HMMoce: an R package for improved analysis of marine animal movement
data using hidden Markov models
Camrin D. Braun1,2*, Benjamin Galuardi3,4, Paul Gatti5, Simon R. Thorrold2
- Massachusetts Institute of Technology-Woods Hole Oceanographic Institution Joint Program in Oceanography/Applied Ocean Science and Engineering, Cambridge, MA 02139
- Biology Department, Woods Hole Oceanographic Institution, Woods Hole, MA 02543
- School of Marine Science and Technology, University of Massachusetts Dartmouth, Fairhaven, MA 02719
- Greater Atlantic Regional Fisheries Office, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Gloucester, MA 01930
- Centre for Fisheries Ecosystems Research, Fisheries and Marine Institute of Memorial University of Newfoundland
All the tweaks and improvements made over the last 1-2 years have been folded into v1.2 which is now on the master branch. The next version will likely be v2.0 and will lack backwards compatibility with previous versions. The new version is currently under development in the -dev branch and pull requests are welcome!
Many thanks to Paul Gatti for catching many small bugs and providing several (very!) useful new functions for what will be the next version. Check out the -dev branch to learn more.
Electronic tagging of marine animals is common throughout the world oceans. Many of these studies have deployed archival tags that rely on light levels and sea-surface temperatures to retrospectively track movements of tagged animals. 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 disregarded the oceanographic profiles collected by the tag as a potential way of refining light-level geolocation estimates provided by electronic tags.
Open-source oceanographic measurements and outputs from high-resolution
models are increasingly available and accessible. We integrated
temperature and depth profiles recorded by electronic tags, with
empirical data and model outputs, to construct likelihoods and improve
geolocation estimates for marine animals using an existing, but
modified, state-space hidden Markov model (HMM). Our model (HMMoce)
exhibited as much as 6-fold improvement in pointwise error as compared
to traditional light-level geolocation approaches and produced the
lowest mean error in 3 of 4 cases when compared to the state-of-the-art
tag manufacturer’s HMM (GPE3). HMMoce contained behavior
state-switching capability not found in other comparable methods. The
use of profile-based likelihood estimates proved useful when we removed
data to emulate data returned from species that yield poor quality light
data. The results demonstrated the general applicability of the HMMoce
model to marine animals, particularly those that do not frequent surface
waters during crepuscular periods. Our model is available as an
open-source R package, HMMoce, that uses a state-space HMM approach
and leverages available tag and oceanographic data to improve position
estimates derived from electronic tags.
Braun, C. D., Galuardi, B., and Thorrold, S. R. (2018). HMMoce: An R package for improved geolocation of archival-tagged fishes using a hidden Markov method. Methods in Ecology and Evolution, 9(5), 1212-1220.
The package is structured as follows: * Load the relevant tag data and establish a study area of interest. * Get the environmental data to base the likelihood calculations on. * Calculate the desired likelihoods (e.g. depth-temperature profiles, SST, etc) * Estimate parameters and run the model. Results are written out along the way. * Perform model checking and choose a final model.
HMMoce can be installed from CRAN from within R using
install.packages('HMMoce'). To get the latest developments, get it
from GitHub using devtools::install_github('camrinbraun/HMMoce', ref='dev')
For an example use of the package, please see the vignette using
vignette('HMMoce').
