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

参考文献 50

[1]	Brontons L, Thuiller W, Araujo MB, Hirzel AH (2004) Presence-absence versus presence-only modelling methods for predicting bird habitat suitability. Ecography, 27, 437-448. DOI URL
[2]	Carpenter G, Gillson AN, Winter J (1993). DOMAIN: a flexible modeling procedure for mapping potential distributions of plants and animals. Biodiversity and Conservation, 2, 667-680. DOI URL
[3]	Chen PF, Wiley EO, Mcnyset KM (2006). Ecological niche modeling as a predictive tool: silver and bighead carps in North America. Biological Invasions, 9, 43-51. DOI URL
[4]	Early R, Anderson B, Thomas CD (2008). Using habitat distribution models to evaluate large-scale landscape priorities for spatially dynamic species. Journal of Applied Ecology, 45, 228-238. DOI URL
[5]	Elith J, Graham CH, Anderson RP, Dudík M, Ferrier S, Gusian A, Hijmans RJ, Huettmann F, Leathwick JR, Lehmann A, Li J, Lohmann LG, Loiselle BA, Manion G, Moritz C, Nakamura M, Nakazawa Y, Overton JMC, Peterson AT, Phillips SJ, Richardson K, Pereira RS, Schapire RE, Soberón J, Williams S, Wisz MS, Zimmermann NE (2006). Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29, 129-151. DOI URL
[6]	Engler R, Guisan A, Rechsteiner L (2004). An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo- absence data. Journal of Applied Ecology, 41, 263-274. DOI URL
[7]	Fang JY, Yoda K (1991). Climate and vegetation in China V. Effect of climatic factors on the upper limit of distribution of evergreen broadleaf forest. Ecological Research, 6, 113-125. DOI URL
[8]	Fischer J, Lindenmayer DB, Nix HA, Stein JL, Stein JA (2001). Climate and animal distribution: a climatic analysis of the Australian marsupial Trichosurus caninus. Journal of Biogeography, 28, 293-304. DOI URL
[9]	Fu HG (傅焕光), Yu GM (于光明) (1978). Cultivation and Utilization of Quercus Variabilis (栓皮栎栽培与利用). China Forestry Publishing House,Beijing. (in Chinese)
[10]	Gao XM (高贤明), Wang W (王巍), Du XJ (杜晓军), Ma KP (马克平) (2001). Size structure, ecological significance and population origin of Quercus wutaishanica forest in Beijing mountainous area. Acta Phytoecologica Sinica (植物生态学报), 25, 673-678. (in Chinese with English abstract)
[11]	Gao ZT (高志涛), Wu XC (吴晓春) (2005). Discussion on regulation on geographic distribution in Mengguli. Protection Forest Science and Technology (防护林科技), 2, 83-84. (in Chinese)
[12]	Graham CH, Ferrier S, Huettman F, Moritz C, Peterson AT (2004). New developments in museum-based informatics and applications in biodiversity analysis. Trends in Ecology and Evolution, 19, 497-503. DOI URL PMID
[13]	Guisan A, Thuiller W (2005). Predicting species distribution: offering more than simple habitat models. Ecology Letters, 8, 993-1009. DOI URL
[14]	Guisan A, Zimmermann NE (2000). Predictive habitat distribution models in ecology. Ecological Modelling, 135, 147-186. DOI URL
[15]	Hernandez PA, Graham CH, Master LL, Albert DL (2006). The effect of sample size and species characteristics on performance of different species distribution modelling methods. Ecography, 29, 773-785. DOI URL
[16]	Hirzel A, Guisan A (2002). Which is the optimal sampling strategy for habitat suitability modelling? Ecological Modelling, 157, 331-341. DOI URL
[17]	Hirzel AH, Hausser J, Chessel D, Perrin N (2002). Ecological-niche factor analysis: how to compute habitat-suitability maps without absence data? Ecology, 83, 2027-2036. DOI URL
[18]	Hutchinson MF (2004). ANUSPLIN Version 4.3 User Guide. The Australian National University, Center for Resource and Environmental Studies,Canberra.
