Landscape planning that ensures the ecological integrity of ecosystems is critical in the face of rapid, human driven habitat conversion and development pressure. Wildlife tracking data provide unique and valuable information on animal distribution and location specific behaviors that can serve to increase the efficacy of such planning efforts. Given the spatio-temporal complexity inherent to animal movements, the interaction between movement behavior and a location is often oversimplified in commonly applied analyses of tracking data. Here, we jointly analyze GPS tracking derived metrics of intensity of use, structural properties (based on network theory), and properties of the movement path (speed and directionality) using machine learning to define homogeneous spatial movement types. We applied our approach to a long-term tracking dataset of over 130 African elephants (Loxodonta africana) in an area facing emerging pressures from infrastructure development. We identified five unique location specific movement categories displayed by elephants, generally defined as high, mid, and low use intensity, and two types of connectivity corridors associated with fast and slow movements. High use and slow movement corridors were associated with similar landscape characteristics associated to productive areas near water, while low use and fast corridors were characterized by low productivity areas further from water. By combining information on intensity of use, movement path properties, and structural aspects of movement across the landscape, our approach provides explicit definition of the functional role of areas for movement across the landscape which we term the "movescape". This combined, high resolution information regarding wildlife space use offers mechanistic information that can progress landscape planning efforts. This article is protected by copyright. All rights reserved.

中文翻译：

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表征运动景观以识别重要的野生动物栖息地和走廊

面对快速的、人类驱动的栖息地转换和发展压力，确保生态系统生态完整性的景观规划至关重要。野生动物追踪数据提供了关于动物分布和特定地点行为的独特且有价值的信息，可用于提高此类规划工作的效率。鉴于动物运动固有的时空复杂性，运动行为和位置之间的相互作用在跟踪数据的常用分析中往往被过度简化。在这里，我们使用机器学习来共同分析 GPS 跟踪衍生的使用强度指标、结构特性（基于网络理论）和运动路径的特性（速度和方向性），以定义同质空间运动类型。我们将我们的方法应用于一个长期跟踪数据集的 130 多头非洲象 (Loxodonta Africana)，该地区面临着基础设施发展带来的新压力。我们确定了大象显示的五个独特的位置特定运动类别，通常定义为高、中和低使用强度，以及与快速和慢速运动相关的两种类型的连接走廊。高利用率和缓慢移动的走廊与与靠近水的生产区相关的相似景观特征相关，而低利用率和快速走廊的特点是远离水的低生产率区域。通过结合使用强度、运动路径属性和跨景观运动的结构方面的信息，我们的方法明确定义了跨景观移动区域的功能作用，我们称之为“移动景观”。这种关于野生动物空间使用的综合高分辨率信息提供了可以推进景观规划工作的机械信息。本文受版权保护。版权所有。