Search and foraging behaviors from movement data: A comparison of methods

Ashley Bennison*, Stuart Bearhop, Thomas W. Bodey, Stephen C. Votier, W. James Grecian, Ewan D. Wakefield, Keith C. Hamer, Mark Jessopp

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

52 Citations (Scopus)
5 Downloads (Pure)


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.

Original languageEnglish
Pages (from-to)13-24
Number of pages12
JournalEcology and Evolution
Issue number1
Early online date23 Nov 2017
Publication statusPublished - Jan 2018

Bibliographical note

Funding Information
Natural Environment Research Council. Grant Numbers: IRF NE/M017990/1, NE/H007466/1. Irish Research Council. Grant Number: GOIPG/2016/503
Marine Renewable Energy Ireland (MaREI). The SFI Centre for Marine Renewable Energy Research. Grant Number: 12/RC/2302
We would like to thank all those involved in fieldwork as well as landowners of both colonies for allowing access for research, in particular Sir Hew Hamilton‐Dalrymple for permitting fieldwork at Bass Rock, the Neale family for permitting work on Great Saltee, and the Scottish Seabird Centre for logistical support and advice. This study was funded by NERC Standard Grant NE/H007466/1. AB is funded by the Irish Research Council (Project ID: GOIPG/2016/503). MJ is funded under Marine Renewable Energy Ireland (MaREI), The SFI Centre for Marine Renewable Energy Research (12/RC/2302), and EW is funded by the NERC (IRF NE/M017990/1).


  • behavior
  • first passage time
  • hidden Markov models
  • k-means
  • kernel density
  • machine learning
  • movement
  • state-space models
  • telemetry


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