HMMoce : an R package for improved geolocation of archival-tagged fishes using a hidden Markov method

Abstract

Electronic tagging of marine fishes is commonly achieved with archival tags that rely on light levels and sea surface temperatures to retrospectively estimate movements. However, methodological issues associated with light-level geolocation have constrained meaningful inference to species where it is possible to accurately estimate time of sunrise and sunset. Most studies have largely ignored the oceanographic profiles collected by the tag as a potential way to refine light-level geolocation estimates. Open-source oceanographic measurements and outputs from high-resolution models are increasingly available and accessible. Temperature and depth profiles recorded by electronic tags can be integrated with these empirical data and model outputs to construct likelihoods and improve geolocation estimates. The R package HMMoce leverages available tag and oceanographic data to improve position estimates derived from electronic tags using a hidden Markov approach. We illustrate the use of the model and test its performance using example blue and mako shark archival tag data. Model results were validated using independent, known tracks and compared to results from other geolocation approaches. HMMoce exhibited as much as 6-fold improvement in pointwise error as compared to traditional light-level geolocation approaches. The results demonstrated the general applicability of HMMoce to marine animals, particularly those that do not frequent surface waters during crepuscular periods.