Linking animal-borne video to accelerometers reveals prey capture variability

Understanding foraging is important in ecology, as it determines the energy gains and, ultimately, the fitness of animals. However, monitoring prey captures of individual animals is difficult. Direct observations using animal-borne videos have short recording periods, and indirect signals (e.g., sto...

Full description

Bibliographic Details
Published in:Proceedings of the National Academy of Sciences
Main Authors: Watanabe, Yuuki Y., Takahashi, Akinori
Format: Text
Language:English
Published: National Academy of Sciences 2013
Subjects:
Online Access:http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3568313
http://www.ncbi.nlm.nih.gov/pubmed/23341596
https://doi.org/10.1073/pnas.1216244110
Description
Summary:Understanding foraging is important in ecology, as it determines the energy gains and, ultimately, the fitness of animals. However, monitoring prey captures of individual animals is difficult. Direct observations using animal-borne videos have short recording periods, and indirect signals (e.g., stomach temperature) are never validated in the field. We took an integrated approach to monitor prey captures by a predator by deploying a video camera (lasting for 85 min) and two accelerometers (on the head and back, lasting for 50 h) on free-swimming Adélie penguins. The movies showed that penguins moved the heads rapidly to capture krill in midwater and fish (Pagothenia borchgrevinki) underneath the sea ice. Captures were remarkably fast (two krill per second in swarms) and efficient (244 krill or 33 P. borchgrevinki in 78–89 min). Prey captures were detected by the signal of head acceleration relative to body acceleration with high sensitivity and specificity (0.83–0.90), as shown by receiver-operating characteristic analysis. Extension of signal analysis to the entire behavioral records showed that krill captures were spatially and temporally more variable than P. borchgrevinki captures. Notably, the frequency distribution of krill capture rate closely followed a power-law model, indicating that the foraging success of penguins depends on a small number of very successful dives. The three steps illustrated here (i.e., video observations, linking video to behavioral signals, and extension of signal analysis) are unique approaches to understanding the spatial and temporal variability of ecologically important events such as foraging.