We developed a zooplankton-based water-quality evaluating method using indices of alpha diversity. Two key objectives were set: (i) the comparison of two-different quality-samples from different areas, and the verification of their differentiation, based on mesozooplankton biodiversity indices; and (ii) the development of a methodology, which was able to assess the quality of new marine water samples. Our analysis was based on a 24-year-long in situ dataset (1987-2010) of 139 samples in which 86 mesozooplankton taxa were identified. High-diversity and high evenness values were reported in the case of the \"good\" status sample, while low diversity, low evenness and high dominance values occurred at the lower quality one. A linear discriminant analysis (LDA) was conducted that discriminated the tested samples at 100%. This LDA was then used to evaluate samples of unknown quality. Finally, 90% of them were classified with a probability of correct classification (posterior probability) >95%. The present study proves that mesozooplankton diversity indices can discriminate different levels of anthropogenic impacts. In this sense, it can be used as a reliable indicator for environmental assessment in the pelagic habitats of the Mediterranean Sea.

In the current study, it was aimed to determine the sediment in Sparus aurata (Linnaeus 1758) tissues and Salicornia europaea L. Cu, Zn Fe, Hg, Cd, Pb and Mn concentration distributions in Güllük Lagoon which is located in the south of Aegean Sea and under the influence of anthropogenic activities by means of metal pollution and health indices and to investigate the effects of these metals on public health. The concentration range of Cu, Zn, Fe, Hg, Cd, Pb and Mn in sediment samples was determined as 12.16-26.00, 1.62-2.03, 7.77-8.36, 7.52-16.15, 0.071-0.40, 7.99-13.74 and 12.11-12.63 mg kg-1, respectively. Cu and Hg concentrations in sediment were found to be higher than sediment quality guidelines standards. In addition, according to the enrichment factor (EF), Hg, Cd and Cu were found to show above moderate enrichment. Cu, Zn, Fe, Hg, Cd and Mn concentrations in S. aurata muscle tissue were 1.31 ± 2.30, 1.01 ± 0.24, 3.43 ± 0.75, 2.79 ± 0.85, 0.01 ± 0.01 and 1.80 ± 1.12 mg kg-1, respectively. S. europaea heavy metals (HMs) concentrations were determined as Cu = 10.97 ± 3.20, Zn = 0.74 ± 0.62, Fe = 5.69 ± 0.22, Hg = 9.62 ± 8.84, Cd = 0.53 ± 0.33, Pb = 0.22 ± 0.26 and Mn = 8.61 ± 0.14 mg kg-1. It was seen that Hg in S. aurata muscle tissue and Hg in S. europaea concentrations exceeded the limit values determined for consumption purposes. Target hazard quotient (THQ) and total target hazard quotient (TTHQ) values were found to be < 1 for S. aurata and S. europaea. When all these results were considered, it was determined that the metals that could pose a potential ecological and health risk were Hg, Cd and Cu in the study area.

This study is a first attempt to assess the trace element contamination status in the surface sediments of the Hellenic Seas since the first environmental studies in the country commenced in the mid-1970s. All available trace element data from the last 20 years have been collected and assessed using sediment quality guidelines and application of single- and multielement pollution indices. Although Hellenic marine sediments initially appear as anthropogenically enriched in Cr and As, this enrichment is attributed to the natural background. Central Greece appears more polluted, followed by Northern Greece and lastly Southern Greece. The element pollution indices featured the influence of industrial activities such as mining, steel industry and chemical factories, shipyards, and secondarily the influence of port activities.

