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

最优觅食理论以生物行为的理性为前提，对当前和未来的收益进行充分估计，并应用最优算法计算觅食策略，这与现实有很大的差距。因此，行为生态学家希望重新建立觅食理论，考虑到实际生物的记忆和学习能力的现实约束。在本研究项目中，我们进行了果蝇（Drosophila melanogasuter）行走轨迹的行为实验。我们希望揭示最终因素（例如，生物适合度）在觅食效率中的作用，分析了杜父鱼觅食轨迹的模式。黑腹菌首先，我们发现，步行回合遵循的混合模型的估计之间的“权力分布”的基础上的Levy-flight步行和“指数分布”推断从随机行走，步行表现出方向偏置的趋势。这一结果可以解释为自然选择促进了Levy-flight步行，提高了觅食效率，并通过对现实生物体的实施约束的组合行为。基于局部平稳自回归模型的时间序列分析表明，在保持同一自回归模型的情况下，运动速度在一定时间内保持不变，而转换转向度则没有变化，表明运动速度存在自然选择。因此，本项目采用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。

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.