This database have been created and used for the study presented in Andrès et al., 2026. It comprises potential density and small particles backscattering (bbprs700) anomalies, derived from BioGeoChemical-Argo profiling floats data and matched with mesoscale eddies Atlas, TOEddies. BGC-Argo profiles have been taken from 01/01/2010 until 31/05/2023 and processed with Sauzede et al., in prep method. Relative anomalies were calculated from BGC-Argo profiles, using climatological products as a reference ( [BGC - climatology] / climatology ). For bbprs700 reference, data came from SOCA-bbp (1, data access) and World Ocean Atlas (2,3,4, data access). These anomalies are cross-referenced with the locations of mesoscale eddies identified from daily maps of Absolute Dynamic Topography (ADT) using the Ocean Eddy Detection and Tracking Algorithms (TOEddies) algorithm (5,6). ADT maps are produced by Ssalto/Duacs and distributed by Copernicus-Marine Environment Services with a resolution of 0.25°x0.25°. TOEddies method detects sea surface height anomalies such as ocean eddies and tracks them through successive maps. The colocalization with BGC-Argo profiles is refered by an update presented in Laxenaire et al. 2022 (7). For each anomaly profile, the dataset provides the following informations : - WMO_CYCLE : the World Meteorological Organization and cycle number, - LON, LAT : the geographical coordinates, - JULD : the julian day, - TIME : the profile date with the following format : YYYY-MM-DD HH:MM:SS, - ABS_MONTH : an absolute month, ie : the southern hemisphere months have been shifted by six months to match the seasonnality of northern hemisphere, - SEASON : the season defined as : Summer=(june, july, august) ; Autumn=(september,october,november) ; Winter=(december, january, february) ; Spring=(march, april, may), - MLD : the Mixed Layer Depth in decibar. - DATA_SELECTION : filled with 1 if the profile is part of the reduced dataset used for the final results in Andrès et al. 2023; 0 if it is not. - DEPTH_CLIM : a depth vector obtained from climatological products. It's worth noting that the depth vector varies slightly between WOA and SOCA datasets. Due to these differences, particulate backscattering and potential density relative anomalies are provided in separate files, containing one of the following two columns : - SIGMA_ANOMALY : potential density relative anomalies (no unit), - BBPRS700_ANOMALY : particulate backscattering relative anomalies (no unit), Furthermore, profiles both inside and outside eddy cores are segregated into two distinct databases. Profiles inside eddies also include : - N_EDDY : the indentification number of the eddy, - N_TRAJ : the identification number of the eddy trajectory, - POLA : the eddy polarity, indicating whether they are located within the core of a cyclonic/anticyclonic eddy (-1/1), - EDDY_THICKNESS : the estimated eddy thickness in decibar, - EDDY_SPEED : the mean azimuthal geostrophic velocity around the eddy core contour, which is defined as the closed contour of the Absolute Dynamic Topography around the eddy center where the mean azimuthal geostrophic velocity is maximum in meters per second, - EDDY_RMAX : the size of the eddy core radius in kilometers, - DISTANCE_CENTER_FLOAT : the distance between the float and the eddy center in kilometers. References : (1) Sauzede R., H. Claustre, J. Uitz, C. Jamet, G. Dall’Olmo, F. D’Ortenzio, B. Gentili, A. Poteau, and C. Schmechtig, 2016: A neural network-based method for merging ocean color and Argo data to extend surface bio-optical properties to depth: Retrieval of the particulate backscattering coefficient, J. Geophys. Res. Oceans, 121, doi:10.1002/2015JC011408. (2) Boyer, Tim P.; García, Hernán E.; Locarnini, Ricardo A.; Zweng, Melissa M.; Mishonov, Alexey V.; Reagan, James R.; Weathers, Katharine A.; Baranova, Olga K.; Paver, Christopher