Biodiv Sci ›› 2016, Vol. 24 ›› Issue (10): 1117-1128.DOI: 10.17520/biods.2016164

• Orginal Article • Previous Articles     Next Articles

Potential distribution of wild Camellia oleifera based on ecological niche modeling

Xiangyan Cui1,2, Wenjuan Wang1,2, Xiaoqiang Yang1,2, Shu Li2, Shengyuan Qin1,2, Jun Rong1,2,*()   

  1. 1 Center for Watershed Ecology, Institute of Life Science, Nanchang University and School of Life Sciences, Nanchang University, Nanchang 330031
    2 Key Laboratory of Poyang Lake Environment and Resource Utilization, Ministry of Education, Nanchang University, Nanchang 330031
  • Received:2016-06-21 Accepted:2016-08-25 Online:2016-10-20 Published:2016-11-10
  • Contact: Rong Jun

Abstract:

Camellia oleifera is the dominant woody oil crop in China, and wild C. oleifera is a valuable genetic resource for C. oleifera breeding. Using distribution data of wild C. oleifera from the Chinese Virtual Herbarium (CVH, http://www.cvh.org.cn/), together with climate and soil data, ecological niche models were constructed with MaxEnt and genetic algorithm for rule-set prediction (GARP) models to predict the potential distribution of wild C. oleifera, and the major environmental factors affecting the distribution of wild C. oleifera were analyzed. Based on the presence probability of wild C. oleifera predicted by the models, the distribution regions of wild C. oleifera were divided into different suitable growing categories, which were then compared with actual distribution data of major C. oleifera production fields to evaluate reliability. Results indicated that the predictions of both MaxEnt and GARP models represented the distributions of C. oleifera well. The potential distribution range predicted by the GARP model was wider, while that predicted by the MaxEnt model was more accurate. Predictions of both the MaxEnt and GARP models showed that the potential distribution regions of wild C. oleifera were located mainly in China and partly in the Indo-China Peninsula. According to predictions of the MaxEnt model, the potential distribution regions of wild C. oleifera in China were matched with the distribution regions of subtropical evergreen broad-leaved forests, and the highly suitable growing regions could be divided into three large regions: (1) northeastern-southwestern trending Wuyi Mountain and adjacent mountainous regions; (2) eastern-western trending Nanling Mountain and adjacent mountainous regions; (3) northeastern-southwestern trending Wuling Mountain and adjacent mountainous regions. The analysis of the MaxEnt model showed that the major environmental factors affecting the distribution of wild C. oleifera were mean monthly diurnal temperature range, precipitation during the driest quarter, and precipitation during the warmest quarter. The vast majority of the regions with large growing areas of C. oleifera were located in the medium to highly suitable growing regions predicted by the MaxEnt model, suggesting that the division of suitable growing regions was reliable. The field investigations showed that the model predictions had high reference values for finding wild C. oleifera resources. Additionally, the study shows that using the plant distribution data from CVH and related environmental data to construct an ecological niche model can help to understand the geographic distribution of crop wild relatives.

http://jtp.cnki.net/bilingual/detail/html/SWDY201610004

Key words: wild Camellia oleifera, geographic distribution, ecological niche model, precipitation, temperature, MaxEnt model, genetic algorithm for rule-set prediction (GARP) model