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ABSTRACT Plants respond to rapid environmental change in ways that depend on both their genetic identity and their phenotypic plasticity, impacting their survival as well as associated ecosystems. However, genetic and environmental effects on phenotype are difficult to quantify across large spatial scales and through time. Leaf hyperspectral reflectance offers a potentially robust approach to map these effects from local to landscape levels. Using a handheld field spectrometer, we analyzed leaf‐level hyperspectral reflectance of the foundation tree species Populus fremontii in wild populations and in three 6‐year‐old experimental common gardens spanning a steep climatic gradient. First, we show that genetic variation among populations and among clonal genotypes is detectable with leaf spectra, using both multivariate and univariate approaches. Spectra predicted population identity with 100% accuracy among trees in the wild, 87%–98% accuracy within a common garden, and 86% accuracy across different environments. Multiple spectral indices of plant health had significant heritability, with genotype accounting for 10%–23% of spectral variation within populations and 14%–48% of the variation across all populations. Second, we found gene by environment interactions leading to population‐specific shifts in the spectral phenotype across common garden environments. Spectral indices indicate that genetically divergent populations made unique adjustments to their chlorophyll and water content in response to the same environmental stresses, so that detecting genetic identity is critical to predicting tree response to change. Third, spectral indicators of greenness and photosynthetic efficiency decreased when populations were transferred to growing environments with higher mean annual maximum temperatures relative to home conditions. This result suggests altered physiological strategies further from the conditions to which plants are locally adapted. Transfers to cooler environments had fewer negative effects, demonstrating that plant spectra show directionality in plant performance adjustments. Thus, leaf reflectance data can detect both local adaptation and plastic shifts in plant physiology, informing strategic restoration and conservation decisions by enabling high resolution tracking of genetic and phenotypic changes in response to climate change. [ABSTRACT FROM AUTHOR]

This study proposes a data-driven statistical model using multi sensor fusion and Kalman filtering for real-time water quality assessment in lakes. A recursive estimation technique, the Kalman Filter, is employed to handle uncertainties and enhance computational efficiency. The fusion process integrates data from sensors monitoring parameters like chlorophyll concentration, surface water elevation, temperature, and precipitation, producing Markov features to capture temporal transitions and environmental dynamics. Data synchronization and fusion are achieved through recursive KF methods, enabling real-time adaptive management in response to environmental fluctuations such as seasonal changes, precipitation (6–18%), and evaporation rates (1.2–11.9 mm/day). Over a 30-day evaluation period, the model accurately predicted chlorophyll concentrations, reaching 128 mg / m 3 in mid-level inflow regions (3.6 m water elevation) compared to 86 mg / m 3 in extreme inflow areas (5.5 m). The integration of Markov feature extraction and eigenvalue estimation enhanced prediction stability and sensitivity, with the KF maintaining computational efficiency at 7.8 ms per computation cycle. The model's accuracy was validated by achieving a residual error of less than 0.05 with minimal noise interference. Overall, the system provides a resilient and precise framework for real-time lake water quality assessment, capable of handling multi-parameter uncertainties and dynamic environmental changes, thereby supporting informed decision-making for aquatic ecosystem management. [ABSTRACT FROM AUTHOR]

Mature 5-year-old Prunus domestica trees were used as research objects in plantation areas in arid and semi-arid regions of northern China in Hohhot city; three irrigation gradients and a natural control were set up according to the determination of wilting coefficient and field capacity, and FluorCam 1000-H chlorophyll fluorescence parameters of plant leaves were determined by using a portable chlorophyll fluorescence imager. The results showed that the effects of irrigation gradients on potential photochemical efficiency, maximum photochemical efficiency and non-photochemical burst were not significant (p > 0.05), and the effects on photochemical burst were significant (p < 0.05) at different fertility periods. The potential photochemical efficiency, maximum photochemical efficiency and photochemical burst basically showed a trend of increasing and then decreasing, and the non-photochemical burst showed a trend of decreasing and then increasing during different fertility periods. During the same fertility period, the potential and maximum photochemical efficiencies were significantly increased, and the photochemical and non-photochemical bursts were somewhat reduced under the full irrigation gradient. Therefore, the chlorophyll fluorescence characteristics of Prunus domestica trees at different fertility stages can be regulated by controlling the irrigation gradient to increase the activity of photosystem II (PSII), which in turn improves the photosynthesis of the plants. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The comparative efficacy of silicon (Si) and zinc (Zn) nanoparticles (NPs) in mitigating drought stress in fennel (Foeniculum vulgare) remains largely unexplored. This study evaluated the impact of Si NPs and Zn NPs on enhancing plant growth and physiological-biochemical attributes of fennel under varying irrigation regimes. The 2-year study was a split-pot design with irrigation at three irrigation levels (100, 75, and 50% field capacity, FC) and five treatments of foliar application of Si and Zn NPs (control, 1 mM Si NP, 2 mM Si NP, 1 mM Zn NP, 2 mM Zn NP). Results showed that drought stress reduced plant performance. Increases in superoxide dismutase (SOD, 131%) and catalase (CAT, 276%) were seen after a 50% FC drought without the use of Si and Zn NPs. Conversely, biological yield (34%), seed yield (44%), chlorophyll a + b (26%), relative water content (RWC, 21%), and essential oil (EO) yield (50%) were all reduced. However, application of Zn and Si, particularly 1 mM Si and 2 mM Zn, greatly mitigated drought stress via lowering CAT and SOD activity and enhancing plant yield, chlorophyll content, RWC, and EO. The composition of the EO consisted primarily of anethole, followed by limonene, fenchone, and estragole. During drought conditions, monoterpene hydrocarbons increased while oxygenated monoterpenes decreased. The opposite trend was observed for Si and Zn NPs. Our results suggest that applying Zn NPs at 2 mM followed by Si NPs at 1 mM improved plant resilience and EO yield in fennel plants under water stress. This study investigated the physiological and biochemical effects of foliar application of Si and Zn nanoparticles (NPs) on fennel (Foeniculum vulgare) plants under different irrigation levels. Si NPs and Zn NPs improved chlorophyll and water content of plants, and superoxide dismutase activity under drought stress, but essential oil content generally decreased by severe drought (50% field capacity). Superoxide dismutase and catalase activities were sensitive to drought, making them potential markers for monitoring fennel under drought stress. [ABSTRACT FROM AUTHOR]

In this study, we performed continuous high spatial resolution temperature measurements (six thermistors per 1–1.4 m water column) at six sites in three shallow temperate ponds during the warm season. These measurements were accompanied by experiments on plankton and sediment respiration rates, including the relationship between temperature and sediment respiration. Continuous temperature measurements allowed the construction of detailed stratification profiles, as well as the construction of seasonal trends in both plankton respiration and sediment oxygen demand, all with high temporal resolution (at 1-h intervals). A peak in plankton respiration was detected in mid-summer, while sediment oxygen demand showed two peaks, one before and one after the plankton peak. Sediment respiration made a significant contribution to total respiration in the water column and even exceeded plankton respiration during certain periods. The criteria for determining stratification and its thresholds, which differ for shallow and deep waters, are discussed in detail. We discuss the uncertainties in estimating respiration and explore contributing factors, such as limitation of sediment oxygen demand by low oxygen concentrations near the bottom and sampling time. In addition, we used statistics to analyze the effect of environmental factors on sediment surface oxygen. On the basis of statistical analysis, water column turbidity was found to be the most significant factor, followed by duration of stratification breakdown, dissolved oxygen (DO) in the surface layer, water column depth, chlorophyll a concentration in the water column, time of day, plankton respiration near the pond bottom, duration of stratification, and temperature near the pond bottom. [ABSTRACT FROM AUTHOR]

Drought stress inhibits oat growth and yield. The application of fulvic acid (FA) can improve the drought resistance of oats, but the corresponding molecular mechanism of FA-mediated drought resistance remains unclear. Here, we studied the effects of FA on the drought tolerance of oat leaves through physiological, transcriptomic, and metabolomics analyses, and identified FAinduced genes and metabolites related to drought tolerance. Physiological analysis showed that under drought stress, FA increased the relative water and chlorophyll contents of oat leaves, enhanced the activity of antioxidant enzymes (SOD, POD, PAL, CAT and 4CL), inhibited the accumulation of malondialdehyde (MDA), hydrogen peroxide (H2O2) and dehydroascorbic acid (DHA), reduced the degree of oxidative damage in oat leaves, improved the drought resistance of oats, and promoted the growth of oat plants. Transcriptome and metabolite analyses revealed 652 differentially expressed genes (DEGs) and 571 differentially expressed metabolites (DEMs) in FA-treated oat leaves under drought stress. These DEGs and DEMs are involved in a variety of biological processes, such as phenylspropanoid biosynthesis and glutathione metabolism pathways. Additionally, FA may be involved in regulating the role of DEGs and DEMs in phenylpropanoid biosynthesis and glutathione metabolism under drought stress. In conclusion, our results suggest that FA promotes oat growth under drought stress by attenuating membrane lipid peroxidation and regulating the antioxidant system, phenylpropanoid biosynthesis, and glutathione metabolism pathways in oat leaves. This study provides new insights into the complex mechanisms by which FA improves drought tolerance in crops. [ABSTRACT FROM AUTHOR]

