Phytoplankton Community Structure Research Articles

Stable phytoplankton community compositions in Lake Mead (Nevada-Arizona, USA) during two decades of severe drought

Lake Mead, a large reservoir on the Colorado River and a critical drinking water source for the southwestern United States, typically exhibits high water quality, characterized by low nutrient and chlorophyll-a concentrations. This stability persists despite the inflow of highly treated wastewater since the 1960s and significant water level declines since 2000, driven by the ongoing Megadrought and basin-wide consumptive use. Such environmental changes may alter phytoplankton communities, potentially leading to increased cyanobacteria abundance, which could negatively impact water quality and the aquatic ecosystem through harmful algal blooms and toxin production. Here we analyzed 17 years of phytoplankton community structure and chlorophyll-a concentrations in Lake Mead, alongside quantitative water quality data, including nutrients, temperature, and water clarity, to assess the effects of environmental changes on phytoplankton communities. Contrary to the hypothesis that cyanobacteria abundance would have increased throughout the reservoir, our results indicate that phytoplankton community structures have remained largely stable, except for shallow areas where increases in temperature or phosphorus levels were observed. Additionally, we evaluated machine learning models for predicting changes in phytoplankton community structures. While the models confidently predicted changes in total phytoplankton biovolume and chlorophyll-a concentrations within the input parameter boundaries, predictions of peak biovolume showed considerable uncertainty, emphasizing the importance of incorporating uncertainty analysis in forecasting and communicating results. This study underscores the current buffering capacity of large, oligotrophic reservoirs like Lake Mead to maintain stable phytoplankton communities despite environmental changes. However, it also highlights the potential for significant community shifts if this buffering capacity is exceeded.

Environmental and Climatic Drivers of Phytoplankton Communities in Central Asia.

Artificial water bodies in Central Asia offer unique environments in which to study plankton diversity influenced by topographic barriers. However, the complexity of these ecosystems and limited comprehensive studies in the region challenge our understanding. In this study, we systematically investigated the water environment parameters and phytoplankton community structure by surveying 14 artificial waters on the southern side of the Altai Mountains and the northern and southern sides of the Tianshan Mountains in the Xinjiang region. The survey covered physical and nutrient indicators, and the results showed noticeable spatial differences between waters in different regions. The temperature, dissolved oxygen, total nitrogen, and total phosphorus of artificial water in the southern Altai Mountains vary greatly. In contrast, the waters in the northern Tianshan Mountains have more consistent physical indicators. The results of phytoplankton identification showed that the phytoplankton communities in different regions are somewhat different, with diatom species being the dominant taxon. The cluster analysis and the non-metric multidimensional scaling (NMDS) results also confirmed the variability of the phytoplankton communities in the areas. The variance partitioning analysis (VPA) results showed that climatic and environmental factors can explain some of the variability of the observed data. Nevertheless, the residual values indicated the presence of other unmeasured factors or the influence of stochasticity. This study provides a scientific basis for regional water resource management and environmental protection.

Methylparaben changes the community composition, structure, and assembly processes of free-living bacteria, phytoplankton, and zooplankton

Parabens are common contaminants in river and lake environments. However, few studies have been conducted to determine the effects of parabens on bacteria, phytoplankton, and zooplankton communities in aquatic environments. In this study, the effect of methylparaben (MP) on the diversity and community structure of the aquatic plankton microbiome was investigated by incubating a microcosm with MP at 0.1, 1, 10, and 100 μg/L for 7 days. The results of the Simpson index showed that MP treatment altered the α-diversity of free-living bacteria (FL), phytoplankton, and zooplankton but had no significant effect on the α-diversity of particle-attached bacteria (PA). Further, the relative abundances of the sensitive bacteria Chitinophaga and Vibrionimonas declined after MP addition. Moreover, the relative abundances of Desmodesmus sp. HSJ717 and Scenedesmus armatus, of the phylum Chlorophyta, were significantly lower in the MP treatment group than in the control group. In addition, the relative abundance of Stoeckeria sp. SSMS0806, of the Dinophyta phylum, was higher than that in the control group. MP addition also increased the relative abundance of Arthropoda but decreased the relative abundance of Rotifera and Ciliophora. The β-diversity analysis showed that FL and phytoplankton communities were clustered separately after treatment with different MP concentrations. MP addition changed community assembly mechanisms in the microcosm, including increasing the stochastic processes for FL and the deterministic processes for PA and phytoplankton. Structural equation modeling analysis showed a significant negative relationship between bacteria richness and phytoplankton richness, and a significant positive relationship between phytoplankton (richness and community composition) and zooplankton. Overall, this study emphasizes that MP, at environmental concentrations, can change the diversity and structure of plankton microbial communities, which might have a negative effect on ecological systems.