[19]	Jiang X (蒋霞), Ni J (倪健) (2005). Species-climate relationships of 10 desert plant species and their estimated potential distribution range in the arid lands of northwestern China. Acta Phytoecologica Sinica (植物生态学报), 29, 98-107. (in Chinese with English abstract)
[20]	Kadmon R, Farber O, Danin A (2003). A systematic analysis of factors affecting the performance of climatic envelope models. Ecological Applications, 13, 853-867. DOI URL
[21]	Li F (李峰), Zhou GS (周广胜), Cao MC (曹铭昌) (2006). Responses of Larix gmelinii geographical distribution to future climate change: A simulation study. Chinese Journal of Applied Ecology (应用生态学报), 17, 2255-2260. (in Chinese with English abstract)
[22]	Liu CY (刘春迎) (1999). The application of KIRA’s indices to the study of vegetation-climatic interaction in China. Acta Phytoecologica Sinica (植物生态学报), 23, 125-138. (in Chinese with English abstract)
[23]	Liu MS (刘茂松), Hong BG (洪必恭) (1999). The analysis of distribution pattern of Fagaceae in China. Journal of Nanjing Forestry University (南京林业大学学报), 23(5), 18-22. (in Chinese with English abstract)
[24]	Loiselle BA, Howell CA, Graham CH, Goerck JM, Brooks T, Smith KG, Williams PH (2003). Avoiding pitfalls of using species distribution models in conservation planning. Conservation Biology, 17, 1591-1600. DOI URL
[25]	Luoto M, Pöyry J, Heikkinen PK, Saarinen K (2005). Uncertainty of bioclimate envelope models based on the geographical distribution of species. Global Ecology and Biogeography, 14, 575-584. DOI URL
[26]	McPherson JM, Jetz W, Rogers DJ (2004). The effects of species’ range sizes on the accuracy of distribution models: ecological phenomenon or statistical artifact? Journal of Applied Ecology, 41, 811-823. DOI URL
[27]	Meng M (孟猛), Ni J (倪健), Zhang ZG (张治国) (2004). Aridity index and its applications in geo-ecological study. Acta Phytoecologica Sinica (植物生态学报), 28, 853-861. (in Chinese with English abstract)
[28]	Meynecke JO (2004). Effects of global climate change on geographic distributions of vertebrates in North Queensland. Ecological Modelling, 174, 347-357. DOI URL
[29]	Monserud RA, Leemans R (1992). The comparison of global vegetation maps. Ecological Modelling, 62, 275-293. DOI URL
[30]	Ni J (倪健), Song YC (宋永昌) (1997a). Relationships between geographical distribution of Cyclobalanopsis glauca and climate in China. Acta Botanica Sinica (植物学报), 39, 451-460. (in Chinese with English abstract)
[31]	Ni J (倪健), Song YC (宋永昌) (1997b). Relationships between climate and distribution of main species of subtropical evergreen broad-leaved forest in China. Acta Phytoecologica Sinica (植物生态学报), 21, 115-129. (in Chinese with English abstract)
[32]	Nix HA (1986). A biogeographic analysis of Australian Elapid snakes. In: Longmore R ed. Snakes: Atlas of Elapid snakes of Australia. Bureau of Flora and Fauna, Canberra, 4-10.
