Optimizing the use of biologgers for movement ecology research

Autores
Williams, Hannah J.; Taylor, Lucy; Benhamou, Simon; Bijleveld, Allert; Clay, Thomas; de Grissac, Sophie; Demsar, Urska; English, Holly M.; Franconi, Novella; Gómez Laich, Agustina Marta; Griffiths, Rachael; Kay, William P.; Morales, Juan Manuel; Potts, Jonathan; Rogerson, Katharine F.; Rutz, Christian; Spelt, Anouk; Trevail, Alice; Wilson, Rory P.; Börger, Luca
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The paradigm-changing opportunities of biologging sensors for ecological research, especially movement ecology, are vast, but the crucial questions of how best to match the most appropriate sensors and sensor combinations to specific biological questions and how to analyse complex biologging data, are mostly ignored. Here, we fill this gap by reviewing how to optimize the use of biologging techniques to answer questions in movement ecology and synthesize this into an Integrated Biologging Framework (IBF). We highlight that multisensor approaches are a new frontier in biologging, while identifying current limitations and avenues for future development in sensor technology. We focus on the importance of efficient data exploration, and more advanced multidimensional visualization methods, combined with appropriate archiving and sharing approaches, to tackle the big data issues presented by biologging. We also discuss the challenges and opportunities in matching the peculiarities of specific sensor data to the statistical models used, highlighting at the same time the large advances which will be required in the latter to properly analyse biologging data. Taking advantage of the biologging revolution will require a large improvement in the theoretical and mathematical foundations of movement ecology, to include the rich set of high-frequency multivariate data, which greatly expand the fundamentally limited and coarse data that could be collected using location-only technology such as GPS. Equally important will be the establishment of multidisciplinary collaborations to catalyse the opportunities offered by current and future biologging technology. If this is achieved, clear potential exists for developing a vastly improved mechanistic understanding of animal movements and their roles in ecological processes and for building realistic predictive models.
Fil: Williams, Hannah J.. Swansea University; Reino Unido
Fil: Taylor, Lucy. University of Oxford; Reino Unido. Save The Elephants; Kenia
Fil: Benhamou, Simon. Centre National de la Recherche Scientifique; Francia
Fil: Bijleveld, Allert. Utrecht University; Países Bajos
Fil: Clay, Thomas. University of Liverpool; Reino Unido
Fil: de Grissac, Sophie. Swansea University; Reino Unido
Fil: Demsar, Urska. University of St. Andrews; Reino Unido
Fil: English, Holly M.. Swansea University; Reino Unido
Fil: Franconi, Novella. Swansea University; Reino Unido
Fil: Gómez Laich, Agustina Marta. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto de Biología de Organismos Marinos; Argentina
Fil: Griffiths, Rachael. Swansea University; Reino Unido
Fil: Kay, William P.. Swansea University; Reino Unido
Fil: Morales, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina
Fil: Potts, Jonathan. University of Sheffield; Reino Unido
Fil: Rogerson, Katharine F.. University of East Anglia; Reino Unido
Fil: Rutz, Christian. University of St. Andrews; Reino Unido
Fil: Spelt, Anouk. University of Bristol; Reino Unido
Fil: Trevail, Alice. University of Liverpool; Reino Unido
Fil: Wilson, Rory P.. Swansea University; Reino Unido
Fil: Börger, Luca. Swansea University; Reino Unido
Materia
ACCELEROMETER
BIG DATA
DATA VISUALIZATION
GPS
INTEGRATED BIOLOGGING FRAMEWORK
MOVEMENT ECOLOGY
MULTIDISCIPLINARY COLLABORATION
MULTISENSOR APPROACH
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/106769
Acceder
id CONICETDig_4820191c0371df713f6b703ef31aae3f
oai_identifier_str oai:ri.conicet.gov.ar:11336/106769
