Environmental drivers of mesozooplankton dynamics in the Munroe Island, adjacent to Ashtamudi Estuary, India.

The response of mesozooplankton is critical in assessing the health of an estuarine ecosystem. Reports on the spatial and temporal dynamics of mesozooplankton community in estuarine and backwaters of the Southern parts of India are scanty. In this scenario, we appraised the community structure of mesozooplankton and their spatio-temporal dynamics based on various multivariate statistical assessments. A total of 31 taxa were identified and the abundance was principally dominated by Copepoda followed by Luciferidae during three sampling seasons. The most abundant species were: Paracalanus parvus parvus, Pseudodiaptomus aurivillii, Temora stylifera, and Pseudodiaptomus serricaudatus. Canonical correspondence analysis and Spearman's correlation coefficients underlined that salinity, turbidity, conductivity, temperature, dissolved oxygen, chlorophyll a (Chl-a), and nutrients were the principal environmental variables strongly linked with mesozooplankton dynamics in Munroe Island. The highest abundance of mesozooplankton was recorded in MoN (monsoon), followed by PrM (pre-monsoon), and PoM (post-monsoon). Hierarchical clustering confirmed that the grouping of sampling stations is based on the estuarine and freshwater influences on mesozooplankton abundance. During the entire investigation, various ecological indices were observed in good condition. Moreover, the optimum environmental conditions during the PoM season are marked with the highest indices values. Overall, multivariate investigations undoubtedly proved the suitability of mesozooplankton communities as potential bioindicators for spatial and seasonal ecological assessments. Our investigation emphasizes the high assemblages of mesozooplankton and their responses to various environmental variables and highlights the significance of long-term ecological monitoring in a threatened ecosystem like Munroe Island.

Identifying environmental impacts on planktonic algal proliferation and associated risks: a five-year observation study in Danjiangkou Reservoir, China.

Understanding the risks of planktonic algal proliferation and its environmental causes is crucial for protecting water quality and controlling ecological risks. Reservoirs, due to the characteristics of slow flow rates and long hydraulic retention times, are more prone to eutrophication and algal proliferation. Chlorophyll-a (Chl-a) serves as an indicator of planktonic algal biomass. Exploring the intricate interactions and driving mechanisms between Chl-a and the water environment, and the potential risks of algal blooms, is crucial for ensuring the ecological safety of reservoirs and the health of water users. This study focused on the Danjiangkou Reservoir (DJKR), the core water source of the Middle Route of the South-to-North Water Diversion Project of China (MRSNWDPC). The multivariate statistical methods and structural equation modeling were used to explore the relationships between chlorophyll-a (Chl-a) contents and water quality factors and understand the driving mechanisms affecting Chl-a variations. The Copula function and Bayesian theory were combined to analyze the risk of changes in Chl-a concentrations at Taocha (TC) station, which is the core water source intake point of the MRSNWDPC. The results showed that the factors driving planktonic algal proliferation were spatially heterogeneous. The main factors affecting Chl-a concentrations in Dan Reservoir (DR) were water physicochemical factors (water temperature, dissolved oxygen, pH value, and turbidity) with a total contribution rate of 60.18%, whereas those in Han Reservoir (HR) were nutrient factors (total nitrogen, total phosphorus, and ammonia nitrogen) with a total contribution rate of 73.58%. In TC, the main factors were water physicochemical factors (turbidity, pH, and water temperature) and nutrient factors (total phosphorus) with total contribution rates of 39.76% and 45.78%, respectively. When Chl-a concentrations in other areas of the DJKR ranged from the minimum to the uppermost quartile, the probabilities that Chl-a concentrations at the TC station exceeded 3.4 µg/L (the benchmark value of Chl-a for lakes in the central-eastern lake area of China) owing to the influence of these areas were all less than 10%. Thus, the risk of planktonic algal proliferation at the MRSNWDPC intake point is low. This study developed an integrated framework to investigate spatiotemporal changes in algal proliferation and their driving factors in reservoirs, which can be used to support water quality management in mega hydro projects.