The Aegean Sea is one of the most contaminated by marine litter (ML) in the World. In this study, the Turkish Aegean Region was evaluated in light of the sources, abundance and composition of ML along Çakalburnu Lagoon coast. Macroscopic ML with > 3 cm was collected and separated into composition and sources categories. ML abundance was calculated by its density in items/m2 (Mean ± SD). Beach cleanliness was evaluated according to Clean-Coast Index (CCI). Seasonality was found as factor for ML composition, sources and abundance at Çakalburnu coast. Plastic was the most abundant material, followed by unidentifiable items. The major sources of ML were mixed packaging, domestic and fisheries activities. The mean ML density was 0.64 ± 0.09 items/m2. Çakalburnu coast was classified as dirty during all seasons. Therefore, ML contamination on Çakalburnu coast represent a potential threat to coastal and marine environments. Thus, the present study can serve as a base for the elaboration of mitigating actions urgently needed at Çakalburnu Lagoon.

The European spiny lobster is a species of great commercial value, yet a limited scientific knowledge exists on its biology, ecology, and physiology, especially for the stocks from east Mediterranean waters. The northern brown shrimp, a non-indigenous established species, is commercially exploited in regions of the Mediterranean Sea. Both species\' proximate composition and fatty acid profile were assessed for the first time in the Mediterranean region, exhibiting an overall significant statistical difference. Protein, fat, and energy contents were significantly higher in the northern brown shrimp, whereas moisture and ash contents were significantly higher in the European spiny lobster. The proximate composition for both species was well within the reported range for other lobster and prawn species in the Mediterranean Sea.

BACKGROUND: The Indo-Pacific sea urchin Diadema setosum has invaded the Mediterranean Sea and has spread along many locations in the southeastern part of the basin, where established populations exist on the shallow subtidal rocky shore. Diadema setosum is a ubiquitous species, of particular ecological importance due to the high levels of grazing pressure it imposes on benthic communities. Its biology, however, is not adequately studied, especially along its introduced range of distribution. The present study examines the population status of D. setosum outside its native range, in the Dodecanese island complex, south Aegean Sea. Thirty-four stations located across 16 islands were surveyed by scientific SCUBA-diving (up to a depth of 10 m) in December 2019 and June-July 2020. Samplings included: (i) visual census along transects to estimate relative abundance and population density, and (ii) random collection of specimens from densely populated stations to assess biometry and reproductive condition (histological examination of gonads) of D. setosum.
RESULTS: Diadema setosum was found in 21 out of the 34 surveyed stations. The species had sparse populations of well-hidden individuals in rocky crevices, but with dense localized patches in Agathonisi, Leros, Kalymnos, Pserimos, Symi, Alimia and Chalki islands. In those seven islands, mean population density was 2.5 ± 1.48 individuals m-2. Diadema setosum had denser populations in shallower depths but larger dimensions in deeper; these results suggest segregated density and size patterns along a depth gradient. The size structure, according to the size frequency distribution of the test diameter, was unimodal with a fitted mode at 4.0-4.5 and 6.5-7.0 cm in shallow and deep populations, respectively. The examined morphometric relationships followed negative allometry, as previously suggested for the species within its native range of distribution, and test diameter appeared to be a good predictor of biomass. Diadema setosum specimens had immature gonads in winter and mature in summer, suggesting a synchronous reproductive pattern. These results conform to previous data from temperate populations of the species.
CONCLUSIONS: Differences in local environmental conditions, e.g. hydrodynamics and habitat type, together with biotic interactions, e.g. recruitment and competition, probably shape D. setosum population in the south Aegean distributional range. The establishment of D. setosum has severe implications on benthic communities and local sea urchin populations demanding management measures to prevent the forecasted further expansion of this invasive species.

