Phytoplankton Populations Research Articles

Seasonal dynamics and diversity of Antarctic marine viruses reveal a novel viral seascape

The Southern Ocean microbial ecosystem, with its pronounced seasonal shifts, is vulnerable to the impacts of climate change. Since viruses are key modulators of microbial abundance, diversity, and evolution, we need a better understanding of the effects of seasonality on the viruses in this region. Our comprehensive exploration of DNA viral diversity in the Southern Ocean reveals a unique and largely uncharted viral landscape, of which 75% was previously unidentified in other oceanic areas. We uncover novel viral taxa at high taxonomic ranks, expanding our understanding of crassphage, polinton-like virus, and virophage diversity. Nucleocytoviricota viruses represent an abundant and diverse group of Antarctic viruses, highlighting their potential as important regulators of phytoplankton population dynamics. Our temporal analysis reveals complex seasonal patterns in marine viral communities (bacteriophages, eukaryotic viruses) which underscores the apparent interactions with their microbial hosts, whilst deepening our understanding of their roles in the world’s most sensitive and rapidly changing ecosystem.

A portable and low-cost optical device for pigment-based taxonomic classification of microalgae using machine learning

The proliferation of certain phytoplankton species may lead to harmful algal blooms (HABs) that can affect living resources and human health. Therefore, an accurate identification of phytoplankton populations is essential for the sustainable management of some activities relevant for the blue economy, such as aquaculture, being also relevant for environmental monitoring and marine research purposes. Microalgae taxonomic discrimination, based on their pigment composition, is a versatile and promising technique to detect and identify potential HABs. In this work, a portable and low-cost device for taxonomic identification of microalgae, based on the pigment composition of 16 species belonging to 6 different phyla, was developed. It uses the fluorescence intensity signal emitted by each species at three wavelengths (575 nm, 680 nm and 730 nm) when excited at five wavelengths (405 nm, 450 nm, 500 nm, 520 nm and 623 nm) to create a fluorescence signature for each species. Furthermore, several machine learning classifiers were studied using this fluorescence signature as features to train and classify each species according to their respective taxonomic group. The Extreme Gradient Boosting (XGBoost) classifier was able to correctly predict microalgae monocultures with 97 % accuracy at the phylum level and 92 % accuracy at the order level. The obtained results confirm the potential of this technique for fast, accurate and low-cost identification of microalgae.

Structure and Dynamics of Phytoplankton Populations in the Black Sea from 2014 to 2017

This study investigated the seasonal variations in phytoplankton communities along the Turkish Black Sea coast (2014-2017). Analyzing data from 20 stations revealed high biodiversity (175 species across 14 classes). Phytoplankton abundance showed distinct seasonality, with a peak in summer (2017, 96.6%) and lows in winter, characterized by dinoflagellate dominance. In terms of biomass, diatoms dominated in half of the sampling seasons, while dinoflagellates dominated in the other half at the surface during the study period. The dominance of dinoflagellates corresponded to the winters and summers of 2015 and 2016. The winter conditions of 2015 were harsh. The surface water community composition sometimes varied from that of the water column. Importantly, 44 potentially toxic species were identified, comprising 25% of the community and significantly contributing to both abundance (73.2-98.6%) and biomass (64-90.2%) throughout the year. These results highlight the unique structure of the Black Sea phytoplankton community, characterized by seasonal dominance of diatoms and a significant presence of potentially toxic species. Season, year, and water depth significantly influenced the phytoplankton assemblages. Water temperature was negatively correlated with dissolved oxygen and nitrogen-based nutrients.

Drivers of phytoplankton distribution, abundance and community composition off East Antarctica, from 55-80°E (CCAMLR Division 58.4.2 East)