Assessment of the impacts of constructing artificial structures on the water quality and hydrological environment of a meandering river.

When an artificial structure is built in a river, the river changes significantly in water quality and hydraulic properties. In this study, the effects of the weirs constructed in the middle section of a river as a four major rivers restoration project in Korea on water quality and hydrological characteristics were analyzed. For multi-dimensional data analysis, a self-organizing map was applied, and statistical techniques including analysis of variation were used. As a result of analysis, the cross-sectional area of the river increased significantly after the construction of the weir compared to before the construction of the weir, and the flow velocity decreased at a statistically significant level. In the case of water quality, nitrogen, phosphorus, and suspended solids tended to improve after weir construction, and chlorophyll-a and bacteria tended to deteriorate. Some water quality parameters such as chlorophyll-a were also affected by seasonal influences. In order to improve the water quality deteriorated by the construction of the weir, it is necessary to consider how to improve the flow velocity of the river through partial opening or operation of the weir. In addition, in order to determine the effect of sedimentation of particulate matter due to the decrease in flow rate, it is necessary to conduct investigations on sediments around weirs in the future. PRACTITIONER POINTS: Compared to before the construction of the weir, there was no significant change in the flow rate of the river after the construction of the weir. In the case of chlorophyll-a and bacteria, the water quality was deteriorated after weir construction. To improve the deteriorated water quality, it is required to consider the fundamental management of each pollutant source and the flexible operation of both weirs. For some improved water quality parameters, further research is needed to determine whether these improvements are directly attributable to the construction of a weir.

Blending census and paleolimnological data allows for tracking the establishment and growth of a major gannet colony over several centuries.

Seabird colonies with long-term monitoring records, i.e., > 50 years, are rare. The population data for northern gannets (Morus bassanus) in Cape St. Mary's (CSM) Ecological Reserve (Newfoundland and Labrador, Canada) is robust, extending back to 1883 when the colony was presumed established. We inferred the colony's historical population shifts by measuring ornithogenic proxies in a dated sediment record collected from a nearby pond. Our record extended to the early eighteenth century, but the proxy data only began to show significant signs of seabird presence between ca. 1832 and 1910, aligning with the period gannets were first observed at CSM. Through the twentieth century, we observed significant increases in δ15N, P, Zn, Cd, and chlorophyll a, coeval with a shift in the dominant diatom species, indicating rapid colony growth. The proxies were overall highest in ca. 2005, corresponding to the reported historical maximum of the gannet colony in 2009. Our results validate that paleo-reconstructions using ornithogenic proxies can accurately reflect population trends and provide a stronger understanding of the colony's establishment and growth. This study highlights the value of applying paleolimnological methods in seabird population studies to frame the history of a colony's dynamics and inform conservation efforts.

Identification of the nitrate pollution in surface water of Dongshan Bay based on stable isotope technology.

Identifying and quantifying water nitrate pollution is crucial for managing aquatic environment of a bay. Dongshan Bay, a significant semi-enclosed bay in the southeastern coastal area of Fujian Province, features mangrove and coral reef ecosystems at its estuary and bay mouth, respectively. Dongshan Bay is impacted by human activities such as mariculture. We quantified and analyzed nitrate pollution status in the surface waters of Dongshan Bay by measuring physicochemical parameters, stable isotopes (δ15N-NO3-, δ18O-NO3- and δ15N-NH4+) of the surface waters, and using statistical methods including the MixSIAR isotope mixing model. The results showed that the concentrations of chlorophyll a and dissolved inorganic nitrogen in the surface waters exhibited a noticeable gradient change, decreasing from the estuary of the Zhangjiang River to the mouth of Dongshan Bay. The maximum concentrations of chlorophyll a, NH4+, NO3- and NO2- were 45.2 µg·L-1, 52.67 µmol·L-1, 379.2 µmol·L-1 and 3.93 µmol·L-1, respectively. The nitrogen and oxygen isotope values of NH4+ and NO3- in the surface waters showed significant spatial variations. According to the MixSIAR model results, nitrogen sources in the surface waters of Dongshan Bay were mainly freshwater inputs of the Zhangjiang River estuary, aquaculture wastewater, and groundwater. The freshwater input from the Zhangjiang River estuary contributed the most (25.2%), while aquaculture wastewater, groundwater and urban sewage accounted for 24.6%, 19.0%, and 15.1%, respectively. It is evident that freshwater input from the Zhangjiang River estuary is the primary source of nitrate in the surface waters of Dongshan Bay.