A pot experiment was carried out to determine the effect of Serendipita indica on the salt response of Lolium multiflorum Lam. Although the salinity decreased the root colonisation of S. indica by 28.34%, successful colonisation of S. indica increased the seed germination rate, fresh weight, leaf relative water content and chlorophyll content by 28.09, 59.01, 38.78 and 28.80%, respectively, compared with uncolonised seedlings. Under salinity, leaf malondialdehyde content, leaf relative electrical conductivity, as well as Na+ content and Na+/K+ ratio in leaves and roots of S. indica-colonised seedlings were decreased by 33.99, 33.31, 63.40% and 47.42, 85.66 and 55.88%, respectively, compared with uncolonised seedlings. Meanwhile, compared with uncolonised seedlings under salinity, the contents of proline in leaves, N, P and K+ in leaves and roots of the S. indica-colonised seedlings were increased by 47.47, 45.69 and 30.05%, and 41.77, 19.51, 19.18 and 155.00%, respectively. These results indicate that S. indica colonisation confers salt tolerance in L. multiflorum seedlings by enhancing osmotic adjustment via actively accumulating proline and K+, increasing the uptake of nutrients such as N and P, and improving Na+/K+ homoeostasis. The study would provide a new idea for the combined application of salt-tolerant plants and symbiotic microorganisms in the ecological restoration of saline-alkali lands. [ABSTRACT FROM AUTHOR]

Satellite data are extensively used for water quality monitoring purposes, offering a significantly reduced cost compared to in situ data sampling. Using past measurements to predict future conditions remains a challenging task, because of the complexity of the natural phenomena that are involved, with great potential in terms of water resources management. This paper proposes a model that can be used to forecast Chlorophyll‐α $\alpha $ (Chl‐α $\alpha $) values in water bodies, which are a common water quality indicator. The operation of the model lays on the fact that typically Chl‐α $\alpha $ increases and decreases periodically. First, we apply C2RCC, which is a common atmospheric correction algorithm, to Sentinel‐2 images to get Chl‐α $\alpha $ maps for 15 lakes for 12 consecutive months around Europe. Then, we use this data set (∼ ${\sim} $1,000 Sentinel‐2 images) to train a Generative Adversarial Network (GAN) to recognize spatiotemporal patterns. To accomplish this task, pix2pix algorithm is employed, matching consecutive past and current Chl‐α $\alpha $ maps to future Chl‐α $\alpha $ maps. This model has been applied to 3 water bodies around Europe that are not included in the 15‐lakes training data set and has been found to perform accurately, achieving high Pearson and Spearman correlations and low RMSE values. Overall, the model can be used to make Chl‐α $\alpha $ maps' predictions with low computational cost and without using any in situ data and without the requirement of training for every water body. Key Points: Use of Sentinel 2 data for creating inland water bodies' Chlorophyll‐α $\alpha $ time‐seriesGenerative Adversarial Networks are trained to recognize Chlorophyll‐α $\alpha $ spatio‐temporal patternsA continental‐scale model is created for Chlorophyll‐α $\alpha $ short term predictions [ABSTRACT FROM AUTHOR]

This study examines the dynamics of limnological parameters of a South American lake located in southern Chile with the objective of predicting chlorophyll-a levels, which are a key indicator of algal biomass and water quality, by integrating combined remote sensing and machine learning techniques. Employing four advanced machine learning models (recurrent neural network (RNNs), long short-term memory (LSTM), recurrent gate unit (GRU), and temporal convolutional network (TCNs)), the research focuses on the estimation of chlorophyll-a concentrations at three sampling stations within Lake Ranco. The data span from 1987 to 2020 and are used in three different cases: using only in situ data (Case 1), using in situ and meteorological data (Case 2), using in situ, and meteorological and satellite data from Landsat and Sentinel missions (Case 3). In all cases, each machine learning model shows robust performance, with promising results in predicting chlorophyll-a concentrations. Among these models, LSTM stands out as the most effective, with the best metrics in the estimation, the best performance was Case 1, with R2 = 0.89, an RSME of 0.32 µg/L, an MAE 1.25 µg/L and an MSE 0.25 (µg/L)2, consistently outperforming the others according to the static metrics used for validation. This finding underscores the effectiveness of LSTM in capturing the complex temporal relationships inherent in the dataset. However, increasing the dataset in Case 3 shows a better performance of TCNs (R2 = 0.96; MSE = 0.33 (µg/L)2; RMSE = 0.13 µg/L; and MAE = 0.06 µg/L). The successful application of machine learning algorithms emphasizes their potential to elucidate the dynamics of algal biomass in Lake Ranco, located in the southern region of Chile. These results not only contribute to a deeper understanding of the lake ecosystem but also highlight the utility of advanced computational techniques in environmental research and management. [ABSTRACT FROM AUTHOR]

The article discusses how public gardens, such as botanical gardens and arboreta, can serve as valuable resources for scientific research by providing curated plant collections that offer high levels of biodiversity. These living collections allow researchers to explore ecological and evolutionary questions, such as the impact of phylogenetic history on plant trait evolution and plant-soil interactions. Public gardens prioritize scientific inquiry and conservation, recording plant provenance and housing replicates for each species to study within-species variation, local adaptation, and niche evolution. The article emphasizes the importance of utilizing public garden collections to address pressing ecological questions and understand how evolution shapes species' responses to changing environments. [Extracted from the article]

Climate change severely affects crop production. Cotton is one of the primary fiber crops in the world and its production is susceptible to various environmental stresses, especially drought and salinity. Development of stress tolerant genotypes is the only way to escape from these environmental constraints. We identified sixteen homologs of the Arabidopsis JUB1 gene in cotton. Expression of GhJUB1_3‐At was significantly induced in the temporal expression analysis of GhJUB1 genes in the roots of drought tolerant (H177) and susceptible (S9612) cotton genotypes under drought. The silencing of the GhJUB1_3‐At gene alone and together with its paralogue GhJUB1_3‐Dt reduced the drought tolerance in cotton plants. The transgenic lines exhibited tolerance to the drought and salt stress as compared to the wildtype (WT). The chlorophyll and relative water contents of wildtype decreased under drought as compared to the transgenic lines. The transgenic lines showed decreased H2O2 and increased proline levels under drought and salt stress, as compared to the WT, indicating that the transgenic lines have drought and salt stress tolerance. The expression analysis of the transgenic lines and WT revealed that GAI was upregulated in the transgenic lines in normal conditions as compared to the WT. Under drought and salt treatment, RAB18 and RD29A were strongly upregulated in the transgenic lines as compared to the WT. Conclusively, GhJUB1_3‐At is not an auto activator and it is regulated by the crosstalk of GhHB7, GhRAP2‐3 and GhRAV1. GhRAV1, a negative regulator of abiotic stress tolerance and positive regulator of leaf senescence, suppresses the expression of GhJUB1_3‐At under severe circumstances leading to plant death. [ABSTRACT FROM AUTHOR]

A joint spatio–temporal distribution model of mackerel (ages 3–10) was developed to investigate the age-based responses of mackerel to three environmental drivers: sea surface temperature (SST), mixed layer depth, and chlorophyll-a concentration during the summer months 2010–20 in the Nordic Seas. The study showed that SST was the most important variable amongst the ones tested and had the strongest impact on the distribution of the younger age classes (3–5), which had a narrower range of favourable SST and a stronger aversion to cold temperatures than older individuals. Consequently, the impact of SST differed regionally; in the polar front regions, SST explained up to 61% of the variability in the observed density of young individuals, where Arctic water masses likely acted as a barrier to these young individuals. That said, part of it could be confounded with the limited migration capability of young mackerels, which could not reach the furthest frontal regions. In warmer southern waters, the same environmental variables had less explanatory power for mackerel of all ages. Individuals in the south were likely not constrained by temperature and perhaps more influenced by other variables, such as food availability or ocean current (throughout their migration path), for which appropriate data are lacking. Moreover, the model showed that older mackerel were distributed more to the north and west, and their migration pattern changed when the 2013 year-class no longer migrated to the west compared to previous year-classes. Additionally, all-year classes started migrating more eastward from summer 2018. [ABSTRACT FROM AUTHOR]

