样本容量和物种特征对BIOCLIM模型模拟物种分布准确度的影响——以12个中国特有落叶栎树种为例

doi:10.3773/j.issn.1005-264x.2009.05.005

植物生态学报 ›› 2009, Vol. 33 ›› Issue (5): 870-877.DOI: 10.3773/j.issn.1005-264x.2009.05.005

样本容量和物种特征对BIOCLIM模型模拟物种分布准确度的影响——以12个中国特有落叶栎树种为例

邵慧¹^,³, 田佳倩¹, 郭柯¹, 孙建新²^,^*()

1 中国科学院植物研究所植被与气候变化国家重点实验室,北京 100093
2 北京林业大学森林培育与保护教育部重点实验室,北京 100083
3 中国科学院研究生院,北京 100049

收稿日期:2009-03-23 修回日期:2009-05-15 出版日期:2009-03-23 发布日期:2009-09-30
通讯作者: 孙建新
作者简介:*(sunjianx@bjfu.edu.cn)
基金资助:
国家自然科学基金创新研究群体项目(30821062);国家科技基础条件建设项目(2005DKA21400)

EFFECTS OF SAMPLE SIZE AND SPECIES TRAITS ON PERFORMANCE OF BIOCLIM IN PREDICTING GEOGRAPHICAL DISTRIBUTION OF TREE SPECIES—A CASE STUDY WITH 12 DECIDUOUS QUERCUS SPECIES INDIGENOUS TO CHINA

SHAO Hui¹^,³, TIAN Jia-Qian¹, GUO Ke¹, Osbert Jianxin Sun²^,^*()

¹State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
²Key Laboratory for Silviculture and Conservation of Ministry of Education, Beijing Forestry University, Beijing 100083, China
³Graduate University of Chinese Academy of Sciences, Beijing 100049, China

Received:2009-03-23 Revised:2009-05-15 Online:2009-03-23 Published:2009-09-30
Contact: Osbert Jianxin Sun

摘要/Abstract

摘要：

物种分布模型被广泛应用于生态学、生物地理学及保护生物学等领域的研究。由于难于取样或标本记录不完善等原因, 真正能够用于模型预测的物种分布数据非常有限。因此, 有必要搞清楚样本容量和物种特征对模型模拟准确度的影响, 为确定以物种特征为区分条件的最小样本容量奠定基础。为了探讨应用BIOCLIM模型预测中国特有植物种的效果, 以12个落叶栎树种为例, 从不同的样本容量和生态特征两方面研究其对BIOCLIM模型模拟准确度的影响。结果表明: BIOCLIM模型模拟准确度随样本容量的增加在初期几乎呈直线增加趋势至样本容量达到25, 随后渐变平缓至样本容量为75~100时达到最大值。此外, 生态幅窄和环境特化物种比生态幅宽和对环境耐受性强的物种更容易获得较高的准确度。结果说明, BIOCLIM可有效地用于样本数量较小的狭域型物种分布预测。

关键词: 物种分布预测, 落叶栎, BIOCLIM模型, 模型准确度, 样本容量, 物种特征

Abstract:

Aims Our objective was to evaluate the performance of BIOCLIM in predicting geographical distributions of tree species in China and to determine the impacts of sample size and species traits on predictive accuracy.
Methods BIOCLIM model was used to predict the geographical distribution of 12 deciduous Quercus species differing in ecological traits and sample sizes. We used data from museum or herbarium collections and 18 layers of grid-data of meteorological variables at 6′×6′ resolution to run the model. We evaluated the quality of model predictions with area under the receiver operating characteristic curve (AUC) and Kappa statistic.
Important findings Model performance was improved and the variability in predictive accuracy was reduced with increasing sample size. Reasonable predictions to county-level resolution can be achieved with a sample size of 25 data points. On average, 75-100 observations were found to be sufficient to obtain maximal accuracy. Furthermore, we found that accuracy of BIOCLIM was greater for species with narrower geographical range and limited environmental tolerance than those with broader range and greater tolerance.