Enhanced quantification of chlorophyll a and its degradation products in olive oil using time-resolved laser-induced fluorescence fingerprint analysis.

Potential errors in the fluorescence analysis of chlorophylls and their degradation products, primarily due to spectral overlap and inner filter, are widely acknowledged. This study aimed to devise a sensitivity-enhanced technique for the concurrent quantification of chlorophyll a and its degradation products while minimizing effects from type-B chlorophylls. Initially, a time-resolved laser-induced fluorescence spectroscopic system was designed and tested on stardard chlorophyll samples. The origins, implications, and mitigation strategies of spectral overlap and the inner filter effect on the measured fluorescence intensity were thoroughly examined. Then, this methodology was proved to be efficacious within complex liquid matrices derived from olive oil. The experimental outcomes not only shed additional light on the mechanisms of chlorophyll fluorescence overlap and the inner filter effect, but also establish a general framework for developing spectrally and timely resolved fluorescence fingerprint analysis for the simultaneous quantification of chlorophylls and their degradation products at high concentrations.

Smartphone as an alternative to measure chlorophyll-a concentration in small waterbodies.

Monitoring chlorophyll-a concentrations (Chl-a, µg·L-1) in aquatic ecosystems has attracted much attention due to its direct link to harmful algal blooms. However, there has been a lack of a cost-effective method for measuring Chl-a in small waterbodies. Inspired by the increase of smartphone photography, a Smartphone-based convolutional neural networks (CNN) framework (SCCA) was developed to estimate Chl-a in Aquatic ecosystem. To evaluate the performance of SCCA, 238 paired records (a smartphone image with a 12-color background and a measured Chl-a value) were collected from diverse aquatic ecosystems (e.g., rivers, lakes and ponds) across China in 2023. Our performance-evaluation results revealed a NS and R2 value of 0.90 and 0.94 in Chl-a estimation, demonstrating a satisfactory (NS = 0.84, R2 = 0.86) model fit in lower Chl-a (<30 µg L-1) conditions. SCCA had involved a realtime-update method with hyperparameter optimization technology. In comparison with the existing methods of measuring Chl-a, SCCA provides a useful screening tool for cost-effective measurement of Chl-a and has the potential for being an algal bloom screening means in small waterbodies, using Huajin River as a case study, especially under limited resources for water measurement. Overall, we highlight that the SCCA can be potentially integrated into a smartphone application in the future to diverse waterbodies in environmental management.

Machine learning-driven assessment of biochemical qualities in tomato and mandarin using RGB and hyperspectral sensors as nondestructive technologies.

