Markerless tracking of an entire insect colony

Bożek, Katarzyna; Hebert, Laetitia; Portugal, Yoann; Stephens, Greg

doi:10.1101/2020.03.26.007302

Cited by 15 publications

(12 citation statements)

References 71 publications

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“…We will explore recent efforts in automatic tracking of bees, such as works by ref. 30 . The ability to track the scenting behavior of bees over time will allow us to answer interesting questions regarding the roles worker bees play in this swarming context.…”

Section: Discussionmentioning

confidence: 99%

Flow-mediated olfactory communication in honeybee swarms

Nguyen

Iuzzolino

Mankel

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

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Honeybee swarms are a landmark example of collective behavior. To become a coherent swarm, bees locate their queen by tracking her pheromones. But how can distant individuals exploit these chemical signals, which decay rapidly in space and time? Here, we combine a behavioral assay with the machine vision detection of organism location and scenting (pheromone propagation via wing fanning) behavior to track the search and aggregation dynamics of the honeybee Apis mellifera L. We find that bees collectively create a scenting-mediated communication network by arranging in a specific spatial distribution where there is a characteristic distance between individuals and directional signaling away from the queen. To better understand such a flow-mediated directional communication strategy, we developed an agent-based model where bee agents obeying simple, local behavioral rules exist in a flow environment in which the chemical signals diffuse and decay. Our model serves as a guide to exploring how physical parameters affect the collective scenting behavior and shows that increased directional bias in scenting leads to a more efficient aggregation process that avoids local equilibrium configurations of isotropic (nondirectional and axisymmetric) communication, such as small bee clusters that persist throughout the simulation. Our results highlight an example of extended classical stigmergy: Rather than depositing static information in the environment, individual bees locally sense and globally manipulate the physical fields of chemical concentration and airflow.

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Section: Discussionmentioning

confidence: 99%

Flow-mediated olfactory communication in honeybee swarms

Nguyen

Iuzzolino

Mankel

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

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“…While there is considerable variance in task allocation, even among bees of the same age, it is unknown to what extent this variation is reflected in the social networks. In large social groups, like honey bee colonies, typically only a subset of individuals are tracked, or tracking is limited to short time intervals (Blut et al 2017; Bozek et al 2020; Naug 2008).…”

Section: Introductionmentioning

confidence: 99%

Social networks predict the life and death of honey bees

Wild

Dormagen

Zachariae

et al. 2020

Preprint

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In many social systems, an individual's role is reflected by its interactions with other members of the group (Gordon 2010, Pinter-Wollmann et al. 2014, Krause 2015, Farine & Whitehead 2015. In honey bee colonies ( Apis mellifera ), workers generally perform different tasks as they age, yet there is high behavioral variation in same-aged bees (Seeley 1982, Robinson 1992, Huang and Robinson 1996, Johnson 2010. It is unknown how social interactions within the colony relate to an individual's tasks throughout her life. We propose a new method to extract a single number from each individual's interaction patterns in multimodal social networks that captures her current role in the colony. This "network age" is better than biological age at predicting task allocation (+99%), survival (+157%), and activity patterns (+44-108%) and even predicts task allocation up to one week (around a sixth of her typical lifespan) into the future. Network age identifies distinct developmental paths and task changes throughout a bee's life: We show that individuals change tasks gradually and exhibit high task repeatability, and that same aged bees form stable behavioral subgroups in which they predominantly interact with one another. While we derived interaction networks by automatically tracking a fully tagged colony, we show that tracking only 5% of the bees is sufficient to extract a meaningful representation of the individuals' interaction patterns, demonstrating the feasibility of our method for detecting complex social structures with reduced experimental effort. Since network age more accurately predicts task allocation than biological age, it could be used in experimental manipulations to quantify shifts in the timing of task transitions as a response. We extend our method to extract interaction patterns relevant to other attributes of the individuals, such as their mortality, opening up a broad range of possible applications. Our approach is a scalable instrument to study individual behavior through the lens of social interactions over time in honey bees and other complex social systems.

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“…with two‐dimensional barcodes in laboratory environments: Gernat et al., 2018; Greenwald et al., 2015; Heyman et al., 2017; Stroeymeyt et al., 2018; Figure 1a). Untagged animals may also be detected (Bozek et al., 2020; Gal et al., 2020; Hein et al., 2018; Pinter‐Wollman et al., 2011; Rosenthal et al., 2015; Strandburg‐Peshkin et al., 2013; Figure 1b,c). Furthermore, recent developments in drone technology and acoustic monitoring are bringing these machine‐vision‐based tools into the field; cameras deployed on drones can monitor the behaviour of animals in their natural environment (Torres et al., 2018).…”

Section: An Automated Toolkit For Studying Social Behaviourmentioning

confidence: 99%

Observing the unwatchable: Integrating automated sensing, naturalistic observations and animal social network analysis in the age of big data

Smith

Pinter‐Wollman

2020

Journal of Animal Ecology

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1. In the 4.5 decades since Altmann (1974) published her seminal paper on the methods for the observational study of behaviour, automated detection and analysis of social interaction networks have fundamentally transformed the ways that ecologists study social behaviour. 2. Methodological developments for collecting data remotely on social behaviour involve indirect inference of associations, direct recordings of interactions and machine vision. 3. These recent technological advances are improving the scale and resolution with which we can dissect interactions among animals. They are also revealing new intricacies of animal social interactions at spatial and temporal resolutions as well as in ecological contexts that have been hidden from humans, making the unwatchable seeable. 4. We first outline how these technological applications are permitting researchers to collect exquisitely detailed information with little observer bias. We further recognize new emerging challenges from these new reality-mining approaches. 5. While technological advances in automating data collection and its analysis are moving at an unprecedented rate, we urge ecologists to thoughtfully combine these new tools with classic behavioural and ecological monitoring methods to place our understanding of animal social networks within fundamental biological contexts.

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Markerless tracking of an entire insect colony

Cited by 15 publications

References 71 publications

Flow-mediated olfactory communication in honeybee swarms

Flow-mediated olfactory communication in honeybee swarms

Social networks predict the life and death of honey bees

Observing the unwatchable: Integrating automated sensing, naturalistic observations and animal social network analysis in the age of big data

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