Composition and temporal dynamics of the phytoplankton community in Laizhou Bay revealed by microscopic observation and rbcL gene sequencing

Laizhou Bay, a major breeding ground for economic marine organisms in the northern waters of China, is facing rapid environmental degradation. In this study, field surveys in this area were conducted in the spring, summer, and autumn of 2020. Microscopic observation and RuBisCO large subunit (rbcL) gene analysis were employed to understand the community structure and temporal dynamics of phytoplankton. The phytoplankton community structures detected by the two methods showed significant differences. Microscopic observation revealed the dominance of dinoflagellates in spring that shifted to the dominance of diatoms in summer and autumn. However, rbcL gene sequencing consistently identified diatoms as dominant throughout all three seasons, with their relative abundance showing an increasing trend. Conversely, the relative abundance of the second- and third-most abundant taxa, namely, haptophytes and ochrophytes, decreased as the seasons transitioned. rbcL gene sequencing annotated more species than microscopy. It could detect haptophytes and cryptophytes, which were overlooked by microscopy. In addition, rbcL gene sequencing detected a remarkable amount of Thalassiosira profunda, which was previously unidentified in this sea area. However, it appeared to underestimate the contribution of dinoflagellates considerably, with most taxa being only identified through microscopic identification. The two methods jointly identified 28 harmful algal bloom taxa with similar detection quantities but substantial differences in species composition. Phytoplankton communities were influenced by temperature, salinity, and nutrients. The results of this work suggest that a combination of multiple techniques is necessary for a comprehensive understanding of phytoplankton.

Long-term monitoring of water quality and phytoplankton community structure of Lake Manzala, Mediterranean Coast of Egypt

Coastal wetlands play a crucial role in supporting biodiversity, providing valuable ecosystem services, and contributing to the resilience of coastal ecosystems, making the preservation and restoration of these wetlands essential for sustainable development in coastal regions. This study focuses on Lake Manzala, a coastal wetland located on the Mediterranean Coast of Egypt, highlighting the significance of conserving and managing this unique environment. The objective of this research was to evaluate the water quality and phytoplankton structure of Lake Manzala and establish an updated long-term ecological database for the region, specifically evaluating the effectiveness of development plans that were carried out in 2017. Surface water samples were collected seasonally at eleven sites between 2010 to 2022. The findings revealed that prior to the development plans, the phytoplankton abundance in Lake Manzala exhibited high levels of eutrophication, characterized by increased abundance and species richness. The dominant phytoplankton classes in Lake Manzala were Bacillariophyceae, Chlorophyceae, and Cyanophyceae. Prior to development plans, they accounted for 46.5% and 45.0% respectively. Post-development, Bacillariophyceae increased to 62.8%, while Chlorophyceae decreased to 25.1%. Dinophyceae increased from 1.3% to 9.04%, while Cyanophyceae decreased from 6.1% to 1.6%. Based on the Trophic State Index for chlorophyll a, Lake Manzala underwent a shift from predominantly hypertrophic to eutrophic conditions. The study explored the relationship between biological factors and environmental conditions using principal component analysis, cluster analysis, and the modified water quality index (WQI). The results indicated positive signs of improvement in Lake Manzala during the post-development phase, as it transitioned from a poor to a moderate state. This research emphasizes the need for integrated land and water management approaches. By informing policy direction and development, this research underscores the importance of preserving and restoring ecosystems for the long-term well-being of both local communities and the global environment.

