From ecological functions to ecosystem services: linking coastal lagoons biodiversity with human well-being

Hydrobiologia, Год журнала: 2023, Номер 850(12-13), С. 2611 - 2653

Опубликована: Март 10, 2023

Язык: Английский

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The unpredictability of scyphozoan jellyfish blooms

Frontiers in Marine Science, Год журнала: 2024, Номер 11

Опубликована: Фев. 2, 2024

The study of jellyfish blooms has gained attention in the recent decades because importance forecasting and anticipating them avoiding their interference with human activities. However, after thirty years scientific effort (monitoring systems, empirical laboratory field studies, modeling, etc.), occurrence remains unpredictable, consequences unavoidable. Climate change, eutrophication, overfishing, coastal construction, species translocation have been suggested as stressors that increase them, but robust evidence to support these claims is limited. widespread belief are “increasing number” challenged years. Among gelatinous zooplankton, bloom forming concentrated class Scyphozoa, number at least one recorded increased during last decade. analyses long-term time series show seasonality dynamic each blooming population, vary intensity there an unexplained absence jellyfish. In this review, we focus on current state knowledge, uncertainties gaps critical points can strongly influence or even lead medusa population. These include ephyrae, planulae scyphistoma natural, predatory fishing mortality, molecular pathway strobilation, benthic population dynamics, planula settlement ephyra transition success. Some account for certain under controlled conditions, difficult be studied field, different sources non-typically variability need addressed improve our understanding dynamics.

Язык: Английский

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Past and Future Grand Challenges in Marine Ecosystem Ecology

Frontiers in Marine Science, Год журнала: 2020, Номер 7

Опубликована: Июнь 3, 2020

SPECIALTY GRAND CHALLENGE article Front. Mar. Sci., 03 June 2020 | https://doi.org/10.3389/fmars.2020.00362

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A new approach to monitor water quality in the Menor sea (Spain) using satellite data and machine learning methods

Environmental Pollution, Год журнала: 2021, Номер 286, С. 117489 - 117489

Опубликована: Май 31, 2021

Язык: Английский

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Data-driven modelling for assessing trophic status in marine ecosystems using machine learning approaches

Environmental Research, Год журнала: 2023, Номер 242, С. 117755 - 117755

Опубликована: Ноя. 25, 2023

Assessing eutrophication in coastal and transitional waters is of utmost importance, yet existing Trophic Status Index (TSI) models face challenges like multicollinearity, data redundancy, inappropriate aggregation methods, complex classification schemes. To tackle these issues, we developed a novel tool that harnesses machine learning (ML) artificial intelligence (AI), enhancing the reliability accuracy trophic status assessments. Our research introduces an improved data-driven methodology specifically tailored for (TrC) waters, with focus on Cork Harbour, Ireland, as case study. innovative approach, named Assessment (ATSI) model, comprises three main components: selection pertinent water quality indicators, computation ATSI scores, implementation new scheme. optimize input minimize employed ML techniques, including advanced deep methods. Specifically, CHL prediction model utilizing ten algorithms, among which XGBoost demonstrated exceptional performance, showcasing minimal errors during both training (RMSE = 0.0, MSE MAE 0.01) testing phases. Utilizing linear rescaling interpolation function, calculated scores evaluated model's sensitivity efficiency across diverse application domains, employing metrics such R2, Nash-Sutcliffe (NSE), factor (MEF). The results consistently revealed heightened all domains. Additionally, introduced brand scheme ranking waters. assess spatial sensitivity, applied to four distinct waterbodies comparing assessment outcomes Estuaries Bays Ireland (ATSEBI) System. Remarkably, significant disparities between ATSEBI System were evident except Mulroy Bay. Overall, our significantly enhances assessments marine ecosystems. combined cutting-edge techniques scheme, represents promising avenue evaluating monitoring conditions TrC study also effectiveness assessing various waterbodies, lakes, rivers, more. These findings make substantial contributions field ecosystem management conservation.

Язык: Английский

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Extreme eutrophication and salinisation in the Coorong estuarine-lagoon ecosystem of Australia's largest river basin (Murray-Darling)

Marine Pollution Bulletin, Год журнала: 2023, Номер 188, С. 114648 - 114648

Опубликована: Янв. 31, 2023

Estuaries in rainfall poor regions are highly susceptible to climatic and hydrological changes. The Coorong, a Ramsar-listed estuarine-coastal lagoon at the end of Murray-Darling Basin (Australia), has experienced declining ecological health over recent decades. Twenty years environmental data were analysed assess patterns drivers water quality Large areas Coorong now persistently hyper-saline (salinity >80 psu) hypereutrophic (total nitrogen, TN > 4 mg L-1, total phosphorus, TP 0.2 chlorophyll 50 μg L-1) which coincided with reduced flushing due diminished freshwater inflows increasing evapo-concentration. Sediment also was related flushing, higher concentrations organic carbon, TN, sulfides as salinity increased. While nutrient levels very high, dissolved inorganic nutrients generally low. Increased lagoonal would be beneficial reduce hypersalinisation hypereutrophication improve ecosystem health.