Estimation of fruit quality parameters are usually based on destructive techniques which are tedious, costly and unreliable when dealing with huge amounts of fruits. Alternatively, non-destructive techniques such as image processing and spectral reflectance would be useful in rapid detection of fruit quality parameters. This research study aimed to assess the potential of image processing, spectral reflectance indices (SRIs), and machine learning models such as decision tree (DT) and random forest (RF) to qualitatively estimate characteristics of mandarin and tomato fruits at different ripening stages. Quality parameters such as chlorophyll a (Chl a), chlorophyll b (Chl b), total soluble solids (TSS), titratable acidity (TA), TSS/TA, carotenoids (car), lycopene and firmness were measured. The results showed that Red-Blue-Green (RGB) indices and newly developed SRIs demonstrated high efficiency for quantifying different fruit properties. For example, the R2 of the relationships between all RGB indices (RGBI) and measured parameters varied between 0.62 and 0.96 for mandarin and varied between 0.29 and 0.90 for tomato. The RGBI such as visible atmospheric resistant index (VARI) and normalized red (Rn) presented the highest R2 = 0.96 with car of mandarin fruits. While excess red vegetation index (ExR) presented the highest R2 = 0.84 with car of tomato fruits. The SRIs such as RSI 710,600, and R730,650 showed the greatest R2 values with respect to Chl a (R2 = 0.80) for mandarin fruits while the GI had the greatest R2 with Chl a (R2 = 0.68) for tomato fruits. Combining RGB and SRIs with DT and RF models would be a robust strategy for estimating eight observed variables associated with reasonable accuracy. Regarding mandarin fruits, in the task of predicting Chl a, the DT-2HV model delivered exceptional results, registering an R2 of 0.993 with an RMSE of 0.149 for the training set, and an R2 of 0.991 with an RMSE of 0.114 for the validation set. As well as for tomato fruits, the DT-5HV model demonstrated exemplary performance in the Chl a prediction, achieving an R2 of 0.905 and an RMSE of 0.077 for the training dataset, and an R2 of 0.785 with an RMSE of 0.077 for the validation dataset. The overall outcomes showed that the RGB, newly SRIs as well as DT and RF based RGBI, and SRIs could be used to evaluate the measured parameters of mandarin and tomato fruits.

Monitoring subtropical aquatic ecosystems: evaluating the use of Trophic State Indices (TSI) and Aquatic Life Protection (API) as baseline indices by monitoring an urban reservoir in southeastern Brazil.

The use of indices is recommended for continuous monitoring and assessment of aquatic ecosystems, as they summarize the technical complexity of the results of multiple analyzes performed and translate these results into quality classes that reflect the actions taken and indicate ways to recover and conserve the resources. Environmental trophic state indices provide information on how nutrients, light availability and other factors promote the development of algal biomass and contribute to increased enrichment status of aquatic systems. Lamparelli's Trophic State Index (TSI) distinguishes between lentic (lower phytoplankton productivity) and lotic (higher phosphorus concentrations and lower chlorophyll-a concentrations) environments. The Aquatic Life Protection Index (ALPI) reflects water quality based on the trophic state of the environment, determines the degree of toxicity to biota and indicates deficiencies in variables and conditions essential for the protection of aquatic life. The indices were applied to a long data series to monitor the Guarapiranga Reservoir, an urban reservoir in the state of São Paulo in southeastern Brazil, which suffers from the urbanization of its surroundings and the discharge of domestic wastewater. The evaluation of the time series from 1978 to 2021 using these indices showed a deterioration in the trophic status and conservation of aquatic life in the reservoir and in one of its tributaries. Considering that the joint assessment of the two indices provides an approach to environmental conservation, their long-term use reflected changes and impacts on the environment and showed the best-preserved sites. Both indices are suitable for application in a baseline network in subtropical environments. They can pinpoint locations for better monitoring and are sensitive to environmental changes.

Planktonic ciliate communities along an environmental gradient in the Nile Delta (Damietta region, Egypt).

The spatial patterns of planktonic ciliate communities were studied from May to June 2019 in the Nile Delta's Damietta region, southeastern Mediterranean. The ciliate communities were sampled from twenty-five sites of five stressed domains with spatial gradients of environmental status. A total of 32 ciliate taxa with six dominant species were identified, comprising 21 tintinnids and 11 aloricate ciliates. The abundance and richness of each ciliate group varied geographically and were most strongly influenced by salinity variations; tintinnid ciliates attained high abundance and richness at high salinity sites in the harbour and coastal region and decreased within the estuary upstream. Aloricate ciliates were poorly represented at most sites but were a substantial proportion of upstream estuarine sites. Multivariate/univariate analyses demonstrated that spatial patterns of the ciliate communities were significantly correlated with environmental variables, especially salinity, chlorophyll-a, and nutrients, either alone or in combination with one another. These results indicate that the ciliates can be useful bioindicators in stressed environments while also allowing the detection of impacts on short time scales by rapidly responding to environmental variations.

