Multibeam echosounders are widely used for 3D bathymetric mapping, and increasingly for water column studies. However, they rapidly collect huge volumes of data, which poses a challenge for water column data processing that is often still manual and time-consuming, or affected by low efficiency and high false detection rates if automated. This research describes a comprehensive and reproducible workflow that improves efficiency and reliability of target detection and classification, by calculating metrics for target cross-sections using a commercial software before feeding into a feature-based semi-supervised machine learning framework. The method is tested with data collected from an uncalibrated multibeam echosounder around an offshore gas platform in the Adriatic Sea. It resulted in more-efficient target detection, and, although uncertainties regarding user labelled training data need to be underlined, an accuracy of 98% in target classification was reached by using a final pre-trained stacking ensemble model.

The Mediterranean Sea is recognized today as the World’s most invaded marine region, but observations of species occurrences remain scattered in the scientific literature and scarcely accessible. Here we introduce the ORMEF database: a first comprehensive and robust compilation of exotic fish observations recorded over more than a century in the Mediterranean. ORMEF consists today of 4015 geo-referenced occurrences from 20 Mediterranean Countries, extracted from 670 scientific published papers. We collated information on 188 fish taxa that are thus divided: 106 species entered through the Suez Canal; 25 species introduced by shipping, mariculture, aquarium release or by means of other human activities; 57 Atlantic species, whose arrival in the Mediterranean has been attributed to the unassisted immigration through the strait of Gibraltar. Each observation included in the ORMEF database was submitted to a severe quality control and checked for geographical and taxonomic biases. ORMEF is a new authoritative reference for Mediterranean bio-invasion research and a living archive to inform management strategies and policymakers in a period of rapid environmental transformation.

In this paper we describe a 50-year (1965–2015) ecological database containing data collected in the northern Adriatic Sea (NAS) at one of the 25 research parent sites belonging to the Italian Long Term Ecological Research Network (LTER-Italy, http://www.lteritalia.it, last access: February 2020). LTER-Italy is a formal member of the International (https://www.ilter.network, last access: February 2020) and European (http://www.lter-europe.net/, last access: February 2020) LTER networks. The NAS is undergoing a process, led by different research institutions and projects, of establishing a marine ecological observatory, building on the existing facilities, infrastructure, and long-term ecological data. During this process, the implementation of open-access and open-science principles has been started by creating an open-research life cycle that involves sharing ideas and results (scientific papers), data (raw and processed), metadata, methods, and software. The present data paper is framed within this wider context. The database is composed of observations on abiotic parameters and phytoplankton and zooplankton abundances, collected during 299 cruises in different sampling stations, in the Gulf of Venice in particular. Here we describe the sampling and analytical activities, the parameters, and the structure of the database. The database is available at https://doi.org/10.5281/zenodo.3516717 (Acri et al., 2019), and it was also uploaded in the DEIMS-SDR repository (Dynamic Ecological Information Management System – Site and Dataset Registry, https://deims.org/), which is the official site and data registry for the International LTER network.