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Cited by 653

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

CONTINUOUS‐TIME CORRELATED RANDOM WALK MODEL FOR ANIMAL TELEMETRY DATA

Open access
Johnson, Devin S., London, Joshua M., Lea, Mary-Anne, Durban, John W.·2008·Ecology

Abstract

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 Two previously unpublished marine mammal telemetry data sets were analyzed to illustrate use of the model, by‐products available from the analysis, and different modifications which are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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Metadata

Authors
Johnson, D. S., London, J. M., Lea, M.-A., & Durban, J. W.
Venue
Ecology
Publisher
Wiley
Published
2008-05
Volume
89
Issue
5
Pages
1208-1215
ISSN
  • 0012-9658 (ISSN-L)
  • 1939-9170
OpenAlex
W2080842868
Cited by
653
References
1
FWCI
9.04
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: CONTINUOUS‐TIME CORRELATED RANDOM WALK MODEL FOR ANIMAL TELEMETRY DATA. https://app.cochat.ai/papers/10.1890/07-1032.1
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Johnson, D. S., London, J. M., Lea, M.-A., & Durban, J. W. (2008). CONTINUOUS‐TIME CORRELATED RANDOM WALK MODEL FOR ANIMAL TELEMETRY DATA. Ecology. https://doi.org/10.1890/07-1032.1

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.