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.

最佳觅食理论的前提是，有机体在合理地估计当前和未来的利润，并计算出使用远非现实主义的最佳算法的觅食策略。因此，行为生态学家想考虑到对实际生物的记忆和学习能力的现实限制，以重建觅食理论。在本研究项目中，我们进行了有关果蝇果蝇果蝇中的行走轨迹的行为实验。我们预计将揭示觅食效率的最终因素（例如生物适应性），分析了D. melanogaster的饮食搜索轨迹中的模式。首先，我们发现步行回合遵循了基于征收飞行步行和从随机步行中推断出的“电源分布”之间的混合模型的估计，并且步行显示了方向偏见的趋势。该结果可以通过自然选择来解释为联合行为，从而促进了征收飞行的行走，从而提高了觅食效率和对现实有机体的实施限制。此外，我们基于局部固定自动回归模型进行时间序列分析，我们表明运动速度在一定时期内保持了一定时期，并保持了相同的自动回归模型，并且不进行转换转弯度，这表明对运动速度进行了自然选择。因此，通过物理和统计方法订购的当前项目分析了决策过程，它显示了超出当前行为生态的独特结果。