EN中文

Journal of Resources and Ecology >

2018 , Vol. 9 >Issue 6: 681 - 689

DOI: https://doi.org/10.5814/j.issn.1674-764x.2018.06.011

Resources and Ecology in the Qinghai-Tibet Plateau

Predicting Potential Geographic Distribution of Tibetan Incarvillea younghusbandii Using the Maxent Model

Expand
  • 1. College of Geophysics, Chengdu University of Technology, Chengdu 610059, China;
    2. State Key Laboratory of Resource and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;
    3. School of Resources and Environmental Engineering, Ludong University, Yantai, Shandong 264025, China;
    4. Institute of Science & Technology Information of Tibet Autonomous Region, Lhasa 850000, China;
    5. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China;
    6. School of Environmental & Resource Sciences, Zhejiang Agriculture & Forestry University, Hangzhou 311300, China

Received date: 2018-04-03

  Revised date: 2018-07-28

  Online published: 2018-11-30

Supported by

National Key Technologies Research and Development Program of China (2014BAL07B02); Tibet Autonomous Region Science-technology Support Projects (201DKJGX01-38).

Fold

Abstract

Incarvillea younghusbandii is a well-known Tibetan medicinal plant with considerable development and research value distributed widely throughout the Tibetan plateau. It is important to study spatial distribution patterns of the plant in order to develop effective protection measures. Based on field survey work and environmental data, the potential geographic distribution of Incarvillea younghusbandii was delineated using a Maximum Entropy (Maxent) model with 28 environmental variables that screened for climate, topography, human activity and biological factors. Our results showed that the main geographic range of Incarvillea younghusbandii included the valley between the Yarlung Zangbo river and the Duoxiong Zangbo river, the valley in the middle section of the Himalaya Mountains, and the area between the north side of the east section of the Himalayas and the south bank of the middle reach of the Yarlung Zangbo river. Distribution may spread to parts of the eastern Himalayas. The Jackknife test indicated that soil types, ratio of precipitation to air temperature, extreme atmospheric pressure differences and annual precipitation were the most important predictive factors for the model, while other variables made relatively small contributions.

Key words： Incarvillea younghusbandii; Himalaya Mountains; Maxent Model; potential distribution

Cite this article

KAN Aike, YANG Xiao, LI Guoqing, WANG Yingjie, TESREN Luobu, ZENG Yelong, CHENG Zhenlong . Predicting Potential Geographic Distribution of Tibetan Incarvillea younghusbandii Using the Maxent Model[J]. Journal of Resources and Ecology, 2018 , 9(6) : 681 -689 . DOI: 10.5814/j.issn.1674-764x.2018.06.011

