Possible Models for Combining Tracking Data with Conventional Tagging Data

Abstract

Advection-diffusion models have been used successfully to describe the time and place of recapture of tuna tagged with conventional dart tags. Such models are the continuous analogs of a biased random walk. This paper demonstrates how biased random walks can be used to simulate large scale movements of tunas as recorded by archival tags in a way that captures all of the major characteristics of the tracks. The parameters of the biased random walk model are identical to the parameters of the advection diffusion model, suggesting that a joint parameter estimation procedure might be feasible. Finally, the potential application of the Kalman filter to the analysis of tracking data is discussed. This statistical model has the potential to increase the accuracy of geoposition estimates from tracking devices as well as to estimate biased random walk parameters from tracking data.