Lowther Andrew D, Lydersen Christian, Fedak Mike A, Lovell Phil, Kovacs Kit M
Norwegian Polar Institute, Fram Centre, N-9296, Tromsø, Norway.
Sea Mammal Research Unit, Scottish Oceans Institute, University of St Andrews, Fife, Scotland, United Kingdom.
PLoS One. 2015 Apr 23;10(4):e0124754. doi: 10.1371/journal.pone.0124754. eCollection 2015.
Understanding how an animal utilises its surroundings requires its movements through space to be described accurately. Satellite telemetry is the only means of acquiring movement data for many species however data are prone to varying amounts of spatial error; the recent application of state-space models (SSMs) to the location estimation problem have provided a means to incorporate spatial errors when characterising animal movements. The predominant platform for collecting satellite telemetry data on free-ranging animals, Service Argos, recently provided an alternative Doppler location estimation algorithm that is purported to be more accurate and generate a greater number of locations that its predecessor. We provide a comprehensive assessment of this new estimation process performance on data from free-ranging animals relative to concurrently collected Fastloc GPS data. Additionally, we test the efficacy of three readily-available SSM in predicting the movement of two focal animals. Raw Argos location estimates generated by the new algorithm were greatly improved compared to the old system. Approximately twice as many Argos locations were derived compared to GPS on the devices used. Root Mean Square Errors (RMSE) for each optimal SSM were less than 4.25 km with some producing RMSE of less than 2.50 km. Differences in the biological plausibility of the tracks between the two focal animals used to investigate the utility of SSM highlights the importance of considering animal behaviour in movement studies. The ability to reprocess Argos data collected since 2008 with the new algorithm should permit questions of animal movement to be revisited at a finer resolution.
了解动物如何利用其周围环境需要准确描述其在空间中的移动。卫星遥测是获取许多物种移动数据的唯一手段，然而数据容易出现不同程度的空间误差；状态空间模型（SSM）最近应用于位置估计问题，为在描述动物移动时纳入空间误差提供了一种方法。用于收集自由放养动物卫星遥测数据的主要平台——Service Argos，最近提供了一种替代的多普勒位置估计算法，据称该算法更准确，能生成比其前身更多的位置。我们全面评估了这种新估计过程相对于同时收集的Fastloc GPS数据对自由放养动物数据的性能。此外，我们测试了三种现成的状态空间模型在预测两只重点动物移动方面的效果。与旧系统相比，新算法生成的原始Argos位置估计有了很大改进。在所使用的设备上，与GPS相比，导出的Argos位置数量大约是其两倍。每个最优状态空间模型的均方根误差（RMSE）小于4.25千米，有些模型的均方根误差小于2.50千米。用于研究状态空间模型效用的两只重点动物之间轨迹的生物学合理性差异，凸显了在移动研究中考虑动物行为的重要性。使用新算法对自2008年以来收集的Argos数据进行重新处理的能力，应该能够以更高的分辨率重新审视动物移动的问题。
