Part 2: GPS Telemetry Detection Rates (Northern Arizona GPS Test Collar Data), GCS NAD 83 (2015)

Bias correction in GPS telemetry data-sets requires a strong understanding of the mechanisms that result in missing data. We tested wildlife GPS collars in a variety of environmental conditions to derive a predictive model of fix acquisition. We found terrain exposure and tall over-story vegetation are the primary environmental features that affect GPS performance. Model evaluation showed a strong correlation (0.924) between observed and predicted fix success rates (FSR) and showed little bias in predictions. The model's predictive ability was evaluated using two independent data-sets from stationary test collars of different make/model, fix interval programming, and placed at different study sites. No statistically significant differences (95% CI) between predicted and observed FSRs, suggest changes in technological factors have minor influence on the models ability to predict FSR in new study areas in the southwestern US. The model training data are provided here for fix attempts by hour. This table can be linked with the site location shapefile using the site field.

GPS遥测数据集（GPS telemetry data-sets）的偏差校正，需要深刻理解引发数据缺失的内在作用机制。本研究在多种环境条件下开展野生动物GPS项圈测试，以构建定位获取（fix acquisition）的预测模型。研究发现，地形暴露度与高大上层植被是影响GPS性能的两类主要环境特征。模型评估结果显示，观测得到的定位成功率（FSR）与预测值之间存在极强相关性（相关系数达0.924），且预测偏差极小。我们采用两套独立的固定式测试项圈数据集对模型的预测能力进行验证，这些测试项圈的品牌型号、定位间隔编程方案各不相同，且部署于不同的研究样地。预测与观测得到的定位成功率之间不存在统计学显著差异（95%置信区间CI），这表明技术因素的变化对该模型在美国西南部新研究样地中预测定位成功率的能力影响微弱。本文提供了按小时统计的定位尝试次数的模型训练数据，该表格可通过样地字段与样地位置形状文件（shapefile）建立关联。