Taxon:
Halichoerus grypus

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Scientific Name
Halichoerus grypus
Common Name
Atlantic gray seal
Atlantic seal
Baltic gray seal
Gray Seal
horsehead
Taxa Group
Phocidae
Environment
Move Mode

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2015 - 20192
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Start date: 2015 × End date: 2019 ×

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    Data from: A novel approach to quantifying the spatiotemporal behavior of instrumented grey seals used to sample the environment
    (2015-09-15) Lidgard, Damian C.; Bowen, W. Don; Iverson, Sara J.
    Background: Paired with satellite location telemetry, animal-borne instruments can collect spatiotemporal data describing the animal’s movement and environment at a scale relevant to its behavior. Ecologists have developed methods for identifying the area(s) used by an animal (e.g., home range) and those used most intensely (utilization distribution) based on location data. However, few have extended these models beyond their traditional roles as descriptive 2D summaries of point data. Here we demonstrate how the home range method, T-LoCoH, can be expanded to quantify collective sampling coverage by multiple instrumented animals using grey seals (Halichoerus grypus) equipped with GPS tags and acoustic transceivers on the Scotian Shelf (Atlantic Canada) as a case study. At the individual level, we illustrate how time and space-use metrics quantifying individual sampling coverage may be used to determine the rate of acoustic transmissions received. Results: Grey seals collectively sampled an area of 11,308 km 2 and intensely sampled an area of 31 km 2 from June-December. The largest area sampled was in July (2094.56 km 2 ) and the smallest area sampled occurred in August (1259.80 km 2 ), with changes in sampling coverage observed through time. Conclusions: T-LoCoH provides an effective means to quantify changes in collective sampling effort by multiple instrumented animals and to compare these changes across time. We also illustrate how time and space-use metrics of individual instrumented seal movement calculated using T-LoCoH can be used to account for differences in the amount of time a bioprobe (biological sampling platform) spends in an area.
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    Data package
    Data from: State-switching continuous-time correlated random walks
    (2019-02-28) McConnell, Bernie J.
    (1) Continuous‐time models have been developed to capture features of animal movement across temporal scales. In particular, one popular model is the continuous‐time correlated random walk, in which the velocity of an animal is formulated as an Ornstein–Uhlenbeck process, to capture the autocorrelation in the speed and direction of its movement. In telemetry analyses, discrete‐time state‐switching models (such as hidden Markov models) have been increasingly popular to identify behavioural phases from animal tracking data. (2) We propose a multistate formulation of the continuous‐time correlated random walk, with an underlying Markov process used as a proxy for the animal’s behavioural state process. We present a Markov chain Monte Carlo algorithm to carry out Bayesian inference for this multistate continuous‐time model. (3) Posterior samples of the hidden state sequence, of the state transition rates, and of the state‐dependent movement parameters can be obtained. We investigate the performance of the method in a simulation study, and we illustrate its use in a case study of grey seal (Halichoerus grypus) tracking data. (4) The method we present makes use of the state‐space model formulation of the continuous‐time correlated random walk, and can accommodate irregular sampling frequency and measurement error. It will facilitate the use of continuous‐time models to estimate movement characteristics and infer behavioural states from animal telemetry data.