DOI: 10.5061/dryad.j86198f
title: Data from: Joint modelling of multi-scale animal movement data using hierarchical hidden Markov models

description: 1. Hidden Markov models are prevalent in animal movement modelling, where
 they are widely used to infer behavioural modes and their drivers from
 various types of telemetry data. To allow for meaningful inference,
 observations need to be equally spaced in time, or otherwise regularly
 sampled, where the corresponding temporal resolution strongly affects what
 kind of behaviours can be inferred from the data. 2. Recent advances in
 biologging technology have led to a variety of novel telemetry sensors
 which often collect data from the same individual simultaneously at
 different time scales, e.g. step lengths obtained from GPS tags every
 hour, dive depths obtained from time-depth recorders once per dive, or
 accelerations obtained from accelerometers several times per second.
 However, to date, statistical machinery to address the corresponding
 complex multi-stream and multi-scale data is lacking. 3. We propose
 hierarchical hidden Markov models as a versatile statistical framework
 that naturally accounts for differing temporal resolutions across multiple
 variables. In these models, the observations are regarded as stemming from
 multiple, connected behavioural processes, each of which operates at the
 time scale at which the corresponding variables were observed. 4. By
 jointly modelling multiple data streams, collected at different temporal
 resolutions, corresponding models can be used to infer behavioural modes
 at multiple time scales, and in particular help to draw a much more
 comprehensive picture of an animal's movement patterns, e.g. with
 regard to long-term vs. short-term movement strategies. 5. The suggested
 approach is illustrated in two real-data applications, where we jointly
 model i) coarse-scale horizontal and fine-scale vertical Atlantic cod
 (Gadus morhua) movements throughout the English Channel, and ii)
 coarse-scale horizontal movements and corresponding fine-scale
 accelerations of a horn shark (Heterodontus francisci) tagged off the
 Californian coast.
description: Horizontal movement data
 and accelerations collected from a single horn shark at Santa Catalina
 Island, California, USA2 sources of data were
 collected from a single horn shark (Heterodontus francisci) at Santa
 Catalina Island, California, USA, over a period of 24 hours in June 2017:
 geopositional and acceleration data. Custom tag packages were externally
 attached to the dorsal fin of the shark that included an accelerometer
 data logger (Technosmart, AxyDepth) and an acoustic transmitter (Vemco V9
 continuous pulse pinger, 2000 milliseconds). The shark was actively
 tracked from a tracking vessel above using a Vemco VR100 on-board receiver
 and VH110 directional hydrophone. During active tracking, geopositions
 were estimated for the tracking vessel relative to that of the tagged
 shark. Although boat position was recorded every 2 seconds, position data
 were filtered according to the closest possible estimations of the shark
 to the boat. Geopositional error for the shark was estimated to be 5
 metres (via range tests at the study location). To characterise the
 shark's horizontal movements, we processed the geopositions into step
 lengths by calculating the Euclidean distances between consecutive
 geopositions. Acceleration data were recorded continuously at 25 hertz in
 3 orthogonal dimensions, the dorso-ventral, anterior-posterior, and
 lateral axes. We summarised the acceleration signal across the 3
 dimensions by calculating the overall dynamic body acceleration. The
 workspace contains 2 lists, entitled steps (containing the step lengths)
 and odbas (containing the accelerations).horn_shark_data_set.RData