Evidence for seasonal cycles in deep-sea fish abundances: A great migration in the deep SE Atlantic?

Trophic state and potential productivity assessment for Qaroun Lake using spatial techniques

Qaroun Lake is one of the most important Egyptian lakes which, recently, have been exposed to severe degradation in water quality and fish productivity. In this manuscript, Carlson's trophic state index (CTSI) was used to evaluate the trophic state, while the trophometric index (TMI) was used to assess the potential productivity of Qaroun Lake. The present study is one of the initial attempts to investigate these indices in Qaroun Lake. To achieve this work, an integrated multidisciplinary approach was adopted integrating field investigation, geographic information system, and data analysis. CTSI combines three variables of water quality: chlorophyll-a (CHL-a), total phosphorus (TP), and transparency measured by Secchi disk depth (SDD). The result of overall CTSI showed the hypereutrophic state is represented by 62% and eutrophic state is represented by 38% of the total lake's area. Moreover, the calculated TMI indicated the average potential productivity value (PP) is 619 t. It can be concluded that the hypereutrophic is the dominant state in Qaroun Lake. The present study recommends the application of TMI model to evaluate and monitor the changes in Qaroun Lake's potential productivity in response to the changing environmental conditions and other biological pressures (e.g., Isopoda paraside).

Image dataset for benchmarking automated fish detection and classification algorithms

Multiparametric video-cabled marine observatories are becoming strategic to monitor remotely and in real-time the marine ecosystem. Those platforms can achieve continuous, high-frequency and long-lasting image data sets that require automation in order to extract biological time series. The OBSEA, located at 4 km from Vilanova i la Geltrú at 20 m depth, was used to produce coastal fish time series continuously over the 24-h during 2013-2014. The image content of the photos was extracted via tagging, resulting in 69917 fish tags of 30 taxa identified. We also provided a meteorological and oceanographic dataset filtered by a quality control procedure to define real-world conditions affecting image quality. The tagged fish dataset can be of great importance to develop Artificial Intelligence routines for the automated identification and classification of fishes in extensive time-lapse image sets.

Deep demersal fish communities respond rapidly to warming in a frontal region between Arctic and Atlantic waters

The assessment of climate impact on marine communities dwelling deeper than the well-studied shelf seas has been hampered by the lack of long-term data. For a long time, the prevailing expectation has been that thermal stability in deep ocean layers will delay ecosystem responses to warming. Few observational studies have challenged this view and indicated that deep organisms can respond exceptionally fast to physical change at the sea surface. To address the depth-specific impact of climate change, we investigated spatio-temporal changes in fish community structure along a bathymetry gradient of 150-1500 m between 1998 and 2016 in East Greenland. Here, the Arctic East Greenland Current and the Atlantic Irminger Current meet and mix, representing a sub-Arctic transition zone. We found the strongest signals of community reorganizations at depths between 350 and 1000 m and only weak responses in the shallowest and deepest regions. Changes were in synchrony with atmospheric warming, loss in sea ice and variability in physical sea surface conditions both within our study region and North of the Denmark Strait. These results suggest that interannual variability and long-term climate trends of the larger ecoregion can rapidly affect fish communities down to 1000-m depth through atmospheric ocean coupling and food web interactions.

An Automated Pipeline for Image Processing and Data Treatment to Track Activity Rhythms of Paragorgia arborea in Relation to Hydrographic Conditions

Imaging technologies are being deployed on cabled observatory networks worldwide. They allow for the monitoring of the biological activity of deep-sea organisms on temporal scales that were never attained before. In this paper, we customized Convolutional Neural Network image processing to track behavioral activities in an iconic conservation deep-sea species—the bubblegum coral Paragorgia arborea—in response to ambient oceanographic conditions at the Lofoten-Vesterålen observatory. Images and concomitant oceanographic data were taken hourly from February to June 2018. We considered coral activity in terms of bloated, semi-bloated and non-bloated surfaces, as proxy for polyp filtering, retraction and transient activity, respectively. A test accuracy of 90.47% was obtained. Chronobiology-oriented statistics and advanced Artificial Neural Network (ANN) multivariate regression modeling proved that a daily coral filtering rhythm occurs within one major dusk phase, being independent from tides. Polyp activity, in particular extrusion, increased from March to June, and was able to cope with an increase in chlorophyll concentration, indicating the existence of seasonality. Our study shows that it is possible to establish a model for the development of automated pipelines that are able to extract biological information from times series of images. These are helpful to obtain multidisciplinary information from cabled observatory infrastructures.

Towards Naples Ecological REsearch for Augmented Observatories (NEREA): The NEREA-Fix Module, a Stand-Alone Platform for Long-Term Deep-Sea Ecosystem Monitoring

Deep-sea ecological monitoring is increasingly recognized as indispensable for the comprehension of the largest biome on Earth, but at the same time it is subjected to growing human impacts for the exploitation of biotic and abiotic resources. Here, we present the Naples Ecological REsearch (NEREA) stand-alone observatory concept (NEREA-fix), an integrated observatory with a modular, adaptive structure, characterized by a multiparametric video-platform to be deployed in the Dohrn canyon (Gulf of Naples, Tyrrhenian Sea) at ca. 650 m depth. The observatory integrates a seabed platform with optoacoustic and oceanographic/geochemical sensors connected to a surface transmission buoy, plus a mooring line (also equipped with depth-staged environmental sensors). This reinforced high-frequency and long-lasting ecological monitoring will integrate the historical data conducted over 40 years for the Long-Term Ecological Research (LTER) at the station “Mare Chiara”, and ongoing vessel-assisted plankton (and future environmental DNA-eDNA) sampling. NEREA aims at expanding the observational capacity in a key area of the Mediterranean Sea, representing a first step towards the establishment of a bentho-pelagic network to enforce an end-to-end transdisciplinary approach for the monitoring of marine ecosystems across a wide range of animal sizes (from bacteria to megafauna).