Magowan E A, Maguire I E, Smith S, Redpath S, Marks N J, Wilson R P, Menzies F, O'Hagan M, Scantlebury D M
School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast, BT9 5DL Northern Ireland UK.
Randox Laboratories Ltd. Crumlin, Antrim, Northern Ireland UK.
Anim Biotelemetry. 2022;10(1):10. doi: 10.1186/s40317-022-00282-2. Epub 2022 Mar 10.
Recent developments in both hardware and software of animal-borne data loggers now enable large amounts of data to be collected on both animal movement and behaviour. In particular, the combined use of tri-axial accelerometers, tri-axial magnetometers and GPS loggers enables animal tracks to be elucidated using a procedure of 'dead-reckoning'. Although this approach was first suggested 30 years ago by Wilson et al (1991), surprisingly few measurements have been made in free-ranging terrestrial animals. The current study examines movements, interactions with habitat features, and home-ranges calculated from just GPS data and also from dead-reckoned data in a model terrestrial mammal, the European badger ().
Research was undertaken in farmland in Northern Ireland. Two badgers (one male, one female) were live-trapped and fitted with a GPS logger, a tri-axial accelerometer, and a tri-axial magnetometer. Thereafter, the badgers' movement paths over 2 weeks were elucidated using just GPS data and GPS-enabled dead-reckoned data, respectively.
Badgers travelled further using data from dead-reckoned calculations than using the data from only GPS data. Whilst once-hourly GPS data could only be represented by straight-line movements between sequential points, the sub-second resolution dead-reckoned tracks were more tortuous. Although there were no differences in Minimum Convex Polygon determinations between GPS- and dead-reckoned data, Kernel Utilisation Distribution determinations of home-range size were larger using the former method. This was because dead-reckoned data more accurately described the particular parts of landscape constituting most-visited core areas, effectively narrowing the calculation of habitat use. Finally, the dead-reckoned data showed badgers spent more time near to field margins and hedges than simple GPS data would suggest.
Significant differences emerge when analyses of habitat use and movements are compared between calculations made using just GPS data or GPS-enabled dead-reckoned data. In particular, use of dead-reckoned data showed that animals moved 2.2 times farther, had better-defined use of the habitat (revealing clear core areas), and made more use of certain habitats (field margins, hedges). Use of dead-reckoning to provide detailed accounts of animal movement and highlight the minutiae of interactions with the environment should be considered an important technique in the ecologist's toolkit.
动物携带的数据记录器在硬件和软件方面的最新进展，现在能够收集大量关于动物运动和行为的数据。特别是，三轴加速度计、三轴磁力计和GPS记录器的联合使用，使得能够通过“航位推算”程序来阐明动物的踪迹。尽管这种方法早在30年前就由威尔逊等人（1991年）提出，但令人惊讶的是，在自由放养的陆生动物中进行的测量却很少。当前的研究考察了一种典型的陆生哺乳动物——欧洲獾，其仅根据GPS数据以及根据航位推算数据计算出的运动、与栖息地特征的相互作用和活动范围。
研究在北爱尔兰的农田中进行。捕获了两只獾（一雄一雌），并为它们安装了GPS记录器、三轴加速度计和三轴磁力计。此后，分别仅使用GPS数据和启用GPS的航位推算数据来阐明獾在两周内的运动路径。
与仅使用GPS数据相比，使用航位推算计算的数据时，獾的移动距离更远。虽然每小时一次的GPS数据只能由连续点之间的直线运动来表示，但亚秒级分辨率的航位推算轨迹更加曲折。尽管在GPS数据和航位推算数据之间，最小凸多边形的测定没有差异，但使用前一种方法测定的活动范围大小的核利用分布更大。这是因为航位推算数据更准确地描述了构成最常访问核心区域的景观的特定部分，有效地缩小了栖息地利用的计算范围。最后，航位推算数据显示，獾在田边和树篱附近花费的时间比简单的GPS数据所显示的要多。
当比较仅使用GPS数据或启用GPS的航位推算数据进行的栖息地利用和运动分析时，会出现显著差异。特别是，使用航位推算数据表明，动物移动的距离远2.2倍，对栖息地的利用更明确（揭示了清晰的核心区域），并且更多地利用了某些栖息地（田边、树篱）。使用航位推算来详细描述动物运动并突出与环境相互作用的细节，应被视为生态学家工具包中的一项重要技术。
