Artificial neural networks reveal a high-resolution climatic signal in leaf physiognomy
文献类型：期刊论文
| 作者 | Li, Shu-Feng; Jacques, Frederic M. B.; Spicer, Robert A.; Su, Tao; Spicer, Teresa E. V.; Yang, Jian3; Zhou, Zhe-Kun |
| 刊名 | PALAEOGEOGRAPHY PALAEOCLIMATOLOGY PALAEOECOLOGY
 |
| 出版日期 | 2016 |
| 卷号 | 442页码:1-11 |
| 关键词 | Artificial neural networks Climate CLAMP CLANN Fossil Leaf physiognomy |
| ISSN号 | 0031-0182 |
| DOI | 10.1016/j.palaeo.2015.11.005 |
| 文献子类 | Article |
| 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 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. |
| 学科主题 | Geography, Physical ; Geosciences, Multidisciplinary ; Paleontology |
| 出版地 | AMSTERDAM |
| 电子版国际标准刊号 | 1872-616X |
| 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) |
| 语种 | 英语 |
| WOS记录号 | WOS:000369681300001 |
| 出版者 | ELSEVIER SCIENCE BV |
| 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] |
| 源URL | [http://ir.ibcas.ac.cn/handle/2S10CLM1/25070]  |
| 专题 | 植物研究所_系统与进化植物学研究中心_系统与进化植物学研究中心_学位论文 |
| 作者单位 | 1.Chinese Acad Sci, Inst Bot, State Key Lab Systemat & Evolutionary Bot, Beijing 100093, Peoples R China 2.Chinese Acad Sci, Nanjing Inst Geol & Paleontol, State Key Lab Paleobiol & Stratig, Nanjing 210008, Peoples R China 3.[Spicer, Teresa E. V. 4.[Spicer, Robert A.] Open Univ, Ctr Earth Planetary Space & Astron Res, Environm Earth & Ecosyst, Milton Keynes, Bucks, England 5.Chinese Acad Sci, Kunming Inst Bot, Key Lab Biogeog & Biodivers, Kunming 650204, Peoples R China 6.Chinese Acad Sci, Key Lab Trop Forest Ecol, Xishuangbanna Trop Bot Garden, Mengla 666303, Peoples R China 7.Su, Tao 8.Jacques, Frederic M. B. 9.[Li, Shu-Feng |
| 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. |
入库方式： OAI收割
来源：植物研究所
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