Evidence map›Paper›PMID 31173339›Full record

ArticleThe Journal of animal ecology2020

Estimates for energy expenditure in free-living animals using acceleration proxies: A reappraisal.

Rory P Wilson, Luca Börger, Mark D Holton, D Michael Scantlebury, Agustina Gómez-Laich, Flavio Quintana, Frank Rosell, Patricia M Graf, Hannah Williams, Richard Gunner and 10 more

Open access · bronzeAbstract read
In one paragraph

Article in The Journal of animal ecology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 97 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
97citing papers in PubMed, 1 pooled it
35.1field-weighted citation impact, top 1% 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

97 citing papers in PubMed, 1 synthesis or guideline pooled it, 274 citations in OpenAlex.

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37 more citing papers are in PubMed but not listed here.

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

20 authors at 11 institutions in 9 countries.

Rory P WilsonDepartment of Biosciences, Swansea University, Swansea, UK.ORCID 0000-0003-3177-0107
Luca BörgerDepartment of Biosciences, Swansea University, Swansea, UK.
Mark D HoltonDepartment of Computing Science, Swansea University, Swansea, UK.
D Michael ScantleburySchool of Biological Sciences, Institute for Global Food Security, Queen's University Belfast, Belfast, UK.ORCID 0000-0001-8327-0556
Agustina Gómez-LaichInstituto de Biología de Organismos Marinos IBIOMAR-CONICET, Puerto Madryn, Argentina.
Flavio QuintanaInstituto de Biología de Organismos Marinos IBIOMAR-CONICET, Puerto Madryn, Argentina.
Frank RosellDepartment of Natural Sciences and Environmental Health, Faculty of Technology, Natural Sciences, and Maritime Sciences, University of South-Eastern Norway, Bø i Telemark, Norway.
Patricia M GrafDepartment of Forestry and Renewable Forest Resources, Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia.
Hannah WilliamsDepartment of Biosciences, Swansea University, Swansea, UK.ORCID 0000-0002-6338-529X
Richard GunnerDepartment of Biosciences, Swansea University, Swansea, UK.
Lloyd HopkinsDepartment of Biosciences, Swansea University, Swansea, UK.
Nikki MarksSchool of Biological Sciences, Institute for Global Food Security, Queen's University Belfast, Belfast, UK.
Nathan R GeraldiRed Sea Research Centre and Computational Biology Research Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.ORCID 0000-0002-2669-3867
Carlos M DuarteRed Sea Research Centre and Computational Biology Research Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
Rebecca ScottGeomar Helmholz Centre for Ocean Research Kiel, Kiel, Germany.
Michael S StranoDepartment of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Hermina RobotkaMax Planck Institute for Ornithology, Seewiesen, Germany.
Christophe EizaguirreSchool of Biological and Chemical Sciences, Queen Mary University of London, London, UK.
Andreas FahlmanDepartamento de Investigación, Fundación Oceanogràfic de la Comunidad Valenciana, Valencia, Spain.
Emily L C ShepardDepartment of Biosciences, Swansea University, Swansea, UK.
Swansea University · GBConsejo Nacional de Investigaciones Científicas y Técnicas · ARKing Abdullah University of Science and Technology · SAQueen's University Belfast · GBFundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana · ESGEOMAR Helmholtz Centre for Ocean Research Kiel · DEMassachusetts Institute of Technology · USMax Planck Institute for Ornithology · DEQueen Mary University of London · GBUniversity of Ljubljana · SIUniversity of South-Eastern Norway · NO

Funding

European Research Council 715874
6 · The paper itself

Abstract

It is fundamentally important for many animal ecologists to quantify the costs of animal activities, although it is not straightforward to do so. The recording of triaxial acceleration by animal-attached devices has been proposed as a way forward for this, with the specific suggestion that dynamic body acceleration (DBA) be used as a proxy for movement-based power. Dynamic body acceleration has now been validated frequently, both in the laboratory and in the field, although the literature still shows that some aspects of DBA theory and practice are misunderstood. Here, we examine the theory behind DBA and employ modelling approaches to assess factors that affect the link between DBA and energy expenditure, from the deployment of the tag, through to the calibration of DBA with energy use in laboratory and field settings. Using data from a range of species and movement modes, we illustrate that vectorial and additive DBA metrics are proportional to each other. Either can be used as a proxy for energy and summed to estimate total energy expended over a given period, or divided by time to give a proxy for movement-related metabolic power. Nonetheless, we highlight how the ability of DBA to predict metabolic rate declines as the contribution of non-movement-related factors, such as heat production, increases. Overall, DBA seems to be a substantive proxy for movement-based power but consideration of other movement-related metrics, such as the static body acceleration and the rate of change of body pitch and roll, may enable researchers to refine movement-based metabolic costs, particularly in animals where movement is not characterized by marked changes in body acceleration.

Indexed as

AccelerationEnergy MetabolismAnimalsMovementdoubly labelled waterdynamic body accelerationenergy expendituremovement costswild animals

Identifiers

PMID31173339
PMCPMC7030956
OpenAlexW2951394506

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.