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A probabilistic algorithm to process geolocation data

机译:一种处理地理定位数据的概率算法

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Background The use of light level loggers (geolocators) to understand movements and distributions in terrestrial and marine vertebrates, particularly during the non-breeding period, has increased dramatically in recent years. However, inferring positions from light data is not straightforward, often relies on assumptions that are difficult to test, or includes an element of subjectivity. Results We present an intuitive framework to compute locations from twilight events collected by geolocators from different manufacturers. The procedure uses an iterative forward step selection, weighting each possible position using a set of parameters that can be specifically selected for each analysis. The approach was tested on data from two wide-ranging seabird species - black-browed albatross Thalassarche melanophris and wandering albatross Diomedea exulans – tracked at Bird Island, South Georgia, during the two most contrasting periods of the year in terms of light regimes (solstice and equinox). Using additional information on travel speed, sea surface temperature and land avoidance, our approach was considerably more accurate than the traditional threshold method (errors reduced to medians of 185?km and 145?km for solstice and equinox periods, respectively). Conclusions The algorithm computes stable results with uncertainty estimates, including around the equinoxes, and does not require calibration of solar angles. Accuracy can be increased by assimilating information on travel speed and behaviour, as well as environmental data. This framework is available through the open source R package probGLS , and can be applied in a wide range of biologging studies.
机译:背景技术近年来,尤其是在非繁殖时期,使用光水平记录仪(地质仪)来了解陆地和海洋脊椎动物的运动和分布的情况急剧增加。然而,从光数据推断位置并不简单,通常依赖于难以检验的假设,或包含主观性的要素。结果我们提供了一个直观的框架,可以根据来自不同制造商的地理定位器收集的暮光事件来计算位置。该过程使用迭代的前进步骤选择,使用可以为每种分析专门选择的一组参数对每个可能的位置加权。该方法已针对两种广泛海鸟物种(黑眉信天翁Thalassarche melanophris和流浪信天翁Diomedea exulans)的数据进行了测试,这些数据是在一年中两个相对较轻的时期(在光度方面,在南乔治亚州伯德岛进行追踪的(冬至)和春分)。使用有关行进速度,海面温度和避免陆地的其他信息,我们的方法比传统的阈值方法要准确得多(冬至和春分期间的误差分别降至185?km和145?km的中值)。结论该算法可通过不确定性估计(包括昼夜平分点附近)来计算稳定的结果,并且不需要校准太阳角。通过吸收有关行进速度和行为的信息以及环境数据,可以提高准确性。该框架可通过开放源代码R包probGLS获得,并可应用于广泛的生物记录研究。

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