Simone Bonamano

Mathematical models supporting the monitoring of Civitavecchia harbour (Rome)

Knowledge of the sources and types of pollutants, of the hydrodynamic field and of the health status of the marine ecosystems subjected to stress is needed to monitor coastal marine environments. The building of new piers and docks and the extension of a breakwater in Civitavecchia harbour have required extensive dredging that was authorised by the Minister of Environment with the prescription to monitor the coastal marine ecosystems with reference to Posidonia oceanica and benthic biocenoses. The structure of benthic communities and the health status of P. oceanica meadows are important indicators of the Ecological Quality Status of coastal marine waters (WFD, 2000/60/CE). In 2012, a multi-platform observing system (C-CEMS) was tested taking into account: a) the distribution of benthic biocenoses; b) physical and biological data acquired by fixed stations and periodic in situ samplings; and c) the results of numerical simulations of sediment particle tracking. This approach was used along the coastline of Northern Latium (Italy) between Tarquinia and Santa Severa. The dispersion of suspended and deposited materials calculated by numerical model is strongly related to the decrement of the shoots density of P. oceanica and to changes of benthic community’s structures.

Cost-Effective Technologies to Study the Arctic Ocean Environment

The Arctic region is known to be severely affected by climate change, with evident alterations in both physical and biological processes. Monitoring the Arctic Ocean ecosystem is key to understanding the impact of natural and human-induced change on the environment. Large data sets are required to monitor the Arctic marine ecosystem and validate high-resolution satellite observations (e.g., Sentinel), which are necessary to feed climatic and biogeochemical forecasting models. However, the Global Observing System needs to complete its geographic coverage, particularly for the harsh, extreme environment of the Arctic Region. In this scenario, autonomous systems are proving to be valuable tools for increasing the resolution of existing data. To this end, a low-cost, miniaturized and flexible probe, ArLoC (Arctic Low-Cost probe), was designed, built and installed on an innovative unmanned marine vehicle, the PROTEUS (Portable RObotic TEchnology for Unmanned Surveys), during a preliminary scientific campaign in the Svalbard Archipelago within the UVASS project. This study outlines the instrumentation used and its design features, its preliminary integration on PROTEUS and its test results.

Circulation during Storms and Dynamics of Suspended Matter in a Sheltered Coastal Area

The Gulf of Gaeta, in the western margin of central Italy, is characterized by a coastal morphology that creates a natural sheltered area in which fine sediment settles. The new port regulatory plan provides for dock expansions and dredging works that could alter the suspended particulate matter (SPM) concentration. The present study investigates the dynamics of the Gulf of Gaeta with a focus on the dynamic processes that affect the fine particle concentration. The study was conducted through a multidisciplinary approach that involves remote sensing acquisitions (satellite imagery and X-band radar), measurements in situ (water sampling, wave buoy, weather station, turbidity station, CTD profiles), and numerical modelling (SWAN and Delft3D FLOW). The X-band radar system supports the analysis of the dynamic processes of the SPM concentration providing a large dataset useful for the hydrodynamic model’s validation. The analysis reveals a strong influence of nearby rivers in modulating the SPM at the regional scale. Short-term high and low fluctuations in SPM concentration within the gulf are triggered by the local effect of the main physical forces. In particular, the direction of events and bottom sediment resuspension play a key role in modulating the SPM concentration while micro-tidal regime does not appear to influence turbidity in the study area. This approach represents an important tool in improving the long-term coastal management strategy from the perspective of sustainable human activities in marine coastal ecosystems.

Landsat 8 OLI satellite data for mapping of the Posidonia oceanica and benthic habitats of coastal ecosystems

ABSTRACT The benthic seabeds and seagrass ecosystems, in particular the vulnerable Posidonia oceanica (PO), are increasingly threatened by climate change and other anthropogenic pressures. Along the 8000 km coastline of Italy, they are often poorly mapped and monitored to properly evaluate their health status. Thus to support these monitoring needs, the improved capabilities of the Landsat 8 Operational Land Imager (OLI) Earth Observation (EO) satellite system were tested for PO mapping by coupling its atmospherically corrected multispectral data with near-synchronous sea truth information. Two different approaches for the necessary atmospheric correction were exploited focusing on the Aerosol Optical Depth (AOD) and adjacency noise effects, which typically occur at land–sea interfaces. The general achievements demonstrated the effectiveness of High Resolution (HR) spectral responses captured by OLI sensor, for monitoring seagrass and sea beds in the optically complex Tyrrhenian shallow waters, with performance level dependent on the type of applied atmospheric pre-processing. The distribution of the PO leaf area index (LAI) on different substrates has been most effectively modelled using on purpose developed spectral indices. They were based on the coastal and blue-green OLI bands, atmospherically corrected using a recently introduced method for AOD retrieval, based on the Short Wave Infrared (SWIR) reflectance. The alternative correction method including a less effective AOD assessment but the removal of adjacency effects has proven its efficacy for improving the thematic discriminability of the seabed types characterized by different PO cover–substrate combinations.

Computational modeling of immune system of the fish for a more effective vaccination in aquaculture

Motivation: A computational model equipped with the main immunological features of the sea bass (Dicentrarchus labrax L.) immune system was used to predict more effective vaccination in fish. The performance of the model was evaluated by using the results of two in vivo vaccinations trials against L. anguillarum and P. damselae. Results: Tests were performed to select the appropriate doses of vaccine and infectious bacteria to set up the model. Simulation outputs were compared with the specific antibody production and the expression of BcR and TcR gene transcripts in spleen. The model has shown a good ability to be used in sea bass and could be implemented for different routes of vaccine administration even with more than two pathogens. The model confirms the suitability of in silico methods to optimize vaccine doses and the immune response to them. This model could be applied to other species to optimize the design of new vaccination treatments of fish in aquaculture. Availability and implementation: The method is available at http://www.iac.cnr.it/˜filippo/c‐immsim/ Contact: nromano@unitus.it Supplementary information: Supplementary data are available at Bioinformatics online.