Phytoplankton community structure at the eastern entrance of the Gulf of California during El Niño 2023

This work aims to report the phytoplankton species composition and cell density in the Mazatlán coastal region (eastern entrance of the Gulf of California (GC), Mexico) during the strong El Niño event of 2023. Surface water samples (at 2 m depth) for phytoplankton cell determinations were collected in December of 2023, a month in which the numerical value of the Oceanic Niño Index was 2.0. The results showed a total of 197 species, including 113 diatoms (with a total cell density of 51,444 cells L-1), 76 dinoflagellates (with a total cell density of 31,260 cells L-1), four silicoflagellates (with a total cell density of 420 cells L-1), two cyanobacteria (with a total cell density of 2,020 cells L-1), one Euglenophyta (with a total cell density of 900 cells L-1) and one ciliate (with a total cell density of 7,820 cells L-1). The diatom Dactyliosolen phuketensis (B.G.Sundström) G.R. Hasle, 1996 and the dinoflagellate Gyrodinium fusiforme (Kofoid & Swezy, 1921) presented the highest cell densities with 4,100 and 5,900 cells L-1, respectively, species that have been previously reported in high abundances in Mexican waters in years of warming events, including El Niño. The results presented here contribute to understanding the effects of strong El Niño events on the phytoplankton community structure of the southeastern GC. This topic still needs to be fully addressed. This study also provides an update on the taxonomic lists available for the region of a group of organisms whose nomenclature has been constantly changing in recent years.

Effect of the ten-year fishing ban on change of phytoplankton community structure: Insights from the Gan River.

The Yangtze River is one of the largest riverine ecosystems in the world and is a biodiversity hotspot. In recent years, this river ecosystem has undergone rapid habitat deterioration due to anthropogenic disturbances, leading to a decrease in freshwater biodiversity. Owing to these anthropogenic impacts, the Chinese government imposed a "Ten-year fishing ban" (TYFB) in the Yangtze River and its tributaries. Ecological changes associated with this decision have not been documented to evaluate the level of success. This study evaluates the success of the TYFB by analyzing the changes in phytoplankton communities and comparing them to periods before the TYFB was imposed. A total of 325 phytoplankton species belonging to 7 phyla and 103 genera dominated by Chlorophyceae and Bacillariophyceae were identified. Species diversity according to the Shannon-Wiener ranged between 1.19 and 3.00. The results indicated that phytoplankton diversity increased, while both density and biomass decreased after the TYFB. The health of the aquatic ecosystem appeared to have improved after the TYFB, as indicated by the phytoplankton-based index of biotic integrity. Significant seasonal and spatial differences were found among the number of species, density, and biomass of phytoplankton, where these differences were correlated with pH, water depth, chlorophyll-a, permanganate index, transparency, copper, ammonia nitrogen, and total phosphorus based on redundancy analysis. Results from this study indicate that the TYFB played an important role in the restoration of freshwater ecosystem in the Yangtze River and its tributaries.

Seasonal variability of phytoplankton community structure in a coastal station of the Argentine continental shelf based on a chemotaxonomic approach

Seasonal variability of phytoplankton community structure in a coastal station of the Argentine continental shelf based on a chemotaxonomic approach

Phytoplankton dynamics in a shellfish farming lagoon in a deltaic system threatened by ongoing climate change

Global climate change has generated an increasing number of environmental problems, especially in Mediterranean coastal areas, such as the Po Delta (PD), where shellfish production has undergone an overall decline because of strong environmental changes. The present study is centred on assessing the fundamental ecological aspects in one of the most crucial European shellfish production lagoons, Sacca degli Scardovari (SC), addressing phytoplankton community parameters directly affecting shellfish production, namely, chemotaxonomic composition, size fractions, and total biomass, in relation to the physicochemical properties of the water column and mussel filtering activity. Our findings suggest that the phytoplankton community structure, its role within the lagoon food web and its production cycles depend on two distinct allogenic inputs, which shape the community differently and exert substantial control on shellfish production. At the same time, the suspended mussel biomass strongly controls the phytoplankton size composition, as their growth is largely supported by nanophytoplankton. As the Po River collects the drainage waters of the Italian side of the entire Alpine Arch, the phytoplankton dynamics reported here represent a useful baseline for further addressing issues of climatic changes affecting lagoon ecology. We believe that our study presents an innovative tool for the planning and management of interventions aimed at enhancing national mussel production without neglecting aspects of environmental protection or the integrity of the coastal system, with significant scientific implications.

