Quantifying harbour porpoise foraging behaviour in CPOD data
Harbour porpoises (Phocoena phocoena) are regularly monitored to assess how they are impacted by the construction and operation of offshore wind farms. A suitable method to do this is passive acoustic monitoring (PAM) by stationary hydrophones, for example CPODs. These devices provide information on...
| Published in: | Proceedings of Meetings on Acoustics, 178th Meeting of the Acoustical Society of America |
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| Main Authors: | , , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Acoustical Society of America
2019
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| Subjects: | |
| Online Access: | https://research.wur.nl/en/publications/quantifying-harbour-porpoise-foraging-behaviour-in-cpod-data https://doi.org/10.1121/2.0001214 |
| Summary: | Harbour porpoises (Phocoena phocoena) are regularly monitored to assess how they are impacted by the construction and operation of offshore wind farms. A suitable method to do this is passive acoustic monitoring (PAM) by stationary hydrophones, for example CPODs. These devices provide information on echolocation click activity, which can then be analysed. Prey occurrence is considered one of the main drivers in porpoise distribution and successful feeding is vital to the fitness and survival of individual porpoises. Information on foraging behavior, however, is difficult to obtain in the field, in particular as animals feed under water. Harbour porpoise use narrow band high frequency signals in a sequence of clicks (called click trains) for echolocation, communication and foraging. The different behaviors are characterised by the modulation in time lag between clicks (inter-click interval). Using CPOD data collected in Dutch water during and after pile driving noise exposure, the present study first investigated different data processing methods for the quantification of foraging behavior. The results indicate that: (1) a click-based classification provides the best results (as opposed to using click trains), (2) foraging events could be detected in sufficient numbers to reveal patterns over time, such as correlation with pile driving activities. |
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