ArticleAnimal biotelemetry2022
Dead-reckoning elucidates fine-scale habitat use by European badgers
Article in Animal biotelemetry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 33 citations in OpenAlex.
- Data-driven analysis of fine-scale badger movement in the UK.PLoS computational biology · 2025Article
- Landscape as a Shared Space for Badgers and Cattle: Insights Into Indirect Contact and Bovine Tuberculosis Transmission Risk.Ecology and evolution · 2025Article
- Badger Ecology, Bovine Tuberculosis, and Population Management: Lessons from the Island of Ireland.Transboundary and emerging diseases · 2024Review
- A novel method for identifying fine-scale bottom-use in a benthic-foraging pinniped.Movement ecology · 2023Article
- Impact of test, vaccinate or remove protocol on home ranges and nightly movements of badgers a medium density population.Scientific reports · 2023Article
- Increasingly detailed insights in animal behaviours using continuous on-board processing of accelerometer data.Movement ecology · 2022Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Recent developments in both hardware and software of animal-borne data loggers now enable large amounts of data to be collected on both animal movement and behaviour. In particular, the combined use of tri-axial accelerometers, tri-axial magnetometers and GPS loggers enables animal tracks to be elucidated using a procedure of 'dead-reckoning'. Although this approach was first suggested 30 years ago by Wilson et al Methods: Research was undertaken in farmland in Northern Ireland. Two badgers (one male, one female) were live-trapped and fitted with a GPS logger, a tri-axial accelerometer, and a tri-axial magnetometer. Thereafter, the badgers' movement paths over 2 weeks were elucidated using just GPS data and GPS-enabled dead-reckoned data, respectively. Results: Badgers travelled further using data from dead-reckoned calculations than using the data from only GPS data. Whilst once-hourly GPS data could only be represented by straight-line movements between sequential points, the sub-second resolution dead-reckoned tracks were more tortuous. Although there were no differences in Minimum Convex Polygon determinations between GPS- and dead-reckoned data, Kernel Utilisation Distribution determinations of home-range size were larger using the former method. This was because dead-reckoned data more accurately described the particular parts of landscape constituting most-visited core areas, effectively narrowing the calculation of habitat use. Finally, the dead-reckoned data showed badgers spent more time near to field margins and hedges than simple GPS data would suggest. Conclusion: Significant differences emerge when analyses of habitat use and movements are compared between calculations made using just GPS data or GPS-enabled dead-reckoned data. In particular, use of dead-reckoned data showed that animals moved 2.2 times farther, had better-defined use of the habitat (revealing clear core areas), and made more use of certain habitats (field margins, hedges). Use of dead-reckoning to provide detailed accounts of animal movement and highlight the minutiae of interactions with the environment should be considered an important technique in the ecologist's toolkit.
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