In the fields of agriculture and forestry, the Normalized Difference Vegetation Index (NDVI) is a critical indicator for assessing the physiological state of plants. Traditional imaging sensors can only collect two-dimensional vegetation distribution data, while dual-wavelength LiDAR technology offers the capability to capture vertical distribution information, which is essential for forest structure recovery and precision agriculture management. However, existing LiDAR systems face challenges in detecting echoes at two wavelengths, typically relying on multiple detectors or array sensors, leading to high costs, bulky systems, and slow detection rates. This study introduces a time-stretched method to separate two laser wavelengths in the time dimension, enabling a more cost-effective and efficient dual-spectral (600 nm and 800 nm) LiDAR system. Utilizing a supercontinuum laser and a single-pixel detector, the system incorporates specifically designed time-stretched transmission optics, enhancing the efficiency of NDVI data collection. We validated the ranging performance of the system, achieving an accuracy of approximately 3 mm by collecting data with a high sampling rate oscilloscope. Furthermore, by detecting branches, soil, and leaves in various health conditions, we evaluated the system's performance. The dual-wavelength LiDAR can detect variations in NDVI due to differences in chlorophyll concentration and water content. Additionally, we used the radar equation to analyze the actual scene, clarifying the impact of the incidence angle on reflectance and NDVI. Scanning the Red Sumach, we obtained its NDVI distribution, demonstrating its physical characteristics. In conclusion, the proposed dual-wavelength LiDAR based on the time-stretched method has proven effective in agricultural and forestry applications, offering a new technological approach for future precision agriculture and forest management. [ABSTRACT FROM AUTHOR]

This study was conducted in a newly established (2-year-old) almond orchard to investigate the effects of five different mulching materials (woven and nonwoven fabric, black and white polyethylene, almond shell) and flame treatments applied at two different frequencies (FL20 and FL30) on weed control and almond growth compared with those of conventional herbicide (glyphosate) application and weedy control. Thus, this study included nine different treatments. The impacts of these treatments on weed density and coverage were periodically monitored. Additionally, the biomass of the weeds was measured at the end of the season to evaluate the effects of the treatments. Because the almond orchard was not yet in the economic fruit-bearing stage, the effects of the treatments were examined in terms of parameters that characterize almond growth, such as plant height, trunk diameter, shoot length, and shoot thickness. The chlorophyll content and water potential values of the trees were also determined. The results of this two-season study indicated that synthetic mulches provided the best outcomes in terms of weed control and almond growth. No weed emergence was observed throughout the season in any of the synthetic mulch treatments. Although almond shells used as organic mulch were highly effective for blocking sunlight, they failed to prevent the growth of some vigorously growing perennials such as Cynodon dactylon and Sorghum halepense that emerged from gaps. Flame treatments demonstrated rapid and effective results; however, they were less successful against the aforementioned monocot perennial weeds and required frequent repetition because of the lack of residual effects. Glyphosate, an herbicide that is commonly used in conventional orcharding, was applied five times throughout the experiment and proved effective weed management compared with that of the weedy control. However, considering the increasing herbicide resistance, environmental and health issues, and growing interest in organic almond cultivation, synthetic mulch applications have emerged as good options. Despite the initially higher establishment costs, synthetic mulches effectively controlled weeds and reduced water stress, thereby promoting almond tree growth. [ABSTRACT FROM AUTHOR]

To test the potential of high‐resolution satellite image analysis for assessing and predicting the mesoscale (<10 km in this study) effects of climate and environmental change on temperature and primary productivity in fishing grounds, we conducted satellite image analysis around an island in a coastal strait west of Japan from 2018 to 2023. We observed a distinct north–south gradient in sea surface temperature (SST) and chlorophyll a concentration (CHL) over approximately 20 km of the transect, which was likely affected by the current system. The model configuration suggests that the frequency of southward currents during winter–spring can control the magnitude of spring phytoplankton blooms. In the study region, an increase in SST at a rate of 0.06–0.13°C y−1 occurred during the study period, accompanied by a decrease in CHL. The north–south gradient in the rate of change suggests that the variation in the temperature and flow rate of the Kuroshio Current into the study area was due to these abrupt changes. The relationship between the annual mean SST and CHL was also spatially heterogeneous, showing a higher sensitivity of CHL to SST in the southwest of the island than in the north. In addition to the intrusion of warm and oligotrophic Kuroshio waters, the spread of less saline and more eutrophic coastal waters likely influenced this spatial heterogeneity. The satellite image analysis in the present study successfully revealed mesoscale mosaics of environmental conditions in coastal fishery grounds. [ABSTRACT FROM AUTHOR]

The generation of reactive oxygen/nitrogen species under either natural or stressful conditions activates antioxidant systems to balance the redox status in plant cells. The effect of seed priming with low concentrations of H2O2 and/or arginine (Arg) was examined on redox status of salinity-stressed Vicia faba in the present study. Salinity stress produced H2O2 and caused oxidative damage to photosynthetic pigments and membrane lipids represented by reduction in chlorophyll contents and carbohydrate production, and high accumulation of malondialdehyde, denoted as oxidative distress. Under saline conditions, Arg and/or H2O2 priming increased the activity of antioxidant enzymes (superoxide dismutase, catalase and ascorbate peroxidase), non-enzymatic antioxidants (phenols, flavonoids, anthocyanins and carotenoids), proline and total antioxidant activity (based on the FRAP method) followed by a reduction in the malondialdehyde content and an increase in the chlorophylls and water soluble carbohydrates contents. Altogether, the seed priming with H2O2 and Arg could constitute a 'priming memory' in seeds of V. faba, which recruited upon a subsequent salinity stress-exposure and induced stress-tolerance of primed beans trough invoking antioxidant systems. [ABSTRACT FROM AUTHOR]

Satellite remote sensing of ocean colour properties allows observation of the ocean with high temporal and spatial coverage, facilitating the better assessment of changes in marine primary production. Ocean productivity is often assessed using satellite-derived chlorophyll a concentrations, a commonly used proxy for phytoplankton concentration. We used the Copernicus GlobColour remote sensing chlorophyll a surface concentration to investigate seasonal and non-seasonal variability, temporal trends, and changes in spring bloom chlorophyll a magnitude. Complementary, we analysed the chlorophyll a relationship with sea surface temperature and mixed-layer depth in the German Bight from 1998 to 2020. Empirical orthogonal functions were employed in order to investigate dominant spatial and temporal patterns (modes) related to the main processes of chlorophyll a variability. Multi covariance analysis was used to extract the dominant structures that maximize the covariance between chlorophyll a and sea surface temperature mixed-layer depth fields. High levels of chlorophyll a were found near the coast, showing a decreasing gradient towards offshore waters. A significant chlorophyll a positive trend was observed close to the Elbe estuary and adjacent area, while 55 % of the German Bight was characterized by a significant chlorophyll a negative trend. The chlorophyll a non-seasonal variability showed that the first four modes explained around 45 %, with the first and second modes related to inter- and intra-annual variability, respectively, observed in the temporal principal components spectral analyses. Monthly chlorophyll a concentration anomalies co-varied by 45 % with sea surface temperature anomalies and 23 % with mixed-layer depth anomalies. The monthly averages of chlorophyll a anomaly fields were suitable to investigate long-term trends and variability. The rising water temperature, combined with its indirect effects on other variables, can partially explain the observed trends in chlorophyll a. [ABSTRACT FROM AUTHOR]

A multidisciplinary approach demonstrates how submerged macrophytes generate high phenological variability in Hungary's Lake Balaton. A 239‐month time series of water chlorophyll indices derived from Landsat 7 imagery from 1999 to 2019 was used. These data facilitated the generation of area‐based phenological patterns, which allowed an assessment of phenological variability by correlating chlorophyll index sequences with spatially adjacent values. The results showed that phenological variability was consistently low (below 5%) at the farthest points from the shore, indicating uniform phenological processes in the pelagic zone of Lake Balaton. Conversely, the littoral zone showed almost eight times higher variability, indicating increased diversity in shallow water areas. In particular, extensive macrophyte biomass datasets revealed a direct relationship between increased phenological variability in the littoral zone and macrophyte biomass (Spearman rank correlation: 0.893). This research highlights contrasting phenological patterns between phytoplankton and macrophyte communities, driven by different life cycles, and the possibility of effectively using satellite data to delineate phenological separation within lakes. [ABSTRACT FROM AUTHOR]

BACKGROUND: Water content and chlorophyll content are important indicators for monitoring rice growth status. Simultaneous detection of water content and chlorophyll content is of significance. Different varieties of rice show differences in phenotype, resulting in the difficulties of establishing a universal model. In this study, hyperspectral imaging was used to detect the Soil and Plant Analyzer Development (SPAD) values and water content of fresh rice leaves of three rice varieties (Jiahua 1, Xiushui 121 and Xiushui 134). RESULTS: Both partial least squares regression and convolutional neural networks were used to establish single‐task and multi‐task models. Transfer component analysis (TCA) was used as transfer learning to learn the common features to achieve an approximate identical distribution between any two varieties. Single‐task and multi‐task models were also built using the features of the source domain, and these models were applied to the target domain. These results indicated that for models of each rice variety the prediction accuracy of most multi‐task models was close to that of single‐task models. As for TCA, the results showed that the single‐task model achieved good performance for all transfer learning tasks. CONCLUSION: Compared with the original model, good and differentiated results were obtained for the models using features learned by TCA for both the source domain and target domain. The multi‐task models could be constructed to predict SPAD values and water content simultaneously and then transferred to another rice variety, which could improve the efficiency of model construction and realize rapid detection of rice growth indicators. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