On 30 October 2020, a strong normal-faulting earthquake struck Samos Island in Greece and İzmir Province in Turkey, both in the eastern Aegean Sea. The earthquake generated a tsunami that hit the coasts of Samos Island, Greece and İzmir, Turkey. National teams performed two post-tsunami field surveys on 31 October to 1 November 2020, and 4-6 November 2020, along the Turkish coastline; while the former was a quick survey on the days following the tsunami, the latter involved more detailed measurement and investigation focusing on a ~ 110-km-long coastline extending from Alaçatı (Çeşme District of İzmir) to Gümüldür (Menderes District of İzmir). The survey teams measured runup and tsunami heights, flow depths, and inundation distances at more than 120 points at eight different localities. The largest tsunami runup among the surveyed locations was measured as 3.8 m in Akarca at a distance of 91 m from the shoreline. The maximum tsunami height of 2.3 m (with a flow depth of 1.4 m) was observed at Kaleiçi region in Sığacık, where the most severe tsunami damage was observed. There, the maximum runup height was measured as 1.9 m at the northeastern side of the bay. The survey team also investigated tsunami damage to coastal structures, noticing a gradual decrease in the impact from Gümüldür to further southeast. The findings of this field survey provide insights into the coastal impact of local tsunamis in the Aegean Sea.

Aerial and underwater imaging is being widely used for monitoring litter objects found at the sea surface, beaches and seafloor. However, litter monitoring requires a considerable amount of human effort, indicating the need for automatic and cost-effective approaches. Here we present an object detection approach that automatically detects seafloor marine litter in a real-world environment using a Region-based Convolution Neural Network. The neural network is trained on an imagery with 11 manually annotated litter categories and then evaluated on an independent part of the dataset, attaining a mean average precision score of 62%. The presence of other background features in the imagery (e.g., algae, seagrass, scattered boulders) resulted to higher number of predicted litter items compare to the observed ones. The results of the study are encouraging and suggest that deep learning has the potential to become a significant tool for automatically recognizing seafloor litter in surveys, accomplishing continuous and precise litter monitoring.

In this research, sea surface temperature (SST) variability for the summer season in the Aegean Sea was analysed over a period of 30 years by using the Landsat thermal infrared bands. A total of 88 Landsat 5, 7, and 8 thermal infrared satellite images from June, July, and August were used for each year from 1989 to 2019. To estimate SST from the thermal infrared band data, thermal infrared sensor at-sensor spectral radiance, and thermal infrared sensor top-of-atmosphere brightness temperatures were used. SST pixel values were extracted from thermal images for the 3-month summer season of each year. In order to validate the findings, regression analysis was performed between the Mediterranean Sea Ultra High Resolution SST L4 data and Landsat data for the 2008-2019 period. Regression constant R-squared values were found to be 0.9672 for June, 0.9550 for July, 0.9634 for August, and 0.9634 for all summer seasons. It was calculated that the minimum value of the average SST was 18.44 ± 2.87 °C in 1992, and the maximum value of the average SST was 23.45 ± 0.70 °C in 2018. According to the Landsat data, over the past 30 years, the annual average SST changes were estimated to be 0.11 °C, and the total changes of average SST were estimated to be 3.19 ± 1.26 °C. As a result of the analysis and the validation, we can see that there is a rising trend in sea surface temperature in the Aegean Sea. However, it is difficult to determine whether this upward trend is related to global climate change.

Location selection for offshore wind farms is a major challenge for renewable energy policy, marine spatial planning, and environmental conservation. This selection constitutes a multi-criteria decision-making problem, through which parameters like wind velocity, water depth, shorelines, fishing areas, shipping routes, environmental protection areas, transportation, and military zones should be jointly investigated. The aim of the present study was thus to develop an integrated methodology for assessing the siting of bottom-fixed offshore wind farms in two different countries (with different legal, political, and socio/economic characteristics). Our methodology combined multi-criteria decision-making methods and geographical information systems and was implemented in Cyclades (Greece) and in the sea area of İzmir region (Turkey). Experts used fuzzy sets and linguistic terms to achieve more consistent and independent rankings and results. In the Turkish region, the results showed that 519 km2 (10.23%) of the study area is suitable for offshore wind farms, while in the Greek region, only 289 km2 (3.22%) of the study area was found to be suitable. This spatial suitability analysis may contribute to provide some useful recommendations for the spatial marine planning at the regional scale, as well as for the preliminary assessment of new offshore wind farms in both countries.