References

[1] Cao Yong, DeWalt R E, Robinson J L, et al., 2013. Using Maxent to model the historic distributions of stonefly species in Illinois streams: The effects of regularization and threshold selections.Ecological Modelling, 259: 30-39.
[2] Chen Jian, Yang Shengtian, Li Hongwei, et al., 2013. Research on geographical environment unit division based on the method of natural breaks (Jenks). ISPRS - International archives of the photogrammetry. Remote Sensing and Spatial Information Sciences, XL-4/W3: 47-50.
[3] Cui Shaopeng, Luo Xiao, Li Chunwang, et al.2018, Predicting the potential distribution of white-lipped deer using the MaxEnt model.Biodiversity Science, 26(2): 171-176. (in Chinese).
[4] Drake J M, Lodge D M, 2006. Forecasting potential distribution non-indigenous species with a genetic algorithm. Fisheries, 31(1): 9-16.
[5] Franklin J, Miller J A, 2009. Mapping species distributions: spatial inference and prediction. Cambridge University Press, Cambridge.
[6] Graham C H, Ferrier S, Huettman F, et al., 2004. New developments in museum-based informatics and applications in biodiversity analysis.Trends in Ecology and Evolution, 19(9): 497-503.
[7] Hernandez P A, Graham C H, Master L L, et al.2006. The effect of sample size and species characteristics on performance of different species distribution modeling methods.Ecography, 29(5): 773-785.
[8] Hirzel A H, Hausser J, Chessel D,et al.2002. Ecological niche factor analysis: How to compute habitat suitability maps without absence data.Ecology, 83: 2027-2036.
[9] Hirzel A, Guisan A, 2002. Which is the optimal sampling strategy for habitat suitability modelling?Ecological Modelling, 157(2-3): 331-341.
[10] Jan Harff, Michael Meyer.2009. Reconstruction of paleocoastlines for the northwestern South China Sea since the Last Glacial Maximum.Science China (Earth Sciences), 8: 1127-1136
[11] Jenks G F, 1967. The data model concept in statistical mapping. International Year Book Cartography 7, 186-190.
[12] Jenks G F, Caspall FC, 1971. Error on choroplethic maps: definition, measurement, reduction.Annals of the Association of American Geographers. 61(2): 217-244.
[13] Kumar S, Neven L G, Yee W L, 2014. Evaluating correlative and mechanistic niche models for assessing the risk of pest establishment.Ecosphere, 5(7): 1-23.
[14] Kumar S, Neven LG, Zhu Hongyu, et al., 2015. Assessing the global risk of establishment of cydia pomonella (Lepidoptera: Tortricidae) using CLIMEX and Maxent niche models.Journal of Economic Entomology, 108(4): 1708-1719.
[15] Li Kai, Sun Yuedi, Jiang Baohua, et al., 2014. Analysis on spatial-temporal patterns of the vegetation coverage and landslides in Bailongjiang River Basin based on the Dimidiate Pixel Model.Journal of Lanzhou University (Natural Sciences), 50: 376-382. (in Chinese)
[16] Li Zhonghui, Wang Enyuan, Ou Jianchun, et al., 2015. Hazard evaluation of coal and gas outbursts in a coal-mine roadway based on logistic regression model.International Journal of Rock Mechanics & Mining Sciences, 80: 185-195.
[17] Luo Hui, Zhong Gejia, Yue Lifei, et al., 2015. Traditional Tibetan medicine in China: A systematic overview of randomized clinical trials.European Journal of Integrative Medicine, 7(5): 450-459.
[18] Ma Aimin, Wang Jinfeng, Wang Dongquan, et al., 2014. Prediction of potential distribution of Anopheles sinensis in China based on Maxent. Chinese Journal of Vector Biology and Control, 25(5): 393-398.
[19] McPherson J M, Walter J, Rogers D J, 2004. The effects of species’ range sizes on the accuracy of distribution models: ecological phenomenon or statistical artefact?.Journal of Applied Ecology, 41: 811-823.
[20] Pan Weigao, Jiang Siping, Luo Peng,et al., 2012. Extracts from the roots of Incarvillea younghusbandii on antioxidant effects and life span prolonging in Drosophila melanogaster.Chinese Journal of Natural Medicines, 10(1): 48-52. (in Chinese)
[21] Pearson R G, Raxworthy C J, Nakamura M, et al., 2007. Predicting species distributions from small numbers of occurrence records: A test case using cryptic geckos in Madagascar.Journal of Biogeography, 34: 102-117.
