Description Author(s) References See Also
The HMMoce package provides a workflow for leveraging all available data from tags deployed on marine animals to estimate movements, space use, and behavior. Marine animals, mostly fish, are notoriously difficult to track with electronic tags because these devices require occupation of the surface-air interface to record satellite-based geolocations or occupation of the photic zone to collect light levels to estimate position. It is common among fishes to avoid the photic zone during daylight hours thus rendering this geolocation approach useless. In the HMMoce package, we leverage all tag-based data streams, like depth-temperature profiles, in conjunction with whatever traditional geolocation data (e.g. light) is available to calculate the most probable movements of the tagged animal. This is performed in a hidden Markov framework originally developed by Pedersen et al. 2008.
Maintainer: Camrin Braun camrin.braun@gmail.com
Authors:
Benjamin Galuardi drdrumfish@gmail.com
Other contributors:
Benjamin Jones (Contributed to earlier version of some of the download functions.) [contributor]
Martin Pedersen (Developed an earlier version of some of the HMM framework and helper functions.) [contributor]
Pedersen MW, Righton D, Thygesen UH, et al. (2008) Geolocation of North Sea cod (Gadus morhua) using hidden Markov models and behavioural switching. Can J Fish Aquat Sci 65:2367-2377.
Pedersen MW, Patterson TA, Thygesen UH, Madsen H (2011) Estimating animal behavior and residency from movement data. Oikos 120:1281-1290.
Woillez M, Fablet R, Ngo TT, et al. (2016) A HMM-based model to geolocate pelagic fish from high-resolution individual temperature and depth histories: European sea bass as a case study. Ecol Modell 321:10-22.
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