2007 Fiscal Year Final Research Report Summary
Foraging and reproduction based on memory and learning in flys : foraging trajectory and chaotic attractor shift
| Project/Area Number |
18570012
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Ecology/Environment
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| Research Institution | The University of Tokyo |
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Principal Investigator |
SHIMADA Masakazu The University of Tokyo, Graduate School of Arts and Science, Professor (40178950)
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| Project Period (FY) |
2006 – 2007
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| Keywords | Optimal foraging theory / Memory / Learning / Drosophila melanofaster / Walking trajectory / Random walking / Levy-flight walking / Autoregression model |
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| Research Abstract |
Optimal foraging theory has a premise that organisms behave rationally estimating current and future profits completely and calculates the foraging strategy applying the optimal algorism that are far from the realism. Therefore, behavioral ecologists would like to re-build the foraging theory, taking into account the realistic constrain on memory and learning ability of actual organisms. In the present research project, we conducted the behavior experiment on walking trajectory in the fruit fly, Drosophila melanogasuter. We expected to reveal the ultimate factor (e.g., biological fitness) in the foraging efficiency, analyzing the pattern in the diet-searching trajectory of D. melanogaster. Firstly, we discovered that a walking bout follows estimation of the mixed model that are in between "power distribution" based on Levy-flight walking and "exponential distribution" inferred from random walking, and that the walking showed a directionally-biased tendency. This results can be explained as combined behaviors by natural selection which promoted Levy-flight walking increasing the foraging efficiency and by embodiment constraint on a realistic organism. Furthermore, conducting the time-series analysis based on locally stationary auto-regression model, we showed that the movement speed was kept for a certain period with maintaining the same auto-regression model and that the conversion turn degree was not, suggesting a natural selection on the movement speed. Thus, the present project that was ordered by physical and statistical methods analyzed decision-making processes, and it showed unique results beyond the current behavioral ecology.
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