Chin J Plant Ecol ›› 2019, Vol. 43 ›› Issue (4): 273-283.doi: 10.17521/cjpe.2018.0237

• Reviews •     Next Articles

Research advances in modelling plant species distribution in China

LIU Xiao-Tong1,YUAN Quan1,NI Jian1,2,*()   

  1. 1 College of Chemistry and Life Sciences, Zhejiang Normal University, Jinhua, Zhejiang 321004, China
    2 Jinhua Mountain Observation and Research Station for Subtropical Forest Ecosystems, Jinhua, Zhejiang 321004, China
  • Received:2018-09-25 Revised:2019-03-20 Online:2019-04-23 Published:2019-04-20
  • Contact: NI Jian ORCID:0000-0001-6198-4849 E-mail:nijian@zjnu.edu.cn
  • Supported by:
    Supported by the National Natural Science Foundation of China(41471049)

Abstract:

Species distribution models (SDMs) have been extensively used in simulations of geographical distribution of animal and plant species during the past 20 years. Taking the simulation of plant species distribution as an example, we used both the digitized and library databases including the China National Knowledge Infrastructure (CNKI), the VIP Chinese Journal Database (VIP) and the Web of Science (WoS) to compile available literatures published from 2000 to 2018. The number of publications, SDMs used, target plant species, data sources, and the purpose of studies about using various SDMs to simulate plant species distribution in China was statistically investigated. In total 366 publications were collected. Further analysis and synthesis showed that the application of SDMs in simulating Chinese plant species distribution has developed rapidly since 2011, especially during the past five years. SDMs have been used in studies of ecology, Chinese traditional medicine, agriculture, and forestry. The Maximum Entropy Model (MaxEnt) is the most widely used model among 33 commonly used SDMs. A half of the studies use climate data only, and another half of the studies use both climate, soil and topography data. The source of both environmental data and plant distribution data are diverse, derived from international and domestic databases. In these studies, researchers have simulated the distribution of 562 plant species, in which 52.7% are woody species and 41.8% are herbaceous species, including a large number of Chinese medicinal plants, fruit trees, garden plants, and crops. Studies aim mainly on two aspects, i.e. the impact of climate change on plant species distribution and their predicted pattern in the past, present, and future climate scenarios, and the assessment of the potential distribution of plant species and biodiversity trends (including the risk of invasive species). In future studies, more attention should be paid to both the basic science on the modelling of potential distribution of plant species and the impact from climate change, and the applied science on the prediction of suitable distribution area of plant species in order to popularize their plantation. More applications of SDMs in multiple disciplines and in multiple industries such as ecology, forestry, crop science and Chinese traditional medicine should be further developed. Joint simulations and inter-comparisons using multiple plant species, more SDMs and multiple data sources of environmental data, as well as the development of new and mechanism SDMs are encouraged. The extension of model applications in new research fields is also needed.

Key words: species distribution models, climate change, biodiversity conservation, potential distribution area, Maximum Entropy Model

Fig. 1

Number of papers about the distribution of plant species simulated by species distribution models in China."

Table 1

Top journals publishing articles of plant distribution modelling from China (from 2001-01 to 2018-01)"

排名
Rank
期刊
Journal
载文量(篇)
Number
of papers published
1 中国中药杂志
China Journal of Chinese Materia Medica
23
2 植物生态学报 Chinese Journal of Plant Ecology 15
3 生态学报 Acta Ecologica Sinica 14
4 应用生态学报 Chinese Journal of Applied Ecology 13
5 生态学杂志 Chinese Journal of Ecology 12
6 PLOS ONE 11
7 林业科学 Scientia Silvae Sinica 7
8 中药材 Journal of Chinese Medicinal Materials 6
生物多样性 Biodiversity Science 6
9 生物安全学报 Journal of Biosafety 5
广东农业科学 Guangdong Agricultural Sciences 5
草业学报 Acta Prataculturae Sinica 5
Scientific Reports 5
10 广西植物 Guihaia 4
Ecology and Evolution 4
Polish Journal of Ecology 4

Table 2

Statistics of publications modeling plant species in China using species distribution models"

模型名称
Model
应用数量
Number of
publications
模型名称
Model
应用数量
Number of
publications
最大熵模型 Maximum Entropy Model (MaxEnt)
基于规则集的遗传算法 Genetic Algorithm for Rule-set
Prediction (GARP)
生物气候模型 BIOCLIM
广义线性模型 Generalized Linear Model (GLM)
广义相加模型 Generalized Additive Model (GAM)
随机森林 Random Forest (RF)
DOMAIN
推进式回归树 Generalized Boosted Regression Models/Boosted Regression Tree (GBM/BRT)
多元适应回归样条函数 Multivariate Adaptive Regression Splines (MARS)
人工神经网络 Artificial Neural Network (ANN)
柔性判别分析 Flexibled Discriminant Analysis (FDA)
支持向量机 Support Vector Machine (SVM)
分类树分析 Classification Tree Analysis (CTA)
分类回归树 Classification and Regression Tree (CART)
表面分布区分室模型 Surface Range Envelope (SRE)
复合型广义相加模型运算系 Mixed GAM Computation
Vehicle (MGCV)
296
27

26
21
21
19
13
13

12

11
10
9
8
6
6
3
拟合神经网络 Fit Neural Networks (NNET)
循环分区回归树 Recursive Partitioning and Regression Trees (RPART)
Logistic回归模型 Logistic Regression (LR)
CLIMEX
作物生态需求 Crop Ecological Requirements (ECOCROP)
农业生态区模型 Agriculture Ecological Zone Model (AEZ)
决策树模型 Classification Tree Model (CT)
生态位因子分析模型 Ecological Niche Factor Analysis (ENFA)
GREEN
生境适生性模型 Habitat Suitability Model (HSM)
线性判别分析 Linear Discriminant Analysis (LDA)
马氏距离 Mahalanobis Distance (MAHAL)
迭代决策树算法 Multiple Additive Regression Tree (MART)
空间明晰物种组合模型 Spatially Explicit Species Assemblage Model (SESAM)
n维环境资源模型 n-Dimentional Environment and Resource Model
生态位模型 Niche model
随机预测模型 Random Predictive model
3
3

3
2
2
1
1
1
1
1
1
1
1
1

1

1
1

Fig. 2

Number of species distribution models used in modelling China’s plant species distribution. The small plot is the number of models with MaxEnt excluded. See Table 2 for models."

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