CONTINUOUS‐TIME CORRELATED RANDOM WALK MODEL FOR ANIMAL TELEMETRY DATA
We propose a continuous‐time version of the correlated random walk model for animal telemetry data. The continuous‐time formulation allows data that have been nonuniformly collected over time to be modeled without subsampling, interpolation, or aggregation to obtain a set of locations uniformly spac...
| Published in: | Ecology |
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| Main Authors: | , , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Wiley
2008
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| Subjects: | |
| Online Access: | http://dx.doi.org/10.1890/07-1032.1 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1890%2F07-1032.1 https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.1890/07-1032.1 |
| Summary: | We propose a continuous‐time version of the correlated random walk model for animal telemetry data. The continuous‐time formulation allows data that have been nonuniformly collected over time to be modeled without subsampling, interpolation, or aggregation to obtain a set of locations uniformly spaced in time. The model is derived from a continuous‐time Ornstein‐Uhlenbeck velocity process that is integrated to form a location process. The continuous‐time model was placed into a state–space framework to allow parameter estimation and location predictions from observed animal locations. Two previously unpublished marine mammal telemetry data sets were analyzed to illustrate use of the model, by‐products available from the analysis, and different modifications which are possible. A harbor seal data set was analyzed with a model that incorporates the proportion of each hour spent on land. Also, a northern fur seal pup data set was analyzed with a random drift component to account for directed travel and ocean currents. |
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