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Optimizing the use of biologgers for movement ecology researchWilliams, Hannah J.Taylor, LucyBenhamou, SimonBijleveld, AllertClay, Thomasde Grissac, SophieDemsar, UrskaEnglish, Holly M.Franconi, NovellaGómez Laich, Agustina MartaGriffiths, RachaelKay, William P.Morales, Juan ManuelPotts, JonathanRogerson, Katharine F.Rutz, ChristianSpelt, AnoukTrevail, AliceWilson, Rory P.Börger, LucaACCELEROMETERBIG DATADATA VISUALIZATIONGPSINTEGRATED BIOLOGGING FRAMEWORKMOVEMENT ECOLOGYMULTIDISCIPLINARY COLLABORATIONMULTISENSOR APPROACHhttps://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1The paradigm-changing opportunities of biologging sensors for ecological research, especially movement ecology, are vast, but the crucial questions of how best to match the most appropriate sensors and sensor combinations to specific biological questions and how to analyse complex biologging data, are mostly ignored. Here, we fill this gap by reviewing how to optimize the use of biologging techniques to answer questions in movement ecology and synthesize this into an Integrated Biologging Framework (IBF). We highlight that multisensor approaches are a new frontier in biologging, while identifying current limitations and avenues for future development in sensor technology. We focus on the importance of efficient data exploration, and more advanced multidimensional visualization methods, combined with appropriate archiving and sharing approaches, to tackle the big data issues presented by biologging. We also discuss the challenges and opportunities in matching the peculiarities of specific sensor data to the statistical models used, highlighting at the same time the large advances which will be required in the latter to properly analyse biologging data. Taking advantage of the biologging revolution will require a large improvement in the theoretical and mathematical foundations of movement ecology, to include the rich set of high-frequency multivariate data, which greatly expand the fundamentally limited and coarse data that could be collected using location-only technology such as GPS. Equally important will be the establishment of multidisciplinary collaborations to catalyse the opportunities offered by current and future biologging technology. If this is achieved, clear potential exists for developing a vastly improved mechanistic understanding of animal movements and their roles in ecological processes and for building realistic predictive models.Fil: Williams, Hannah J.. Swansea University; Reino UnidoFil: Taylor, Lucy. University of Oxford; Reino Unido. Save The Elephants; KeniaFil: Benhamou, Simon. Centre National de la Recherche Scientifique; FranciaFil: Bijleveld, Allert. Utrecht University; Países BajosFil: Clay, Thomas. University of Liverpool; Reino UnidoFil: de Grissac, Sophie. Swansea University; Reino UnidoFil: Demsar, Urska. University of St. Andrews; Reino UnidoFil: English, Holly M.. Swansea University; Reino UnidoFil: Franconi, Novella. Swansea University; Reino UnidoFil: Gómez Laich, Agustina Marta. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto de Biología de Organismos Marinos; ArgentinaFil: Griffiths, Rachael. Swansea University; Reino UnidoFil: Kay, William P.. Swansea University; Reino UnidoFil: Morales, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; ArgentinaFil: Potts, Jonathan. University of Sheffield; Reino UnidoFil: Rogerson, Katharine F.. University of East Anglia; Reino UnidoFil: Rutz, Christian. University of St. Andrews; Reino UnidoFil: Spelt, Anouk. University of Bristol; Reino UnidoFil: Trevail, Alice. University of Liverpool; Reino UnidoFil: Wilson, Rory P.. Swansea University; Reino UnidoFil: Börger, Luca. Swansea University; Reino UnidoWiley Blackwell Publishing, Inc2020-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/106769Williams, Hannah J.; Taylor, Lucy; Benhamou, Simon; Bijleveld, Allert; Clay, Thomas; et al.; Optimizing the use of biologgers for movement ecology research; Wiley Blackwell Publishing, Inc; Journal Of Animal Ecology; 89; 1; 1-2020; 1-210021-87901365-2656CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1111/1365-2656.13094info:eu-repo/semantics/altIdentifier/url/https://besjournals.onlinelibrary.wiley.com/doi/epdf/10.1111/1365-2656.13094info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:38:55Zoai:ri.conicet.gov.ar:11336/106769instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:38:55.787CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Optimizing the use of biologgers for movement ecology research
title Optimizing the use of biologgers for movement ecology research
spellingShingle Optimizing the use of biologgers for movement ecology research
Williams, Hannah J.