To understand the scavenging action of chlorophyll (Chla) found in most of the vegetables towards hydroxyl ( OH ∙ ) radicals, its reactions with OH ∙ radicals via RAF, HAT, and SET mechanisms have been investigated theoretically using two layer ONIOM [M06-2X/6-31G(d) (High):M06-2X/3-21G (Low)] method and M06-2X/6–311 + G(d,p) level of density functional theory. The molecular electrostatic potential (MEP), highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) plots, HOMO–LUMO energy gap, global hardness (η), global softness (S), electronegativity (χ), and electrophilicity index (ω) of Chla molecule were computed and analyzed to determine its stability and reactive sites. It is found that RAF and HAT reactions are exergonic in both gaseous and aqueous media whereas SET reactions are endergonic in both media. However, all the RAF, HAT and SET reactions studied here are found to be more favourable in aqueous media vs. gas phase. The rate constants of RAF reactions at different sites are found to be of the order of ~ 6.2 × 107–1.8 × 1010 s−1 indicating that RAF reactions would be appreciably fast. This study concludes that chlorophyll can efficiently scavenge OH ∙ radicals preferably via RAF and HAT mechanisms and intake of water with chlorophyll can enhance its scavenging actions. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive satellite-based quantitative analysis spanning two decades (2002–2022) to examine daily, monthly, and annual chlorophyll concentrations (Chl-a), sea surface temperature (SST), and primary productivity (PPeu) in the Arabian Sea (AS) and the Bay of Bengal (BoB). Utilizing MODIS-Aqua satellite data and applying standard ocean colour algorithms, we generated Chl-a, SST, and PPeu products at the scale of 1 km resolution for in-depth analyses, including long-term trends, temporal patterns, and productivity comparisons. Notable findings include elevated Chl-a concentrations in the AS during February–March and in the BoB during August–September. Chl-a in the AS peaked at 5.57 times higher than in the BoB, with primary productivity estimates ranging from 196.48 to 242.65 gC m–2 year–1 in the AS, compared to 144.15 to 172.06 gC m–2 year–1 in the BoB. This suggests that the AS fixes carbon at approximately 1.34 times the rate of the BoB, indicating its higher capacity for carbon fixation. The study highlights the AS's heightened sensitivity to climatic variations compared to the BoB, emphasizing the importance of regional climatic impacts on marine productivity. Research highlights: Comprehensive satellite-based analysis of temporal trends in chlorophyll concentrations, sea surface temperature, and primary productivity in the Arabian Sea and the Bay of Bengal over two decades. Investigation of satellite-derived ratios to study elevated chlorophyll concentrations in both seas. Arabian Sea's Chl-a peaked at 5.57 times that of the Bay of Bengal. Arabian Sea fixes carbon at approximately 1.34 times the rate of the Bay of Bengal. Insights into long-term stability and evolving dynamics of the Arabian Sea and the Bay of Bengal. Potential trajectories of chlorophyll concentrations, sea surface temperature, and primary productivity in the coming years based on 20 years of satellite data. [ABSTRACT FROM AUTHOR]

Background: In the past two decades, the impacts of Helium-Neon (He–Ne) laser on stress resistance and secondary metabolism in plants have been studied, but the signaling pathway which by laser regulates this process remains unclear. Therefore, the current study sought to explore the role of RBOH-dependent signaling in He–Ne laser-induced salt tolerance and elicitation of secondary metabolism in Salvia officinalis. Seeds were primed with He–Ne laser (6 J cm− 2) and peroxide hydrogen (H2O2, 5 mM) and 15-old-day plants were exposed to two salinity levels (0, 75 mM NaCl). Results: Salt stress reduced growth parameters, chlorophyll content and relative water content (RWC) and increased malodialdehyde (MDA) and H2O2 contents in leaves of 45-old-day plants. After 48 h of salt exposure, higher transcription levels of RBOH (encoding NADPH oxidase), PAL (phenylalanine ammonia-lyase), and RAS (rosmarinic acid synthase) were recorded in leaves of plants grown from seeds primed with He–Ne laser and/or H2O2. Despite laser up-regulated RBOH gene in the early hours of exposing to salinity, H2O2 and MDA contents were lower in leaves of these plants after 30 days. Seed pretreatment with He–Ne laser and/or H2O2 augmented the accumulation of anthocyanins, total phenol, carnasol, and rosmarinic acid and increased total antioxidant capacity under non-saline and more extensively at saline conditions. Indeed, these treatments improved RWC, and K+/Na+ ratio, enhanced the activities of superoxide dismutase and ascorbate peroxidase and proline accumulation, and significantly decreased membrane injury and H2O2 content in leaves of 45-old-day plants under salt stress. However, applying diphenylene iodonium (DPI as an inhibitor of NADPH oxidase) and N, N-dimethyl thiourea (DMTU as a H2O2 scavenger) after laser priming reversed the aforementioned effects which in turn resulted in the loss of laser-induced salt tolerance and secondary metabolism. Conclusions: These findings for the first time deciphered that laser can induce a transient RBOH-dependent H2O2 burst, which might act as a downstream signal to promote secondary metabolism and salt stress alleviation in S. officinalis plants. [ABSTRACT FROM AUTHOR]

The English Channel has the highest long-finned squid landings in the Northeast Atlantic, making squid one of the most valuable resources exploited by demersal fisheries operating in this area. This resource consists of two short-lived longfinned squid species: Loligo forbesii and L. vulgaris, which have a similar appearance (they are not distinguished by fishers) but differ in the timing of their life cycle: in L. forbesii, the recruitment peak occurs in July while in L. vulgaris recruitment peak occurs in November. The abundance and distribution of cephalopod species, such as Loligo spp., depends on favourable environmental conditions to support growth, reproduction and successful recruitment. This study investigated the role of several environmental variables (bottom temperature, salinity, current velocity, phosphate and chlorophyll concentrations) on recruitment biomass (in July for L. forbesii and November for L. vulgaris), as based on environmental data for pre-recruitment period from the Copernicus Marine Service and commercial catches of French bottom trawlers during the recruitment period over the years 2000 to 2021. To account for non-linear relationship between environmental descriptors and the biological response, General Additive Models (GAM) were fitted to the data. Separate models were obtained to forecast L. vulgaris and L. forbesii biomass indices during their respective recruitment periods. These models explain a high percentage of variation in biomass indices (65.8% for L. forbesii and 56.7% for L. vulgaris) and may be suitable to forecast the abundance (in terms of biomass) and spatial distribution of the resource. Such forecasts are desirable tools to guide fishery managers. Since these models can be fitted shortly before the start of the fishing season, their routine implementation would take place in real-time fishery management (as promoted by fishery scientists dealing with short-lived species). [ABSTRACT FROM AUTHOR]

DNA-binding one zinc finger (DOF) transcription factors are crucial plant-specific regulators involved in growth, development, signal transduction, and abiotic stress response generation. However, the genome-wide identification and characterization of AcDOF genes and their regulatory elements in kiwifruit (Actinidia chinensis) has not been thoroughly investigated. In this study, we screened the kiwifruit genome database and identified 42 AcDOF genes (AcDOF1 to AcDOF42). Phylogenetic analysis facilitated the categorization of these genes into five subfamilies (DOF-a, DOF-b, DOF-c, DOF-d, and DOF-e). We further analyzed the motifs, conserved domains, gene structures, and collinearity of the AcDOFgene family. Gene ontology (GO) enrichment analysis indicated significant enrichment in the "flower development" term and the "response to abiotic stress" category. Promoter prediction analysis revealed numerous cis-regulatory elements related to responses to light, hormones, and low-temperature and drought stress in AcDOF promoters. RNA-seq expression profiles demonstrated the tissue-specific expression of AcDOF genes. Quantitative real-time PCR results showed that six selected genes (AcDOF04, AcDOF09, AcDOF11, AcDOF13, AcDOF21, and AcDOF22) were differentially induced by abscisic acid (ABA), methyl jasmonate (MeJA), and cold, salt, and drought stresses, with AcDOF22 specifically expressed at high levels in drought-tolerant cultivars. Further experiments indicated that transient AcDOF22 overexpression in kiwifruit leaf disks reduced water loss and chlorophyll degradation. Additionally, AcDOF22 was localized to the nucleus and exhibited transcriptional activation, enhancing drought resistance by activating the downstream drought marker gene AcDREB2A. These findings lay the foundation for elucidating the molecular mechanisms of drought resistance in kiwifruit and offer new insights into drought-resistant breeding. [ABSTRACT FROM AUTHOR]

