2005 Fiscal Year Final Research Report Summary
Spati-Temporal Dynamics of Bio-Production Systems by Chaos Time Series Analysis
| Project/Area Number |
14360148
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
農業機械学
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| Research Institution | Tokyo University of Agriculture and Technology |
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Principal Investigator |
SAKAI Kenshi Tokyo University of Agriculture and Technology, Institute of Symbiotic Science and Technology, Associate Professor, 大学院・共生科学技術研究部, 助教授 (40192083)
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| Co-Investigator(Kenkyū-buntansha) |
HOSHINO Yoshinobu Tokyo University of Agriculture and Technology, Institute of Symbiotic Science and Technology, Associate Professor, 大学院・共生科学技術研究部, 助教授 (00143636)
KANZAKI Nobuo Tokyo University of Agriculture and Technology, Institute of Symbiotic Science and Technology, Associate Professor, 大学院・共生科学技術研究部, 助教授 (80234152)
SASAO Akira Tokyo University of Agriculture and Technology, Institute of Symbiotic Science and Technology, Professor, 大学院・共生科学技術研究部, 教授 (70032993)
SHIBUSAWA Sakae Tokyo University of Agriculture and Technology, Institute of Symbiotic Science and Technology, Professor, 大学院・共生科学技術研究部, 教授 (50149465)
OKAMOTO Hiroshi Hokkaido University, Professor, 農学研究科, 助手 (40322838)
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| Project Period (FY) |
2002 – 2005
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| Keywords | Spatio-temporal Dynamics / Chaos / Nonlinear Time Series Analysis / Agriculture / Ecological systems / Embedding / Remote Sensing |
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| Research Abstract |
The concept of deterministic chaos is attractive for ecologists, because it implies an underlying order behind the complexity of ecological systems. Nonlinear time series analysis (NTSA) is used to investigate deterministic chaos. However, most ecological time series are too short to perform NTSA, which requires a time series whose size is in the thousands. On the application of NTSA on ecological dynamics., we need to develop appropriate way to reconstruct ecological dynamics with limited data, and observation. Here we propose "ensemble reconstruction" of the dynamics from a very short ecological time series whose size is smaller than ten. In most tree crops such as citrus, nuts and acorns, the yield alternates between high and low yielding years. Isagi, et al. proposed a theoretical model that describes masting as chaos that can be applied to alternate bearing. We used an ensemble dataset consisting of the yields of 48 individual trees over 7 years to test our proposed method and successfully validated this method by one-year forward prediction three times in 2002, 2003 and 2004. We also show that conventional tools such as Lyapunov spectrum and correlation dimension can be applied with the ensemble reconstruction method. This method offers an essential tool to realize an ecosystem approach towards and/or adaptive control of real world ecosystems and their biodiversity. We also conducted remote sensing to estimate the spatio-temporal dynamics.
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Research Products
(29 results)
