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Environmental factors influence the detection probability in acoustic telemetry in a marine environment: results from a new setup
Reubens, J.; Verhelst, P.; van der Knaap, I; Deneudt, K.; Moens, T.; Hernandez, F. (2019). Environmental factors influence the detection probability in acoustic telemetry in a marine environment: results from a new setup. Hydrobiologia 845(1): 81-94. https://hdl.handle.net/10.1007/s10750-017-3478-7
In: Hydrobiologia. Springer: The Hague. ISSN 0018-8158; e-ISSN 1573-5117, meer
Peer reviewed article  

Beschikbaar in  Auteurs 

Trefwoord
    Marien
Author keywords
    Range test, Acoustic telemetry, Detection probability, LifeWatch project, North Sea

Auteurs  Top 
  • Reubens, J.
  • Verhelst, P.
  • van der Knaap, I
  • Deneudt, K.
  • Moens, T.
  • Hernandez, F.

Abstract
    Acoustic telemetry is a commonly applied method to investigate the ecology of marine animals and provides a scientific basis for management and conservation. Crucial insight in animal behaviour and ecosystem functioning and dynamics is gained through acoustic receiver networks that are established in many different environments around the globe. The main limitation to this technique is the ability of the receivers to detect the signals from tagged animals present in the nearby area. To interpret acoustic data correctly, understanding influencing factors on the detection probability is critical. Therefore, range test studies are an essential part of acoustic telemetry research. Here, we investigated whether specific environmental factors (i.e. wind, currents, waves, background noise, receiver tilt and azimuth) influence the receiver detection probability for a permanent acoustic receiver network in Belgium. Noise and wind speed in relation to distance, the interaction of receiver tilt and azimuth and current speed were the most influential variables affecting the detection probability in this environment. The study indicated that there is high detection probability up to a distance of circa 200 m. A new setup, making use of features that render valuable information for data analysis and interpretation, was tested and revealed general applicability.

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