Abstract
Automated acoustic indices to infer biological sounds from marine recordings have produced mixed levels of success. The use of such indices in complex marine environments, dominated by several anthropogenic and geophonic sources, have yet to be understood fully. In this study, we introduce a noise resilient method based on complexity-entropy (hereafter named C-H) for the detection of biophonic sounds originating from fish choruses. The C-H method was tested on data collected in Changhua and Miaoli (Taiwan) during the spring in both 2016 and 2017. Miaoli was exposed to continual shipping activity, which led to an increase of ~10 dB in low frequency ambient noise levels (5–500 Hz). The acoustic dataset was successively analyzed via the acoustic complexity index, the acoustic diversity index and the bioacoustic index. The C-H method was found to be strongly correlated with fish chorusing (Pearson correlation: rH < −0.9; rC > 0.89), and robust to noise originating from shipping activity or natural sources, such as wind and tides (rH and rC were between 0.22 and −0.19). Other indices produced lower or null correlations with fish chorusing due to missed identification of the choruses or sensitivity to other sound sources. In contrast to most acoustic indices, the C-H method does not require a prior setting of frequency and amplitude thresholds, and is therefore, more user friendly to untrained technicians. We conclude that the use of the C-H method has potential implications in the efficient detection of fish choruses for management or conservation purposes and could help with overcoming the limitations of acoustic indices in noisy marine environments.
Highlights
Underwater sound exists throughout the world’s oceans
We propose the C-H method as a new tool for the automated detection of fish choruses and test its validity on recordings taken at Changhua (A1) and Miaoli (N1) located at the Eastern Taiwan Strait (ETS)
We introduced a technique based on the C-H method for detecting fish choruses and compared its detection performance with Acoustic Indices (AIs), such as acoustic complexity index (ACI), acoustic diversity index (ADI) and bioacoustic index (BI)
Summary
The issue of an increasing contribution from human activities into the marine environment has received increased focus from scientists and international institutions [1,2,3]. Augmented noise input from human activities in the ocean originates mainly from continual shipping, offshore and coastal construction and industrialization, and such noise has been found to produce severe negative impacts on marine species [4,5,6,7,8,9,10]. Passive acoustic monitoring (PAM) has been used for the long-term monitoring of marine biodiversity and as a proxy for population health [11,12,13,14]. Extracting significant details regarding marine communities or specific species remains a challenging task [16,17]
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