Silver nanoparticles alter planktonic community structure and promote ecosystem respiration in freshwater mesocosms

The widespread use of silver nanoparticles (AgNPs) has resulted in their release into the aquatic environment, which threatens the health of aquatic ecosystems. Although the ecotoxicological effects of AgNPs have been widely reported at individual and population levels, the impact of long-term exposure to AgNPs on community structure and ecosystem function in aquatic ecosystems remains poorly understood. Herein, the present study investigated the effects of long-term exposure (28 d) to environmentally relevant concentrations (1 μg/L and 10 μg/L) of AgNPs on the community structure and function of freshwater ecosystems by artificially constructed 28 mesocosms freshwater ecosystem in experimental greenhouses, using plastic water tanks and food web manipulation. The results showed that long-term exposure to AgNPs significantly altered the community structure of zooplankton, phytoplankton, and bacterioplankton in the aquatic ecosystem. Exposure to 10 μg/L AgNPs significantly reduced the zooplankton density (70.3%, p < 0.05) and increased the phytoplankton biomass and bacterial richness and diversity via a “top-down effect.” With regards to ecosystem function, AgNPs exposure significantly increased the respiration in freshwater ecosystems but did not have a significant effect on decomposition. The partial least squares path modeling (PLS-PM) further revealed that AgNPs may have a negative impact on ecosystem functions by reducing zooplankton community density and thus increasing phytoplankton biomass. This study is the first to show that long-term exposure to environmentally relevant concentrations of AgNPs leads to alterations in plankton community structure and promotes respiration in freshwater ecosystems. It emphasizes the need for assessing the environmental risk of long-term exposure to AgNPs at the ecosystem level.

Research on the Relationship between Eukaryotic Phytoplankton Community Structure and Key Physiochemical Properties of Water in the Western Half of the Chaohu Lake Using High-Throughput Sequencing

Eukaryotic phytoplankton play a major role in the circulation of material and energy in a lake’s ecosystem. The acquisition of information on the eukaryotic phytoplankton community is extremely significant for handling and regulating the ecosystems of lakes. In this study, samples were collected from the western half of Chaohu Lake in the summer and winter periods. Analyses revealed that the eukaryotic phytoplankton in this region comprised 70 genera, 34 orders, and 7 phyla. There were 61 genera, 29 orders, and 7 phyla in summer, and 25 genera, 14 orders, and 5 phyla in winter. The dominant genus was Chlamydomonas of Chlorophyta in summer. In contrast, the dominant genus was Mychonastes of Chlorophyta in winter. The diversity index analysis revealed that the eukaryotic phytoplankton community exhibited greater fluctuation in the summer than in the winter. Moreover, analysis of the physiochemical properties of the water samples showed considerable spatial and temporal differences in the water quality. This paper focusses primarily on analysing the influence of the physiochemical properties of water on the eukaryotic phytoplankton community. In particular, the effects of the major physicochemical properties of water on the community evolution of eukaryotic phytoplankton classes were evaluated using the redundancy analysis method. The findings demonstrated that total phosphorus (TP), PO4−-P, NH4+-N, and total nitrogen (TN) were the primary influencing factors in summer, whereas NO3−-N, DO, and water temperature (WT) were the major influencing factors in winter. Subsequently, the Mantel test revealed that the phylum level of the eukaryotic phytoplankton community was significantly correlated with WT, DO, NH4+-N, TN, TP, and Chlorophyll a. Variance partitioning analysis indicated that seasonal factors accounted for a large proportion of the variation in the eukaryotic phytoplankton community, reaching 48.4%. Subsequently, co-occurrence network analysis demonstrated that most families of eukaryotic phytoplankton were facilitated mutually, with the proportion of promotion being 94.1%. This study provides insight into the crucial factors that influence the phytoplankton communities and a reasonable control direction for the positive evolution of the eukaryotic phytoplankton community in the western half of Chaohu Lake.

Exploring the response and prediction of phytoplankton to environmental factors in eutrophic marine areas using interpretable machine learning methods

Coastal marine areas are frequently affected by human activities and face ecological and environmental threats, such as algal blooms and climate change. The community structure of phytoplankton—primary producers in marine ecosystems—is highly sensitive to environmental factors, such as temperature, salinity, and nutrients. However, traditional methods for exploring the relationship between phytoplankton communities and environmental factors in eutrophic marine areas are limited by various factors. Therefore, this study employed interpretable machine learning models, integrating high-dimensional data analysis and complex system modeling, to quantitatively and thoroughly analyze the dynamic relationship between phytoplankton communities and environmental variables in high-frequency samples collected over 53 weeks from eutrophic marine areas. The cell abundance of phytoplankton exhibited a distinct “two-peak pattern” variation. Interpretable machine learning model analysis revealed the dynamic contributions of different environmental factors during changes in the phytoplankton community structure. The results showed that temperature was a key environmental factor that affected phytoplankton growth during peak periods. In addition, the contribution of salinity increased during the second peak in phytoplankton abundance, highlighting its central role in the ecological dynamics of this phase. During green tide outbreaks, particularly in Area 01, the contributions of factors such as temperature and salinity increased, whereas those of phosphates and silicates decreased, indicating that green tide outbreaks substantially altered the nutritional dynamics of the ecosystem. Furthermore, different phytoplankton species, such as Skeletonema costatum, Thalassiosira spp., and Nitzschia spp., exhibit varying responses to environmental factors. Hence, the predictions made using random forest and generalized additive models for phytoplankton cell abundance in two marine areas revealed complex nonlinear relationships between environmental factors, such as temperature, salinity, and phytoplankton abundance.

