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Abstract Current methods for identifying and predicting infectious disease dynamics in wildlife populations are limited. Pathogen transmission dynamics can be complex, influenced by behavioural interactions between and among hosts, pathogens and their environments. These behaviours may also be influenced directly by observers, with observational research methods being limited to habituated species. Banded mongooseMungos mungoare social, medium size carnivores infected with the novel tuberculosis pathogenMycobacterium mungi. This pathogen is principally transmitted during normal olfactory communication behaviours. Banded mongoose behavioural responses to humans change over the landscape, limiting the use of direct observational approaches in areas where mongoose are threatened and flee.The accelerometers in bio‐logging devices have been used previously to identify distinct behaviours in wildlife species, providing a tool to quantifying specific behaviours in ecological studies. We deployed Axy‐5X model accelerometers (TechnoSmArt) on captive mongoose to determine whether accelerometers could be used to identify key mongoose behavioural activities previously associated withM. mungitransmission.After two collaring periods, we determined that three distinct behavioural activities could be identified in the accelerometer data: bipedal vertical vigilance, running and scent marking activity; behaviours that have been shown to vary across land type in the banded mongoose.Results from this work advance current data analytics and provide modifications to data analysis works flows, updating and expanding upon current methodologies. We also provide preliminary evidence of successful mathematical classification of the target behaviours, supporting the future use of these devices. Methods applied here may allow model estimates ofM. mungitransmission in free‐ranging mongoose to be refined with possible application to other systems where direct observation approaches have limited application.
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NAPS : Integrating pose estimation and tag‐based tracking
Abstract Significant advances in computational ethology have allowed the quantification of behaviour in unprecedented detail. Tracking animals in social groups, however, remains challenging as most existing methods can either capture pose or robustly retain individual identity over time but not both.To capture finely resolved behaviours while maintaining individual identity, we built NAPS (NAPS is ArUco Plus SLEAP), a hybrid tracking framework that combines state‐of‐the‐art, deep learning‐based methods for pose estimation (SLEAP) with unique markers for identity persistence (ArUco). We show that this framework allows the exploration of the social dynamics of the common eastern bumblebee (Bombus impatiens).We provide a stand‐alone Python package for implementing this framework along with detailed documentation to allow for easy utilization and expansion. We show that NAPS can scale to long timescale experiments at a high frame rate and that it enables the investigation of detailed behavioural variation within individuals in a group.Expanding the toolkit for capturing the constituent behaviours of social groups is essential for understanding the structure and dynamics of social networks. NAPS provides a key tool for capturing these behaviours and can provide critical data for understanding how individual variation influences collective dynamics.
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- Award ID(s):
- 1734030
- PAR ID:
- 10448349
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Methods in Ecology and Evolution
- Volume:
- 14
- Issue:
- 10
- ISSN:
- 2041-210X
- Format(s):
- Medium: X Size: p. 2541-2548
- Size(s):
- p. 2541-2548
- Sponsoring Org:
- National Science Foundation
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