The HD-ZIP transcription factor family plays crucial roles in plant growth and abiotic stress responses. While its diverse functions and regulatory mechanisms are well-documented, its role in conferring abiotic stress tolerance in peaches remains largely unexplored. Here, we report the bioinformatics profile of PpGL2, a member of the HD-ZIP transcription factor family, and its integration into the soybean genome to assess its potential impact on drought tolerance. Localization studies in onion cells revealed nuclear localization of PpGL2-GFP fusion protein, while yeast hybridization experiments demonstrated its transactivation and DNA binding abilities. PpGL2 overexpression under drought conditions led to reduced accumulation of reactive oxygen species and malondialdehyde compared to wild-type, decreased rate of water loss, and increased chlorophyll and relative water content. Additionally, PpGL2 overexpression promoted soybean height and root length under drought stress, accompanied by altered transcription levels of stress-related genes across different plant genotypes. Furthermore, PpGL2 overexpression enhanced oxidative tolerance. Therefore, our findings suggest that PpGL2 overexpression holds promise for enhancing soybean drought resistance, offering a novel approach to improving soybean drought resistance. [ABSTRACT FROM AUTHOR]

• Methyl jasmonate (MJ) mitigated salt stress effects on purple basil. • MJ increased chlorophyll, anthocyanin, sugars, and phenolics in purple basil. • MJ increased the growth and post-harvest quality of purple basil. The objective of this study was to evaluate the potential of methyl jasmonate (MJ) in mitigating the negative effects of salt stress on purple basil. For this, plants were grown in an entirely randomized design in a 3 × 2 factorial scheme (salinity × MJ treatments), and seven replicates. Salinity levels corresponded to non-stress (irrigation water without NaCl addition), mild stress (40 mM NaCl), and severe stress (80 mM NaCl), and MJ treatments corresponded to control (0 µM – water) or 1 mM solutions, applied as leaf sprays. The growth, physiology, and post-harvest attributes of purple basil were determined. Salt stress impaired growth, photosynthetic capacity, chlorophyll content, and relative water content, while it increased electrolyte leakage (P ≤ 0.05). However, the application of exogenous methyl jasmonate (MJ) mitigated the effects of salt stress on growth and gas exchange in purple basil. MJ also increased chlorophyll and relative water content while decreasing electrolyte leakage. Mild and severe salt stress differentially impacted chlorophyll fluorescence, while methyl jasmonate (MJ) partially mitigated these effects. Salt stress also reduced the contents of anthocyanins, sugars, and phenolic compounds, whereas MJ increased their levels under both non-stressed and salt-stressed conditions. In conclusion, MJ alleviated the effects of mild and severe salt stress and enhanced the post-harvest quality of purple basil. [ABSTRACT FROM AUTHOR]

Dinitrogen (N2) fixation represents a key source of reactive nitrogen in marine ecosystems. While the process has been rather well-explored in low latitudes of the Atlantic and Pacific Oceans, other higher latitude regions and particularly the Indian Ocean have been chronically overlooked. Here, we characterize N2 fixation and diazotroph community composition across nutrient and trace metals gradients spanning the multifrontal system separating the oligotrophic waters of the Indian Ocean subtropical gyre from the high nutrient low chlorophyll waters of the Southern Ocean. We found a sharp contrasting distribution of diazotroph groups across the frontal system. Notably, cyanobacterial diazotrophs dominated north of fronts, driving high N2 fixation rates (up to 13.96 nmol N l−1 d−1) with notable peaks near the South African coast. South of the fronts non-cyanobacterial diazotrophs prevailed without significant N2 fixation activity being detected. Our results provide new crucial insights into high latitude diazotrophy in the Indian Ocean, which should contribute to improved climate model parameterization and enhanced constraints on global net primary productivity projections. [ABSTRACT FROM AUTHOR]

Excess nutrient pollution contributes to the formation of harmful algal blooms (HABs) that compromise fisheries and recreation and that can directly endanger human and animal health via cyanotoxins. Efforts to quantify the occurrence, drivers, and severity of HABs across large areas is difficult due to the resource intensive nature of field monitoring of lake nutrient and chlorophyll‐a concentrations. To better characterize how nutrients interact with other environmental factors to produce algal blooms in freshwater systems, we used spatially explicit and temporally matched climate, landscape, in‐lake characteristic, and nutrient inventory data sets to predict nutrients and chlorophyll‐a across the conterminous US (CONUS). Using a nested modeling approach, three random forest (RF) models were trained to explain the spatiotemporal variation in total nitrogen (TN), total phosphorus (TP), and chlorophyll‐a concentrations across US EPA's National Lakes Assessment (n = 2,062). Concentrations of TN and TP were the most important predictors and, with other variables, the RF model accounted for 68% of variation in chlorophyll‐a. We then used these RF models to extrapolate lake TN and TP predictions to lakes without nutrient observations and predict chlorophyll‐a for ∼112,000 lakes across the CONUS. Risk for high chlorophyll‐a concentrations is highest in the agriculturally dominated Midwest, but other areas of risk emerge in nutrient pollution hot spots across the country. These catchment and lake‐specific results can help managers identify potential nutrient pollution and chlorophyll‐a hot spots that may fuel blooms, prioritize at‐risk lakes for additional monitoring, and optimize management to protect human health and other environmental end goals. Plain Language Summary: When lakes receive large amounts of nutrients from the surrounding landscape due to fertilizer runoff or other sources of nutrient pollution, they can develop algal blooms. Algal blooms are harmful to the lake ecosystem and sometimes produce toxins which are dangerous to humans and animals. To assess this issue, lake chlorophyll‐a, a measure of algal presence, is monitored. This monitoring is limited in reach due to the expense of in‐lake sampling and the limited resolution of satellite technology. However, there is a wealth of climate, nutrient, landscape, and in‐lake characteristic data for the conterminous US (CONUS) which explains much of what contributes to nutrient pollution and algal growth. Here, we use this data in a machine learning model to predict nutrient (total nitrogen and total phosphorus) and chlorophyll‐a concentrations in about 112,000 lakes in the CONUS. We found that high chlorophyll‐a concentrations are more likely in the Midwest where agriculture is prevalent, but other areas with high lake chlorophyll‐a concentrations are present across the CONUS in nutrient pollution hot spots. These predictions of lake nutrient and chlorophyll‐a concentrations can help managers identify areas of concern, prioritize at‐risk lakes for testing, and target management to protect human health and the environment. Key Points: Chlorophyll‐a and nutrient concentrations in ∼112,000 lakes were predicted using widely available national data sets and machine learningNutrients far outweigh other environmental predictors in driving chlorophyll‐a concentrations in lakesWith more information about likely chlorophyll‐a concentrations, managers can prioritize lakes at risk for harmful algal bloom production [ABSTRACT FROM AUTHOR]

The canopy of perennial evergreen fruit trees in southern China has a unique Bidirectional Reflectance Factor (BRF) due to its complex multi-branch structure and density changes. This study aimed to address the lack of clarity regarding the changes in BRF of evergreen fruit trees in southern China. Litchi, a typical fruit tree in this region, was chosen as the subject for establishing a three-dimensional (3D) real structure model. The canopy BRF of litchi was simulated under different leaf components, illumination geometry, observed geometry, and leaf area index (LAI) using a 3D radiation transfer model. The corresponding changes in characteristics were subsequently analyzed. The findings indicate that the chlorophyll content and equivalent water thickness of leaves exert significant influences on canopy BRF, whereas the protein content exhibit relatively weak effects. Variation in illumination and observation geometry results in the displacement of hotspots, with the solar zenith angle and view zenith angle exerting significant influence on the BRF. As the LAI of the litchi orchard increases, the distribution of hotspots becomes more concentrated, and the differences in angle information are relatively smaller when observed from multiple angles. With the increase in LAI in litchi orchards, the BRF on the principal plane would be saturated, but observation at hotspots could alleviate this phenomenon. The above analysis provides a reference for quantitative inversion of vegetation parameters using remote sensing monitoring information of typical perennial evergreen fruit trees. [ABSTRACT FROM AUTHOR]

In regions like Oman, which are characterized by aridity, enhancing the water quality discharged from reservoirs poses considerable challenges. This predicament is notably pronounced at Wadi Dayqah Dam (WDD), where meeting the demand for ample, superior water downstream proves to be a formidable task. Thus, accurately estimating and mapping water quality indicators (WQIs) is paramount for sustainable planning of inland in the study area. Since traditional procedures to collect water quality data are time-consuming, labor-intensive, and costly, water resources management has shifted from gathering field measurement data to utilizing remote sensing (RS) data. WDD has been threatened by various driving forces in recent years, such as contamination from different sources, sedimentation, nutrient runoff, salinity intrusion, temperature fluctuations, and microbial contamination. Therefore, this study aimed to retrieve and map WQIs, namely dissolved oxygen (DO) and chlorophyll-a (Chl-a) of the Wadi Dayqah Dam (WDD) reservoir from Sentinel-2 (S2) satellite data using a new procedure of weighted averaging, namely Bayesian Maximum Entropy-based Fusion (BMEF). To do so, the outputs of four Machine Learning (ML) algorithms, namely Multilayer Regression (MLR), Random Forest Regression (RFR), Support Vector Regression (SVRs), and XGBoost, were combined using this approach together, considering uncertainty. Water samples from 254 systematic plots were obtained for temperature (T), electrical conductivity (EC), chlorophyll-a (Chl-a), pH, oxidation–reduction potential (ORP), and dissolved oxygen (DO) in WDD. The findings indicated that, throughout both the training and testing phases, the BMEF model outperformed individual machine learning models. Considering Chl-a, as WQI, and R-squared, as evaluation indices, BMEF outperformed MLR, SVR, RFR, and XGBoost by 6%, 9%, 2%, and 7%, respectively. Furthermore, the results were significantly enhanced when the best combination of various spectral bands was considered to estimate specific WQIs instead of using all S2 bands as input variables of the ML algorithms. [ABSTRACT FROM AUTHOR]

