珍稀喜马拉雅药用植物长叶小檗的预测分布建模

Predictive distribution modeling for rare Himalayan medicinal plant Berberis aristata DC.

作者信息

Ray Rajasri, Gururaja K V, Ramchandra T V

机构信息

Energy and Wetland Research Group, Centre for Ecological Sciences, Indian Institute of Science, Bangalore, 560 012, India.

出版信息

J Environ Biol. 2011 Nov;32(6):725-30.

PMID:22471208

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.

摘要

对具有高药用价值的珍稀濒危植物印度小檗进行了预测分布建模，旨在了解其在印度喜马拉雅地区的潜在分布区域。利用生物气候和地形变量，借助三种不同算法，即规则集生成遗传算法（GARP）、生物气候模型和最大熵模型（MaxEnt），开发了分布模型。与GARP（4.63%）和生物气候模型（2.44%）相比，最大熵模型预测的潜在分布范围更广（10.36%）。验证证实这些输出结果与印度小檗目前的分布模式具有可比性。该研究突出表明该物种偏好西喜马拉雅地区。然而，GARP和MaxEnt对东喜马拉雅邦（即阿鲁纳恰尔邦、那加兰邦和曼尼普尔邦）的预测也表明这些地区是潜在的出现地点，需要进一步探索。