Animal movement study often relies on individual tracking. The data scale (in time and space) varies according to the species, the environment where individuals live, or the exogenous processes that drive movement. To explore freshwater fish movement in rivers, fine-scale data are needed. Also, in rivers, recorded telemetry frequently shows missing data and location errors. The irregular ... [Show full abstract] time-steps, huge amount of data, environmental complexity (river section) and how fish move in such anisotropic environments undermine the use of statistical frameworks such as state-space models. To deal with these specificities, data pre-treatment can be required. We propose a generic method of telemetry data pre-processing, which can be transposed to other datasets. This framework includes interpolation to handle trajectories at fine time scales and performs data analysis within a state-space model.
We combined analyses on observed and simulated data at various interpolation time-steps to choose the one that best preserves the general movement while reducing the total amount of data required. First, we directly compared raw and interpolated data, and the results of parameter inference of a simple state-space model using the interpolated data. The state-space model infers behavioural state based on speed and turning angle between successive locations in animal trajectories. We also included two additional variables computed from raw data: a quantitative indicator of the correspondence between the interpolated trajectory and the raw data, and the variance of turning angles of raw data within the interpolation time-step. We were finally able to determine the most appropriate time-step to obtain locations that were regularly spaced in time and to reduce the amount of data while maintaining the precision of the raw data. Computational time was reduced 12-fold by using a 30-second time-step to interpolate data simulated at 3-second intervals. The inclusion of the two variables derived from raw data compensated for the loss of information in interpolated trajectories and allowed more efficient discrimination between behaviours.