Spatial phytoplankton community structure revealed by photosynthetic pigments in the tropical estuarine-coastal zone (Bangladesh)

To explore the variation of phytoplankton community along the Bakkhali river estuary and its adjacent coastal water in the north of the Bay of Bengal, total Chl-a (TChl-a) concentrations and group-specific photosynthetic pigments were investigated during April 2017. Distinct spatial distribution was observed in temperature, turbidity and nutrient concentrations as well as in TChl-a concentrations, showing a seaward decreasing pattern. The different distribution of phytoplankton pigments and functional groups along the gradients was also observed. Chlorophyll-b and zeaxanthin showed their highest abundance in the turbid riverine water, while alloxanthin and prasinoxanthin dominated in the coastal water. High concentrations of fucoxanthin, peridinin and hex-fucoxanthin were associated with high-light availability and showed a seaward increasing trend. Three phytoplankton groups can be classified: the riverine group (chlorophytes and cyanobacteria), the coastal group (cryptophytes and prasinophytes) and the offshore group (diatoms, dinoflagellate and haptophytes_type 6). The predominance of cryptophytes (avg. 48%) over diatoms (avg. 28%) was basically influenced by the scarcity of nitrogen and silicate relative to phosphate. Not only availability of nutrients, the photosynthetically active radiation also plays a key role in regulating TChl-a, photosynthetic pigments and functional groups in this tropical estuarine-coastal zone.

Community Structure and Toxicity Potential of Cyanobacteria during Summer and Winter in a Temperate-Zone Lake Susceptible to Phytoplankton Blooms.

Cyanobacterial blooms are increasingly common during winters, especially when they are mild. The goal of this study was to determine the summer and winter phytoplankton community structure, cyanotoxin presence, and toxigenicity in a eutrophic lake susceptible to cyanobacterial blooms throughout the year, using classical microscopy, an analysis of toxic cyanometabolites, and an analysis of genes involved in biosynthesis of cyanotoxins. We also assessed whether cyanobacterial diversity in the studied lake has changed compared to what was reported in previous reports conducted several years ago. Moreover, the bloom-forming cyanobacterial strains were isolated from the lake and screened for cyanotoxin presence and toxigenicity. Cyanobacteria were the main component of the phytoplankton community in both sampling times, and, in particular, Oscillatoriales were predominant in both summer (Planktothrix/Limnothrix) and winter (Limnothrix) sampling. Compared to the winter community, the summer community was denser; richer in species; and contained alien and invasive Nostocales, including Sphaerospermopsis aphanizomenoides, Raphidiopsis raciborskii, and Raphidiopsis mediterranea. In both sampling times, the blooms contained toxigenic species with genetic determinants for the production of cylindrospermopsin and microcystins. Toxicological screening revealed the presence of microcystins in the lake in summer but no cyanotoxins in the winter period of sampling. However, several cyanobacterial strains isolated from the lake during winter and summer produced anabaenopeptins and microcystins. This study indicates that summer and winter blooms of cyanobacteria in the temperate zone can differ in biomass, structure, and toxicity, and that the toxic hazards associated with cyanobacterial blooms may potentially exist during winter.

