Artificial neural networks reveal a high-resolution climatic signal in leaf physiognomy
Li, Shu-Feng1,5; Jacques, Frederic M. B.1; Spicer, Robert A.3; Su, Tao1; Spicer, Teresa E. V.4; Yang, Jian4; Zhou, Zhe-Kun1,2
2016-01-15
Source PublicationPALAEOGEOGRAPHY PALAEOCLIMATOLOGY PALAEOECOLOGY
Volume442Pages:1-11
AbstractThe 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.
KeywordArtificial Neural Networks Climate Clamp Clann Fossil Leaf Physiognomy
DOI10.1016/j.palaeo.2015.11.005
Indexed BySCI
Language英语
WOS IDWOS:000369681300001
Citation statistics
Document Type期刊论文
Identifierhttp://ir.kib.ac.cn/handle/151853/25959
Collection中国科学院东亚植物多样性与生物地理学重点实验室
Affiliation1.Chinese Acad Sci, Key Lab Trop Forest Ecol, Xishuangbanna Trop Bot Garden, Mengla 666303, Peoples R China
2.Chinese Acad Sci, Kunming Inst Bot, Key Lab Biogeog & Biodivers, Kunming 650204, Peoples R China
3.Open Univ, Ctr Earth Planetary Space & Astron Res, Environm Earth & Ecosyst, Milton Keynes, Bucks, England
4.Chinese Acad Sci, Inst Bot, State Key Lab Systemat & Evolutionary Bot, Beijing 100093, Peoples R China
5.Chinese Acad Sci, Nanjing Inst Geol & Paleontol, State Key Lab Paleobiol & Stratig, Nanjing 210008, Peoples R China
Recommended Citation
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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