Search Results (137)

The effect of periodical heatwaves and related thermal stratification in freshwater aquatic ecosystems has been a hot research issue. A large dataset of samples was generated from samples exposed to temporary thermal stratification in mesocosms mimicking shallow eutrophic freshwater lakes. Temperature regimes were based on IPCC climate warming scenarios, enabling simulation of future warming conditions. Surface oxygen levels reached 19.37 mg/L, while bottom layers dropped to 0.07 mg/L during stratification. Analysis by FlowCAM revealed dominance of Cyanobacteria under ambient conditions (up to 99.2%), while Cryptophyta (up to 98.9%) and Chlorophyta (up to 99.9%) were predominant in the A2 and A2+50% climate scenarios, respectively. We identified temperature changes and shifts in nutrient concentrations, particularly phosphate, as critical factors in microbial community composition. Furthermore, five distinct Microcystis morphospecies identified by FlowCAM-based analysis were associated with different microbial clusters. The combined use of imaging flow cytometry, which differentiates phytoplankton based on morphological parameters, and nanopore long-read sequencing analysis has shed light into the dynamics of microbial communities associated with different Microcystis morphospecies. In our observations, a peak of algicidal bacteria abundance often coincides with or is followed by a decline in the Cyanobacteria. These findings highlight the importance of species-level classification in the analysis of complex ecosystem interactions and the dynamics of algal blooms in freshwater bodies in response to anthropogenic effects and climate change. Full article

This study presents the living coccolithophore communities and the morphological variability of Emiliania huxleyi in the South Aegean Sea from three sampling regions during winter-early spring (March 2017, March 2019) and summer (August 2019). Emphasis is given to March 2017 to monitor the variations in coccolithophore assemblages after an exceptionally cold event in December 2016, which resulted in newly produced dense waters that ventilated the Aegean deep basins. The assemblages displayed distinct seasonality with the predominance of E. huxleyi and Syracosphaera molischii during winter-early spring, associated with the water column mixing. By contrast, summer assemblages were featured by holococcolithophores and typical taxa of warm, oligotrophic upper waters. It seems that the phytoplanktonic succession as well as the nutrient supply to the upper euphotic layers were affected by the water column perturbation during the extreme winter of 2016–2017, which led to strong convective mixing and dense water formation. The decreased coccosphere densities during March 2017, accompanied by the notable presence of diatoms, were most probably associated with a prolonged diatom bloom, causing delay in the development of the coccolithophore community and resulting in a nitrogen-limited setting. Emiliania huxleyi morphometry showed the characteristic seasonal calcification trend of the Aegean, with the dominance of smaller coccoliths in the summer and increased coccolith length and width during the cold season. The intense cold conditions and wind-induced mixing during the winter of 2016–2017 possibly increased the absorption of atmospheric CO₂ in surface waters, causing increased acidity and the subsequent presence of etched/undercalcified E. huxleyi coccoliths and other taxa, most probably implying in situ calcite dissolution. Full article

Since the mid-1980s, the Mediterranean Sea’s surface and deeper layers have warmed at unprecedented rates, with recent projections identifying it as one of the regions most impacted by rising global temperatures. Metrics that characterize phytoplankton abundance, phenology and size structure are widely utilized as ecological indicators that enable a quantitative assessment of the status of marine ecosystems in response to environmental change. Here, using an extensive, updated in situ pigment dataset collated from numerous past research campaigns across the Mediterranean Sea, we re-parameterized an abundance-based phytoplankton size class model that infers Chl-a concentration in three phytoplankton size classes: pico- (<2 μm), nano- (2–20 μm) and micro-phytoplankton (>20 μm). Following recent advancements made within this category of size class models, we also incorporated information of sea surface temperature (SST) into the model parameterization. By tying model parameters to SST, the performance of the re-parameterized model was improved based on comparisons with concurrent, independent in situ measurements. Similarly, the application of the model to remotely sensed ocean color observations revealed strong agreement between satellite-derived estimates of phytoplankton size structure and in situ observations, with a performance comparable to the current regional operational datasets on size structure. The proposed conceptual regional model, parameterized with the most extended in situ pigment dataset available to date for the area, serves as a suitable foundation for long-term (1997–present) analyses on phytoplankton size structure and ecological indicators (i.e., phenology), ultimately linking higher trophic level responses to a changing Mediterranean Sea. Full article