Spatial-temporal distributions of phytoplankton shifting, chlorophyll-a, and their influencing factors in shallow lakes using remote sensing

Understanding phytoplankton dynamics is crucial for assessing water ecosystem health. However, traditional in-situ investigations often fall short of capturing the spatial-temporal variations of phytoplankton community structure and biomass on large scales. This study introduces a novel approach that harnesses the power of machine learning models coupled with Sentinel-2 satellite data to predict phytoplankton shifting and chlorophyll-a (Chla) in three large shallow lakes in central China from 2016 to 2021. We employed an array of machine learning algorithms, including the extreme gradient boosting method (XGBoost), random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN) to retrieve Chla and phytoplankton groups clustered by hierarchical cluster analysis based on the Bray-Curtis similarity index (HCA). The influences of environmental factors, including meteorology and nutrients, on phytoplankton dynamics were then explored. Our results identified four distinct phytoplankton groups, each exhibiting unique structural characteristics according to the HCA. The RF and XGBoost, utilizing top-of-atmosphere reflectance, provided the most accurate estimations of phytoplankton shifting and Chla, respectively. The red edge-red ratio emerged as a key variable in both models, underlining the significance of red edge bands in phytoplankton monitoring. We mapped spatial-temporal patterns of phytoplankton group occurrence and average Chla concentration across the three lakes. Our findings indicated that eutrophication extended periods of cyanobacteria domination and algal blooms, particularly in bays and nearshore areas. Redundancy analysis and classification and regression tree model highlighted temperature as a significant driver of phytoplankton shifting, while nutrient levels exhibited stronger influences on Chla. Our study underscores the potential of integrating machine learning models with Sentinel-2 data to enhance the monitoring and prediction of phytoplankton dynamics in shallow lakes. This approach offers valuable insights for the early detection of algal blooms and informed management strategies, contributing to the preservation and sustainable management of aquatic ecosystems.

Different pacemakers of marine organic carbon burial in the North Yellow Sea from the early to late Holocene

Marginal seas play a crucial role in the global carbon cycle, contributing approximately 20% of the net CO2 uptake by the world's oceans. The North Yellow Sea (NYS), a semi-enclosed marginal sea in the western Pacific Ocean, serves as a significant carbon sink influenced by factors such as sea level changes, monsoon dynamics, ocean currents, and El Niño-Southern Oscillation (ENSO) activity. This study examines a 538 cm-long sediment core W03 from the NYS (10.3 kyr BP to present). We analyzed Organic Carbon (OC) proxies, including total organic carbon (TOC), the stable carbon isotope (δ13C) of TOC, total nitrogen (TN) and marine-produced lipid biomarkers. Our findings demonstrate a strong correlation between OC burial and sea-level fluctuations during the early Holocene, driven by the exposure of the continental shelf. During the mid-Holocene, the intrusion of the Yellow Sea Warm Current increased salinity and temperature conditions and nutrient levels of sea water, thereby enhancing marine OC burial. Over the last 3.3 kyr BP, ENSO variability and intensified East Asian Winter Monsoon (EAWM) strengthened the coastal currents, contributing to better OC preservation. Additionally, human activities on the surrounding mainland increased terrestrial input, altering the OC burial. The heightened activity of the Kuroshio Current also introduced more saline and warm water masses into the NYS, impacting phytoplankton productivity and community structure, as indicated by increased C37 alkenones. This study highlights the complex interplay of geological factors in OC burial processes within the NYS, providing valuable insights into OC dynamics in marginal seas under the impact of global change.

Different amino acid compositions and food quality of particulate organic matter driven by two major phytoplankton groups in the Ross Sea

Understanding the amino acid (AA) composition of phytoplankton-derived particulate organic matter (POM) is crucial for unraveling phytoplankton physiology and assessing food quality. This study investigated AA compositions and food quality of POM associated with two phytoplankton groups in the Ross Sea polynya. The Phaeocystis antarctica (P. antarctica)-predominant group, with P. antarctica overwhelmingly contributing to its composition, demonstrated a fresh state and heightened physiological activity. In contrast, the P. antarctica and diatoms-mixed group exhibited superior essential amino acids (EAAs) contributions and a higher EAA index (EAAI), indicating enhanced food quality. Dissimilarity Index values provided nuanced insights into degradation stages not captured by other indices. Despite lower physiological activity, diatoms in the mixed group stood out as crucial sources of high-quality nutrition for higher trophic levels in the Ross Sea polynya. The observed variations in AA compositions not only reflected the phytoplankton community structure but also provided insights into their physiological conditions. Given the ongoing climate-induced environmental changes potentially influencing phytoplankton communities, this study underscores the potential impacts on the intricate food web dynamics in the Ross Sea polynya. The assessment of AA composition emerged as a valuable tool for understanding the ecological implications for higher trophic levels in this polar region.