Numerous policies have been proposed by international and supranational institutions, such as the European Union, to surveil Earth from space and furnish indicators of environmental conditions across diverse scenarios. In tandem with these policies, different initiatives, particularly on both sides of the Atlantic, have emerged to provide valuable data for environmental management such as the concept of essential climate variables. However, a key question arises: do the available data align with the monitoring requirements outlined in these policies? In this paper, we concentrate on Earth Observation (EO) optical data applications for environmental monitoring, with a specific emphasis on ocean colour. In a rapidly changing climate, it becomes imperative to consider data requirements for upcoming space missions. We place particular significance on the application of these data when monitoring lakes and marine protected areas (MPAs). These two use cases, albeit very different in nature, underscore the necessity for higher-spatial-resolution imagery to effectively study these vital habitats. Limnological ecosystems, sensitive to ice melting and temperature fluctuations, serve as crucial indicators of a climate in change. Simultaneously, MPAs, although generally small in size, play a crucial role in safeguarding marine biodiversity and supporting sustainable marine resource management. They are increasingly acknowledged as a critical component of global efforts to conserve and manage marine ecosystems, as exemplified by Target 3 of the Kunming–Montreal Global Biodiversity Framework (GBF), which aims to effectively conserve 30% of terrestrial, inland water, coastal, and marine areas by 2030 through protected areas and other conservation measures. In this paper, we analysed different policies concerning EO data and their application to environmental-based monitoring. We also reviewed and analysed the existing relevant literature in order to find gaps that need to be bridged to effectively monitor these habitats in an ecosystem-based approach, making data more accessible, leading to the generation of water quality indicators derived from new high- and very high-resolution satellite monitoring focusing especially on Chlorophyll-a concentrations. Such data are pivotal for comprehending, at small and local scales, how these habitats are responding to climate change and various stressors. [ABSTRACT FROM AUTHOR]

Water supply limitations will likely impose increasing restrictions on future crop production, underlining a need for crops that use less water per mass of yield. Water use efficiency (WUE) therefore becomes a key consideration in developing resilient and productive crops. In this study, we hypothesized that it is possible to improve WUE under drought conditions via modulation of chloroplast signals for stomatal opening by up-regulation of non-photochemical quenching (NPQ). Nicotiana tabacum plants with strong overexpression of the PsbS gene encoding PHOTOSYSTEM II SUBUNIT S, a key protein in NPQ, were grown under differing levels of drought. The PsbS -overexpressing lines lost 11% less water per unit CO2 fixed under drought and this did not have a significant effect on plant size. Depending on growth conditions, the PsbS -overexpressing lines consumed from 4–30% less water at the whole-plant level than the corresponding wild type. Leaf water and chlorophyll contents showed a positive relation with the level of NPQ. This study therefore provides proof of concept that up-regulation of NPQ can increase WUE, and as such is an important step towards future engineering of crops with improved performance under drought. [ABSTRACT FROM AUTHOR]

In order to solve the eutrophication problem in Lake Dali, the variation of chlorophyll-a concentration in the west, center and east of Lake Dali was simulated and predicted by Hybrid Evolutionary Algorithm (HEA) based on the monitoring points data of water quality in Dali from 2000 to 2020, and the three monitoring points of chlorophyll-a concentration were revealed. The threshold and sensitivity analysis were combined to quantify the relationship between chlorophyll-a concentration and environmental factors. The results showed that: (1) the concentration of chlorophyll-a showed significant seasonal variations, which was significantly higher in summer and autumn than spring, but there was no significant difference in three monitoring points; (2) the correlation coefficients (R² ) between simulated values and actual monitoring values in the western, central and eastern parts of Lake Dali were 0.50 0.62 and 0.75, respectively. And the root mean square error (RMSE) were 2.08, 1.62 and 1.09 mg . m-3, respectively; (3) under certain range and threshold conditions, chlorophyll-a concentration had a positive correlation with permanganate index, biochemical oxygen demand, ammonia nitrogen and water temperature, and a negative correlation with water depth and Secchi disk depth, but no obvious correlation with pH. The results showed that the HEA could explain and simulate the changes of chlorophyll-a, and the accuracy of the model was high, which provided a certain data basis for the early prediction and early warning of lake Dali bloom. [ABSTRACT FROM AUTHOR]

As one of the important lakes in the "One Lake and Two Seas" of the Inner Mongolia Autonomous Region, the monitoring of water quality in Lake Daihai has attracted increasing attention, and the concentration of chlorophyll-a directly affects the water quality, making the monitoring of chlorophyll-a concentration in Lake Daihai particularly crucial. Traditional methods of monitoring chlorophyll-a concentration are not only inefficient but also require significant human and material resources. Remote sensing technology has the advantages of wide coverage and short update cycles. For lakes such as Daihai with a high salinity content, salinity is considered a key factor when inverting the concentration of chlorophyll-a. In this study, machine learning models, including model stacking from ensemble learning, a ridge regression model, and a random forest model, were constructed. After comparing the training accuracy of the three models on Zhuhai-1 satellite data, the random forest model, which had the highest accuracy, was selected as the final training model. By comparing the accuracy changes before and after adding salinity factors to the random forest model, a high-precision model for inverting chlorophyll-a concentration in hypersaline lakes was obtained. The research results show that, without considering the salinity factor, the root mean square error (RMSE) of the model was 0.056, and the coefficient of determination (R2) was 0.64, indicating moderate model performance. After adding the salinity factor, the model accuracy significantly improved: the RMSE decreased to 0.047, and the R2 increased to 0.92. This study provides a solid basis for the application of remote sensing technology in hypersaline aquatic environments, confirming the importance of considering salinity when estimating chlorophyll-a concentration in hypersaline waters. This research helps us gain a deeper understanding of the water quality and ecosystem evolution in Daihai Lake. [ABSTRACT FROM AUTHOR]

In recent years, the frequent occurrence of eutrophication problems in water bodies has been caused by changes in the climate environment and overexploitation of natural resources by humans. Chlorophyll-a, as a key indicator for water body assessment, plays an important role in eutrophication research and has a profound impact on the global biogeochemical cycle of the climate process. Studies have shown that temperature can directly or indirectly affect the concentration of chlorophyll-a by influencing the growth of algae and water quality indicators in water bodies. Considering the temperature factor in the inversion of chlorophyll-a concentration is a novel research approach. Based on the influence of temperature on chlorophyll-a concentration, we propose the idea of inverting global ocean chlorophyll-a concentration based on temperature zoning. Using monthly average remote sensing reflectance data from VIIRS (Visible and Infrared Imaging Radiometer Suite), combined with the results of temperature zoning, the OC3V(SST) model was constructed to invert the monthly average chlorophyll-a concentration in the global ocean in October 2018. The OC3V(SST) model has been validated by applying it to the remaining 11 months of January, April, July, and October in 2017, 2018, and 2019, as well as the entire 31-day dataset of October 2018. The results indicate that temperature zonation can significantly improve the inversion accuracy of chlorophyll-a and further explore the spatial distribution patterns of global chlorophyll-a concentrations across various temperature ranges based on monthly averages from the global ocean. Additionally, the study investigates the continuity issues of various models and the correlation between temperature and chlorophyll-a. [ABSTRACT FROM AUTHOR]

Water deficit is the main limiting factor constraining sugarcane productivity, and its impact is expected to increase due to climate changes. During prolonged drought periods, most plants become extremely vulnerable to ROS accumulation, which can severely damage their photosynthetic apparatus. The PetC gene, encoding a Rieske FeS protein (ISP), has been shown to regulate the electron transport chain and protect photosystems (PSs) under drought conditions in some plant species. In sugarcane, transcriptome analysis revealed that ScPetC is repressed during drought stress in the field. In this study, we have overexpressed ScPetC in tobacco plants and evaluated its role in water-deficit tolerance. Our results indicate that the ScPetC protein structure is conserved when compared to other species. ScPetC overexpression reduced the negative impact of water deficit on plant development. This effect was associated with a positive impact on ScPetC quantum efficiency and the electron transport rate of PSII, the photosynthetic rate, and water use efficiency. The total chlorophyll content under water deficit was higher in plants overexpressing ScPetC, and this was correlated with less chlorophyll degradation from oxidative damage. Together, these results demonstrate that ScPetC confers tolerance to water deficit and oxidative stresses, making it a candidate gene for crop improvement. [ABSTRACT FROM AUTHOR]

