BEESCOUT: A model of bee scouting behaviour and a software tool for characterizing nectar/pollen landscapes for BEEHAVE.
BEESCOUT: A model of bee scouting behaviour and a software tool for characterizing nectar/pollen landscapes for BEEHAVE.
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DOI：
10.1016/j.ecolmodel.2016.09.013
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发表时间：
2016-11-24
影响因子：
3.1
通讯作者：
Osborne, J. L.
中科院分区：
文献类型：
--
作者：
Becher, M. A.;Grimm, V.;Knapp, J.;Horn, J.;Twiston-Davies, G.;Osborne, J. L.
BEESCOUT是一个空间上明确的，基于个体的侦察蜜蜂模型。它决定了食物来源的检测概率。它可以连接到蜜蜂模型BEEHAVE来预测群体的发展。群居蜂是从周围景观中收集花卉资源的主要觅食者，但人们对侦察蜂找到特定花地的可能性知之甚少。因此，我们开发了一个软件工具，BEESCOUT，从理论上研究蜜蜂如何探索景观，并在时间和空间上分布它们的侦察活动。可以导入图像文件，该模型将其解释为“饲料地图”，其中某些颜色代表用户指定的某些作物或栖息地类型。BEESCOUT计算这些潜在食物源在该景观中相对于蜂群的大小和位置。然后，基于个体的模型根据从雷达跟踪的蜜蜂和大黄蜂的飞行模式收集的参数值来确定蜜蜂对食物斑块的检测概率。各种各样的“搜寻模式”描述了侦察蜂长距离探索的假设搜寻策略。由此产生的检测概率的饲料补丁可以作为输入最近开发的蜜蜂模型BEEHAVE，探索现实的情况下，殖民地的增长和死亡，以应对不同的压力。在示例模拟中，我们发现，接近殖民地的食物源的检测概率相当好地拟合经验数据。然而，对于更远的食物来源，没有经验数据可用于验证模型输出。模拟的检测概率在很大程度上取决于蜜蜂的搜索模式，以及它们是否交换有关食物源位置的信息。尽管如此，我们表明景观结构和食物来源的连通性会对结果产生强烈影响。我们相信，BEESCOUT是一个有价值的工具，更好地了解如何景观配置和蜜蜂的搜索行为影响检测概率的食物来源。它还可以指导相关数据的收集和实验设计，以缩小知识差距，并提供了一个有用的扩展BEEHAVE蜜蜂模型，使未来的用户能够探索景观结构和食物供应如何影响蜜蜂的觅食决策和补丁访问率，从而预测殖民地的发展和生存。
BEESCOUT is a spatially explicit, individual-based model of scouting bees. It determines the detection probabilities of food sources. It can be linked to the honey bee model BEEHAVE to predict colony development. Social bees are central place foragers collecting floral resources from the surrounding landscape, but little is known about the probability of a scouting bee finding a particular flower patch. We therefore developed a software tool, BEESCOUT, to theoretically examine how bees might explore a landscape and distribute their scouting activities over time and space. An image file can be imported, which is interpreted by the model as a “forage map” with certain colours representing certain crops or habitat types as specified by the user. BEESCOUT calculates the size and location of these potential food sources in that landscape relative to a bee colony. An individual-based model then determines the detection probabilities of the food patches by bees, based on parameter values gathered from the flight patterns of radar-tracked honeybees and bumblebees. Various “search modes” describe hypothetical search strategies for the long-range exploration of scouting bees. The resulting detection probabilities of forage patches can be used as input for the recently developed honeybee model BEEHAVE, to explore realistic scenarios of colony growth and death in response to different stressors. In example simulations, we find that detection probabilities for food sources close to the colony fit empirical data reasonably well. However, for food sources further away no empirical data are available to validate model output. The simulated detection probabilities depend largely on the bees’ search mode, and whether they exchange information about food source locations. Nevertheless, we show that landscape structure and connectivity of food sources can have a strong impact on the results. We believe that BEESCOUT is a valuable tool to better understand how landscape configurations and searching behaviour of bees affect detection probabilities of food sources. It can also guide the collection of relevant data and the design of experiments to close knowledge gaps, and provides a useful extension to the BEEHAVE honeybee model, enabling future users to explore how landscape structure and food availability affect the foraging decisions and patch visitation rates of the bees and, in consequence, to predict colony development and survival.
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