Biodiversity Science ›› 2017, Vol. 25 ›› Issue (8): 799-806.doi: 10.17520/biods.2015218

• Original Papers: Plant Diversity • Previous Article     Next Article

Potential distribution range and conservation strategies for the endangered species Amygdalus pedunculata

Jianmin Chu1, Yifu Li2, Lei Zhang1, Bin Li1, Mingyuan Gao1, Xiaoqian Tang1, Jianwei Ni1, Xinqiao Xu1, *()   

  1. 1 Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091
    2 College of Forestry, Southwest Forestry University, Kunming 650224
  • Received:2016-11-22 Accepted:2017-02-17 Online:2017-08-31
  • Xu Xinqiao E-mail:xqx210@126.com

The endangered shrub species Amygdalus pedunculata is distributed in Inner Mongolia Autonomous Region and Shaanxi Province, China. However, little is known about its resource quantity and conservation status. A field investigation was conducted to determine the natural distribution range of A. pedunculata. Eight environmental factors were chosen to build models with the genetic algorithm for rule-set prediction (GARP) model and maximum entropy (MaxEnt) model. We predicted the potential distribution of A. pedunculata in China. Using four model evaluation metrics (Kappa, true skill statistic (TSS), overall accuracy and area under the receiver operating characteristic curve (AUC)), we assessed the predictive performance of both models. The Jackknife method was used to investigate the most important environmental factors for the distribution of A. pedunculata. The results indicated that both models were effective for predicting the distribution of A. pedunculata, but MaxEnt performed better than GARP in terms of the four evaluation metrics. The species was predicted to have a broad suitable area, which ranged from the south of Daxing’anling to the east of Helan Mountains and the Ulan Buh Desert. Amygdalus pedunculata is mainly distributed in the middle regions of Inner Mongolia, including Mu Us Sandy Land, Kubuqi desert, Otindag Sandy Land, and the Wulashan and Daqingshan Mountains. Low suitable sites occurred in some regions of Liaoning, Hebei, Shanxi and Shaanxi. Besides, and there were some sporadic low suitable areas in the middle regions of the Ningxia Hui Autonomous Region and Gansu Province. Variable importance analysis showed that the variables relevant to precipitation had more significant effects on the geographic distribution of A. pedunculata. These results have important implications for resource conservation and ecology including the revegetation of semi-arid ecosystems.

Key words: Amygdalus pedunculata, potential distribution area, MaxEnt model, GARP model, species conservation

Table 1

Environmental variables used in the distribution modeling of Amygdalus pedunculata"

数据代码
Code
环境因子
Environmental variables
BIO2 平均温度日较差 Mean diurnal temperature range
BIO3 等温性 Isothermality
BIO5 最热月最高温度 Max. temperature of the warmest month
BIO6 最冷月最低温度 Min. temperature of the coldest month
BIO7 平均温度年较差 Temperature annual range
BIO13 最湿月降水量 Precipitation of wettest month
BIO14 最干月降水量 Precipitation of driest month
BIO15 降水量季节性 Precipitation seasonality

Fig. 1

Potential distributions of Amygdalus pedunculata predicted by genetic algorithm for rule-set prediction (GARP) model and maximum entropy (MaxEnt) model"

Table 2

Predictive accuracy of genetic algorithm for rule-set prediction (GARP) model and maximum entropy (MaxEnt) model"

模型类型
Model type
Kappa 真实技巧统计法
True skill statistic (TSS)
总体精度
Overall accuracy
受试者工作特征曲线下面积 Area under the receiver operating characteristic curve (AUC)
MaxEnt 0.98 ± 0.01 0.98 ± 0.14 0.99 ± 0.01 1.00 ± 0.002
GARP 0.95 ± 0.01 0.95 ± 0.01 0.98 ± 0.01 0.98 ± 0.01

Table 3

Comparison of prediction area between the GARP model and the MaxEnt model"

预测面积 Prediction area (×10,000 km2)
MaxEnt GARP
高适宜区 High suitability area 11.9373 48.2647
次适宜区 Moderate suitability area 12.1625 24.2406
低适宜区 Low suitability area 26.0358 18.4617
总计 Total 50.1356 90.9670

Fig. 2

Importance of environmental factors estimated by MaxEnt model. Blue bars indicate the importance of the variable of interest. Green bars indicate the importance of all variables without the variable of interest. The longer the bar, the more important the variables. The abbreviations of climatic variables are the same as in Table 1."

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