globalchange  > 过去全球变化的重建
DOI: 10.1016/j.palaeo.2015.11.005
论文题名:
Artificial neural networks reveal a high-resolution climatic signal in leaf physiognomy
作者: Li S.-F.; Jacques F.M.B.; Spicer R.A.; Su T.; Spicer T.E.V.; Yang J.; Zhou Z.-K.
刊名: Palaeogeography, Palaeoclimatology, Palaeoecology
ISSN: 0031-0182
出版年: 2016
卷: 442
起始页码: 1
结束页码: 11
语种: 英语
英文关键词: Artificial neural networks ; CLAMP ; CLANN ; Climate ; Fossil ; Leaf physiognomy
英文摘要: 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 R2, 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. © 2015 Elsevier B.V.
Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/68621
Appears in Collections:过去全球变化的重建

Files in This Item:

There are no files associated with this item.


作者单位: Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla, China; Key Laboratory of Biogeography and Biodiversity, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, China; Environment, Earth and Ecosystems, Centre for Earth, Planetary, Space and Astronomical Research, The Open University, United Kingdom; State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, The Chinese Academy of Sciences, Beijing, China; State Key Laboratory of Paleobiology and Stratigraphy, Nanjing Institute of Geology and Paleontology, Chinese Academy of Sciences, Nanjing, China

Recommended Citation:
Li S.-F.,Jacques F.M.B.,Spicer R.A.,et al. Artificial neural networks reveal a high-resolution climatic signal in leaf physiognomy[J]. Palaeogeography, Palaeoclimatology, Palaeoecology,2016-01-01,442
Service
Recommend this item
Sava as my favorate item
Show this item's statistics
Export Endnote File
Google Scholar
Similar articles in Google Scholar
[Li S.-F.]'s Articles
[Jacques F.M.B.]'s Articles
[Spicer R.A.]'s Articles
百度学术
Similar articles in Baidu Scholar
[Li S.-F.]'s Articles
[Jacques F.M.B.]'s Articles
[Spicer R.A.]'s Articles
CSDL cross search
Similar articles in CSDL Cross Search
[Li S.-F.]‘s Articles
[Jacques F.M.B.]‘s Articles
[Spicer R.A.]‘s Articles
Related Copyright Policies
Null
收藏/分享
所有评论 (0)
暂无评论
 

Items in IR are protected by copyright, with all rights reserved, unless otherwise indicated.