Harmful algal blooms (HABs), driven by environmental pollution, pose significant threats to water quality, public health, and aquatic ecosystems. This study enhances the prediction of HABs in Lake Erie, part of the Great Lakes system, by utilizing ensemble machine learning (ML) models coupled with explainable artificial intelligence (XAI) for interpretability. Using water quality data from 2013 to 2020, various physical, chemical, and biological parameters were analyzed to predict chlorophyll-a (Chl-a) concentrations, which are a commonly used indicator of phytoplankton biomass and a proxy for algal blooms. This study employed multiple ensemble ML models, including random forest (RF), deep forest (DF), gradient boosting (GB), and XGBoost, and compared their performance against individual models, such as support vector machine (SVM), decision tree (DT), and multi-layer perceptron (MLP). The findings revealed that the ensemble models, particularly XGBoost and deep forest (DF), achieved superior predictive accuracy, with R² values of 0.8517 and 0.8544, respectively. The application of SHapley Additive exPlanations (SHAPs) provided insights into the relative importance of the input features, identifying the particulate organic nitrogen (PON), particulate organic carbon (POC), and total phosphorus (TP) as the critical factors influencing the Chl-a concentrations. This research demonstrates the effectiveness of ensemble ML models for achieving high predictive accuracy, while the integration of XAI enhances model interpretability. The results support the development of proactive water quality management strategies and highlight the potential of advanced ML techniques for environmental monitoring. Full article

The present dataset served to evaluate the plankton community composition and abundance in Nuevo Gulf (42°42′ S, 64°30′ W), a World Heritage Site in Argentinian Patagonia and part of the Valdés Biosphere Reserve. It reports zooplankton abundance (>300 µm) and phytoplankton concentration (10–200 μm) during the spring and summer seasons from 2019 to 2021. Special attention was given to the taxonomic classification of zooplankton, leading to the first identification of jellyfish species within the Gulf and the detection of an unreported copepod for the area (Drepanopus forcipatus). Samples were collected at two depths—a surface and a deeper layer—to assess vertical distribution patterns of plankton communities and explore potential environmental drivers influencing their variability. This dataset provides a valuable baseline for future studies analyzing temporal variations in the Gulf’s plankton communities. Moreover, it encourages the local scientific community to contribute data and promote open access to marine biodiversity records in the region. Full article

In eutrophic aquatic ecosystems like the Archipelago Sea in the northern Baltic, the role of the sediment as a sink and source of nutrients is especially important. Based on previous research on the Archipelago Sea, it can be concluded that the amount of stored phosphorus that can be released with time from sediments is large, and internal phosphorus recycling processes may thus play a key role in phosphorus fluxes in the coastal zone. The release of nutrients from the sediment has been suggested as an explanation for the fact that a substantial reduction in the external nutrient load does not always result in a corresponding reduction in nutrient concentrations and phytoplankton biomass in recipient waters. However, the magnitude of the actual internal phosphorus load in the Archipelago Sea has not been successfully estimated, or the estimates have been evidently too high. In this study, calculations were performed based on the measured water quality data to estimate how much phosphorus has been transported from the bottom water layer to the surface layers during the biological production season for use in algal production. In other words, the magnitude of the effective internal phosphorus load in the Archipelago Sea was determined. The calculations resulted in a summer internal net phosphorus load in the surface layer of approximately 270 tons over 5 months. The other total phosphorus load in the Archipelago Sea, which mainly (60%) originates from the catchment area, was 575 t/a. A permanent way to mitigate internal loading has been thought to be to reduce external loading. However, a decrease in internal loading occurs with an unknown delay, making it impossible to predict the recovery rate. Full article

To examine the bio-optical response to coastal upwelling, we measured inherent optical properties (IOPs) and biogeochemical parameters simultaneously off Hainan Island in the northwest part of the South China Sea (SCS) during late summer 2013. Bio-optical relationships between IOPs and phytoplankton were used for calculating vertical profiles of the total chlorophyll a concentration (Chl-a) and the absorption by coloured detrital matter (CDM). These bio-optical properties, which showed distinct horizontal and vertical distributions across the continental shelf, were strongly influenced by upwelling processes, as well as the shelf topography. Phytoplankton biomass and CDM absorption in surface waters showed much higher values along the coast, with their spatial distributions related to topographic variability. Vertical distributions of phytoplankton were characterised by a subsurface chlorophyll maximum (SCM) layer. The strongest SCM (Chl-a = 4.22 mg m⁻³) was observed at 24 m depth in coastal waters near the northeast cape of Hainan Island. The depth of the SCM varied between 16 and 60 m at different stations, appearing to coincide with the isotherm of 22 °C. The SCM depth was inversely correlated with the magnitude of the SCM. Different shapes of Chl-a profiles were observed, which suggested that the vertical distributions of phytoplankton biomass were driven by different environmental factors. Elevated concentrations of CDM were mainly observed near the bottom, which suggest that the benthic nepheloid layer may be an important source of detrital material. The relationship between the absorption coefficient of CDM at 443 nm, a_CDM(443), and Chl-a exhibited distinct differences between waters in upper ocean and in bottom layers, with the threshold depth being modulated by shelf topography. Our results highlight the utility of bio-optical observations with high resolution for better understanding the coupling between physical forcing and biogeochemical variability. Full article

