Data from: Novel, continuous monitoring of fine-scale movement using fixed-position radiotelemetry arrays and random forest location fingerprinting

1. Radio-tag signals from fixed-position antennas are most often used to indicate presence/absence of individuals, or to estimate individual activity levels from signal strength variation within an antenna’s detection zone. The potential of such systems to provide more precise information on tag location and movement has not been explored in great detail in an ecological setting. 2. By reversing the roles that transmitters and receivers play in localization methods common to the telecommunications industry, we present a new telemetric tool for accurately estimating the location of tagged individuals from received signal strength values. The methods used to characterize the study area in terms of received signal strength are described, as is the random forest model used for localization. The resulting method is then validated using test data before being applied to true data collected from tagged individuals in the study site. 3. Application of the localization method to test data withheld from the learning dataset indicated a low average error over the entire study area (< 1m) while application of the localization method to real data produced highly probable results consistent with field observations. 4. This telemetric approach provided detailed movement data for tagged fish along a single axis (a migratory path) and is particularly useful for monitoring passage along migratory routes. The new methods applied in this study can also be expanded to include multiple axes (x, y, z) and multiple environments (aquatic and terrestrial) for remotely monitoring wildlife movement.1. 由固定位置天线采集的无线电标记（radio-tag）信号，通常用于指示个体的存在或缺失，或是基于天线探测区域内的信号强度变化估算个体的活动水平。此类系统在生态学研究场景中，其提供标记个体位置与移动轨迹的更精准信息的潜力尚未得到充分深入的挖掘。 2. 本研究借鉴电信行业通用的定位方法，反转发射机与接收机的角色，提出了一种全新的遥测工具（telemetric tool），可基于接收信号强度（received signal strength）值精准估算标记个体的位置。本文阐述了以接收信号强度为依据表征研究区域的方法，以及用于定位的随机森林模型（random forest model）。所得定位方法先通过测试数据完成验证，随后再应用于研究样地中标记个体的实测采集数据。 3. 将定位方法应用于从学习数据集（learning dataset）中预留的测试数据时，结果显示整个研究区域内的平均误差较低（<1米）；而将该方法应用于实测数据时，所得结果与野外观测结果高度吻合，可信度极高。 4. 本研究提出的遥测方法可为沿单一轴（迁移路径）活动的标记鱼类提供精细化的移动数据，尤其适用于监测沿迁移路线的通行情况。本研究采用的新型方法还可进一步拓展至多轴（x、y、z轴）及多环境（水生与陆生）场景，以实现野生动物移动的远程监测。