Acoustic Complexity of vocal fish communities: a field and controlled validation.

Sci Rep. 2018 Jul 12;8(1):10559. doi: 10.1038/s41598-018-28771-6. Bolgan M1, Amorim MCP2,3, Fonseca PJ3, Di Iorio L4, Parmentier E5. Author information 1 Laboratoire de Morphologie Fonctionnelle et Evolutive, Institut de Chimie-B6C, Université de Liège, Liège, Belgium. mbolgan@uliege.be. 2 MARE, Marine and Environmental Sciences Centre, ISPA - Instituto Universitário, Lisbon, Portugal. 3 Departamento de Biologia Animal and Centre for Ecology, Evolution and Environmental Changes, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal. 4 CHORUS Institute, INP Phelma Minatec, 3 Parvis Louis Néel, 38016, Grenoble, France. 5 Laboratoire de Morphologie Fonctionnelle et Evolutive, Institut de Chimie-B6C, Université de Liège, Liège, Belgium. Abstract The Acoustic Complexity Index (ACI) is increasingly applied to the study of biodiversity in aquatic habitats. However, it remains unknown which types of acoustic information are highlighted by this index in underwater environments. This study explored the robustness of the ACI to fine variations in fish sound abundance (i.e. number of sounds) and sound diversity (i.e. number of sound types) in field recordings and controlled experiments. The ACI was found to be sensitive to variations in both sound abundance and sound diversity, making it difficult to discern between these variables. Furthermore, the ACI was strongly dependent on the settings used for its calculation (i.e. frequency and temporal resolution of the ACI algorithm, amplitude filter). Care should thus be taken when comparing ACI absolute values between studies, or between sites with site-specific characteristics (e.g. species diversity, fish vocal community composition). As the use of ecoacoustic indices presents a promising tool for the monitoring of vulnerable environments, methodological validations like those presented in this paper are of paramount importance in understanding which biologically important information can be gathered by applying acoustic indices to Passive Acoustic Monitoring data. PMID: 30002420 PMCID: PMC6043554 DOI: 10.1038/s41598-018-28771-6 Free PMC Article