Dougherty Eric R, Carlson Colin J, Blackburn Jason K, Getz Wayne M
Department of Environmental Science, Policy, and Management, University of California, Berkeley, Berkeley, CA USA.
Spatial Epidemiology and Ecology Research Laboratory, Department of Geography, University of Florida, Gainesville, FL USA.
Mov Ecol. 2017 Sep 6;5:19. doi: 10.1186/s40462-017-0110-4. eCollection 2017.
With decreasing costs of GPS telemetry devices, data repositories of animal movement paths are increasing almost exponentially in size. A series of complex statistical tools have been developed in conjunction with this increase in data. Each of these methods offers certain improvements over previously proposed methods, but each has certain assumptions or shortcomings that make its general application difficult. In the case of the recently developed Time Local Convex Hull (T-LoCoH) method, the subjectivity in parameter selection serves as one of the primary impediments to its more widespread use. While there are certain advantages to the flexibility it offers for question-driven research, the lack of an objective approach for parameter selection may prevent some users from exploring the benefits of the method.
Here we present a cross-validation-based approach for selecting parameter values to optimize the T-LoCoH algorithm. We demonstrate the utility of the approach using a case study from the Etosha National Park anthrax system.
Utilizing the proposed algorithm, rather than the guidelines in the T-LoCoH documentation, results in significantly different values for derived site fidelity metrics.
Due to its basis in principles of cross-validation, the application of this method offers a more objective approach than the relatively subjective guidelines set forth in the T-LoCoH documentation and enables a more accurate basis for the comparison of home ranges among individuals and species, as well as among studies.
随着全球定位系统遥测设备成本的降低,动物移动路径的数据存储库规模几乎呈指数级增长。随着数据量的增加,一系列复杂的统计工具也应运而生。这些方法相较于之前提出的方法都有一定改进,但每种方法都有一些假设或缺点,导致其难以广泛应用。以最近开发的时间局部凸包(T-LoCoH)方法为例,参数选择的主观性是其更广泛应用的主要障碍之一。虽然它为问题驱动的研究提供了灵活性方面的某些优势,但缺乏客观的参数选择方法可能会阻碍一些用户探索该方法的益处。
在此,我们提出一种基于交叉验证的方法来选择参数值,以优化T-LoCoH算法。我们通过埃托沙国家公园炭疽系统的案例研究来证明该方法的实用性。
使用所提出的算法,而非T-LoCoH文档中的指导方针,得出的地点保真度指标值有显著差异。
由于该方法基于交叉验证原则,其应用提供了一种比T-LoCoH文档中相对主观的指导方针更客观的方法,为个体间、物种间以及不同研究间的家域比较提供了更准确的依据。