Advanced   Register
XTBG OpenIR  > 古生态研究组  > 期刊论文

title: Artificial neural networks reveal a high-resolution climatic signal in leaf physiognomy
author: Li, Shu-Feng;  Jacques, Frederic M. B.;  Spicer, Robert A.;  Su, Tao;  Spicer, Teresa E. V.;  Yang, Jian;  Zhou, Zhe-Kun
Issued Date: 2016
Abstract: 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.
Appears in Collections:古生态研究组_期刊论文

Files in This Item:

File SizeFormat
Artificial neural networks reveal a high-resolution climatic signal in leaf physiognomy.pdf614KbAdobe PDFView  Download

全文许可: Creative Commons 署名-非商业性使用-相同方式共享 3.0

Recommended Citation:
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(X):1-11.

SCI Citaion Data:
 Recommend this item
 Sava as my favorate item
 Show this item's statistics
 Export Endnote File
Google Scholar
 Similar articles in Google Scholar
 [Li, Shu-Feng]'s Articles
 [Jacques, Frederic M. B.]'s Articles
 [Spicer, Robert A.]'s Articles
CSDL cross search
 Similar articles in CSDL Cross Search
 [Li, Shu-Feng]‘s Articles
 [Jacques, Frederic M. B.]‘s Articles
 [Spicer, Robert A.]‘s Articles
Scirus search
 Similar articles in Scirus
Related Copyright Policies
Social Bookmarking
  Add to CiteULike  Add to Connotea  Add to  Add to Digg  Add to Reddit 
所有评论 (0)
内 容:
Email:  *
验证码:   刷新
标 题:
内 容:
Email:  *
验证码:   刷新

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



Valid XHTML 1.0!
Powered by CSpace