Artificial neural networks reveal a high-resolution climatic signal in leaf physiognomy
文献类型：期刊论文
| 作者 | Zhou, Zhe-Kun ; Su, Tao
|
| 刊名 | PALAEOGEOGRAPHY PALAEOCLIMATOLOGY PALAEOECOLOGY
 |
| 出版日期 | 2016 |
| 卷号 | 442期号:x页码:1-11 |
| 关键词 | Artificial neural networks Climate CLAMP CLANN Fossil Leaf physiognomy |
| 中文摘要 | 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. |
| 公开日期 | 2016-03-30 |
| 源URL | [http://ir.xtbg.org.cn/handle/353005/9705]  |
| 专题 | 西双版纳热带植物园_古生态研究组 |
| 通讯作者 | Zhou, Zhe-Kun |
| 推荐引用方式 GB/T 7714 | Zhou, Zhe-Kun,Su, Tao. Artificial neural networks reveal a high-resolution climatic signal in leaf physiognomy[J]. PALAEOGEOGRAPHY PALAEOCLIMATOLOGY PALAEOECOLOGY,2016,442(x):1-11. |
| APA | Zhou, Zhe-Kun,&Su, Tao.(2016).Artificial neural networks reveal a high-resolution climatic signal in leaf physiognomy.PALAEOGEOGRAPHY PALAEOCLIMATOLOGY PALAEOECOLOGY,442(x),1-11. |
| MLA | Zhou, Zhe-Kun,et al."Artificial neural networks reveal a high-resolution climatic signal in leaf physiognomy".PALAEOGEOGRAPHY PALAEOCLIMATOLOGY PALAEOECOLOGY 442.x(2016):1-11. |
入库方式： OAI收割
来源：西双版纳热带植物园
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