Berberisaristata DC
Author Details
RajasriRay
Energy
and Wetland Research Group, Centre for Ecological Sciences, Indian Institute
of Science,
Bangalore - 560 012, India
K.V.Gururaja
Energy
and Wetland Research Group, Centre for Ecological Sciences, Indian Institute
of Science,
Bangalore - 560 012, India
T.V.Ramchandra
(Corresponding
author)
Energy
and Wetland Research Group, Centre for Ecological Sciences, Indian Institute
of Science,
Bangalore - 560 012, India
e-mail: cestvr@ces.iisc.ernet.in
Publication Data
Paper received:
15
May 2010
Revised
received:
21
October 2010
Accepted:
20 November 2010
Abstract
Predictive distribution modelling of Berberisaristata 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 favoursWestern Himalaya.
However, GARP and MaxEnt?s prediction of Eastern
Himalayan states (i.e.ArunachalPradesh,
Nagaland and Manipur) are also identified as
potential occurrence places require further exploration.
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Key
words
Berberisaristata, Bioclim
and Maximum entropy, Distribution modeling, GARP, Indian Himalayan region
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