Stark Danica J, Vaughan Ian P, Ramirez Saldivar Diana A, Nathan Senthilvel K S S, Goossens Benoit
Organisms and Environment Division, Cardiff School of Biosciences, Cardiff University, Sir Martin Evans Building, Museum Avenue, Cardiff, United Kingdom.
Danau Girang Field Centre, c/o Sabah Wildlife Department, Wisma Muis, Kota Kinabalu, Sabah, Malaysia.
PLoS One. 2017 Mar 31;12(3):e0174891. doi: 10.1371/journal.pone.0174891. eCollection 2017.
The development of GPS tags for tracking wildlife has revolutionised the study of home ranges, habitat use and behaviour. Concomitantly, there have been rapid developments in methods for estimating habitat use from GPS data. In combination, these changes can cause challenges in choosing the best methods for estimating home ranges. In primatology, this issue has received little attention, as there have been few GPS collar-based studies to date. However, as advancing technology is making collaring studies more feasible, there is a need for the analysis to advance alongside the technology. Here, using a high quality GPS collaring data set from 10 proboscis monkeys (Nasalis larvatus), we aimed to: 1) compare home range estimates from the most commonly used method in primatology, the grid-cell method, with three recent methods designed for large and/or temporally correlated GPS data sets; 2) evaluate how well these methods identify known physical barriers (e.g. rivers); and 3) test the robustness of the different methods to data containing either less frequent or random losses of GPS fixes. Biased random bridges had the best overall performance, combining a high level of agreement between the raw data and estimated utilisation distribution with a relatively low sensitivity to reduced fixed frequency or loss of data. It estimated the home range of proboscis monkeys to be 24-165 ha (mean 80.89 ha). The grid-cell method and approaches based on local convex hulls had some advantages including simplicity and excellent barrier identification, respectively, but lower overall performance. With the most suitable model, or combination of models, it is possible to understand more fully the patterns, causes, and potential consequences that disturbances could have on an animal, and accordingly be used to assist in the management and restoration of degraded landscapes.
用于追踪野生动物的全球定位系统(GPS)标签的发展彻底改变了对动物活动范围、栖息地利用和行为的研究。与此同时,从GPS数据估计栖息地利用的方法也有了迅速发展。综合起来,这些变化在选择估计活动范围的最佳方法时可能会带来挑战。在灵长类动物学领域,这个问题很少受到关注,因为迄今为止基于GPS项圈的研究很少。然而,随着技术的进步使项圈研究变得更加可行,分析工作需要与技术同步推进。在这里,我们使用来自10只长鼻猴(Nasalis larvatus)的高质量GPS项圈数据集,旨在:1)将灵长类动物学中最常用的方法——网格单元法——与最近为大型和/或时间相关的GPS数据集设计的三种方法所估计出的活动范围进行比较;2)评估这些方法识别已知物理屏障(如河流)的能力;3)测试不同方法对包含较少频率或随机丢失GPS定位数据的稳健性如何。偏差随机桥接方法总体表现最佳——原始数据与估计的利用分布之间一致性程度高,同时对定位频率降低或数据丢失的敏感度相对较低——它估计长鼻猴的活动范围为24 - 165公顷(平均80.89公顷)。网格单元法和基于局部凸包的方法分别具有一些优点,包括简单性和出色的屏障识别能力,但总体表现较低。使用最合适的模型或模型组合,有可能更全面地了解干扰对动物可能产生的模式、原因和潜在后果,从而可用于协助退化景观的管理和恢复。