Long-term trend forecast of chlorophyll-a concentration over eutrophic lakes based on time series decomposition and deep learning algorithm.

Long-term trend forecast of chlorophyll-a concentration (Chla) holds significant implications for eutrophication management and pollution control planning on lakes, especially under the background of climate change. However, it is a challenging task due to the mixture of trend, seasonal and residual components in time series and the nonlinear relationships between Chla and the hydro-environmental factors. Here we developed a hybrid approach for long-term trend forecast of Chla in lakes, taking the Lake Taihu as an instantiation case, by the integration of Seasonal and Trend decomposition using Loess (STL), wavelet coherence, and Convolutional Neural Network with Bidirectional Long Short-Term Memory (CNN-BiLSTM). The results showed that long-term trends of Chla and the hydro-environmental factors could be effectively separated from the seasonal and residual terms by STL method, thereby enhancing the characterization of long-term variation. The resonance pattern and time lag between Chla and the hydro-environmental factors in the time-frequency domain were accurately identified by wavelet coherence. Chla responded quickly to variations in TP, but showed a time lag response to variations in WT in Lake Taihu. The forecasting method using multivariate and CNN-BiLSTM largely outperformed the other methods for Lake Taihu with regards to R2, RMSE, IOA and peak capture capability, owning to the combination of CNN for extracting local features and the integration of bidirectional propagation mechanism for the acquisition of higher-level features. The proposed hybrid deep learning approach offers an effective solution for the long-term trend forecast of algal blooms in eutrophic lakes and is capable of addressing the complex attributes of hydro-environmental data.

Ecological investigations of giant Phaeocystis colonies in Viet Nam: I. Cell abundance and elemental composition.

Phaeocystis globosa is an important bloom-forming marine phytoplankton species that often accumulates to large levels in temperate and tropical waters and has significant impacts on food webs and biogeochemical cycles. It can form "giant" colonies that reach 3 cm in diameter. Microscopic observations, colony elemental composition, and pigment composition were analyzed to assess the characteristics of colonies as a function of colony size. Particulate organic carbon (POC) per unit surface area, colonial cell density, and chlorophyll a per unit surface area all increased with colony size, in contrast to results from temperate waters. Cellular chl a averaged 0.85 pg chl · cell-1. Colonies had both photosynthetic and protective pigments, with fucoxanthin being the dominant accessory pigment. Based on chl a and pigment levels, it appears colonies were acclimated to relatively low irradiances, likely due to their life cycle and the extremely turbulent environment in which they grew. Mucous carbon ranged from 16.2% to 79.2% of the total POC, and mucous carbon per unit surface area increased with colony size, suggesting that the mucous envelope did not thin as the colony grew. Based on elemental composition, nitrogen did not appear to limit growth, but phosphorus:carbon ratios were similar to those of P-limited cultures. Giant colonies represent an extreme response to the environment, but they do not appear to have greatly different characteristics than other tropical strains.

Environmental drivers behind the exceptional increase in cyanobacterial blooms in Okavango Delta, Botswana.

