Evidence map›Paper›PMID 37521810›Full record

ArticleAnimal biotelemetry2022

Dead-reckoning elucidates fine-scale habitat use by European badgers

E A Magowan, I E Maguire, S Smith, S Redpath, N J Marks, R P Wilson, F Menzies, M O'Hagan, D M Scantlebury

Open access · goldAbstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
5.6field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 33 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 2 institutions in 1 country.

E A Magowan *School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast, BT9 5DL Northern Ireland UK.
I E Maguire *School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast, BT9 5DL Northern Ireland UK.
S SmithSchool of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast, BT9 5DL Northern Ireland UK.
S RedpathSchool of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast, BT9 5DL Northern Ireland UK.
N J MarksSchool of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast, BT9 5DL Northern Ireland UK.
R P WilsonDepartment of Biological Sciences, Institute of Environmental Sustainability, Swansea University, Swansea, UK.
F MenziesDepartment of Agriculture, Environment and Rural Affairs, Veterinary Epidemiology Unit, Belfast, UK.
M O'HaganDepartment of Agriculture, Environment and Rural Affairs, Veterinary Epidemiology Unit, Belfast, UK.
D M ScantleburySchool of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast, BT9 5DL Northern Ireland UK.ORCID 0000-0001-8327-0556
Queen's University Belfast · GBSwansea University · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

AccelerometerBadgerDead-reckoningDistance travelledGPSHome-rangeLoggerMovementTuberculosis

Identifiers

PMID37521810
PMCPMC8908954
OpenAlexW4220819059

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.