There is a vital need to understand wildlife movement and space-use patterns to inform conservation efforts given current rates of anthropogenic environmental change. Grizzly bears (Ursus arctos), among other large predators, are especially vulnerable to landscape change given their need to travel across large areas in search of seasonally available foods. Understanding how grizzly bears make movement decisions based on learned knowledge and how these decisions lead to home range size is key in developing effective conservation and management decisions. While previous research has provided insight into bear relationships to various landscape features, many conventional approaches are challenged by the need to understand how complex movement decisions over time allow bears to access habitat providing necessary resources and establish a home range. We present a novel agent-based model (ABM) that simulates individual bear movement decisions at multiple scales that are governed by learning and memory in a dynamic landscape. Using GPS-radio collared data of bear movement in west-central Alberta, our model successfully identifies movement behaviours that lead to the emergence of home ranges and provides new insight into how bears use previously acquired landscape data to maximize use of high-quality areas within a heterogeneous landscape. Future modelling efforts should continue to explore the intricacies of movement behaviour in wide-ranging species, including the movement-memory interface.

迫切需要了解野生动植物的活动和空间利用方式，以根据当前人为环境变化的速度为保护工作提供信息。灰熊（Ursus arctos）和其他大型掠食者一样，由于他们需要穿越大片地区寻找季节性食物，因此尤其容易受到景观变化的影响。了解灰熊如何根据所学知识做出运动决定，以及这些决定如何导致家庭范围扩大，对于制定有效的保护和管理决定至关重要。尽管先前的研究提供了关于熊与各种景观特征之间关系的见识，但许多传统方法都面临着需要了解随着时间的推移复杂的移动决策如何使熊进入提供必要资源并建立家园的栖息地的挑战。我们提出了一个新颖的基于主体的模型（ABM），该模型可以模拟在动态范围内学习和记忆所控制的多个尺度上的单个熊运动决策。我们的模型使用艾伯塔中西部中部熊的GPS无线电项圈数据，成功地识别了导致家园范围出现的运动行为，并提供了新的见解，使熊能够如何使用先前获取的景观数据来最大程度地利用内部的高质量区域异质的景观。未来的建模工作应继续探索各种物种在运动行为方面的复杂性，包括运动-记忆界面。