Lidar technology is increasingly applied to the inversion of oceanic biological parameters and optical properties based on empirical and semi-empirical bio-optical models. However, these models cannot be directly applied to inland waters due to their complex composition, and research on the biological parameters and optical properties of inland waters remains limited. In this study, the Fernald method was employed to retrieve the vertical distribution of optical properties in Qiandao Lake for the first time using shipborne lidar data obtained in June 2019. By quantifying the depth-resolved optical contributions of biological components, the vertical distributions of chlorophyll-a concentration were mapped with greater precision. The lidar-estimated optical properties exhibited characteristic spatiotemporal distributions, which were closely related to water quality. At the inflow of Xin’an River, the attenuation and scattering coefficient showed a gradual increase with depth. At the north–south-oriented reservoir area and the outflow of Qiandao Lake, an apparently continuous subsurface layer with the maximum signal occurred at approximately 3.5 m. The vertical distributions of chlorophyll-a profiles were consistently classified as subsurface chlorophyll maxima, with the maximum value of chlorophyll-a concentration fluctuating between 4 and 12 μg/L. The subsurface phytoplankton layer was observed at water depths ranging from 1.5 to 3.5 m, with a thickness of 3 to 6 m. Furthermore, the influences of lidar ratio S_p(z) and reference value b_bp(z_m) were discussed as significant sources of inversion error in the Fernald method. These results indicate that lidar technology holds great potential for the long-term monitoring of lakes. Full article

Estuaries are dynamic and resource-rich ecosystems renowned for their high productivity and ecological significance. The Rías Baixas, located in the northwest of the Iberian Peninsula, consist of four highly productive estuaries that support the region’s economy through key fisheries and aquaculture activities. Numerical modeling of biogeochemical processes in the rias is essential to address environmental and anthropogenic pressures, particularly in areas facing intense human development. This study presents a high-resolution water quality model developed using Delft3D 4 software, integrating the hydrodynamic (Delft3D-FLOW) and water quality (Delft3D-WAQ) modules. Calibration and validation demonstrate the robust performance and reliability of the model in simulating critical biogeochemical processes, such as nutrient cycling and phytoplankton dynamics. The model effectively captures seasonal and spatial variations in water quality parameters, including water temperature, salinity, inorganic nutrients, dissolved oxygen, and chlorophyll-a. Of the variables studied, the model performed best for dissolved oxygen, followed by nitrates, phosphates, ammonium, silicate, and chlorophyll-a. While some discrepancies were observed in the inner zones and deeper layers of the rias, the overall performance metrics aligned closely with the observed data, enhancing confidence in the model’s utility for future research and resource management. These results highlight the model’s value as a tool for research and managing water and marine resources in the Rías Baixas. Full article

Diarrhetic shellfish poisoning (DSP) toxins and pectenotoxins (PTXs) produced by endemic species of Dinophysis, mainly D. acuta and D. acuminata, threaten public health and negatively impact the shellfish industry worldwide. Despite their socioeconomic impact, research on the environmental drivers of DSP outbreaks in the Chilean fjords is scanty. From 22 to 24 March 2017, high spatial–temporal resolution measurements taken in Puyuhuapi Fjord (Northern Patagonia) illustrated the short-term (hours, days) response of the main phytoplankton functional groups (diatoms and dinoflagellates including toxic Dinophysis species) to changes in water column structure. Results presented here highlight the almost instantaneous coupling between time–depth variation in density gradients, vertical shifts of the subsurface chlorophyll maximum, and its evolution to a buoyancy-driven thin layer (TL) of diatoms just below the pycnocline the first day. A second shallower TL of dinoflagellates, including Dinophysis acuta, was formed on the second day in a low-turbulence lens in the upper part of the pycnocline, co-occurring with the TL of diatoms. Estimates of in situ division rates of Dinophysis showed a moderate growth maximum, which did not coincide with the cell density max. This suggests that increased cell numbers resulted from cell entrainment of off-fjord populations combined with in situ growth. Toxin profiles of the net tow analyses mirrored the dominance of D. acuminata/D. acuta at the beginning/end of the sampling period. This paper provides information about biophysical interactions of phytoplankton, with a focus on Dinophysis species in a strongly stratified Patagonian fjord. Understanding these interactions is crucial to improv predictive models and early warning systems for toxic HABs in stratified systems. Full article

