Variations in copepod composition and diversity in relation to eutrophication and hydrology in İzmir Bay, Aegean Sea

Hyrographic and trophic gradient along Izmir Bay used to assess their effects on the copepod composition and diversity. Trophic state of the bay varied from oligotrophic in the outer region to hypertrophic condition in the inner region as stated by trophic index (TRIX). Aegean Sea water entrance and trophic conditions of the bay affected species assemblages. Oithona davisae, Oithona nana and Acartia clausi were associated with hyper-eutrophic conditions whereas the typical Aegean Sea species, such as Oncaea media group, Oithona similis, Paracalanus parvus, A. clausi, Farranula rostrata and Calocalanus styliremis were related to oligotrophic condition. Copepod diversity indices decreased linearly along the trophic gradient, from oligotrophic to hyper-eutrophic conditions, highlighting the substantial impact of eutrophication on the copepod community structure in the inner region. These findings emphasize TRIX's value in assessing eutrophication in anthropized systems, and immense potential for using copepod composition and diversity in monitoring programs for ecological assessments studies.

Use of Sentinel-3 OLCI Images and Machine Learning to Assess the Ecological Quality of Italian Coastal Waters

Understanding and monitoring the ecological quality of coastal waters is crucial for preserving marine ecosystems. Eutrophication is one of the major problems affecting the ecological state of coastal marine waters. For this reason, the control of the trophic conditions of aquatic ecosystems is needed for the evaluation of their ecological quality. This study leverages space-based Sentinel-3 Ocean and Land Color Instrument imagery (OLCI) to assess the ecological quality of Mediterranean coastal waters using the Trophic Index (TRIX) key indicator. In particular, we explore the feasibility of coupling remote sensing and machine learning techniques to estimate the TRIX levels in the Ligurian, Tyrrhenian, and Ionian coastal regions of Italy. Our research reveals distinct geographical patterns in TRIX values across the study area, with some regions exhibiting eutrophic conditions near estuaries and others showing oligotrophic characteristics. We employ the Random Forest Regression algorithm, optimizing calibration parameters to predict TRIX levels. Feature importance analysis highlights the significance of latitude, longitude, and specific spectral bands in TRIX prediction. A final statistical assessment validates our model's performance, demonstrating a moderate level of error (MAE of 0.51) and explanatory power (R2 of 0.37). These results highlight the potential of Sentinel-3 OLCI imagery in assessing ecological quality, contributing to our understanding of coastal water ecology. They also underscore the importance of merging remote sensing and machine learning in environmental monitoring and management. Future research should refine methodologies and expand datasets to enhance TRIX monitoring capabilities from space.

Evaluation of the water quality and the eutrophication risk in Ramsar site on Moroccan northern Mediterranean (Marchica lagoon): A multivariate statistical approach

This study aimed to assess the extent of eutrophication in the Marchica lagoon, using the trophic index (TRIX), trophic status index (TSI) and Redfield ratio (N/P). Seawater samples were collected monthly from March 2018 to February 2019 from ten locations in the lagoon. The TRIX index ranged from 1.25 to 8.87, indicating oligotrophic to eutrophic conditions. TSI values ranged from 28.4 to 65.1, indicating ultraoligotrophic to supereutrophic seawater. The Redfield ratio consistently showed N/P values below 16, designating phosphorus as the limiting factor for algal growth. Principal component analysis (PCA) and cluster analysis (CA) revealed distinct coastal water masses based on seasons and sampling regions. Given these findings, urgent conservation and management measures are recommended to address the eutrophication issues threatening the delicate balance of the Marchica lagoon.

Water masses seasonality and meteorological patterns drive the biogeochemical processes of a subtropical and urbanized watershed-bay-shelf continuum

Understanding the different scales of temporal variability is crucial to improve the knowledge of the biogeochemical processes in the land-ocean interface. In this study, we evaluated the role of continental runoff and intrusion of oceanic water masses in the trophic state of the Bay of Santa Catarina Island (BSCI) over the last three decades (1993-2019) by using multiple biogeochemical and eutrophication assessment tools. The sub-watersheds of BSCI showed high concentrations of nutrients, fecal coliform and chlorophyll-a, directly correlated to the number of inhabitants. Worst-case scenarios were found in summer and fall seasons due to sewage inputs caused by mass tourism and the inefficiency or even absence of treatment systems, boosted by strong rainfall. The intrusion of the South Atlantic Central Water and the Plata Plume Water into the BSCI favored autotrophy in the summer and heterotrophy in the winter, coupled with low and high residence time, respectively. El Niño events enhanced rainfall and continental runoff, exporting elevated nutrients and phytoplankton biomass loads from the eutrophic rivers to the continental shelf. The pattern reverses during La Niña, when chlorophyll and nutrient peaks were detected inside the bay. Eutrophication evaluation indicated that the trophic state oscillated from moderate to high and that these conditions tend to remain the same in future scenarios due to the moderate residence time of the water, anthropogenic pressures, periodic algal blooms and the intrusion of nutrient-rich oceanic water masses. Management actions, such as the improvement of the wastewater treatment system and wetlands restoration, are needed in order to mitigate eutrophication and the loss of ecosystem services and functions.

