DataSheet_3_How artificial potential field algorithms can help to simulate trade-offs in movement behaviour of reef fishes.docx

IntroductionSpace use patterns in fish result from the interactions between individual movement behaviour and characteristics of the environment. Herbivorous parrotfishes, for instance, are constrained by the availability of resources and different predation risks. The resulting spatial distribution of the fish population can strongly influence community composition and ecosystem resilience.MethodsIn a novel approach, we combine individual-based modelling (IBM) with an artificial potential field algorithm to realistically represent fish movements and the decision-making process. Potential field algorithms, which are popular methods in mobile robot path planning, efficiently generate the best paths for an entity to navigate through vector fields of repellent and attracting forces. In our model the repellent and attracting forces are predation risk and food availability, both implemented as separate grid-based vector fields. The coupling of individual fish bioenergetics with a navigation capacity provides a mechanistic basis to analyse how the habitat structure influences population dynamics and space utilization.ResultsModel results indicate that movement patterns and the resulting spatial distributions strongly depend on habitat fragmentation with the bioenergetic capacity to spawn and reproduce being particularly susceptible processes at the individual level. The resulting spatial distributions of the population are more irregularly distributed among coral reef patches the more the coral reef habitat becomes fragmented and reduced.DiscussionThis heterogeneity can have strong implications for the delivered ecosystem functioning, e.g., by concentrating or diluting the grazing effort. Our results also highlight the importance of incorporating individual foraging-path patterns and the spatial exploitation of microhabitats into marine spatial planning by considering the effects of fragmentation. The integration of potential fields into IBMs represents a promising strategy to advance our understanding of complex decision-making in animals by implementing a more realistic and dynamic decision-making process, in which each fish weighs different rewards and risks of the environment. This information may help to identify core areas and essential habitat patches and assist in effective marine spatial management.

鱼类在空间中的利用模式源于个体运动行为与环境特征的相互作用。例如，草食性鹦鹉鱼受到资源可用性和不同捕食风险的限制。由此产生的鱼类种群空间分布可以显著影响群落组成和生态系统韧性。

在一种新颖的方法中，我们结合了基于个体的建模（IBM）与人工势场算法，以真实地模拟鱼类运动和决策过程。势场算法，作为移动机器人路径规划中的流行方法，能够有效地为实体在排斥力和吸引力矢量场中生成最佳路径。在我们的模型中，排斥力和吸引力分别对应捕食风险和食物可用性，两者均作为独立的基于网格的矢量场实现。将个体鱼的生物能量学与其导航能力相结合，为分析栖息地结构如何影响种群动态和空间利用提供了机制基础。

模型结果表明，运动模式和由此产生的空间分布强烈依赖于栖息地破碎化，尤其是在个体层面上的产卵和繁殖的生物能量能力尤其敏感。随着珊瑚礁栖息地的破碎化和减少，种群的空间分布在不同珊瑚礁斑块中呈现更加不规则的模式。

这种异质性对生态系统功能的实现具有强烈的影响，例如通过集中或稀释放牧努力。我们的结果还强调了将个体觅食路径和微生境的空间利用纳入海洋空间规划的重要性，并考虑破碎化的影响。将势场算法融入IBM中，代表了一种有前景的策略，通过实施更真实、动态的决策过程来推进我们对动物复杂决策过程的理解，在这个过程中，每条鱼都会权衡环境中的不同奖励和风险。这些信息有助于识别核心区域和关键栖息地斑块，并有助于有效的海洋空间管理。