The diel vertical variations of chlorophyll a (Chl-a) concentration are thought of primarily as an external manifestation of regulating phytoplankton’s biomass, which is essential for dynamically estimating the biogeochemical cycle in inland waters. However, information on these variations is limited due to insufficient measurements. Undersampled observations lead to delayed responses in phytoplankton assessment, impacting accurate evaluations of carbon export and water quality in dynamic inland waters. Here, we report the first lidar-observed diel vertical variations of inland Chl-a concentration. Strong agreement with r² of 0.83 and a root mean square relative difference (RMSRD) of 9.0% between the lidar-retrieved and in situ measured Chl-a concentration verified the feasibility of the Mie–fluorescence–Raman lidar (MFRL). An experiment conducted at a fixed observatory demonstrated the lidar-observed diel Chl-a concentration variations. The results showed that diel variations of Chl-a and the formation of subsurface phytoplankton layers were driven by light availability and variations in water temperature. Furthermore, the facilitation from solar radiation-regulated water temperature on the phytoplankton growth rate was revealed by the high correlation between water temperature and Chl-a concentration anomalies. Lidar technology is expected to provide new insights into continuous three-dimension observations and be of great importance in dynamic inland water ecosystems. Full article

The modern Arctic is characterized by a decreased ice cover and significant interannual variability. However, the reaction of the High Arctic ecosystem to such changes is still being determined. This study tested the hypothesis that the key drivers of changes in phytoplankton are the position and intensity of Atlantic water (AW) flow. The research was conducted in August 2017 in the northern part of the Barents Sea and in August 2020 in the Nansen Basin. In 2017, the Nansen Basin was ice covered; in 2020, the Nansen Basin had open water up to 83° N. A comparative analysis of phytoplankton composition, dominant species, abundance, and biomass at the boundary of the ice and open water in the marginal ice zone (MIZ) as well as in the open water was carried out. The total biomass of the phytoplankton in the photic layer of MIZ is one and a half orders of magnitude greater than in open water. In 2017, the maximum abundance and biomass of phytoplankton in the MIZ were formed by cold-water diatoms Thalassiosira spp. (T. gravida, T. rotula, T. hyalina, T. nordenskioeldii), associated with first-year ice. They were confined to the northern shelf of the Barents Sea. The large diatom Porosira glacialis grew intensively in the MIZ of the Nansen Basin under the influence of Atlantic waters. A seasonal thermocline, above which the concentrations of silicon and nitrogen were close to zero, and deep maxima of phytoplankton abundance and biomass were recorded in the open water. Atlantic species—haptophyte Phaeocystis pouchettii and large diatom Eucampia groenlandica—formed these maxima. P. pouchettii were observed in the Nansen Basin in the Atlantic water (AW) flow (2020); E. groenlandica demonstrated a high biomass (4848 mg m⁻³, 179.5 mg C m⁻³) in the Franz Victoria trench (2017). Such high biomass of this species in the northern Barents Sea shelf has not been observed before. The variability of the phytoplankton composition and biomass in the Franz Victoria trench and in the Nansen Basin is related to the intensity of the AW, which comes from the Frame Strait as the Atlantic Water Boundary Current. Full article

