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Using the segmentation algorithm within Matecho (Perrot et al., 2018) we are able to deliver 15 descriptors to characterize the acoustic micronektonic layers in the water column. Even if the species composition is not known, these descriptors which are obtained using the same methodology allow for comparison between ecosystems and to study inter-annual variability. Some of these descriptors are new and others are based on the ones usually used to characterize pelagic fish schools using echointegration per shoal (Weill et al., 1993). In this work we will focus on the new ones and show some application cases in the three Atlantic African Large Marine Ecosystems, to monitor potential perturbations due to global change. All layer descriptors are estimated per layer and per elementary sampling unit of 0.1 nautical miles (ESU) with an accuracy of 1 meter depth. In this study we present four classes of descriptors: spatial (e.g. altitude, mean depth, minimal depth); morphological (e.g. width, ESU number, filling rate of water column); acoustic (e.g. mean volume backscattering strength Sv (dB)) and the layer number per ESU. In this study we focus on the original descriptors: (i) Filling rate of the water column (%): this indicator is based on the calculation of the width of the micronektonic layer vs. the local bottom depth. (ii) Filling rate contribution of first layer (%): this indicator shows the contribution of the first layer (the closest layer of surface) in the global filling rate. It is computed by dividing the filling rate of first layer by the filling rate of all layers. (iii) Number of layers: this indicator is calculated for each ESU, giving the number of layers in this water column. The descriptors have been computed over more than 1 million of ESUs, 992 737 in the CCLME, 166 183 in the GCLME and 462 807 in the BCLME. Such descriptors allow classification of micronekton layers and appear relevant to monitor changes in the ecosystem. Next step will be to use multifrequency or even wide-band data to improve the quality of descriptors. They were efficiently applied to study diel vertical behaviour as well as the effect of water mass characteristics on the spatial structure of the layers. In future applications it should help in the classification of the layers per functional group as well as to improve our knowledge on ecosystem organization and functioning.

Diel vertical migration (DVM) of micronekton is a behavioural mechanism driven by a trade-off between predator avoidance and access to prey. This trade-off is controlled by environmental forcing that can lead to changes of DVM pattern under changing environmental conditions. Time series of hydro acoustic surveys between 1995 – 2015 of three large Atlantic ecosystems (Canary Current - CCLME, Guinea Current - GCLME, and Benguela Current - BCLME) were analysed to calculate DVM patterns based on volume backscattering strength (Sv). DVM related descriptors (n=15) were calculated for areas according to bathymetric definitions (shelf = 10 – 150 m bottom depth, slope = 150 – 500 m bottom depth, and plain > 500 m bottom depth). Typical DVM I pattern, with micronekton descending during daytime and ascending during night-time, were observed on the slope and plain in all three ecosystems, but not on the shelf with only negative day-night values in the CCLME and BCLME. Lower daytime Sv values during the day compared to night-time suggest either less dense patches of micronekton leading to negative day-night differences in the CCLME and GCLME or insufficient measurements of certain depth strata (e.g., 0 – 10 m surface). Only a few significant and different DVM descriptors suggest a change in the CCLME and the GCLME in the last 20 years. All other insignificant descriptors assume natural variability in large Atlantic ecosystems. Our results provide insight into inter-annual variability in micronekton DVM patterns.

The understand of the fine scale process occurring in the ocean needs high resolution data and ad hoc analysis approach to improve the knowledge of ecosystem functioning. During an international survey carry out in 2014'AWA’ on-board the research vessel Thalassa (Ifremer, Brest) along the coast of Mauritania and Senegal we have used simultaneously multifrequency scientific echosounder and a Scanfish, both system allow a continuous acquisition of high- quality data at high spatial and temporal resolution over long distance. The functional data analyses have recently raising in serval field of statistics and appear to be well suited for the analysis of this dataset. In fact such data has spatial-functional nature and may be considered as observations of a stochastic process X in space of continuous functions over an interval T. Let X1(t), X2(t),.., Xn(t), t T, be the collection of n observations from X. First, we study an eventual horizontally or vertically variation of the acoustic intensity, we consider for a given frequency (here 200 kHz) and one vessel radial the two cases: vertical and horizontal variations of the acoustic intensity. Unsupervised functional classification used, shows a horizontal and vertical variation of acoustic intensity for a given frequency and a given radial. The approach can led to scrutinized at fine scale the processes occurring in three dimensions in the pelagic environment. The statistical functional classification applied to this case study appears powerful, ad hoc for ecological studies of marine ecosystem and will be extend to model the spatial structuration of the pelagic ecosystem according to the physcio-chemical parameters of the water mass which will allow to improve the forecast of the effect of the environment on marine ecosystem organization.