ACCELEROMETER
BIG DATA
DATA VISUALIZATION
GPS
INTEGRATED BIOLOGGING FRAMEWORK
MOVEMENT ECOLOGY
MULTIDISCIPLINARY COLLABORATION
MULTISENSOR APPROACH
title_short Optimizing the use of biologgers for movement ecology research
title_full Optimizing the use of biologgers for movement ecology research
title_fullStr Optimizing the use of biologgers for movement ecology research
title_full_unstemmed Optimizing the use of biologgers for movement ecology research
title_sort Optimizing the use of biologgers for movement ecology research
dc.creator.none.fl_str_mv Williams, Hannah J.
Taylor, Lucy
Benhamou, Simon
Bijleveld, Allert
Clay, Thomas
de Grissac, Sophie
Demsar, Urska
English, Holly M.
Franconi, Novella
Gómez Laich, Agustina Marta
Griffiths, Rachael
Kay, William P.
Morales, Juan Manuel
Potts, Jonathan
Rogerson, Katharine F.
Rutz, Christian
Spelt, Anouk
Trevail, Alice
Wilson, Rory P.
Börger, Luca
author Williams, Hannah J.
author_facet Williams, Hannah J.
Taylor, Lucy
Benhamou, Simon
Bijleveld, Allert
Clay, Thomas
de Grissac, Sophie
Demsar, Urska
English, Holly M.
Franconi, Novella
Gómez Laich, Agustina Marta
Griffiths, Rachael
Kay, William P.
Morales, Juan Manuel
Potts, Jonathan
Rogerson, Katharine F.
Rutz, Christian
Spelt, Anouk
Trevail, Alice
Wilson, Rory P.
Börger, Luca
author_role author
author2 Taylor, Lucy
Benhamou, Simon
Bijleveld, Allert
Clay, Thomas
de Grissac, Sophie
Demsar, Urska
English, Holly M.
Franconi, Novella
Gómez Laich, Agustina Marta
Griffiths, Rachael
Kay, William P.
Morales, Juan Manuel
Potts, Jonathan
Rogerson, Katharine F.
Rutz, Christian
Spelt, Anouk
Trevail, Alice
Wilson, Rory P.
Börger, Luca
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv ACCELEROMETER
BIG DATA
DATA VISUALIZATION
GPS
INTEGRATED BIOLOGGING FRAMEWORK
MOVEMENT ECOLOGY
MULTIDISCIPLINARY COLLABORATION
MULTISENSOR APPROACH
topic ACCELEROMETER
BIG DATA
DATA VISUALIZATION
GPS
INTEGRATED BIOLOGGING FRAMEWORK
MOVEMENT ECOLOGY
MULTIDISCIPLINARY COLLABORATION
MULTISENSOR APPROACH
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.6
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The paradigm-changing opportunities of biologging sensors for ecological research, especially movement ecology, are vast, but the crucial questions of how best to match the most appropriate sensors and sensor combinations to specific biological questions and how to analyse complex biologging data, are mostly ignored. Here, we fill this gap by reviewing how to optimize the use of biologging techniques to answer questions in movement ecology and synthesize this into an Integrated Biologging Framework (IBF). We highlight that multisensor approaches are a new frontier in biologging, while identifying current limitations and avenues for future development in sensor technology. We focus on the importance of efficient data exploration, and more advanced multidimensional visualization methods, combined with appropriate archiving and sharing approaches, to tackle the big data issues presented by biologging. We also discuss the challenges and opportunities in matching the peculiarities of specific sensor data to the statistical models used, highlighting at the same time the large advances which will be required in the latter to properly analyse biologging data. Taking advantage of the biologging revolution will require a large improvement in the theoretical and mathematical foundations of movement ecology, to include the rich set of high-frequency multivariate data, which greatly expand the fundamentally limited and coarse data that could be collected using location-only technology such as GPS. Equally important will be the establishment of multidisciplinary collaborations to catalyse the opportunities offered by current and future biologging technology. If this is achieved, clear potential exists for developing a vastly improved mechanistic understanding of animal movements and their roles in ecological processes and for building realistic predictive models.
