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.

Eutrophication of inland lakes poses various societal and ecological threats, making water quality monitoring crucial. Satellites provide a comprehensive and cost-effective supplement to traditional in situ sampling. The Sentinel-2 MultiSpectral Instrument (S2 MSI) offers unique spectral bands positioned to quantify chlorophyll a, a water-quality and trophic-state indicator, along with fine spatial resolution, enabling the monitoring of small waterbodies. In this study, two algorithms-the Maximum Chlorophyll Index (MCI) and the Normalized Difference Chlorophyll Index (NDCI)-were applied to S2 MSI data. They were calibrated and validated using in situ chlorophyll a measurements for 103 lakes across the contiguous U.S. Both algorithms were tested using top-of-atmosphere reflectances (ρ t), Rayleigh-corrected reflectances (ρ s), and remote sensing reflectances (R rs ). MCI slightly outperformed NDCI across all reflectance products. MCI using ρ t showed the best overall performance, with a mean absolute error factor of 2.08 and a mean bias factor of 1.15. Conversion of derived chlorophyll a to trophic state improved the potential for management applications, with 82% accuracy using a binary classification. We report algorithm-to-chlorophyll-a conversions that show potential for application across the U.S., demonstrating that S2 can serve as a monitoring tool for inland lakes across broad spatial scales.

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.

The denitrification process in aquaculture systems plays a crucial role in nitrogen (N) cycle and N budget estimation. Reliable models are needed to rapidly quantify denitrification rates and assess nitrogen losses. This study conducted a comparative analysis of denitrification rates in fish, crabs, and natural ponds in the Taihu region from March to November 2021, covering a complete aquaculture cycle. The results revealed that aquaculture ponds exhibited higher denitrification rates compared to natural ponds. Key variables influencing denitrification rates were Nitrate nitrogen (NO3--N), Suspended particles (SPS), and chlorophyll a (Chla). There was a significant positive correlation between SPS concentration and denitrification rates. However, we observed that the denitrification rate initially rose with increasing Chla concentration, followed by a subsequent decline. To develop parsimonious models for denitrification rates in aquaculture ponds, we constructed five different statistical models to predict denitrification rates, among which the improved quadratic polynomial regression model (SQPR) that incorporated the three key parameters accounted for 80.7% of the variability in denitrification rates. Additionally, a remote sensing model (RSM) utilizing SPS and Chla explained 43.8% of the variability. The RSM model is particularly valuable for rapid estimation in large regions where remote sensing data are the only available source. This study enhances the understanding of denitrification processes in aquaculture systems, introduces a new model for estimating denitrification in aquaculture ponds, and offers valuable insights for environmental management.

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.

This comprehensive study examines primary production (PP) within the Spitsbergen fjords, Hornsund, and Kongsfjord, over a 25-year period (1994-2019), across 45 stations and 348 incubation levels at various depths. PP and hydrological parameters were measured at 28 sampling stations in Kongsfjorden and 17 in Hornsund, with the locations of \"Glacier,\" \"Inner,\" and \"Outer\" zones defined to reflect the varying influence of glacial meltwater. Our study revealed spatial and temporal variability in PP, both at the surface and within the water column with very high depth resolution. The highest PP values were observed in the Glacier and Inner zones of Hornsund, particularly in the water layer up to 3 m depth, exceeding 20 mgC m-3 h-1. A notable decline in PP with increasing depth was observed in both fjords, with the Glacier zones displaying the highest productivity at the surface. The study also highlights the influence of glacial meltwater on surface water conditions, affecting the PP in the upper layers of both fjords. The observed gradient in the depth of maximum PP toward the mouth of the fjord varied between the two fjords, with Kongsjord displaying more dynamic variations. The spatial distribution of integrated primary production (Pi) suggested lower productivity in the glacial regions, likely due to light limitation caused by high concentrations of mineral particulate matter. The values of Pi were considerably higher in Hornsund, approximately twice as high overall, with specific emphasis on the Glacier and Inner zones where Pi values were about 6.5 and 2.5 times higher, respectively, when compared to those observed in Kongsfjord.

