Physical energy defines the energy landscape and determines the species-specific cost of movement, thus influencing movement decisions. In unpredictable and dynamic environments, observing the locomotion of others increases individual certainty in the distribution of physical energy to increase movement efficiency. Beyond the physical energy landscape, social sampling increases certainty in all ecological landscapes that influence animal movement (including fear and resource landscapes), and individuals use energy to express each of these. We call for the development of an 'optimal movement theory' (OMT) that integrates the multidimensional reality of movement decisions by combining ecological landscapes according to a single expectation of energy cost–benefit, where social sampling provides up-to-date information under uncertain conditions. This mechanistic framework has implications for predicting individual movement patterns and for investigating the emergence of aggregations.

物理能量定义了能量格局并决定了特定物种的运动成本，从而影响运动决策。在不可预测和动态的环境中，观察他人的运动可以增加个体在物理能量分配方面的确定性，从而提高运动效率。除了物理能量景观之外，社会抽样增加了影响动物运动（包括恐惧和资源景观）的所有生态景观的确定性，并且个人使用能量来表达这些中的每一个。我们呼吁发展一种“最佳运动理论”（OMT），通过根据能源成本效益的单一预期结合生态景观，整合运动决策的多维现实，社会抽样在不确定条件下提供最新信息。这种机械框架对预测个体运动模式和研究聚集体的出现具有重要意义。