dc.contributor.author | Bennison, A | |
dc.contributor.author | Bearhop, S | |
dc.contributor.author | Bodey, TW | |
dc.contributor.author | Votier, SC | |
dc.contributor.author | Grecian, WJ | |
dc.contributor.author | Wakefield, ED | |
dc.contributor.author | Hamer, KC | |
dc.contributor.author | Jessopp, M | |
dc.date.accessioned | 2018-02-13T11:10:43Z | |
dc.date.issued | 2017-11-23 | |
dc.description.abstract | Search behavior is often used as a proxy for foraging effort within studies of animal movement, despite it being only one part of the foraging process, which also includes prey capture. While methods for validating prey capture exist, many studies rely solely on behavioral annotation of animal movement data to identify search and infer prey capture attempts. However, the degree to which search correlates with prey capture is largely untested. This study applied seven behavioral annotation methods to identify search behavior from GPS tracks of northern gannets (Morus bassanus), and compared outputs to the occurrence of dives recorded by simultaneously deployed time-depth recorders. We tested how behavioral annotation methods vary in their ability to identify search behavior leading to dive events. There was considerable variation in the number of dives occurring within search areas across methods. Hidden Markov models proved to be the most successful, with 81% of all dives occurring within areas identified as search. k-Means clustering and first passage time had the highest rates of dives occurring outside identified search behavior. First passage time and hidden Markov models had the lowest rates of false positives, identifying fewer search areas with no dives. All behavioral annotation methods had advantages and drawbacks in terms of the complexity of analysis and ability to reflect prey capture events while minimizing the number of false positives and false negatives. We used these results, with consideration of analytical difficulty, to provide advice on the most appropriate methods for use where prey capture behavior is not available. This study highlights a need to critically assess and carefully choose a behavioral annotation method suitable for the research question being addressed, or resulting species management frameworks established. | en_GB |
dc.description.sponsorship | Natural Environment Research Council,
Grant/Award Number: IRF NE/M017990/1
and NE/H007466/1; Irish Research Council,
Grant/Award Number: GOIPG/2016/503;
Marine Renewable Energy Ireland (MaREI);
The SFI Centre for Marine Renewable Energy
Research, Grant/Award Number: 12/RC/2302 | en_GB |
dc.identifier.citation | Vol. 8 (1), pp. 13 - 24 | en_GB |
dc.identifier.doi | 10.1002/ece3.3593 | |
dc.identifier.uri | http://hdl.handle.net/10871/31437 | |
dc.language.iso | en | en_GB |
dc.publisher | Wiley Open Access | en_GB |
dc.relation.url | https://www.ncbi.nlm.nih.gov/pubmed/29321847 | en_GB |
dc.rights | This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium,
provided the original work is properly cited.
© 2017 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd | en_GB |
dc.subject | behavior | en_GB |
dc.subject | first passage time | en_GB |
dc.subject | hidden Markov models | en_GB |
dc.subject | kernel density | en_GB |
dc.subject | k‐means | en_GB |
dc.subject | machine learning | en_GB |
dc.subject | movement | en_GB |
dc.subject | state‐space models | en_GB |
dc.subject | telemetry | en_GB |
dc.title | Search and foraging behaviors from movement data: A comparison of methods. | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2018-02-13T11:10:43Z | |
dc.identifier.issn | 2045-7758 | |
exeter.place-of-publication | England | en_GB |
dc.description | This is the final version of the article. Available from Wiley via the DOI in this record. | en_GB |
dc.identifier.journal | Ecology and Evolution | en_GB |