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Automated tracking and analysis of ant trajectories shows variation in forager exploration

Determining how ant colonies optimize foraging while mitigating pathogen and predator risks provides insight into how the ants have achieved ecological success. Ants must respond to changing resource conditions, but exploration comes at a cost of higher potential exposure to threats. Fungal infected...

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Autores principales: Imirzian, Natalie, Zhang, Yizhe, Kurze, Christoph, Loreto, Raquel G., Chen, Danny Z., Hughes, David P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6744467/
https://www.ncbi.nlm.nih.gov/pubmed/31519955
http://dx.doi.org/10.1038/s41598-019-49655-3
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author Imirzian, Natalie
Zhang, Yizhe
Kurze, Christoph
Loreto, Raquel G.
Chen, Danny Z.
Hughes, David P.
author_facet Imirzian, Natalie
Zhang, Yizhe
Kurze, Christoph
Loreto, Raquel G.
Chen, Danny Z.
Hughes, David P.
author_sort Imirzian, Natalie
collection PubMed
description Determining how ant colonies optimize foraging while mitigating pathogen and predator risks provides insight into how the ants have achieved ecological success. Ants must respond to changing resource conditions, but exploration comes at a cost of higher potential exposure to threats. Fungal infected cadavers surround the main foraging trails of the carpenter ant Camponotus rufipes, offering a system to study how foragers behave given the persistent occurrence of disease threats. Studies on social insect foraging behavior typically require many hours of human labor due to the high density of individuals. To overcome this, we developed deep learning based computer vision algorithms to track foraging ants, frame-by-frame, from video footage shot under the natural conditions of a tropical forest floor at night. We found that most foragers walk in straight lines overlapping the same areas as other ants, but there is a subset of foragers with greater exploration. Consistency in walking behavior may protect most ants from infection, while foragers that explore unique portions of the trail may be more likely to encounter fungal spores implying a trade-off between resource discovery and risk avoidance.
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spelling pubmed-67444672019-09-27 Automated tracking and analysis of ant trajectories shows variation in forager exploration Imirzian, Natalie Zhang, Yizhe Kurze, Christoph Loreto, Raquel G. Chen, Danny Z. Hughes, David P. Sci Rep Article Determining how ant colonies optimize foraging while mitigating pathogen and predator risks provides insight into how the ants have achieved ecological success. Ants must respond to changing resource conditions, but exploration comes at a cost of higher potential exposure to threats. Fungal infected cadavers surround the main foraging trails of the carpenter ant Camponotus rufipes, offering a system to study how foragers behave given the persistent occurrence of disease threats. Studies on social insect foraging behavior typically require many hours of human labor due to the high density of individuals. To overcome this, we developed deep learning based computer vision algorithms to track foraging ants, frame-by-frame, from video footage shot under the natural conditions of a tropical forest floor at night. We found that most foragers walk in straight lines overlapping the same areas as other ants, but there is a subset of foragers with greater exploration. Consistency in walking behavior may protect most ants from infection, while foragers that explore unique portions of the trail may be more likely to encounter fungal spores implying a trade-off between resource discovery and risk avoidance. Nature Publishing Group UK 2019-09-13 /pmc/articles/PMC6744467/ /pubmed/31519955 http://dx.doi.org/10.1038/s41598-019-49655-3 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Imirzian, Natalie
Zhang, Yizhe
Kurze, Christoph
Loreto, Raquel G.
Chen, Danny Z.
Hughes, David P.
Automated tracking and analysis of ant trajectories shows variation in forager exploration
title Automated tracking and analysis of ant trajectories shows variation in forager exploration
title_full Automated tracking and analysis of ant trajectories shows variation in forager exploration
title_fullStr Automated tracking and analysis of ant trajectories shows variation in forager exploration
title_full_unstemmed Automated tracking and analysis of ant trajectories shows variation in forager exploration
title_short Automated tracking and analysis of ant trajectories shows variation in forager exploration
title_sort automated tracking and analysis of ant trajectories shows variation in forager exploration
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6744467/
https://www.ncbi.nlm.nih.gov/pubmed/31519955
http://dx.doi.org/10.1038/s41598-019-49655-3
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