Abstract
It has been stated that there is a certain amount of intrinsic error inherent in all remote sensing methods, including acoustic telemetry, which has gained popularity in both freshwater and marine environments to record fine-scale movements over small spatial scales. We performed stationary tag trials on three freshwater lakes where we placed transmitters at known locations around the lakes and used radio-acoustic positioning and telemetry (RAPT) system-derived location data to assess the measurement and systematic biases of the system. We used a geostatistical technique called ordinary kriging to deal with the systematic errors and a state-space model to represent the measurement error of the data. Furthermore, we applied the kriging correction and a continuous-time correlated random walk model in a state-space framework to predict locations of a lake trout. The stationary tagging trials produced a complex pattern of spatial error within each lake that could not properly be accounted for by a simple filtering process. Using fivefold cross-validation, positioning error was reduced from 93 to 99 % in three small lakes. We also identified tag depth as a potential source of measurement error. The application of a state-space model resulted in the contraction of home ranges of lake trout by 10–32 % and a 3–32 % reduction in total distance travelled. Our results indicate that the systematic biases were a greater source of error than the measurement errors using a RAPT system. Consequently, the addition of a state-space model had relatively little effect on the quality of the spatial correction compared with the kriging method. The kriging method was able to compensate for the systematic biases produced by the RAPT systems and in turn increased the quality of data returned.
Highlights
It has been stated that there is a certain amount of intrinsic error inherent in all remote sensing methods, including acoustic telemetry, which has gained popularity in both freshwater and marine environments to record fine-scale movements over small spatial scales
We modelled individual fish movement as a correlated random walk (CRW) using R [11] and the “CRAWL” package [12], which allows the flexibility to model continuous movement with data collected at irregular intervals
Using observed telemetry data from a tagged lake trout in each of the three study lakes, we present four scenarios: (1) raw data, (2) data corrected for measurement error, (3) data corrected for systematic error, and (4) data corrected for both measurement and systematic errors
Summary
It has been stated that there is a certain amount of intrinsic error inherent in all remote sensing methods, including acoustic telemetry, which has gained popularity in both freshwater and marine environments to record fine-scale movements over small spatial scales. Where once manually locating an individual animal with a telemetry transmitter was a significant milestone, satellite transmitters and large-scale coastal tracking networks allow for the longrange monitoring of many individuals over large geographic ranges and time spans. Automated positioning systems, often used in aquatic environments, allow for the near-continuous monitoring of multiple animals simultaneously and can provide fine-scale movement data over multiple years [1]. The use of automated telemetry positioning systems in aquatic environments has allowed for detailed examination of fine-scale spatial patterns in fish and other aquatic biota. These telemetry systems incorporate three or more stationary hydrophone receivers that are positioned in a triangular array.
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