Язык: Английский

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Using Machine-Learning Algorithms for Eutrophication Modeling: Case Study of Mar Menor Lagoon (Spain)

International Journal of Environmental Research and Public Health, Год журнала: 2020, Номер 17(4), С. 1189 - 1189

Опубликована: Фев. 13, 2020

The Mar Menor is a hypersaline coastal lagoon with high environmental value and characteristic example of highly anthropized hydro-ecosystem located in the southeast Spain. An unprecedented eutrophication crisis 2016 2019 abrupt changes quality its waters caused great social alarm. Understanding modeling level indicator, such as chlorophyll-a (Chl-a), benefits management this complex system. In study, we investigate potential machine learning (ML) methods to predict Chl-a. Particularly, Multilayer Neural Networks (MLNNs) Support Vector Regressions (SVRs) are evaluated using target dataset information up nine different water parameters. most relevant input combinations were extracted wrapper feature selection which simplified structure model, resulting more accurate efficient procedure. Although performance validation phase showed that SVR models obtained better results than MLNNs, experimental indicated both ML algorithms provide satisfactory prediction Chl-a concentration, reaching 0.7 R2CV (cross-validated coefficient determination) for best-fit models.

Язык: Английский

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Effects of Nutrient Management Scenarios on Marine Food Webs: A Pan-European Assessment in Support of the Marine Strategy Framework Directive

Frontiers in Marine Science, Год журнала: 2021, Номер 8

Опубликована: Март 23, 2021

Eutrophication is one of the most important anthropogenic pressures impacting coastal seas. In Europe, several legislations and management measures have been implemented to halt nutrient overloading in marine ecosystems. This study evaluates impact freshwater control on higher trophic levels (HTL) European ecosystems following descriptors criteria as defined by Marine Strategy Framework Directive (MSFD). We used a novel pan-European modeling ensemble fourteen HTL models, covering almost all EU seas, under two scenarios. Results from our projections suggest that proposed reduction may not significant structure function Among assessed criteria, spawning stock biomass commercially fish stocks small pelagic fishes would be impacted, albeit with values lower than 2.5%. For other criteria/indicators, such species diversity level indicators, was lower. The Black Sea North-East Atlantic were negatively impacted regions, while Baltic only region showing signs improvement. Coastal shelf areas more sensitive environmental changes large regional sub-regional also include open first multi-model comparison assess impacts land-based through set selected ecological indicators. Since are expanding apace environment policy makers need use rapid effective for fast-changing environments, this framework an essential asset supporting guiding needs decisions.

Язык: Английский

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Use of the Sentinel-2 and Landsat-8 Satellites for Water Quality Monitoring: An Early Warning Tool in the Mar Menor Coastal Lagoon

Remote Sensing, Год журнала: 2022, Номер 14(12), С. 2744 - 2744

Опубликована: Июнь 7, 2022

During recent years, several eutrophication processes and subsequent environmental crises have occurred in Mar Menor, the largest hypersaline coastal lagoon Western Mediterranean Sea. In this study, Landsat-8 Sentinel-2 satellites are jointly used to examine evolution of main water quality descriptors during latest ecological crisis 2021, resulting an important loss benthic vegetation unusual mortality events affecting different aquatic species. Several field campaigns were carried out March, July, August, November 2021 measure variables over 10 control points. The validation satellite biogeochemical against on-site measurements indicates precise results algorithms with median errors 0.41 mg/m3 2.04 FNU for chlorophyll-a turbidity, respectively. preprocessing scheme shows consistent performance both satellites; therefore, using them tandem can improve mapping strategies. findings demonstrate suitability methodology capture spatiotemporal distribution turbidity concentration at 10–30 m spatial resolution on a systematic basis cost-effective way. multitemporal products allow identification critical areas close mouth Albujon watercourse beginning process above 3 mg/m3. These innovative tools support decision makers improving current monitoring strategies as early warning systems timely assistance these disasters, thus preventing detrimental conditions lagoon.

Язык: Английский

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A holistic approach for determining the hydrology of the mar menor coastal lagoon by combining hydrological & hydrodynamic models

Journal of Hydrology, Год журнала: 2021, Номер 603, С. 127150 - 127150

Опубликована: Ноя. 6, 2021

A combination of hydrological and hydrodynamic modelling can be applied to understand the hydrology key water balance components lakes lagoons. In this research, Soil Water Assessment Tool (SWAT) model QGIS Ecosystems (QWET) were for Mar Menor coastal lagoon its watershed known as Campo de Cartagena. First, SWAT was calibrated validated based on remote sensing evapotranspiration data. Results showed an acceptable performance in both calibration (R2 = 0.63, NSE 0.62, PBIAS 2.91%) validation 0.68, 2.47%) periods a monthly basis. The simulated streamflow fed into QWET simulate lagoon. evaluated comparison between observed temperatures also estimated evaporation. Simulated daily good agreement with data by capturing timing inter-annual variations, 0.98, BIAS 2.7%. Our estimation, using reference period 2003–2019, yields mean annual rainfall over lake 301 mm evaporation 1325 mm. average surface runoff groundwater discharge are 49 hm3/year 11 hm3/year, respectively. Extreme storm events cause vary 8 202 hm3/year. closed exchange Mediterranean Sea, resulting overall positive flow from Sea 82 study that during summer months, particular, there is considerable inflow lagoon, whereas some autumn winter months (November, December January) net outflow Mediterranean. This novel approach combining complex provides useful tool understanding may play role decision makers when developing strategies mitigating eutrophication.

Язык: Английский

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