Artificial neural networks reveal a high-resolution climatic signal in leaf physiognomy

doi:10.1016/j.palaeo.2015.11.005

	Artificial neural networks reveal a high-resolution climatic signal in leaf physiognomy
	Li, Shu-Feng ; Jacques, Frederic M. B.; Spicer, Robert A.; Su, Tao ; Spicer, Teresa E. V.; Yang, Jian 7; Zhou, Zhe-Kun
	2016
发表期刊	PALAEOGEOGRAPHY PALAEOCLIMATOLOGY PALAEOECOLOGY
ISSN	0031-0182
卷号	442 页码:1-11
摘要	The relationship linking leaf physiognomy and climate has long been used in paleoclimatic reconstructions, but current models lose precision when worldwide data sets are considered because of the broader range of physiognomies that occur under the wider range of climate types represented. Our aim is to improve the predictive power of leaf physiognomy to yield climate signals, and here we explore the use of an algorithm based on the general regression neural network (GRNN), which we refer to as Climate Leaf Analysis with Neural Networks (CLANN). We then test our algorithm on Climate Leaf Analysis Multivariate Program (CLAMP) data sets and digital leaf physiognomy (DLP) data sets, and compare our results with those obtained from other computation methods. We explore the contribution of different physiognomic characters and test fossil sites from North America. The CLANN algorithm introduced here gives high predictive precision for all tested climatic parameters in both data sets. For the CLAMP data set neural network analysis improves the predictive capability as measured by R-2, to 0.86 for MAT on a worldwide basis, compared to 0.71 using the vector-based approach used in the standard analysis. Such a high resolution is attained due to the nonlinearity of the method, but at the cost of being susceptible to 'noise' in the calibration data. Tests show that the predictions are repeatable, and robust to information loss and applicable to fossil leaf data. The CLANN neural network algorithm used here confirms, and better resolves, the global leaf form-climate relationship, opening new approaches to paleoclimatic reconstruction and understanding the evolution of complex leaf function. (C) 2015 Elsevier B.V. All rights reserved.
关键词	Artificial neural networks Climate CLAMP CLANN Fossil Leaf physiognomy
学科领域	Geography, Physical ; Geosciences, Multidisciplinary ; Paleontology
DOI	10.1016/j.palaeo.2015.11.005
收录类别	SCI
语种	英语
WOS关键词	GLOBAL LAND AREAS ; ANGIOSPERM LEAVES ; MARGIN ANALYSIS ; FOSSIL LEAVES ; TEMPERATURE ; EOCENE ; ECOLOGY ; AFRICA ; MODELS ; RECORD
WOS研究方向	Science Citation Index Expanded (SCI-EXPANDED)
WOS记录号	WOS:000369681300001
出版者	ELSEVIER SCIENCE BV
文献子类	Article
出版地	AMSTERDAM
EISSN	1872-616X
资助机构	National Basic Research Program of ChinaNational Basic Research Program of China [2012CB821901] ; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [41372035] ; Foundation of the State Key Laboratory of Paleobiology and Stratigraphy, Nanjing Institute of Geology and Paleontology, Chinese Academy of SciencesChinese Academy of Sciences [153107] ; CAS 135 program [XTBG-F01]
作者邮箱	zhouzk@xtbg.ac.cn
引用统计	被引频次：13[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ibcas.ac.cn/handle/2S10CLM1/25070
专题	系统与进化植物学国家重点实验室
作者单位	1.[Li, Shu-Feng 2.Jacques, Frederic M. B. 3.Su, Tao 4.Chinese Acad Sci, Key Lab Trop Forest Ecol, Xishuangbanna Trop Bot Garden, Mengla 666303, Peoples R China 5.Chinese Acad Sci, Kunming Inst Bot, Key Lab Biogeog & Biodivers, Kunming 650204, Peoples R China 6.[Spicer, Robert A.] Open Univ, Ctr Earth Planetary Space & Astron Res, Environm Earth & Ecosyst, Milton Keynes, Bucks, England 7.[Spicer, Teresa E. V. 8.Chinese Acad Sci, Inst Bot, State Key Lab Systemat & Evolutionary Bot, Beijing 100093, Peoples R China 9.Chinese Acad Sci, Nanjing Inst Geol & Paleontol, State Key Lab Paleobiol & Stratig, Nanjing 210008, Peoples R China
推荐引用方式 GB/T 7714	Li, Shu-Feng,Jacques, Frederic M. B.,Spicer, Robert A.,et al. Artificial neural networks reveal a high-resolution climatic signal in leaf physiognomy[J]. PALAEOGEOGRAPHY PALAEOCLIMATOLOGY PALAEOECOLOGY,2016,442:1-11.
APA	Li, Shu-Feng.,Jacques, Frederic M. B..,Spicer, Robert A..,Su, Tao.,Spicer, Teresa E. V..,...&Zhou, Zhe-Kun.(2016).Artificial neural networks reveal a high-resolution climatic signal in leaf physiognomy.PALAEOGEOGRAPHY PALAEOCLIMATOLOGY PALAEOECOLOGY,442,1-11.
MLA	Li, Shu-Feng,et al."Artificial neural networks reveal a high-resolution climatic signal in leaf physiognomy".PALAEOGEOGRAPHY PALAEOCLIMATOLOGY PALAEOECOLOGY 442(2016):1-11.

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