Foraging and reproduction based on memory and learning in flys : foraging trajectory and chaotic attractor shift

基于苍蝇记忆和学习的觅食和繁殖：觅食轨迹和混沌吸引子转移

基本信息

批准号：
18570012
负责人：
SHIMADA Masakazu
金额：
$ 2.5万
依托单位：
The University of Tokyo
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2006
资助国家：
日本
起止时间：
2006 至 2007
项目状态：
已结题

项目摘要

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.

最优觅食理论的前提是生物体的行为完全理性地估计当前和未来的利润，并应用最优算法计算觅食策略，但与现实相去甚远。因此，行为生态学家希望重建觅食理论，考虑到实际生物体记忆和学习能力的现实限制。在本研究项目中，我们对果蝇 Drosophila melanogsuter 的行走轨迹进行了行为实验。我们希望通过分析黑腹果蝇的饮食搜索轨迹模式来揭示觅食效率的最终因素（例如生物适应性）。首先，我们发现步行回合遵循混合模型的估计，该混合模型介于基于 Levy 飞行步行的“功率分布”和随机步行推断的“指数分布”之间，并且步行表现出方向性偏差的趋势。这一结果可以解释为自然选择的组合行为，自然选择促进了利维飞行行走，提高了觅食效率，并且通过对现实生物体的体现约束。此外，基于局部平稳自回归模型进行时间序列分析，我们发现在保持相同的自回归模型的情况下，运动速度在一定时间内保持不变，而转换转弯程度则不然，这表明自然选择关于移动速度。因此，本项目通过物理和统计方法分析决策过程，并显示出超越当前行为生态学的独特结果。