Honey bee swarms are a landmark example of collective behavior. To become a coherent swarm, bees locate their queen by tracking her pheromones, but how can distant individuals exploit these chemical signals which decay rapidly in space and time? Here, we combine a novel behavioral assay with the machine vision detection of organism location and scenting behavior to track the search and aggregation dynamics of the honey bee Apis mellifera L. We find that bees collectively create a communication network to propagate pheromone signals, by arranging in a specific spatial distribution where there is a characteristic distance between individuals and a characteristic direction in which individuals broadcast the signals. To better understand such a flow-mediated directional communication strategy, we connect our experimental results to an agent-based model where virtual bees with simple, local behavioral rules, exist in a flow environment. Our model shows that increased directional bias leads to a more efficient aggregation process that avoids local equilibrium configurations of isotropic communication, such as small bee clusters that persist throughout the simulation. Our results highlight a novel example of extended classical stigmergy: rather than depositing static information in the environment, individual bees locally sense and globally manipulate the physical fields of chemical concentration and airflow.

中文翻译：

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蜜蜂蜂拥而至的流媒体嗅觉交流

蜜蜂群是集体行为的标志性例子。为了成为一个连贯的群，蜜蜂通过跟踪她的信息素来定位它们的女王，但是远方的人们如何利用这些在空间和时间上迅速衰减的化学信号呢？在这里，我们将新颖的行为检测与机器视觉检测有机物的位置和气味行为相结合，以跟踪蜜蜂Apis mellifera的搜索和聚集动态L.我们发现，蜜蜂被安排在特定的空间分布中，从而集体地建立了传播信息素信号的通信网络，在该空间分布中，个体之间存在特征距离，而个体广播信号的特征方向也不同。为了更好地理解这种以流为媒介的定向通信策略，我们将实验结果连接到基于代理的模型，在该模型中，具有简单，局部行为规则的虚拟蜜蜂存在于流环境中。我们的模型表明，增加的方向偏差会导致更有效的聚合过程，从而避免各向同性通信的局部平衡配置，例如在整个模拟过程中持续存在的小蜂群。我们的研究结果突出了扩展的经典stigmergy的新颖示例：