Abstract

            Background
            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.
          
          
            Results
            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.
          
          
            Conclusions
            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.