Report of acoustic processing routines & quality checking methods. Version 1.1. [D3.1]

Abstract

The mesopelagic (200-1000 m depth), is one of the most understudied regions in the world oceans (St John et al. 2016). Micronekton (~1 to 20 cm in length, Kloser et al. 2009) are an ecologically important component of the mesopelagic community, having potentially large biomasses (Irigoien et al. 2014), high nutritional value (Lea et al. 2002), transferring carbon from the surface to depth (Anderson et al. 2018), and of commercial interest (Gjøsæter and Kawaguchi 1980; St John et al. 2016). Notoriously hard to sample, due to poor sampling efficiency of nets, observations within the mesopelagic zone are frequently made using active acoustics (Simmonds and MacLennan 2005). Whereby, echosounders produce a pulse of sound and receive echoes backscattered from organisms, objects and discontinuities in the water. Measurement of the time delay of the received acoustic signal and quantification of the intensity of the returned sound reveals information about the source of the scattering and where it is in the water column (Benoit-Bird and Lawson 2016). Integrated into marine vessels, echosounders offer the ability to make measurements spanning high and wide spatial and temporal scales. Acoustic methods are widely using in fisheries research for pelagic fish estimation and ecosystembased management (Bertrand et al. 2003; Simmonds and MacLennan 2005). Dedicated acoustic survey programmes to count, map and predict fishing conditions commenced in the 1970s (Fernandes et al. 2002), and have expanded now to multi-national surveys covering sea and basin scales such as the International Blue Whiting Spawning Stock Survey (ICES 2018; WGIPS 2017) and the CCAMLR synoptic survey for Antarctic krill (Hewitt et al. 2004). In addition, as well as fisheries research vessels, many oceanographic research vessels and fishing vessels are equipped with hull mounted echosounders, operating at a variety of frequencies (e.g., Erreur ! Source du renvoi introuvable.). Acoustic data from these and other vessels have been collected for targeted reasons (ecosystem surveys, examples) or opportunistically (as part of transit routes, Kloser et al. 2009; Behagle et al. 2016; Escobar-Flores et al. 2018). As a result acoustic data exist in vast quantities, with extensive geographical and temporal coverage, and could be considered as “big” data (Colosi & Worcester, 2013) within environmental sciences. Modern acoustic data are stored digitally and collected data are archived in data centres (e.g. NOAA National Centers for Environmental Information (https://www.ngdc.noaa.gov/mgg/wcd/), NERC data centres (http://www.datacentres.nerc.ac.uk) and Integrated Marine Observing System (www.imos.au). Raw acoustic data are typically stored in a proprietary format that requires specialized acoustic processing software (e.g. Echoview (www.echoview.com), LSSS (https://www.marec.no) or MOVIES 3D (Trenkel et al. 2009) or a knowledge of the file format and a scientific programming language. As a result, both IMOS and NOAA identified that enabling open-access to quality-checked, calibrated acoustic data would allow greater exploitation by non-acousticians (Kloser et al. 2009; Wall et al. 2016). Stored with a metadata convention to ensure proper documentation of how, when, why and where the data were collected, ensures consistency across datasets (ICES 2014). In order to convert raw acoustic data to a quality-checked, calibrated acoustic data, a number of steps are required (Figure 1). The quantitative use of data from more than one sensor requires that the acoustic instrument is calibrated to allow comparison. This involves characterisation of measurement accuracy and precision, and best practise is a sphere calibration (Foote et al. 1987) that measures the overall performance of an echosounder using reflections from a solid sphere of known backscattering strength (bs (m2)) (Demer et al. 2015).