Remote sensing is a cost-effective method for monitoring chlorophyll- a (Chl- a) concentration, an indicator of eutrophication, due to its spatiotemporal effectiveness and availability of historical data. However, its application in shallow, small water bodies poses challenges due to the need for high spatial and temporal resolutions, significant optical interference from other water constituents, and depth-related accuracy issues. This study assessed the performance of Landsat-8 and Sentinel-2 satellites in estimating Chl- a concentrations in Chitgar Lake, Tehran. Semi-empirical models, constructed with 73 and 122 common data pairs between observed data and Landsat-8 and Sentinel-2 reflectance data, respectively, yielded accurate estimates. Two Band (2BAND) algorithm (green and red band ratio, R2 = 0.8, RMSE (root mean square error) = 1.12 µg.L-1 and NRMSE (normalized root mean square error) = 12.4 %) was identified as the best Landsat-8 based model. Both Normalized Difference Chlorophyll Index (NDCI) algorithm (red and red edge bands composition, R2 = 0.82, RMSE = 1.29 µg.L-1, NRMSE = 7.8 %) and 2BAND algorithm (red and red edge band ratio, R2 = 0.81, RMSE = 1.28 µg.L-1, and NRMSE = 7.7 %) were the best models for Sentinel-2 data, with a Power of 0.8. Overall, remote sensing data from both satellites demonstrated appropriate performance and can accurately estimate Chl- a concentrations in Chitgar Lake as a shallow freshwater body. [ABSTRACT FROM AUTHOR]

This paper presents the results of a mesocosm experiment for the evaluation of remote sensing chlorophyll-a (chl-a) concentration estimations in an anthropogenic water reservoir. The chl-a presence in the water causes changes in the water surface reflectance spectrum, especially in the green and red part, but many factors could affect the remote measurements of chl-a content. The in situ mesocosm method of the experiment was used for investigating the spectral reflectance of the inland water surface in a wide range of chl-a concentrations. Eight specially designed measurement boxes were placed into the water. In half of the boxes, the devices to support the development of the submerged water plant were installed. During the experiment, simultaneously, spectral data from the water surface were gathered and physical–chemical analyses of water were carried out. The obtained results confirm the usefulness of the mesocosm experiment for the remote sensing chl-a concentration algorithms being developed. The concentration of dissolved organic carbon was identified as a key factor that interfered with remote chl-a estimations in the analyzed reservoir. [ABSTRACT FROM AUTHOR]

In order to screen candidate aquaporin genes involved in resisting osmotic stress, we analyzed the physiological responses and the expression levels of aquaporin genes in garlic under drought and salt stress with 'Er Shuizao' as plant material. Different physiological indicators were detected under drought and salt stress treatments. RT-qPCR was used to detect the expression levels of the candidate aquaporin genes in specific tissues. Finally, we screened AsPIP1-3 as a candidate gene and analyzed its function. The results showed that the relative water content and chlorophyll content of leaves decreased, the O2− production rate increased, and H2O2 accumulated in garlic under drought and salt stress. The activities of SOD, POD, and CAT enzymes first increased and then decreased in garlic. The content of soluble sugar and proline increased to maintain cell osmotic balance, and the content of MDA and relative conductivity continued to increase. Most aquaporin gene expression first increased and then decreased in garlic under drought and salt stress. AsPIP1-3 gene expression is up-regulated under drought and salt stress in garlic. The relative expression was the highest on the 6th day of stress, being related to antioxidant enzyme activity and osmotic regulation. The consistent changes in gene expressions and physiological responses indicated that AsPIP1-3 played a role in resisting garlic osmotic stress. AsPIP1-3 was located on the cell membrane, being consistent with the predicted results of subcellular localization. The germination rate and root length of transgenic Arabidopsis under drought stress were significantly different from the wild type. Drought stress reduced the ROS accumulation of transgenic Arabidopsis, and the antioxidant enzyme activity was significantly higher than the wild type. The relative conductivity and MDA content significantly decreased, and the proline content increased under drought stress. The expression level of the genes related to drought stress response (AtRD22, AtP5CS, AtABF3, and AtLEA) significantly increased. The overexpression of AsPIP1-3 genes improved the drought tolerance of transgenic Arabidopsis plants, showing that AsPIP1-3 proteins enhanced drought tolerance. Our study laid a foundation for exploring the regulatory mechanism of garlic to abiotic stress. [ABSTRACT FROM AUTHOR]

Water deficit affects the growth as well as physiological and biochemical processes in plants. The aim of this study was to determine differences in physiological and biochemical responses to drought stress in two wheat cultivars—Chinese Spring (CS) and SQ1 (which are parents of a mapping population of doubled haploid lines)—and to relate these responses to final yield and agronomic traits. Drought stress was induced by withholding water for 14 days, after which plants were re-watered and maintained until harvest. Instantaneous gas exchange parameters were evaluated on the 3rd, 5th, 10th, and 14th days of seedling growth under drought. After 14 days, water content and levels of chlorophyll a+b, carotenoids, malondialdehyde, soluble carbohydrates, phenolics, salicylic acid, abscisic acid (ABA), and polyamines were measured. At final maturity, yield components (grain number and weight), biomass, straw weight, and harvest index were evaluated. Physiological and biochemical parameters of CS responded more than those of SQ1 to the 14-day drought, reflected in a greater reduction in final biomass and yield in CS. Marked biochemical differences between responses of CS and SQ1 to the drought were found for soluble carbohydrates and polyamines. These would be good candidates for testing in the mapping population for the coincidence of the genetic control of these traits and final biomass and yield. [ABSTRACT FROM AUTHOR]

To understand the spatial temporal distribution characteristics of Illex argentinus caught by trawl fishing vessels in the Southwestern Atlantic Ocean and their relationship with key marine environmental factors, this study analyzed the temporal and spatial changes in the fishing ground center of trawl vessels at the ten-day scale from December 2019 to May 2022, combining Chinese trawl fishing log data marine environmental data with satellite remote sensing marine environmental data. Utilizing the Maxent model, ten-day intervals were used as the temporal scale, and ten marine environmental factors, including sea surface temperature, sea surface height, sea surface salinity, chlorophyll concentration, temperature at 50 m and 100 m depth, and the meridional and zonal velocities of ocean currents were quantitatively analyzed to explore the correlation between the spatial distribution of catch and environmental factors. The study reveals that the trawl fishing grounds for Illex argentinus are divided into southern and northern grounds. The southern grounds first appear near 45°20′ S in December, gradually moving southeastward in February and March. The northern grounds do not appear until April, near 42° S in the high seas. On the ten-day time scale, the central fishing grounds of Illex argentinus show significant spatial variability but minor interannual differences. The Maxent model results indicate that sea surface temperature and chlorophyll a concentration are the key environmental factors influencing the spatial and temporal variability of the high seas trawl fishing grounds for most of the time, with high environmental contribution rates during the fishing season. While the range of suitable habitats with an HSI > 0.6 identified by the Maxent model varies significantly between years, a pattern is observed where the range expands at the start and end of the fishing season and contracts during the peak fishing season. This suggests that a more concentrated range of suitable habitats is conducive to accurate predictions of trawl fishing grounds, enabling efficient fishing operations. [ABSTRACT FROM AUTHOR]

Aquaculture plays a vital role in global food production, with fish pond water quality directly impacting aquatic product quality. The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) serves as a key producer of aquatic products in South China. Monitoring environmental changes in fish ponds serves as an indicator of their health. This study employed the extreme gradient boosting tree (BST) model of machine learning, utilizing Landsat imagery data, to assess Chlorophyll-a (Chl-a) concentration in GBA fish ponds from 2013 to 2022. The study also examined the corresponding spatiotemporal variations in Chl-a concentration. Key findings include: (1) clear seasonal fluctuations in Chl-a concentration, peaking in summer (56.7 μg·L−1) and reaching lows in winter (43.5 μg·L−1); (2) a slight overall increase in Chl-a concentration over the study period, notably in regions with rapid economic development, posing a heightened risk of eutrophication; (3) influence from both human activities and natural factors such as water cycle and climate, with water temperature notably impacting summer Chl-a levels; (4) elevated Chl-a levels in fish ponds compared to surrounding natural water bodies, primarily attributed to human activities, indicating an urgent need to revise breeding practices and address eutrophication. These findings offer a quantitative assessment of fish pond water quality and contribute to sustainable aquaculture management in the GBA. [ABSTRACT FROM AUTHOR]

