Chicks make stochastic decisions based on gain rates of different time constants

IF 1.3 4区生物学 Q4 BEHAVIORAL SCIENCES

Behavioural Processes Pub Date : 2025-01-01 DOI:10.1016/j.beproc.2024.105134

Yukiko Ogura , Ai Kawamori , Toshiya Matsushima

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引用次数: 0

Abstract

The marginal value theorem (MVT) predicts that optimal foragers leave a patch when the instantaneous gain rate decreases to the average long-term gain rate. However, various animals systematically deviate from this optimum by staying too long or overharvesting relative to this optimum. We hypothesised that animals do not represent their optimal stay time but instead determine their departure point probabilistically. To test this hypothesis, we conducted behavioural experiments and modelling using chicks. The chicks ran on a treadmill with feeders on both sides, and their travel time to the feeder was experimentally controlled. As predicted by the MVT, the chicks stayed longer at the feeder when forced to run more. However, they stayed even longer than predicted by the MVT. Therefore, we constructed and compared stochastic decision-making models with the MVT-based model. The stochastic models explained the chicks’ behaviour better than the MVT-based model. These results suggest that chicks leave probabilistically based on their immediate foraging history rather than representing an optimal stay time.

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雏鸡根据不同时间常数的增益率做出随机决定。

根据边际值定理（MVT）预测，当瞬时增益率下降到平均长期增益率时，最优采集者会离开一块土地。然而，各种动物由于停留时间过长或相对于这一最佳值的过度收获而系统性地偏离了这一最佳值。我们假设动物并不代表它们的最佳停留时间，而是概率地决定它们的出发点。为了验证这一假设，我们用小鸡进行了行为实验和建模。雏鸡在跑步机上奔跑，两侧都有喂食器，它们到喂食器的旅行时间是实验控制的。正如MVT所预测的那样，当小鸡被迫跑得更多时，它们在喂食器上停留的时间更长。然而，它们停留的时间比MVT预测的还要长。因此，我们构建了随机决策模型，并将其与基于mvt的模型进行了比较。随机模型比基于mvt的模型更能解释雏鸟的行为。这些结果表明，雏鸟离开的概率是基于它们最近的觅食历史，而不是代表一个最佳的停留时间。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Behavioural Processes 生物-动物学

CiteScore

2.70

自引率

7.70%

发文量

144

审稿时长

4-8 weeks

期刊介绍： Behavioural Processes is dedicated to the publication of high-quality original research on animal behaviour from any theoretical perspective. It welcomes contributions that consider animal behaviour from behavioural analytic, cognitive, ethological, ecological and evolutionary points of view. This list is not intended to be exhaustive, and papers that integrate theory and methodology across disciplines are particularly welcome.