The Okavango Delta region in Botswana experienced exceptionally intense landscape-wide cyanobacterial harmful algal blooms (CyanoHABs) in 2020. In this study, the drivers behind CyanoHABs were determined from thirteen independent environmental variables, including vegetation indices, climate and meteorological parameters, and landscape variables. Annual Land Use Land Cover (LULC) maps were created from 2017 to 2020, with ∼89% accuracy to compute landscape variables such as LULC change. Generalized Additive Models (GAM) and Structural Equation Models (SEM) were used to determine the most important drivers behind the CyanoHABs. Normalized Difference Chlorophyll Index (NDCI) and Green Line Height (GLH) algorithms served as proxies for chlorophyll-a (green algae) and phycocyanin (cyanobacteria) concentrations. GAM models showed that seven out of the thirteen variables explained 89.9% of the variance for GLH. The models showcased that climate variables, including monthly precipitation (8.8%) and Palmer Severity Drought Index- PDSI (3.2%), along with landscape variables such as changes in Wetlands area (7.5%), and Normalized Difference Vegetation Index (NDVI) (5.4%) were the determining drivers behind the increased cyanobacterial activity within the Delta. Both PDSI and NDVI showed negative correlations with GLH, indicating that increased drought conditions could have led to large increases in toxic CyanoHAB activity within the region. This study provides new information about environmental drivers which can help monitor and predict regions at risk of future severe CyanoHABs outbreaks in the Okavango Delta, Botswana, and other similar data-scarce and ecologically sensitive areas in Africa. Plain Language Summary: The waters of the Okavango Delta in Northern Botswana experienced an exceptional increase in toxic cyanobacterial activity in recent years. Cyanobacterial blooms have been shown to affect local communities and wildlife in the past. To determine the drivers behind this increased bloom activity, we analyzed the effects of thirteen independent environmental variables using two different statistical models. Within this research, we focused on vegetation indices, meteorological, and landscape variables, as previous studies have shown their effect on cyanobacterial activity in other parts of the world. While driver determination for cyanobacteria has been done before, the environmental conditions most important for cyanobacterial growth can be specific to the geographic setting of a study site. The statistical analysis indicated that the increases in cyanobacterial bloom activity within the region were mainly driven by persistent drier conditions. To our knowledge, this is the first study to determine the driving factors behind cyanobacterial activity in this region of the world. Our findings will help to predict and monitor areas at risk of future severe cyanobacterial blooms in the Okavango Delta and other similar African ecosystems.

Assessing Chlorophyll-a Variations in Caspian Sea during the COVID-19 Pandemic.

The COVID-19 pandemic's disruptions to human activities prompted serious environmental changes. Here, we assessed the variations in coastal water quality along the Caspian Sea, with a focus on the Iranian coastline, during the lockdown. Utilizing Chlorophyll-a data from MODIS-AQUA satellite from 2015 to 2023 and Singular Spectrum Analysis for temporal trends, we found a 22% Chlorophyll-a concentration decrease along the coast, from 3.2 to 2.5 mg/m³. Additionally, using a deep learning algorithm known as Long Short-Term Memory Networks, we found that, in the absence of lockdown, the Chlorophyll-a concentration would have been 20% higher during the 2020-2023 period. Furthermore, our spatial analysis revealed that 98% of areas experienced about 18% Chlorophyll-a decline. The identified improvement in coastal water quality presents significant opportunities for policymakers to enact regulations and make local administrative decisions aimed at curbing coastal water pollution, particularly in areas experiencing considerable anthropogenic stress.

Examining chlorophyll-a concentrations in tropical reservoirs under various land use changes using Sentinel - 2 and Google Earth engine - Bhadra and Tungabhadra, India.