Fil: Williams, Hannah J.. Swansea University; Reino Unido
Fil: Taylor, Lucy. University of Oxford; Reino Unido. Save The Elephants; Kenia
Fil: Benhamou, Simon. Centre National de la Recherche Scientifique; Francia
Fil: Bijleveld, Allert. Utrecht University; Países Bajos
Fil: Clay, Thomas. University of Liverpool; Reino Unido
Fil: de Grissac, Sophie. Swansea University; Reino Unido
Fil: Demsar, Urska. University of St. Andrews; Reino Unido
Fil: English, Holly M.. Swansea University; Reino Unido
Fil: Franconi, Novella. Swansea University; Reino Unido
Fil: Gómez Laich, Agustina Marta. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto de Biología de Organismos Marinos; Argentina
Fil: Griffiths, Rachael. Swansea University; Reino Unido
Fil: Kay, William P.. Swansea University; Reino Unido
Fil: Morales, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina
Fil: Potts, Jonathan. University of Sheffield; Reino Unido
Fil: Rogerson, Katharine F.. University of East Anglia; Reino Unido
Fil: Rutz, Christian. University of St. Andrews; Reino Unido
Fil: Spelt, Anouk. University of Bristol; Reino Unido
Fil: Trevail, Alice. University of Liverpool; Reino Unido
Fil: Wilson, Rory P.. Swansea University; Reino Unido
Fil: Börger, Luca. Swansea University; Reino Unido
description The paradigm-changing opportunities of biologging sensors for ecological research, especially movement ecology, are vast, but the crucial questions of how best to match the most appropriate sensors and sensor combinations to specific biological questions and how to analyse complex biologging data, are mostly ignored. Here, we fill this gap by reviewing how to optimize the use of biologging techniques to answer questions in movement ecology and synthesize this into an Integrated Biologging Framework (IBF). We highlight that multisensor approaches are a new frontier in biologging, while identifying current limitations and avenues for future development in sensor technology. We focus on the importance of efficient data exploration, and more advanced multidimensional visualization methods, combined with appropriate archiving and sharing approaches, to tackle the big data issues presented by biologging. We also discuss the challenges and opportunities in matching the peculiarities of specific sensor data to the statistical models used, highlighting at the same time the large advances which will be required in the latter to properly analyse biologging data. Taking advantage of the biologging revolution will require a large improvement in the theoretical and mathematical foundations of movement ecology, to include the rich set of high-frequency multivariate data, which greatly expand the fundamentally limited and coarse data that could be collected using location-only technology such as GPS. Equally important will be the establishment of multidisciplinary collaborations to catalyse the opportunities offered by current and future biologging technology. If this is achieved, clear potential exists for developing a vastly improved mechanistic understanding of animal movements and their roles in ecological processes and for building realistic predictive models.
publishDate 2020
dc.date.none.fl_str_mv 2020-01
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/106769
Williams, Hannah J.; Taylor, Lucy; Benhamou, Simon; Bijleveld, Allert; Clay, Thomas; et al.; Optimizing the use of biologgers for movement ecology research; Wiley Blackwell Publishing, Inc; Journal Of Animal Ecology; 89; 1; 1-2020; 1-21
0021-8790
1365-2656
CONICET Digital
CONICET
url http://hdl.handle.net/11336/106769
identifier_str_mv Williams, Hannah J.; Taylor, Lucy; Benhamou, Simon; Bijleveld, Allert; Clay, Thomas; et al.; Optimizing the use of biologgers for movement ecology research; Wiley Blackwell Publishing, Inc; Journal Of Animal Ecology; 89; 1; 1-2020; 1-21
0021-8790
1365-2656
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1111/1365-2656.13094
info:eu-repo/semantics/altIdentifier/url/https://besjournals.onlinelibrary.wiley.com/doi/epdf/10.1111/1365-2656.13094
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Wiley Blackwell Publishing, Inc
publisher.none.fl_str_mv Wiley Blackwell Publishing, Inc
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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