Calibrating animal‐borne proximity loggers Article Swipe
Christian Rutz
,
Michael B. Morrissey
,
Zackory T. Burns
,
John M. Burt
,
Brian Otis
,
James J. H. St Clair
,
Richard W. James
·
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.1111/2041-210x.12370
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.1111/2041-210x.12370
Summary Growing interest in the structure and dynamics of animal social networks has stimulated efforts to develop automated tracking technologies that can reliably record encounters in free‐ranging subjects. A particularly promising approach is the use of animal‐attached ‘proximity loggers’, which collect data on the incidence, duration and proximity of spatial associations through inter‐logger radio communication. While proximity logging is based on a straightforward physical principle – the attenuation of propagating radio waves with distance – calibrating systems for field deployment is challenging, since most study species roam across complex, heterogeneous environments. In this study, we calibrated a recently developed digital proximity‐logging system (‘Encounternet’) for deployment on a wild population of New Caledonian crows Corvus moneduloides . Our principal objective was to establish a quantitative model that enables robust post hoc estimation of logger‐to‐logger (and, hence, crow‐to‐crow) distances from logger‐recorded signal‐strength values. To achieve an accurate description of the radio communication between crow‐borne loggers, we conducted a calibration exercise that combines theoretical analyses, field experiments, statistical modelling, behavioural observations, and computer simulations. We show that, using signal‐strength information only, it is possible to assign crow encounters reliably to predefined distance classes, enabling powerful analyses of social dynamics. For example, raw data sets from field‐deployed loggers can be filtered at the analysis stage to include predominantly encounters where crows would have come to within a few metres of each other, and could therefore have socially learned new behaviours through direct observation. One of the main challenges for improving data classification further is the fact that crows – like most other study species – associate across a wide variety of habitats and behavioural contexts, with different signal‐attenuation properties. Our study demonstrates that well‐calibrated proximity‐logging systems can be used to chart social associations of free‐ranging animals over a range of biologically meaningful distances. At the same time, however, it highlights that considerable efforts are required to conduct study‐specific system calibrations that adequately account for the biological and technological complexities of field deployments. Although we report results from a particular case study, the basic rationale of our multi‐step calibration exercise applies to many other tracking systems and study species.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1111/2041-210x.12370
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/2041-210X.12370
- OA Status
- hybrid
- Cited By
- 51
- References
- 45
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2024987570
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- OpenAlex ID
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https://openalex.org/W2024987570Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1111/2041-210x.12370Digital Object Identifier
- Title
-
Calibrating animal‐borne proximity loggersWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2015Year of publication
- Publication date
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2015-03-13Full publication date if available
- Authors
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Christian Rutz, Michael B. Morrissey, Zackory T. Burns, John M. Burt, Brian Otis, James J. H. St Clair, Richard W. JamesList of authors in order
- Landing page
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https://doi.org/10.1111/2041-210x.12370Publisher landing page
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/2041-210X.12370Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/2041-210X.12370Direct OA link when available
- Concepts
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Data logger, Computer science, Logging, Software deployment, Population, Calibration, Field (mathematics), Raw data, Real-time computing, Remote sensing, Geography, Statistics, Mathematics, Forestry, Pure mathematics, Operating system, Sociology, Demography, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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51Total citation count in OpenAlex
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2025: 2, 2024: 19, 2022: 2, 2021: 4, 2020: 2Per-year citation counts (last 5 years)
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45Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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