The understanding of spatio-temporal variation in land use and land cover (LULC) patterns is crucial for managing catchment land use planning, as it directly influences of tropical reservoir water quality and the subsequent Nutrient Contamination (NC) of unmonitored water bodies. The current research attempts to accurately measure the influence of LULC and its associated determinants on the quantities of NC loads by using Chl-a as a proxy, within tropical reservoirs, i.e. Bhadra and Tungabhadra, located in same river catchment. This Chl-a spread calculated by the Maximum Chlorophyll Index (MCI) derived from Sentinel 2 satellite data products covering the period from July 2016 to June 2021 were done using Google Earth Engine (GEE) platform. The validation analysis confirms the robustness of the methodology with a strong correlation between MCI-calculated values and EOMAP (Earth Observation and Environmental Services Mapping) Chl-a (µg/L) data points for both reservoirs, Bhadra (R2 = 0.64) and Tungabhadra (R2 = 0.68). The findings reveal that, Tungabhadra reservoir consistently exhibits an excessive spatial distribution of Chl-a spread area (17 km2 to 335 km2), reflecting nutrient-rich water inflows, particularly evident during the post-monsoon period. This notable rise could be linked to harvesting the Kharif crop, resulting in elevated nutrient concentrations. In contrast Bhadra reservoir, dominated by forested areas, maintains relatively lower Chl-a spread areas (<20 km2), highlighting its pivotal role in maintaining water cleanliness and serves as a riparian boundary. In addition, the changes in LULC classes show a strong relationship with variation in Chl-a during the studied period, for the Bhadra Reservoir R2 = 0.51 (F- statistics = 3.983, p = 0.021), and the Tungabhadra Reservoir R2 = 0.802 (F- statistics = 7.489, p = 0.0143). This highlights how changes in land use significantly shape contamination dynamics, deepening our understanding of nutrient inputs and contamination drivers in tropical reservoirs.

Environmental factors influencing the abundance and spatial distribution of modern dinoflagellate cysts in Izmir Bay (Eastern Mediterranean).

We analyzed surface sediments from 12 stations located in Izmir Bay to determine the impact of anthropogenic pollution on dinoflagellate cysts. Forty-three dinoflagellate cyst taxa and two cyst assemblage zones were identified. Zone 1 is characterized by the dominance of cysts of Gymnodinium nolleri, Selenopemphix nephroides, and Operculodinium centrocarpum from the oligotrophic part of Izmir Bay. Zone 2 is in the highly productive inner part of the bay and is characterized by the high abundances of Lingulodinium machaerophorum, Spiniferites ramosus, cysts of Scrippsiella spp., cysts of Polykrikos spp. and Quinquecuspis concreta. We used multivariate statistical analysis (DCA and CCA) on dinoflagellate cysts and environmental variables to support the identification of Zones 1 and 2. Our analyses also revealed that summer and winter chlorophyll-a as well as elevated nitrate and nitrite concentrations are significant parameters in controlling dinoflagellate cyst distribution in Izmir Bay.

Biodiversity patterns of epipelagic copepods in the South Pacific Ocean: Strengths and limitations of current data bases.

Basin-scale patterns of biodiversity for zooplankton in the ocean may provide valuable insights for understanding the impact of climate change and global warming on the marine ecosystem. However, studies on this topic remain scarce or unavailable in vast regions of the world ocean, particularly in large regions where the amount and quality of available data are limited. In this study, we used a 27-year (1993-2019) database on species occurrence of planktonic copepods in the South Pacific, along with associated oceanographic variables, to examine their spatial patterns of biodiversity in the upper 200 m of the ocean. The aim of this study was to identify ecological regions and the environmental predictors explaining such patterns. It was found that hot and cold spots of diversity, and distinctive species assemblages were linked to major ocean currents and large regions over the basin, with increasing species richness over the subtropical areas on the East and West sides of the South Pacific. While applying the spatial models, we showed that the best environmental predictors for diversity and species composition were temperature, salinity, chlorophyll-a concentration, oxygen concentration, and the residual autocorrelation. Nonetheless, the observed spatial patterns and derived environmental effects were found to be strongly influenced by sampling coverage over space and time, revealing a highly under-sampled basin. Our findings provide an assessment of copepods diversity patterns and their potential drivers for the South Pacific Ocean, but they also stress the need for strengthening the data bases of planktonic organisms, as they can act as suitable indicators of ecosystem response to climate change at basin scale.

Dynamics of Noctiluca scintillans blooms: A 20-year study in Jangmok Bay, Korea.