[33]	Osborne PE, Suárez-Seoane S (2002). Should data be partitioned spatially before building large-scale distribution models? Ecological Modelling, 157, 249-259. DOI URL
[34]	Prentice IC (1990). Bioclimate distribution of vegetation for general circulation model studies. Journal of Geophysical Research, 95, 11811-11830. DOI URL
[35]	Robertson MP, Peter CI, Villet MH, Ripley BS (2003). Comparing models for predicting species’ potential distributions: a case study using correlative and mechanistic predictive modelling techniques. Ecological Modelling, 164, 153-167. DOI URL
[36]	Segurado P, Araújo MB (2004). An evaluation of methods for modelling speices distributions. Journal of Biogeography, 31, 1555-1568. DOI URL
[37]	Stockwell DRB, Peterson AT (2002). Effects of sample size on accuracy of species distribution models. Ecological Modelling, 148, 1-13. DOI URL
[38]	The Research Group of the Deciduous Oaks (Forest Resources College) (落叶栎树研究组) (1988). A synoptic summary of the researches on Chinese deciduous Oaks. Journal of Beijing Forestry University (北京林业大学学报), 10(3), 77-83. (in Chinese with English abstract)
[39]	Tian JQ (田佳倩), Zhou ZY (周志勇), Bao B (包彬), Sun JX (孙建新)(2008). Variations of soil particle size distribution with land-use types and influences on soil organic carbon and nitrogen. Acta Phytoecologica Sinica (植物生态学报), 32, 601-610. (in Chinese with English abstract)
[40]	Tsoar A, Allouche O, Steinitz O, Rotem D, Kadmon R (2007). A comparative evaluation of presence-only methods for modelling species distribution. Diversity and Distributions, 13, 397-405. DOI URL
[41]	Wang J (王娟), Ni J (倪健) (2009). Modelling the distribution of five Caragana species in temperate northern China. Acta Phytoecologica Sinica (植物生态学报), 33, 12-24. (in Chinese with English abstract)
[42]	Wang LM (王良民), Ren XW (任宪威), Liu YJ (刘一樵) (1985). Geographic distribution of deciduous oaks in China. Journal of Beijing Forestry University (北京林业大学学报), 2, 57-69. (in Chinese with English abstract)
[43]	Wisz MS, Hijmans RJ, Li J, Peterson AT, Graham CH, Guisan A, NCEAS Predicting Species Distribution Working Group (2008). Effects of sample size on the performance of species distribution models. Diversity and Distribution, 14, 763-773. DOI URL
[44]	Xu WD (徐文铎) (1983). The relation between distribution of edificatory and companion in zonal vegetation and water-temperature condition. Acta Botanica Sinica (植物学报), 25, 264-274. (in Chinese with English abstract)
[45]	Xu XT (徐晓婷), Yang Y (杨永), Wang LS (王利松) (2008). Geographic distribution and potential distribution estimation of Pseudotaxus chienii. Acta Phytoecologica Sinica (植物生态学报), 32, 1134-1145. (in Chinese with English abstract)
[46]	Yang ZY (杨正宇), Zhou GS (周广胜), Yang DA (杨奠安) (2003). Comparison of simulated vegetation distribution in China produced by four popular climate-vegetation classification models. Acta Phytoecologica Sinica (植物生态学报), 27, 587-593. (in Chinese with English abstract)
[47]	Yu SL (于顺利), Ma KP (马克平), Chen LZ (陈灵芝) (2000). Preliminary discussion on the origin of Quercus mongolica forest in North China. Guihaia (广西植物), 20, 131-137. (in Chinese with English abstract)
[48]	Zhang WH (张文辉), Lu ZJ (卢志军) (2002). A study on the biological and ecological property and geographical distribution of Quercus variabilis population. Acta Botanica Boreali-Occidentalia Sinica (西北植物学报), 22, 1093-1101. (in Chinese with English abstract)
[49]	Zhou GS (周广胜), Zhang XS (张新时) (1996). Study on Chinese climate-vegetation relationship. Acta Phytoec- ologica Sinica (植物生态学报), 20, 113-119. (in Chinese with English abstract)
[50]	Zhu L, Sun OJ, Sang WG, Li ZY, Ma KP (2007). Predicting the spatial distribution of an invasive plant speices (Eupatorium adenophorum) in China. Landscape Ecology, 22, 1143-1154. DOI URL

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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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