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Basic Continuous Time Signals

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Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

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Continuous-time correlated random walk model for animal telemetry data.

Devin S Johnson¹, Joshua M London, Mary-Anne Lea

¹National Marine Mammal Laboratory, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA, 7600 Sand Point Way NE, Seattle, Washington 98115, USA. devin.johnson@noaa.gov

|June 12, 2008

Summary

We introduce a continuous-time correlated random walk model for animal tracking data. This advanced model handles irregular data collection, improving movement analysis for marine mammals.

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Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats

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Area of Science:

Ecology
Movement Ecology
Biomathematics

Background:

Animal telemetry data often has irregular temporal sampling.
Existing models may require data manipulation (subsampling, interpolation, aggregation) to handle irregular intervals.
Accurate modeling of animal movement is crucial for ecological research.

Purpose of the Study:

To develop a continuous-time correlated random walk model for animal telemetry data.
To enable modeling of irregularly collected data without preprocessing.
To provide a flexible framework for analyzing marine mammal movements.

Main Methods:

Derived a continuous-time Ornstein-Uhlenbeck velocity process and integrated it into a location process.
Implemented the model within a state-space framework for parameter estimation and prediction.
Applied the model to two marine mammal telemetry datasets (harbor seal, northern fur seal pup).

Main Results:

Demonstrated the model's ability to handle non-uniformly collected telemetry data.
Successfully analyzed harbor seal movement, incorporating time spent on land.
Modeled northern fur seal pup movements, including a random drift component for directed travel and currents.

Conclusions:

The continuous-time correlated random walk model offers a robust approach for analyzing animal telemetry data with irregular sampling.
The model framework allows for incorporating biological and environmental covariates.
This method enhances the accuracy and flexibility of animal movement studies, particularly for marine species.