The Zhejiang coastal waters (ZCW), which exhibit various turbidity levels, including low, medium, and high turbidity levels, are vital for regional ecological balance and sustainable marine resource utilization. Dissolved oxygen (DO) significantly affects marine organism survival and ecosystem health, yet there is limited research on remote sensing monitoring of DO in the ZCW, and the underlying mechanisms are unclear. This study addresses this gap by utilizing high-resolution Landsat 8/9 imagery and sea surface temperature (SST) data to develop a multiple linear regression (MLR) model for DO estimation. Compared to previous studies that utilize remote sensing band reflectance data as inputs, the results show that the red and blue bands are more suitable for establishing DO inversion models for such water bodies. The model was applied to analyze variations in the DO concentrations in the ZCW from 2013 to 2023, with a focus on Hangzhou Bay (HZB), Xiangshan Bay (XSB), Sanmen Bay (SMB), and Yueqing Bay (YQB). The temporal and spatial distributions of DO concentrations and their relationships with environmental factors, such as chlorophyll-a (Chl-a) concentrations, total suspended matter (TSM) concentrations, and thermal effluents, are analyzed. The results reveal significant seasonal fluctuations in DO concentrations, which peak in winter (e.g., 9.02 mg/L in HZB) and decrease in summer (e.g., 6.83 mg/L in HZB). Changes in the aquatic environment, particularly in the thermal effluents from the Sanmen Nuclear Power Plant (SNPP), significantly decrease coastal dissolved oxygen (DO) concentrations near drainage outlets. Chl-a and TSM directly or indirectly affect DO concentrations, with notable correlations observed in XSB. This study offers a novel approach for monitoring and managing water quality in the ZCW, facilitating the early detection of potential hypoxia issues in critical zones, such as nuclear power plant heat discharge outlets. [ABSTRACT FROM AUTHOR]

Based on contact data, the dynamics of the abundance (N) and biomass (B) of phytoplankton in 2013–2014 and the concentration of chlorophyll a (Cсhl) in 2000–2003 and 2008–2021 were analyzed using fast Fourier transform (FFT) under conditions of changing water temperature (T) in the vicinity of Sevastopol in the Black Sea. Estimates of the contribution made by the variability of the annual and semiannual harmonics of N, B, Cchl, T to the seasonal cycle amounted to more than 56% for all parameters. A significant relationship between B and Cchl (r < –0.83) at two stations indicates the aging of microalgae. The dominance of different groups of microalgae in the phytoplankton biomass has been detected. A typical period of 2–4 years is distinguished in the interannual variability of Cchl and T in different seasons. [ABSTRACT FROM AUTHOR]

The tropical Pacific is one of the largest ocean regions on Earth where the trace element iron limits new primary production and therefore the efficiency of carbon export to the deep sea. Although there is a long history of marine biogeochemical research in the tropical Pacific, recent advancements using GEOTRACES key parameters such as iron and nitrate isotopes (nitrate S15N and S18O) make this a good time to review the current understanding of tropical Pacific nitrate dynamics--how both regional subsurface nitrate characteristics and surface ocean nitrate utilization change with time. While this article provides a comprehensive overview of the biological, chemical, and physical processes shaping equatorial Pacific subsurface-to-surface nutrients, it principally explores the findings from the first nitrate isotope time series in iron-limited high nutrient, low chlorophyll waters. Results indicate that the preferential recycling of bioavailable iron within the euphotic zone is required to explain even the lowest observed nitrate utilization in the eastern equatorial Pacific (EEP). Furthermore, because seasonal-to-interannual nitrate utilization variability in the EEP cannot be driven by changes in iron supply, this work argues that iron recycling (and therefore bioavailable iron) is modulated by upwelling rate changes, creating a predicted and recently observed spectrum of iron limitation in the iron-limited EEP surface waters. In other words, upper ocean physics overwhelmingly dominates seasonal-to-interannual nitrate utilization in the iron-limited EEP. This new understanding of nitrate utilization in iron-limited waters helps to explain long-term changes in past equatorial Pacific nitrate utilization obtained via sedimentary proxy records and potentially complicates the efficacy of future iron fertilization of the equatorial Pacific. [ABSTRACT FROM AUTHOR]

Phytoplankton production as indicated by chlorophyll a concentrations in Lake Karāpiro, the last hydro power station lake on the Waikato River, shows high seasonal variability but a long term trend of decrease, despite an abundance of nutrients (N and P) in the river. Early studies pre-2005 of the river system's eight hydro dams identified that only Lakes Ohakuri and Arapuni thermally stratified in summer, and it was hypothesised that thermal stratification could enhance phytoplankton productivity through increased retention time. During. a more recent study (2013–2014), Lake Karāpiro was also found to be thermally stratified with the thermocline around the depth of the power station intake. In this study we found that thermal stratification enhanced phytoplankton growth by confining them to the well-lit epilimnion. However, growth was limited as the nutrient supply became depleted and zooplankton grazing was found to adversely impact phytoplankton biomass, with highest grazing pressure in spring and lowest in late summer to autumn, consistent with an increase in chlorophyll a concentration. We found that wind stress along the lake axis can induce an internal seiche, which causes a substantial stepwise change in water quality downstream as the thermocline sweeps up and down across the power station intake. [ABSTRACT FROM AUTHOR]

Chlorophyll-a (Chl-a) concentration is one of the important indicators in water bodies for assessing the ecological health of water quality. In this paper, an OGolden-DBO-XGBoost Chl-a concentration inversion model is proposed using Wuliangsu Lake as the study area, and by combining the Sentinel-2 remote-sensing satellite images and measured Chl-a concentration data in Wuliangsu Lake, the XGBoost model is optimized using the hybrid-strategy-improved dung beetle optimization algorithm (OGolden-DBO), and an OGolden-DBO-XGBoost Chl-a concentration inversion model. The OGolden-DBO-XGBoost model's coefficients of determination (R2s) were 0.8936 and 0.8850 on the training set and test set, according to the results. The root mean squared errors (RMSEs) were 3.1353 and 2.9659 μg/L, and the mean absolute errors (MAEs) were 1.8918 and 2.4282 μg/L. The model performed well and provided a strong support for the detection of Chl-a concentration in Wuliangsu Lake. [ABSTRACT FROM AUTHOR]

The Humboldt squid (Dosidicus gigas), is a significant economic species off Peru. The abundance and distribution of the species are highly susceptible to fluctuations in marine environmental conditions. The evolution of mesoscale eddies represents one of the dynamic processes in the ocean, exerting varying degrees of influence on regional biogeochemical processes from generation to dissipation. However, the mechanisms governing the regulation of abundance and spatial distribution of D. gigas off Peru during this evolution remain unclear. Therefore, this study employed normalization techniques and the habitat suitability index (HSI) model, utilizing data from the D. gigas fishery, mesoscale eddies, and marine environmental factors (including sea surface temperature, temperature at 50 m depth, and chlorophyll-a concentration) to analyze the environmental changes and the abundance, spatial distribution, and habitat changes of D. gigas during the evolution of mesoscale eddies. The results indicate that eddies undergo four stages: formation, intensification, maturity, and decay. During eddy evolution, the abundance of D. gigas exhibited an initial increase and a subsequent decrease within cyclonic eddies (CEs), whereas, within anticyclonic eddies (AEs), abundance showed a gradual decline. The outcome of the HSI model revealed that, the habitat suitability and the proportion of suitable habitat areas within eddies were highly consistent with the abundance of D. gigas during all stages of the eddies other than the intensification stage of AEs. The study speculated that both CEs and AEs can create favorable environmental conditions during the maturity stage, thereby leading to an increase in suitable habitat and abundance of D. gigas. These findings emphasize the significant impact of mesoscale eddy evolution on the abundance and habitat distribution of D. gigas. [ABSTRACT FROM AUTHOR]

Monitoring the spatio-temporal variations of surface chlorophyll-a concentration (Chl, a proxy of phytoplankton biomass) greatly benefited from the availability of continuous and global ocean color satellite measurements from 1997 onward. These two decades of satellite observations are however still too short to provide a comprehensive description of Chl variations at decadal to multi-decadal timescales. This paper investigates the ability of a machine learning approach (a non-linear statistical approach based on Support Vector Regression, hereafter SVR) to reconstruct global spatio-temporal Chl variations from selected surface oceanic and atmospheric physical parameters. With a limited training period (13 years), we first demonstrate that Chl variability from a 32- years global physical-biogeochemical simulation can generally be skillfully reproduced with a SVR using the model surface variables as input parameters. We then apply the SVR to reconstruct satellite Chl observations using the physical predictors from the above numerical model and show that the Chl reconstructed by this SVR more accurately reproduces some aspects of observed Chl variability and trends compared to the model simulation. This SVR is able to reproduce the main modes of interannual Chl variations depicted by satellite observations in most regions, including El Niño signature in the tropical Pacific and Indian Oceans. In stark contrast with the trends simulated by the biogeochemical model, it also accurately captures spatial patterns of Chl trends estimated by satellite data, with a Chl increase in most extratropical regions and a Chl decrease in the center of the subtropical gyres, although the amplitude of these trends are underestimated by half. Results from our SVR reconstruction over the entire period (1979--2010) also suggest that the Interdecadal Pacific Oscillation drives a significant part of decadal Chl variations in both the tropical Pacific and Indian Oceans. Overall, this study demonstrates that non-linear statistical reconstructions can be complementary tools to in situ and satellite observations as well as conventional physical-biogeochemical numerical simulations to reconstruct and investigate Chl decadal variability. [ABSTRACT FROM AUTHOR]