Tracking Tool for Fiddler Crabs in Natural Settings to Promote a Model Organism for Synchrony

TL;DR

This paper introduces a tracking tool for fiddler crabs to study synchronized claw waving, promoting them as a model for collective synchronization research.

Contribution

An open-source tracking algorithm for detecting and analyzing synchronized claw waving in fiddler crabs in natural settings.

Findings

The algorithm successfully detects claw wave activity and preserves individual IDs across video recordings.

It works robustly across different fiddler crab species and geographic locations.

The tool enables the translation of field observations into data for studying synchronization.

Abstract

Synchronization has been studied across vastly different spatiotemporal scales in physical, biological, and social systems. Research into the mechanisms that give rise to temporal order, however, has been, by and large, mostly theoretical. Here, we seek to advance the fiddler crab as a model organism for collective synchronization. To attract mates, males of many fiddler crab species wave their claws in sync. These crabs are found in many places around the world, so they are generally accessible and observable. Translating observation (e.g., from recorded video) into actionable data, however, remains a challenge. We provide an easy‐to‐use and open source tracking algorithm that detects claw wave activity in video recordings and preserves IDs over time. We demonstrate the robustness of the algorithm by running it on videos displaying different species from different locations. We discuss possible future directions (both in the lab and the field) and call for renewed interest in these unique animals.

We introduce an easy‐to‐use and open source tracking algorithm that detects claw waves in field video recordings and preserves IDs over time to advance the widely found fiddler crabs as a model organism for collective synchronization. This software translates video observations into actionable data to advance the study of synchronization beyond theory. We demonstrate the robustness of the algorithm by running it on videos of different species and in different locations.

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