R.; Seidov, Dan; Smolyar, Igor V. (2018). World Ocean Atlas 2018. [indicate subset used]. NOAA National Centers for Environmental Information. Dataset. https://www.ncei.noaa.gov/archive/accession/NCEI-WOA18. Accessed 2023. (3) Locarnini, R. A., A. V. Mishonov, O. K. Baranova, T. P. Boyer, M. M. Zweng, H. E. García, J. R. Reagan, D. Seidov, K. Weathers, C. R. Paver, and I. Smolyar, 2019. World Ocean Atlas 2018, Volume 1: Temperature. A. Mishonov Technical Ed.; NOAA Atlas NESDIS 81, 52 pp. (4) Zweng, M. M., J. R. Reagan, D. Seidov, T. P. Boyer, R. A. Locarnini, H. E. García, A. V. Mishonov, O. K. Baranova, K. Weathers, C. R. Paver, and I. Smolyar, 2019. World Ocean Atlas 2018, Volume 2: Salinity. A. Mishonov Technical Ed.; NOAA Atlas NESDIS 82, 50 pp. (5) Laxenaire, R., Speich, S., Blanke, B., Chaigneau, A., Pegliasco, C., & Stegner, A. (2018). Anticyclonic Eddies Connecting the Western Boundaries of Indian and Atlantic Oceans. Journal of Geophysical Research: Oceans, 123(11), 7651–7677. https://doi.org/10.1029/2018JC014270 (6) Laxenaire, R., Speich, S., & Stegner, A. (2020). Agulhas Ring Heat Content and Transport in the South Atlantic Estimated by Combining Satellite Altimetry and Argo Profiling Floats Data. Journal of Geophysical Research: Oceans, 125(9). https://doi.org/10.1029/2019JC015511 (7) Laxenaire, R., Ioannou, A., & Speich, S. (2022). Presentation of the near-real time and delayed time global database of mesoscale ocean eddies detected by TOEddies on altimetry fields and co-located with (BGC-) Argo floats. 2022 Ocean Surface Topography Science Team Meeting, 227.

The use of acoustic scattering models provide estimates of single target echoes that allow acousticians to convert acoustic information into biologically meaningful measures. The literature on organisms’ target strength is extensive but is mainly focused on commercial stocks of small pelagic fishes and zooplankton species. A few models of swimbladdered fishes of the mesopelagic zone are also available. However, deep species of the lower mesopelagic and bathypelagic zones tend to have regressed swimbladders or lack one. These habitats have low numerical densities and thus single target studies and angle variation are of particular relevance. Cyclothone spp, the most abundant fishes in the planet and a major constituent of the biomass in the bathypelagic zone, possess gas-filled swimbladders in the upper mesopelagic zone and in larvae stages of all species, but deeper species gradually fill their swimbladder with age. They thus change from a gas-bearing acoustic scattering to a fluid like type. This study applies the Kirchoff Ray Mode (KRM) model based on real fish body shapes of Cyclothone individuals derived from photographs of organisms captured along the year in the Bay of Biscay in order to obtain target strength (TS) of these individuals. Width versus standard length (SL) values fitted the following equation: width=0.01+0.02*SL. Estimated TS values in the Rayleigh zone at broadside had significant linear correlations with SL that can be employed as an approximation of their scattering (TS = 35*log10(SL) − 119, TS = 35*log10(SL) − 106 and TS = 35*log10(SL) − 97 at 18, 38 and 70 kHz respectively). TS at 120 and 200 kHz were not significantly correlated with standard length. Changes in fish body sound speed and density values highly vary the TS level. Assuming neutral buoyancy (body density close to surrounding seawater density), mean TS values were located at −91, −85, −78, −77, −80 dB at 18, 38 and 70, 120 and 200 kHz respectively. TS changes with orientation were also considered depicting important variations in echo level as well as in TS spectra. This study provides relevant information on the acoustic characteristics of lower mesopelagic and bathypelagic Cyclothone species that can be employed to better infer knowledge from acoustic recordings in those areas.