Optimising the use of bio-loggers for movement ecology research
1.The paradigm-changing opportunities of bio-logging 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 bio-logging da...
| Main Authors: | , , , , , , , , , , , , , , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Wiley
2019
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| _version_ | 1797060281592971264 |
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| author | Williams, HJ Taylor, LA Benhamou, S Bijleveld, AI Clay, TA De Grissac, S Demšar, U English, HM Franconi, N Gómez-Laich, A Griffiths, RC Kay, WP Morales, JM Potts, JR Rogerson, KF Rutz, C Spelt, A Trevail, AM Wilson, RP Börger, L |
| author_facet | Williams, HJ Taylor, LA Benhamou, S Bijleveld, AI Clay, TA De Grissac, S Demšar, U English, HM Franconi, N Gómez-Laich, A Griffiths, RC Kay, WP Morales, JM Potts, JR Rogerson, KF Rutz, C Spelt, A Trevail, AM Wilson, RP Börger, L |
| author_sort | Williams, HJ |
| collection | OXFORD |
| description | 1.The paradigm-changing opportunities of bio-logging 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 bio-logging data, are mostly ignored. 2.Here, we fill this gap by reviewing how to optimise the use of bio-logging techniques to answer questions in movement ecology and synthesise this into an Integrated Bio-logging Framework (IBF). 3.We highlight that multi-sensor approaches are a new frontier in bio-logging, whilst identifying current limitations and avenues for future development in sensor technology. 4.We focus on the importance of efficient data exploration, and more advanced multi-dimensional visualisation methods, combined with appropriate archiving and sharing approaches, to tackle the big data issues presented by bio-logging. 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 bio-logging data. 5.Taking advantage of the bio-logging 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 multi-disciplinary collaborations to catalyse the opportunities offered by current and future bio-logging 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. This article is protected by copyright. All rights reserved. |
| first_indexed | 2024-03-06T20:15:00Z |
| format | Journal article |
| id | oxford-uuid:2bd65c60-c3d6-4ef5-b8c1-e66b7f72c130 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T20:15:00Z |
| publishDate | 2019 |
| publisher | Wiley |
| record_format | dspace |
| spelling | oxford-uuid:2bd65c60-c3d6-4ef5-b8c1-e66b7f72c1302022-03-26T12:33:28ZOptimising the use of bio-loggers for movement ecology researchJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:2bd65c60-c3d6-4ef5-b8c1-e66b7f72c130EnglishSymplectic Elements at OxfordWiley2019Williams, HJTaylor, LABenhamou, SBijleveld, AIClay, TADe Grissac, SDemšar, UEnglish, HMFranconi, NGómez-Laich, AGriffiths, RCKay, WPMorales, JMPotts, JRRogerson, KFRutz, CSpelt, ATrevail, AMWilson, RPBörger, L1.The paradigm-changing opportunities of bio-logging 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 bio-logging data, are mostly ignored. 2.Here, we fill this gap by reviewing how to optimise the use of bio-logging techniques to answer questions in movement ecology and synthesise this into an Integrated Bio-logging Framework (IBF). 3.We highlight that multi-sensor approaches are a new frontier in bio-logging, whilst identifying current limitations and avenues for future development in sensor technology. 4.We focus on the importance of efficient data exploration, and more advanced multi-dimensional visualisation methods, combined with appropriate archiving and sharing approaches, to tackle the big data issues presented by bio-logging. 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 bio-logging data. 5.Taking advantage of the bio-logging 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 multi-disciplinary collaborations to catalyse the opportunities offered by current and future bio-logging 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. This article is protected by copyright. All rights reserved. |
| spellingShingle | Williams, HJ Taylor, LA Benhamou, S Bijleveld, AI Clay, TA De Grissac, S Demšar, U English, HM Franconi, N Gómez-Laich, A Griffiths, RC Kay, WP Morales, JM Potts, JR Rogerson, KF Rutz, C Spelt, A Trevail, AM Wilson, RP Börger, L Optimising the use of bio-loggers for movement ecology research |
| title | Optimising the use of bio-loggers for movement ecology research |
| title_full | Optimising the use of bio-loggers for movement ecology research |
| title_fullStr | Optimising the use of bio-loggers for movement ecology research |
| title_full_unstemmed | Optimising the use of bio-loggers for movement ecology research |
| title_short | Optimising the use of bio-loggers for movement ecology research |
| title_sort | optimising the use of bio loggers for movement ecology research |
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