Chlorella vulgaris is of great importance in numerous exploratory or industrial applications (e.g., medicals, food, and feed additives). Rapid quantification of algal biomass is crucial in photobioreactors for the optimization of nutrient management and the estimation of production. The main goal of this study is to provide a simple, rapid, and not-resource-intensive estimation method for determining the algal density of C. vulgaris according to the measured parameters using UV-Vis spectrophotometry. Comparative assessment measurements were conducted with seven different methods (e.g., filtration, evaporation, chlorophyll a extraction, and detection of optical density and fluorescence) to determine algal biomass. By analyzing the entire spectra of diluted algae samples, optimal wavelengths were determined through a stepwise series of linear regression analyses by a novel correlation scanning method, facilitating accurate parameter estimation. Nonlinear formulas for spectrometry-based estimation processes were derived for each parameter. As a result, a general formula for biomass concentration estimation was developed, with recommendations for suitable measuring devices based on algae concentration levels. New values for magnesium content and the average single-cell weight of C. vulgaris were established, in addition to the development of a rapid, semiautomated cell counting method, improving efficiency and accuracy in algae quantification for cultivation and biotechnology applications.

This study investigated the mitigating effects of spermidine on salinity-stressed yarrow plants (Achillea millefolium L.), an economically important medicinal crop. Plants were treated with four salinity levels (0, 30, 60, 90 mM NaCl) and three spermidine concentrations (0, 1.5, 3 μM). Salinity induced electrolyte leakage in a dose-dependent manner, increasing from 22% at 30 mM to 56% at 90 mM NaCl without spermidine. However, 1.5 μM spermidine significantly reduced leakage across salinities by 1.35-11.2% relative to untreated stressed plants. Photosynthetic pigments (chlorophyll a, b, carotenoids) also exhibited salinity- and spermidine-modulated responses. While salinity decreased chlorophyll a, both spermidine concentrations increased chlorophyll b and carotenoids under most saline conditions. Salinity and spermidine synergistically elevated osmoprotectants proline and total carbohydrates, with 3 μM spermidine augmenting proline and carbohydrates up to 14.4% and 13.1% at 90 mM NaCl, respectively. Antioxidant enzymes CAT, POD and APX displayed complex regulation influenced by treatment factors. Moreover, salinity stress and spermidine also influenced the expression of linalool and pinene synthetase genes, with the highest expression levels observed under 90 mM salt stress and the application of 3 μM spermidine. The findings provide valuable insights into the responses of yarrow plants to salinity stress and highlight the potential of spermidine in mitigating the adverse effects of salinity stress.

Ongoing warming is leading to the accelerated shrinkage of glaciers located on Arctic islands. Consequently, the influence of glacial meltwater on phytoplankton primary production in Arctic bays becomes critically important in an era of warming. This work studies the spatiotemporal variation of primary production and chlorophyll a concentration in the bays along the eastern coast of the Novaya Zemlya archipelago. Data were collected during nine cruises performed from July to October (2013-2022). The effect of underwater photosynthetically available radiation (PAR) and nutrients on primary production was assessed separately for bays influenced by glacial meltwater (glacial bays) and those without such influence (non-glacial bays). The median value of water column-integrated primary production (IPP) for all bays was 38 mgC m-2 d-1, characterizing them as oligotrophic areas. IPP in non-glacial bays was found to be 2.3-fold and 1.4-fold higher than that in glacial bays during summer and autumn, respectively. Underwater PAR was the main abiotic factor determining IPP during the ice-free period. In the entire bays nutrient concentrations were high, exceeding the limiting values for growth and photosynthesis of phytoplankton. It was concluded that the high turbidity from glacial meltwater runoff leads to decreased underwater PAR and, consequently, to a decline in IPP. This study demonstrates that rapid warming could have a negative impact on the productivity of high Arctic bays and their adjacent areas.

Chlorophyll-a (Chl-a) is a crucial pigment in algae and macrophytes, which makes the concentration of total Chl-a in the water column (total Chl-a) an essential indicator for estimating the primary productivity and carbon cycle of the ocean. Integrating the Chl-a concentration at different depths (Chl-a profile) is an important way to obtain the total Chl-a. However, due to limited cost and technology, it is difficult to measure Chl-a profiles directly in a spatially continuous and high-resolution way. In this study, we proposed an integrated strategy model that combines three different machine learning methods (PSO-BP, random forest and gradient boosting) to predict the Chl-a profile in the Mediterranean by using several sea surface variables (photosynthetically active radiation, spectral irradiance, sea surface temperature, wind speed, euphotic depth and KD490) and subsurface variables (mixed layer depth) observed by or estimated from satellite and BGC-Argo float observations. After accuracy estimation, the integrated model was utilized to generate the time series total Chl-a in the Mediterranean from 2003 to 2021. By analysing the time series results, it was found that seasonal fluctuation contributed the most to the variation in total Chl-a. In addition, there was an overall decreasing trend in the Mediterranean phytoplankton biomass, with the total Chl- decreasing at a rate of 0.048 mg/m2 per year, which was inferred to be related to global warming and precipitation reduction based on comprehensive analysis with sea surface temperature and precipitation data.