Abstract Understanding interactions between individual animals and their resources is fundamental to ecology. Agent-Based Models (ABMs) offer an opportunity to study how individuals move given the spatial distribution and characteristics of their resources. When contrasted with empirical individual-resource network data, ABMs can be a powerful method to detect the processes behind observed movement patterns, as they allow for a complete and quantitative analysis of the agent-to-environment relationships. Here we use the small-scale, within-patch movement of bumblebees (Bombus pascuorum) as a case study to demonstrate how ABMs can be combined with network statistics to provide a deeper understanding of the mechanisms behind the interactions between individuals and their resources. We build an ABM that explicitly simulates the influence of distance to the nearest flowering plant (allowing minimal energy expenditure and maximum time spent foraging), plant height and number of flower heads (as a proxy of food availability) on local foraging decisions of bumblebees. The relative importance of these three elements is determined using pattern-oriented modelling (POM), where we confront the network statistics (number of visited plants, number of interactions, nestedness and modularity) of a real B. pascuorum individual-resource network with the emergent patterns of our ABM. We also explore the model results using spatial analysis. The model is able to reproduce the observed network statistics. Despite the complex behaviour of bumblebees, our results show a surprisingly precise match between the structure of the simulated and empirical networks after adjusting a single model parameter controlling the importance of distance to the next plant visited. Our study illustrates the potential of combining field data, ABMs and individual-resource networks for evaluating small-scale, within-patch movement decisions to better understand animal movements in natural habitats. We discuss the benefits of our approach when compared to more classical statistical methods, and its ability to test various scenarios in a new or altered environment.

中文翻译：

---

结合基于代理的建模和网络统计的优势，以了解动物的运动和与资源的相互作用：来自大黄蜂补丁内觅食决策的示例

摘要 了解个体动物与其资源之间的相互作用是生态学的基础。基于代理的模型 (ABM) 提供了一个机会，可以研究个人在给定资源的空间分布和特征的情况下如何移动。与经验性个体资源网络数据相比，ABM 可以成为检测观察到的运动模式背后过程的强大方法，因为它们允许对代理与环境的关系进行完整和定量的分析。在这里，我们使用大黄蜂 (Bombus pascuorum) 的小规模斑块内运动作为案例研究，展示如何将 ABM 与网络统计数据相结合，以更深入地了解个体与其资源之间相互作用背后的机制。我们构建了一个 ABM，它明确模拟到最近开花植物的距离（允许最小的能量消耗和最长的觅食时间）、植物高度和花头数量（作为食物可用性的代表）对大黄蜂局部觅食决策的影响。这三个元素的相对重要性是使用面向模式的建模 (POM) 确定的，我们在其中面对真实 B. pascuorum 个体资源网络的网络统计数据（访问植物数量、交互数量、嵌套性和模块性）我们的 ABM 的涌现模式。我们还使用空间分析探索模型结果。该模型能够重现观察到的网络统计数据。尽管大黄蜂行为复杂，我们的结果显示，在调整单个模型参数后，模拟网络和经验网络的结构之间惊人地精确匹配，该参数控制到下一个访问的工厂的距离的重要性。我们的研究说明了结合现场数据、ABM 和个体资源网络来评估小规模、斑块内运动决策以更好地了解自然栖息地中的动物运动的潜力。我们讨论了我们的方法与更经典的统计方法相比的好处，以及它在新的或改变的环境中测试各种场景的能力。斑块内运动决策，以更好地了解自然栖息地中的动物运动。我们讨论了我们的方法与更经典的统计方法相比的好处，以及它在新的或改变的环境中测试各种场景的能力。斑块内运动决策，以更好地了解自然栖息地中的动物运动。我们讨论了我们的方法与更经典的统计方法相比的好处，以及它在新的或改变的环境中测试各种场景的能力。