logo aquatic noise 2019

A nonlinear model for rocky shore bioacoustic signature off the Cabo Frio Island

F.C. Xavier, fabiofcx(at)gmail.com
L. Calado, lcalado(at)ieapm.mar.mil.br,
N.G. Silveira, nilce.ngs(at)gmail.com
Marine Biotechnology Program, IEAPM, Brazilian Navy, Arraial do Cabo, RJ 28930-000 Brazil
R.G. Menezes, nilce.ngs(at)gmail.com
E.B.F. Netto, eb-netto(at)uol.com.br
A.D. Kassuga, kassuga(at)gmail.com
S.M. Jesus sjesus(at)ualg.pt
LARSyS, University of Algarve, 8005-139 Faro, Portugal.

Comments: download file (poster)
Ref.: Conf. on the Effects of Aquatic Noise, Den Haag (The Netherlands), July 2019.

Different marine habitats have distinct acoustic signatures (Radford et al., 2014). These signatures are composed by anthropogenic, natural and biological sound. In coastal zones, the acoustic signature has a stronger influence of benthic organisms that form the bioacoustic chorus (Butler et al., 2017), that we will term as the Rocky Shore Bioacoustic Signature (RSBS). However, RSBS patterns can be influenced to circadian and lunar cycles, wind, tide, temperature, luminosity and others. Yet, to better understand the influence of abiotic and biotic factors in the RSBS pattern it is very important to model, identify and quantify contributions of each these factors.

This work aims at proposing a nonlinear model for the RSBS, based on data collected off the Cabo Frio Island, Brazil. This area sustains a unique environment due to strong upwelling occurrence and other hydrodynamic characteristics (Ferreira, 2003). A bottom structure with 4 hydrophones, temperature and luminosity sensors was installed near a rocky shore during 82 days. A meteorological dataset (rain, wind, solar radiation) from National Institute of Meteorology (INMET) were utilized for RSBS modelling. The RSBS model was based on a nonlinear least squares multiple regression technique.

Regression analysis revealed that temperature and luminosity explain approximately 50% of the RSBS variance, while other abiotic factors explain just 5%, approximately. Another important result was the nonlinear relationship between luminosity and RSBS. This puts in evidence that the biorhythm can be one of the principal of contributors for RSBS, increasing in twilights. In addition, this model may help to understand RSBS patterns and its variations, and help for developing bioacoustic inversion applications as abiotic data measuring, populational density of benthic organisms and marine health monitoring.