[22] Polak T, Saltz D, 2011. Reintroduction as an ecosystem restoration technique.Conservation Biology, 25(3): 424-427.
[23] Ru Feng, Yin Aijing, Jin Jiaxin,et al., 2016. Prediction of cadmium enrichment in reclaimed coastal soils by classification and regression tree.Estuarine Coastal and Shelf Science, 177: 1-7.
[24] Stephen J T, 2014. Using vegetation data within presettlement land survey records for species distribution modeling: A tale of two datasets.Ecological Modelling, 291: 109-120.
[25] Su Donghai, Jiang Siping, Liu Hao, et al., 2011. Initial research on hematopoietic activity of incarvillea younghusbandii sprague.Sichuan Journal of Zoology, 30(1): 103-106. (in Chinese)
[26] Tan Yufan, Zuo Xiaoqing.2018. Studies on potential suitable growth areas and protection of camellia nitidissima based on GIS and Maxent model.Journal of Tropical and Subtropical Botany, 26(1): 24-32. (in Chinese).
[27] Walther G R, Post E, Convey P, et al., 2002. Ecological responses to recent climate change.Nature, 416(6879): 389-395.
[28] Wang Xiaodan, Zhong Xiaohao, Gao Pan, 2010. A GIS-based decision support system for regional eco-security assessment and its application on the Tibetan Plateau.Journal of Environmental Management, 91(10): 1981-1990.
[29] Wang Yunsheng, Xie Bingyan, Wan Fanghao, et al., 2007. Application of ROC curve analysis in evaluating the performance of alien species’ potential distribution models.Biodiversity Science, 15(4): 365-372.
[30] Warren D L, Seifert S N, 2011. Ecological niche modeling in MaXent: the importance of model complexity and the performance of model selection criteria.Ecology Applications, 21: 335-342.
[31] Warren D L, Wright A N, Seifert S N, et al., 2014. Incorporating model complexity and spatial sampling bias into ecological niche models of climate change risks faced by 90 Califomia vertebrate species of concern.Diversity and Distributions, 20: 334-343
[32] Wisz M S, Hijmans R J, Li Jin, et al., 2008. Effects of sample size on the performance of species distribution models.Diversity and Distributions, 14(5): 763-773.
[33] Wu Z, 1985. Flora of Tibet. Science Press. Beijing, China. (in Chinese)
[34] Yang Xueqing, Kushwaha S P S, Saran S, et al., 2013. MaXent modeling for predicting the potential distribution of medicinal plant, Justicia adhatoda L. in Lesser Himalayan foothills.Ecological Engineering, 51(1): 83-87.
[35] Yi Yujun, Cheng Xi, Yang Zhifeng, et al.2016. MaXent modeling for predicting the potential distribution ofendangered medicinal plant (H. riparia Lour) in Yunnan, China.Ecological Engineering, 92: 260-269.
[36] Yu Shunli, Jiang Siping, Kan Aike, et al., 2012. Investigation of distribution and resource storage amount estimation of Incarvillea compacta: One of the famous tibetan medicine plants.Beijing Agriculture, 15: 74-77. (in Chinese)
[37] Yu Yong, Zhou Pinyang, Wang Yang, et al., 2008. Assessment of risk of introduction of pine wood nematode, bursaphelenchus xylophilus in Yunnan Province using BIOCLIM ecological niche model.Journal of Yunnan Agricultural University, 23(6): 746-753. (in Chinese).
[38] Yuan Haisheng, Wei Yulian, Wang Xugao, 2015. MaXent modeling for predicting the potential distribution of Sanghuang, an important group of medicinal fungi in China.Fungal Ecology, 17: 140-145.
[39] Zhang Jiping, Zhang Yili, Liu Linshanet al., 2011. Predicting Potential Distribution of Tibetan Spruce (Picea smithiana) in Qomolangma (Mount Everest) National Nature Preserve Using Maximum Entropy Niche-based Model.Chinese Geographical Science, 21(4): 417-426.
[40] Zhang Lei, Liu Shirong, Sun Pengsen, et al., 2011. Predicting the Potential Distribution of Phyllostachys edulis with DOMAIN and Neural Ensembles Models.Scientia Silvae Sinicae, 47(7): 20-26.
[41] Zhu Yong, Geng Yupeng, Tersing T S,et al., 2009. High genetic differentiation and low genetic diversity in Incarvillea younghusbandii, an endemic plant of Qinghai-Tibetan Plateau, revealed by AFLP markers.Biochemical Systematics and Ecology, 37(5): 589-596.
Options
Outlines

/

〈 〉