Applying feature-similarity-metrics for long-tailed problem of phytoplankton microscopic images classification

The distribution of phytoplankton in natural water bodies holds significant importance for aquatic for maintaining healthy aquatic environments. While deep learning has become a popular research field of automating phytoplankton identification, its performance is diminished by the uneven species distribution in nature world, which biases classification models towards more advantaged species. Addressing this long-tailed problem, the paper proposed a novel deep learning idea termed feature-similarity-metrics species which utilizes the principle of decreasing intra-class differences and increasing the inter-class differences. A method called feature-center-constraint is introduced to implement the idea. Initially, a center is presupposed for each category, and these centers are distributed evenly and sparsely within the feature space using a uniform distribution initialization method. During model training, the Euclidean distance is used to measure the similarity between sample features and corresponding center. This approach enhanced the model's ability to predict disadvantaged phytoplankton species without compromising the classification accuracy of advantaged ones. Using a phytoplankton dataset of 26,564 images from 68 genera, collected from Chaohu Lake, this method demonstrates superior performance compared to existing techniques. Specially, it achieves a 5.69 % increase in micro-average F1 score and an 11.85 % rise in macro-average F1 score over general method, while F1 score is widely recognized as a reliable metric for measuring classification models' performance. These enhancements not only increase the accuracy of automated phytoplankton identification but also facilitate more effective monitoring of biodiversity and ecological health in aquatic systems. This makes the research highly beneficial for practical applications in environmental science, offering rapid and accurate insights into phytoplankton community structures, for ecological assessments.

High-frequency monitoring reveals phytoplankton succession patterns and the role of cryptophyte in a subtropical river reservoir

Shifts in phytoplankton biodiversity drive frequent community successions within aquatic ecosystems. While significant progress has been made in understanding the factors influencing phytoplankton growth under controlled conditions, the processes driving phytoplankton succession in natural waters remain poorly understood. To address this gap, we conducted high-frequency monitoring of the phytoplankton community in the Jiangdong River Reservoir (Southeast China) from 2021 to 2022. High-frequency data captured 17 complete cycles of phytoplankton growth and decline. Our analysis showed that cryptophytes, though not the dominant group, played a key role in shaping the temporal dynamics of phytoplankton community structure and abundance. The relative abundance of cryptophytes consistently mirrored fluctuations in total chlorophyll-a concentrations. Rainfall-induced low-light conditions enhanced the competitive advantage of cryptophytes. Throughout each cycle of phytoplankton changes, cryptophytes leveraged this short-term advantage to proliferate by utilizing available ammonia nitrogen, ascending from the fourth to the second most abundant group. However, as ammonia nitrogen levels declined to 0.1 mg/L, cryptophyte populations began to decline. These new findings highlight the critical role of cryptophytes in driving phytoplankton succession patterns.

Distribution, community structure and assembly patterns of phytoplankton in the northern South China Sea.

A cruise was conducted in the summer of 2023 from the Pearl River Estuary (PRE) to the adjacent waters of the Xisha Islands in the northern South China Sea (NSCS) to investigate the distribution, community structure, and assembly patterns of eukaryotic and prokaryotic phytoplankton using high-throughput sequencing (HTS) and microscopic observation. Dinophyta were the most abundant phylum in the eukaryotic phytoplankton community based on HTS, accounting for 92.17% of the total amplicon sequence variants (ASVs). Syndiniales was the most abundant order among eukaryotic phytoplankton, whereas Prochlorococcus was the most abundant genus within cyanobacteria. The alpha diversity showed the lowest values in the PRE area and decreased gradually with depth, while cyanobacteria exhibited higher alpha diversity indices in the PRE and at depths ranging from 75 m to 750 m. The morphological results were different from the data based on HTS. Diatoms (37 species) dominated the phytoplankton community, with an average abundance of 3.01 × 104 cells L-1, but only six species of dinoflagellate were observed. Spearman correlation analysis and redundancy analysis (RDA) showed that the distribution and community structure of phytoplankton were largely influenced by geographical location and environmental parameters in the NSCS. The neutral community model (NCM) and null model indicated that deterministic processes played a significant role in the assembly of eukaryotic phytoplankton, with heterogeneous selection and homogeneous selection accounting for 47.27 and 29.95%, respectively. However, stochastic processes (over 60%) dominated the assembly of cyanobacteria and undominated processes accounted for 63.44%. In summary, the formation of eukaryotic phytoplankton was mainly influenced by environmental factors and geographic location, but the assembly of cyanobacteria was shaped by both stochastic processes, which accounted for over 60%, and environmental selection in the NSCS.