The perception of the importance of ciliate in freshwater has changed dramatically since the “microbial loop” conceptualisation, reflecting methodological attempts. The data from two decades (1994–2018) on the surface (0–3 m) ciliate assemblage in the Slapy reservoir (Vltava River, Czech Republic) during two different nutrient-load defined periods were analysed. We grouped the identified, quantified, and biomass-evaluated ciliates in the quantitative protargol-impregnated preparations according to their feeding behaviour. The sampling median and interquartile range data of the ciliates were plotted; the modelled water age, nutrients, bacteria, heterotrophic nanoflagellates, and Rhodomonas spp. were applied as the main explanatory background variables. We validated the differences between the periods, engaging multivariate analyses. The picoplankton-filtering species dominated the assemblages in an annual mean (halteriids and minute strobilidiids followed by peritrichs). Algae hunting urotrichs, Balanion planctonicum, and nanoplankton filtering tintinnids were significant before the spring phytoplankton peak when a maximum of ciliate biomass reflected mixotrophic nanoplankton filtering pelagostrombidiids. Only there did ciliate biomass tightly follow their quantified prey. Heterotrophic and mixotrophic Askenasia and Lagynophrya were typical raptorial/flagellate-hunting cilates; only Mesodinium spp. reached the maximum during autumn. The observed oligotrophication of the reservoir increased the ciliate assemblage biomass in the surface layer during stratification in concordance with the Plankton Ecology Group (PEG) model. Full article

Many studies have consistently demonstrated that the near-surface phytoplankton chlorophyll (Chl) levels in anticyclonic eddies (AEs) are higher than in cyclonic eddies (CEs) in the South Pacific Ocean (SPO), using remote sensing data, which is attributed to higher phytoplankton biomass or physiological adjustments in AEs. However, the characteristics of the Chl profile induced by mesoscale eddies and their underlying dynamic mechanism have not been comprehensively studied by means of field measurement, and the influence mechanism of environmental factors at different depths on Chl has not been investigated. To fill this gap, we utilized Biogeochemical-Argo (BGC-Argo) data to investigate the relationships between Chl concentration and environmental factors at different water layers and the underlying dynamic mechanisms of mesoscale eddies in the SPO. Our findings indicate that the same environmental factor can have different effects on Chl at different depths. Within a mixed layer (ML), the elevated Chl levels in AEs result from both physiological adjustments and increased phytoplankton biomass, and the former plays a more dominant role, which is induced by enhanced nutrient availability and weakened light, due to the deepening ML in AEs. At depths ranging from 50 m to 110 m, and between 110 m and 150 m (near the depth of pycnocline or the bottom of the euphotic zone), the dominant factor contributing to higher Chl levels in CEs is phytoplankton physiological adaptation driven by reduced temperature and light. At depths exceeding 150 m (beyond the euphotic zone), higher Chl in AEs is primarily caused by high phytoplankton biomass as a result of downwelling by eddy pumping. This work should advance our comprehensive understanding of the physical–biological interactions of mesoscale eddies and their impacts on primary productivity throughout the water column, and it should provide some implications for understanding the biogeochemical processes. Full article

Due to the external environment and the buoyancy of cyanobacteria, the inhomogeneous vertical distribution of phytoplankton in eutrophic lakes affects remote sensing reflectance (R_rs) and the inversion of surface chlorophyll-a concentration (Chla). In this study, vertical profiles of Chla(z) (where z is the water depth) and field R_rs (R_rs_F) were collected and utilized to retrieve the vertical profiles of Chla in Lake Chaohu in China. Chla(z) was categorized into vertically uniform (Type 1: N = 166) and vertically non-uniform (Type 2: N = 58) types. Based on the validation of the atmospheric correction performance of the Geostationary Ocean Color Imager (GOCI), a Chla(z) inversion model was developed for Lake Chaohu from 2011 to 2020 using GOCI R_rs data (R_rs_G). (1) Five functions of non-uniform Chla(z) were compared, and the best result was found for Chla(z) = a × exp(b × z) + c (R² = 0.98, RMSE = 38.15 μg/L). (2) A decision tree of Chla(z) was established with the alternative floating algae index (AFAI_Rrs), the fluorescence line height (FLH), and wind speed (WIN), where the overall accuracy was 89% and the Kappa coefficient was 0.79. The Chla(z) inversion model for Type 1 was established using the empirical relationship between Chla (z = surface) and AFAI_Rrs (R² = 0.58, RMSE = 10.17 μg/L). For Type 2, multivariate regression models were established to estimate the structural parameters of Chla(z) combined with R_rs_G and environmental parameters (R² = 0.75, RMSE = 72.80 μg/L). (3) There are obvious spatial variations in Chla(z), especially from the water surface to a depth of 0.1 m; the largest diurnal variations were observed at 12:16 and 13:16 local time. The Chla(z) inversion method can determine Chla in different layers of each pixel, which is important for the scientific assessment of phytoplankton biomass and lake carbon and can provide vertical information for the short-term prediction of algal blooms (and the generation of corresponding warnings) in lake management. Full article