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.