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Measuring activity-rest rhythms under different acclimation periods in a marine fish using automatic deep

Mourad Akaarir1, Martina Martorell-Barceló2, Bernat Morro2

  • 1Laboratorio del Sueño y Ritmos Biológicos, Universitat de les Illes Balears IDISBA, IUNICS, Palma, Spain.

Chronobiology International
|July 8, 2024
PubMed
Summary
This summary is machine-generated.

This study developed a deep learning video tracking method to analyze activity rhythms in Xyrichtys novacula. Longer acclimation periods in the lab improved rhythm synchronization to artificial light cycles.

Keywords:
Activity-rest patternsXyrichtys novaculacircadian rhythmdeep learninglocomotor activityvideo tracking

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Area of Science:

  • Animal behavior
  • Chronobiology
  • Deep learning applications

Background:

  • Circadian rhythms are fundamental biological processes influencing organismal behavior.
  • Understanding activity rhythms in marine species like Xyrichtys novacula is crucial for ecological and physiological studies.
  • Traditional methods for monitoring animal activity can be labor-intensive and limited in scope.

Purpose of the Study:

  • To introduce and validate a novel deep learning-powered video tracking system for assessing biological rhythms.
  • To evaluate the stability, fragmentation, robustness, and synchronization of activity rhythms in Xyrichtys novacula under laboratory conditions.
  • To compare laboratory-observed rhythms with natural behavior patterns of wild Xyrichtys novacula.

Main Methods:

  • Development of a deep learning-based video tracking system for automated activity monitoring.
  • Experimental setup involving three groups of Xyrichtys novacula with varying acclimation periods (1, 2, and 3 weeks) to a 14/10 hours light/dark cycle.
  • Collection and analysis of telemetry data from wild Xyrichtys novacula to establish baseline natural behavior.

Main Results:

  • Wild Xyrichtys novacula exhibited robust, minimally fragmented activity rhythms synchronized to natural photoperiods.
  • Laboratory fish showed activity differences between light and dark phases, but no significant differences in rhythm metrics across acclimation groups.
  • Extended acclimation periods (2 and 3 weeks) increased the proportion of fish synchronizing to the laboratory light/dark cycle.

Conclusions:

  • The novel deep learning video tracking system effectively monitors biological rhythms in laboratory settings.
  • Acclimation duration influences rhythm synchronization to artificial photoperiods in Xyrichtys novacula.
  • The developed system is adaptable for monitoring activity rhythms in other species, offering a powerful tool for chronobiology research.