Tracking all members of a honey bee colony over their lifetime

TL;DR

This paper presents a novel multi-step tracking algorithm that automatically follows approximately 2000 honey bees over 10 weeks, significantly improving ID accuracy and providing the first comprehensive lifetime trajectory dataset for all colony members.

Contribution

The work introduces an improved tracking system and algorithm for lifelong monitoring of all bees in a colony, with open-source tools and a large dataset for further research.

Findings

Reduced ID decoding errors from 13% to 2%

Tracked approximately 2000 bees over 10 weeks

Published the first complete honey bee colony trajectory dataset

Abstract

Computational approaches to the analysis of collective behavior in social insects increasingly rely on motion paths as an intermediate data layer from which one can infer individual behaviors or social interactions. Honey bees are a popular model for learning and memory. Previous experience has been shown to affect and modulate future social interactions. So far, no lifetime history observations have been reported for all bees of a colony. In a previous work we introduced a tracking system customized to track up to $4000$ bees over several weeks. In this contribution we present an in-depth description of the underlying multi-step algorithm which both produces the motion paths, and also improves the marker decoding accuracy significantly. We automatically tracked ${\sim}2000$ marked honey bees over 10 weeks with inexpensive recording hardware using markers without any error correction bits. We found that the proposed two-step tracking reduced incorrect ID decodings from initially ${\sim}13\%$ to around $2\%$ post-tracking. Alongside this paper, we publish the first trajectory dataset for all bees in a colony, extracted from ${\sim} 4$ million images. We invite researchers to join the collective scientific effort to investigate this intriguing animal system. All components of our system are open-source.