Hybrid modeling approach for the northern Adriatic watershed management

Northern Adriatic (NA) is one of the most productive parts of the Mediterranean Sea due to vast nutrient discharges from the contributing watershed. To understand better the excess of nutrients as stressors to the state of the marine ecosystem, a hybrid modeling approach following the DPSIR framework and terminology was developed, linking: 1) the AVGWLF model for modeling the pressures, i.e. nutrients originating from the watershed caused by two major drivers (urbanization and agriculture), 2) the ML tool MTSMOTI for inducing a model tree connecting the pressures with the marine ecosystem state, and 3) the water quality index, TRIX, equation to evaluate the trophic state of the marine ecosystem. Data used for the modeling purpose comprised GIS layers (i.e., digital terrain model, land use/cover data, soil map, locations of hydro-meteorological stations and WWTPs), time series data (i.e., hydro-meteorological data and nutrient concentrations), and statistical data (i.e., number of inhabitants, connections to wastewater treatment, livestock statistics, etc.) as well as physical, chemical and biological parameters, measured at six marine water monitoring stations, located between the Po River delta (Italy) and the city of Rovinj (west Istrian coast, Croatia). Using the model, seven watershed management scenarios related to wastewater treatment and agricultural activities were evaluated for their influence on the state of the NA marine ecosystem. According to the results, the gradual implementation of the UWWTD in the last 10years contributed significantly to the preservation and improvement of the NA marine ecosystem state. However, despite the full implementation of the UWWTD, the state of the NA marine ecosystem could deteriorate in case of increased nutrient loads from agriculture. Since the UWWTD is already close to its full implementation, NA watershed management should focus on controlling agricultural activities in order to maintain 'high' state of the NA marine ecosystem.

Phytoplankton community structure in relation to environmental factors from the New Mangalore Port waters along the southwest coast of India

Seasonal and spatial phytoplankton distribution in relation to environmental factors was investigated in New Mangalore Port, a major port along the west coast of India. A well-mixed water column characterized the non-monsoon seasons, whereas it was weakly stratified during monsoon. Water quality index (TRIX) scores indicated good water quality except during pre-monsoon (inner zone surface) and monsoon (near bottom waters). Surface abundance of tychopelagic diatoms (Paralia sulcata, Melosira nummuloides, Cylindrotheca closterium, and Nitzschia sigma) was higher during non-monsoon seasons. Certain centric diatoms, e.g., Leptocylindrus danicus, P. sulcata, and Rhizosolenia imbricata, dominated during pre-monsoon (inner zone) and positively correlated with TRIX. High Skeletonema costatum and dinoflagellate abundance during the monsoon season coincided with high nutrient concentrations. Five potential toxic and fourteen harmful/bloom forming algal species were encountered at abundances below the level that can be considered as harmful to the ecosystem. In addition to a baseline database, this study highlights the potential use of certain diatom species as indicators of hydrography and water quality for monitoring dynamic coastal marine ecosystems.

Real-time eutrophication status evaluation of coastal waters using support vector machine with grid search algorithm

The development of techniques for real-time monitoring of the eutrophication status of coastal waters is of great importance for realizing potential cost savings in coastal monitoring programs and providing timely advice for marine health management. In this study, a GS optimized SVM was proposed to model relationships between 6 easily measured parameters (DO, Chl-a, C1, C2, C3 and C4) and the TRIX index for rapidly assessing marine eutrophication states of coastal waters. The good predictive performance of the developed method was indicated by the R² between the measured and predicted values (0.92 for the training dataset and 0.91 for the validation dataset) at a 95% confidence level. The classification accuracy of the eutrophication status was 86.5% for the training dataset and 85.6% for the validation dataset. The results indicated that it is feasible to develop an SVM technique for timely evaluation of the eutrophication status by easily measured parameters.