0000000000141123
AUTHOR
Rosalia Ferreri
Identifying small pelagic Mediterranean fish schools from acoustic and environmental data using optimized artificial neural networks
Abstract The Common Fisheries Policy of the European Union aims to exploit fish stocks at a level of Maximum Sustainable Yield by 2020 at the latest. At the Mediterranean level, the General Fisheries Commission for the Mediterranean (GFCM) has highlighted the importance of reversing the observed declining trend of fish stocks. In this complex context, it is important to obtain reliable biomass estimates to support scientifically sound advice for sustainable management of marine resources. This paper presents a machine learning methodology for the classification of pelagic species schools from acoustic and environmental data. In particular, the methodology was tuned for the recognition of an…
Spatio-temporal dynamics of a planktonic system and chlorophyll distribution in a 2D spatial domain: matching model and data
AbstractField data on chlorophyll distribution are investigated in a two-dimensional spatial domain of the Mediterranean Sea by using for phytoplankton abundances an advection-diffusion-reaction model, which includes real values for physical and biological variables. The study exploits indeed hydrological and nutrients data acquired in situ, and includes intraspecific competition for limiting factors, i.e. light intensity and phosphate concentration. As a result, the model allows to analyze how both the velocity field of marine currents and the two components of turbulent diffusivity affect the spatial distributions of phytoplankton abundances in the Modified Atlantic Water, the upper layer…
Automatic classification of acoustically detected krill aggregations: A case study from Southern Ocean
Acoustic surveys represent the standard methodology to assess the spatial distribution and abundance of pelagic organisms characterized by aggregative behaviour. The species identification of acoustically observed aggregations is usually performed by taking into account the biological sampling and according to expert-based knowledge. The precision of survey estimates, such as total abundance and spatial distribution, strongly depends on the efficiency of acoustic and biological sampling as well as on the species identification. In this context, the automatic identification of specific groups based on energetic and morphological features could improve the species identification process, allo…
The Graham Bank (Sicily Channel, central Mediterranean Sea). Seafloor signatures of volcanic and tectonic controls
Abstract Graham Bank is a dominant physiographic element of the NW Sicily Channel (central Mediterranean Sea), affected in the last 100 years by numerous well-documented volcanic eruptions. We present the first results of a geomorphological study where the Graham Bank region in the depth interval 7–350 m was mapped for the first time with multi-beam echosounder and high-resolution seismic and multi-channel seismic reflection profiles. We describe in high resolution the detailed geomorphological features of Graham Bank, and how the superficial expression of different process and dynamics occurring in the sub-seafloor evidence volcanic and tectonic controls on seafloor morphology across a rel…
A novel method to simulate the 3D chlorophyll distribution in marine oligotrophic waters
Abstract A 3D advection-diffusion-reaction model is proposed to investigate the abundance of phytoplankton in a difficult-to-access ecosystem such as the Gulf of Sirte (southern Mediterranean Sea) characterized by oligotrophic waters. The model exploits experimentally measured environmental variables to reproduce the dynamics of four populations that dominate phytoplankton community in the studied area: Synechococcus, Prochlorococcus HL, Prochlorococcus LL and picoeukaryotes. The theoretical results obtained for phytoplankton abundances are converted into chl-a and Dvchl-a concentrations, and the simulated vertical chlorophyll profiles are compared to the corresponding experimentally acquir…
A pattern recognition approach to identify biological clusters acquired by acoustic multi-beam in Kongsfjorden
The Svalbardsis one of the most intensively studied marine regions in the Artic; here the composition and distribution of marine assemblages are changing under the effect of global change, and marine communities are monitored in order to understand the long-term effects on marine biodiversity. In the present work, acoustic data collected in the Kongsfjorden using multi-beam technology was analyzed to develop a methodology for identifying and classifying 3D acoustic patterns related to fish aggregations. In particular, morphological, energetic and depth features were taken into account to develop a multi-variate classification procedure allowing to discriminate fish species. The results obta…