Key words: prediction of species distribution, deciduous oak, BIOCLIM model, predictive accuracy, sample size, species traits

邵慧, 田佳倩, 郭柯, 孙建新. 样本容量和物种特征对BIOCLIM模型模拟物种分布准确度的影响——以12个中国特有落叶栎树种为例. 植物生态学报, 2009, 33(5): 870-877. DOI: 10.3773/j.issn.1005-264x.2009.05.005

SHAO Hui, TIAN Jia-Qian, GUO Ke, Osbert Jianxin Sun. EFFECTS OF SAMPLE SIZE AND SPECIES TRAITS ON PERFORMANCE OF BIOCLIM IN PREDICTING GEOGRAPHICAL DISTRIBUTION OF TREE SPECIES—A CASE STUDY WITH 12 DECIDUOUS QUERCUS SPECIES INDIGENOUS TO CHINA. Chinese Journal of Plant Ecology, 2009, 33(5): 870-877. DOI: 10.3773/j.issn.1005-264x.2009.05.005

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链接本文: https://www.plant-ecology.com/CN/10.3773/j.issn.1005-264x.2009.05.005

https://www.plant-ecology.com/CN/Y2009/V33/I5/870

图/表 6

表1 12种落叶栎树样本容量

Table 1 Sample size of the 12 deciduous Quercus species used in this study

分布类型 Types of distribution	物种 Species	样本大小 Sample size
广布型 Broad-ranged	枹栎 Quercus serrata	138
	槲栎 Q. aliena	176
	槲树 Q. dentata	149
	麻栎 Q. acutissima	308
	锐齿槲栎 Q. aliena var. acutiserrata	209
	栓皮栎 Q. variabilis	294
	白栎 Q. fabri	268
	小叶栎 Q. chenii	59
狭域型 Narrow-ranged	辽东栎 Q. wutaishanica	137
	蒙古栎 Q. mongolica	89
	北京槲栎 Q. aliena var. pekingensis	47
	大叶栎 Q. griffithii	70

表2 AUC (Area under curve)和Kappa指数的准确度

Table 2 Accuracy represented by AUC (Area under curve) & Kappa values

准确度 Accuracy	AUC	Kappa
较差 Poor	0.5 ~ 0.6	0 ~ 0.2
一般 Fair	0.6 ~ 0.7	0.2 ~ 0.4
较准确 Good	0.7 ~ 0.8	0.4 ~ 0.6
很准确 Very good	0.8 ~ 0.9	0.6 ~ 0.8
极准确 Excellent	0.9 ~ 1.0	0.8 ~ 1.0

图1 样本容量与AUC (Area under curve)和Kappa相关性图

Fig. 1 Effects of sample size on predictive accuracy as tested by AUC (Area under curve) & Kappa statistic

表3 3个物种不同样本容量(75~150)的AUC值和 Kappa值的方差分析

Table 3 One-way ANOVA in AUC & Kappa among four sample sizes for three Quercus species

物种 Species	AUC		Kappa
物种 Species	F	p	F	p
麻栎 Q. acutissima	2.139	0.173	0.275	0.842
栓皮栎 Q. variabilis	3.511	0.069	1.121	0.396
白栎 Q. fabri	2.674^*	0.148	1.395	0.313

图2 12种落叶栎树在中国潜在地理分布模拟的AUC和Kappa值广布型 Broad-ranged: 枹栎 Q. serrata 槲栎 Q. aliena 槲树 Q. dentate 麻栎 Q. acutissima 锐齿槲栎 Q. aliena var. acutiserrata 栓皮栎 Q. variabilis 白栎 Q. fabric 小叶栎 Q. chenii 狭域型 Narrow-ranged: 辽东栎 Q. wutaishanica 蒙古栎 Q. mongolica 北京槲栎 Q. aliena var. pekingensis 大叶栎 Q. griffithii

Fig. 2 Mean AUC vs mean Kappa for 12 deciduous Quercus species

图3 两组落叶栎的AUC和Kappa值 **: 表示在0.01水平下差异显著; 误差棒代表平均值的标准误差 Indicating significant differences at 1% level among two types. The error bars represent standard errors of the means

Fig. 3 Mean AUC & Kappa values of two groups of deciduous Quercus species

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样本容量和物种特征对BIOCLIM模型模拟物种分布准确度的影响——以12个中国特有落叶栎树种为例

EFFECTS OF SAMPLE SIZE AND SPECIES TRAITS ON PERFORMANCE OF BIOCLIM IN PREDICTING GEOGRAPHICAL DISTRIBUTION OF TREE SPECIES—A CASE STUDY WITH 12 DECIDUOUS QUERCUS SPECIES INDIGENOUS TO CHINA

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