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A guide to pre-processing high-throughput animal tracking data.

Pratik Rajan Gupte1,2, Christine E Beardsworth2, Orr Spiegel3,4

  • 1Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, The Netherlands.

The Journal of Animal Ecology
|October 17, 2021
PubMed
Summary
This summary is machine-generated.

High-throughput animal tracking generates big data, but location errors can distort behavioral analysis. This study introduces an automated pipeline and R package (atlastools) to clean and process this movement data, improving accuracy and enabling better ecological inferences.

Keywords:
atlastoolsATLAS trackingbig databiotelemetrydata cleaninghigh-throughput movement ecologyresidence patchreverse GPS

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Area of Science:

  • Movement Ecology
  • Bioinformatics
  • Data Science

Background:

  • Modern animal tracking generates massive datasets with high temporal resolution.
  • Location errors in tracking data can exceed animal step size, leading to misinterpretation of behaviors.
  • Standardized, automated methods for cleaning and processing this data are scarce.

Purpose of the Study:

  • To provide guidance on building automated pipelines for pre-processing high-throughput animal tracking data.
  • To introduce the R package atlastools for efficient data cleaning and analysis.
  • To demonstrate the utility of the pipeline and package with simulated and real-world animal tracking datasets.

Main Methods:

  • Development of a pre-processing pipeline balancing ease of use and computational efficiency.
  • Application of the pipeline to simulated data with introduced location errors.
  • Utilizing the pipeline to segment and cluster location data into 'residence patches' for space use analysis.
  • Demonstration using tracking data from the Wadden Sea ATLAS system (WATLAS) and Egyptian fruit bats (Rousettus aegyptiacus).

Main Results:

  • The proposed pipeline effectively reduces location errors while preserving valid animal movements.
  • The R package atlastools facilitates standardized and reproducible data pre-processing.
  • The residence patch method, enabled by the pipeline, provides biologically meaningful insights into animal space use.
  • Pre-processing significantly improves the quality of tracking data and enhances analytical outcomes.

Conclusions:

  • Automated pre-processing pipelines are essential for handling big data in animal tracking.
  • The atlastools package offers a user-friendly and robust solution for movement data analysis.
  • Standardized methods in movement ecology lead to more reliable inferences from tracking data.