This 20-year study (2001-2020) conducted in Jangmok Bay, Korea, assessed the intricate relationships between environmental factors and Noctiluca scintillans blooms. Granger causality tests and PCA analysis were used to assess the impact of sea surface temperature (SST), salinity, dissolved oxygen (DO) concentration, wind patterns, rainfall, and chlorophyll-a (Chl-a) concentration on bloom dynamics. The results revealed significant, albeit delayed, influences of these variables on bloom occurrence, with SST exhibiting a notable 2-month lag and salinity a 1-month lag in their impact. Additionally, the analysis highlighted the significant roles of phosphate, ammonium, and silicate, which influenced N. scintillans blooms with lags of 1 to 3 months. The PCA demonstrates how SST and wind speed during spring and summer, along with wind direction and salinity in winter, significantly impact N. scintillans blooms. We noted not only an increase in large-scale N. scintillans blooms but also a cyclical pattern of occurrence every 3 years. These findings underscore the synergistic effects of environmental factors, highlighting the complex interplay between SST, salinity, DO concentration, and weather conditions to influence bloom patterns. This research enhances our understanding of harmful algal blooms (HABs), emphasizing the importance of a comprehensive approach that considers multiple interconnected environmental variables for predicting and managing N. scintillans blooms.

Climate change triggered planktonic cyanobacterial blooms in a regulated temperate river.

Harmful algae blooms are a rare phenomenon in rivers but seem to increase with climate change and river regulation. To understand the controlling factors of cyanobacteria blooms that occurred between 2017 and 2020 over long stretches (> 250 km) of the regulated Moselle River in Western Europe, we measured physico-chemical and biological variables and compared those with a long-term dataset (1997-2016). Cyanobacteria (Microcystis) dominated the phytoplankton community in the late summers of 2017-2020 (cyano-period) with up to 110 µg Chlorophyll-a/L, but had not been observed in the river in the previous 20 years. From June to September, the average discharge in the Moselle was reduced to 69-76% and water temperature was 0.9-1.8 °C higher compared to the reference period. Nitrogen (N), phosphorus (P) and silica (Si) declined since 1997, albeit total nutrient concentrations remained above limiting conditions in the study period. Cyanobacterial blooms correlated best with low discharge, high water temperature and low nitrate. We conclude that the recent cyanobacteria blooms have been caused by dry and warm weather resulting in low flow conditions and warm water temperature in the regulated Moselle. Under current climate projections, the Moselle may serve as an example for the future of regulated temperate rivers.

Impact of varying dissolved organic carbon load on sediment phosphorus release and its periodic mechanisms.

Phosphorus (P) release from sediment poses a severe challenge for eutrophication management in the aquatic environment. The dissolved organic carbon (DOC) concentrations in riverine ecosystems have shown an increasing trend due to intensified climate change and anthropogenic activities, while their impact on sediment P cycling remains unclear. To investigate the effects of different DOC loads on sediment P release and the underlying mechanisms, we conducted a two-month experiment in 15 plexiglass tanks, with five gradient-increasing target DOC concentrations set according to reality: control (S0), 5 mg/L (S5), 10 mg/L (S10), 15 mg/L (S15), and 20 mg/L (S20). The results demonstrated that: i) DOC enrichment promoted the sediment P mobilization and release, with the underlying mechanisms exhibited periodic characteristics. ii) reduced dissolved oxygen (DO) concentration and stimulated alkaline phosphatase activity (APA) were likely the primary and sustained facilitating mechanisms. While after the termination of DOC load, elevated pH level was also considered a contributing factor when chlorophyll a (Chl a) ranged between 5.9 µg/L and 7.7 µg/L iii) ultimate concentration of total P (TP) in the overlying water depended on DOC load. After DOC addition was terminated, decreased TP concentrations were observed when DOC concentration was in the range of 5-15 mg/L, which may be attributed to the direct uptake of P by phytoplankton counteracting the minor promotion of P release induced by anoxic conditions. However, when DOC concentrations exceeded 15-20 mg/L, there were notable increments in TP concentrations. Our findings provide further insight into the response mechanisms of sediment P release to the increasing organic C load in natural ecosystems. The impact of broader C forms or C loads on sediment P cycling needs to be fully elucidated and even quantified in future studies, especially through large-scale field investigations to further clarify the coupled roles between C and P.