Southern Ocean phytoplankton form the base of the Antarctic food web, influencing higher trophic levels through biomass and community structure. We examined phytoplankton distribution and abundance in the Indian Sector of the Southern Ocean during austral summer as part a multidisciplinary ecosystem survey: Trends in Euphausiids off Mawson, Predators and Oceanography (TEMPO, 2021). Sampling covered six meridional transects from 55-80°E, and from 62°S or 63°S to the ice edge. To determine phytoplankton groups, CHEMTAX analysis was undertaken on pigments measured using HPLC. Diatoms were the dominant component of phytoplankton communities, explaining 56% of variation in chlorophyll a (Chl a), with haptophytes also being a major component. Prior to sampling the sea ice had retreated in a south-westerly direction, leading to shorter ice-free periods in the west (< 44 days, ≤65°E) compared to east (> 44 days, ≥70°E), inducing a strong seasonal effect. The east was nutrient limited, indicated by low-iron forms of haptophytes, and higher silicate:nitrate drawdown ratios (5.1 east vs 4.3 west), pheophytin a (phaeo) concentrations (30.0 vs 18.4 mg m-2) and phaeo:Chl a ratios (1.06 vs 0.53). Biological influences were evident at northern stations between 75-80°E, where krill “super-swarms” and feeding whales were observed. Here, diatoms were depleted from surface waters likely due to krill grazing, as indicated by high phaeo:Chl a ratios (> 0.75), and continued presence of haptophytes, associated with inefficient filtering or selective grazing by krill. Oceanographic influences included deeper mixed layers reducing diatom biomass, and a bloom to the north of the southern Antarctic Circumpolar Current Front in the western survey area thought to be sinking as waters flowed from west to east. Haptophytes were influenced by the Antarctic Slope Front with high-iron forms prevalent to the south only, showing limited iron transfer from coastal waters. Cryptophytes were associated with meltwater, and greens (chlorophytes + prasinophytes) were prevalent below the mixed layer. The interplay of seasonal, biological and oceanographic influences on phytoplankton populations during TEMPO had parallels with processes observed in the BROKE and BROKE-West voyages conducted 25 and 15 years earlier, respectively. Our research consolidates understanding of the krill ecosystem to ensure sustainable management in East Antarctic waters.

Inter-annual variability in the phytoplankton abundance and community structure of a tropical monsoonal estuary in response to variable monsoonal patterns

The study elucidates the inter-annual and seasonal variability in the phytoplankton abundance and community structure concurrent to variable rainfall patterns in Chettuva, a tropical monsoonal estuary along the southwest coast of India. The phytoplankton community exhibited statistically significant inter-annual variation with a 1.2-fold increase in abundance (av. 19.44 ×104cellsL-1) in 2021 in comparison with 2019 (av. 15.30 ×104cellsL-1). A total of 87 phytoplankton species were observed during the study, among which Bacillariophyceae dominated with 52 species, followed by Dinophyceae (20 species), and to a lesser extent by Chlorophyceae (11 species) and Cyanophyceae (2 species). Phytoplankton community structure also showed variation, with 71 species contributing to the diversity in 2021 and 62 species in 2019. Though Bacillariophycea dominated during both years, the community was more diverse in 2021comparatively a more saline year Biddulphia sp. Coscinodiscus sp., Cyclotella striata, Melosira sulcata, Pleurosigma sp., and Skeletonema costatum were the diatoms common to both the years, though with varied abundance. Dinophyceae did not show a conspicuous change in community structure in both years, with mostly the same species contributing to the total phytoplankton population but showed slight variation in abundance. Cyanophycea was observed exclusively during the year 2021. The abundance and community structure of the species varied from year to year, but they also changed seasonally, with lower numbers observed during the monsoon compared to the post-monsoon period in both years. CCA analysis identified two groups of phytoplankton assemblages in relation to the prevailing environmental conditions. The Group I species preferred lower temperature and salinity and elevated nutrient concentrations and they dominated during the monsoon season. Group II species preferred high saline well oxygenated, nutrient-poor waters and they dominated during the post-monsoon. Furthermore, the trophic state index analysis indicated mesotrophic conditions during the monsoon, while eutrophic conditions were observed in the post-monsoon period.

Fingerprints of El Niño Southern Oscillation on global and regional oceanic chlorophyll-a timeseries (1997–2022)

With the advancement of present-day ocean color satellites, the spatiotemporal variations of oceanic Chlorophyll-a (Chl-a) concentration, a proxy for phytoplankton population, can be monitored at regional and global scales. Estimating long-term changes in Chl-a concentration, however, is mainly constrained by the limited availability of ocean color data and the significant influence of climate oscillations like the El Niño Southern Oscillation (ENSO). In this study, we investigate the influence of ENSO on regional and global Chl-a timeseries using two ocean color datasets spanning from September 1997 to December 2022. Our analysis found that global Chl-a concentration exhibits a significant increasing trend over the study period. Most of the increasing trends are detected in the Southern Hemisphere (SH) and high-latitudinal regions, including the Southern Ocean. In light-limited regions like the Southern Ocean, enhanced Chl-a concentration shows a strong positive correlation with warming sea surface temperature (SST). Conversely, most oceans in the Northern Hemisphere (NH) exhibit decreasing trends, which are highly correlated with warming SST and ENSO phases. Additionally, we identify distinctive fingerprints of four extreme ENSO events: two extreme El Niño events (1997–1998 and 2015–2016) and two triple La Niña events (1998–2001 and 2020–2022) in these timeseries. Furthermore, empirical orthogonal function (EOF) analysis reveals that the dominant mode of interannual Chl-a variability is associated with an ENSO-like pattern, accounting for about 13 % of the total variability. Intriguingly, this principal mode is highly correlated with the phases of ENSO (R = -0.87) and Pacific Decadal Oscillation (PDO) (R = −0.90) on an interannual scale. This study provides insights into assessing the impacts of ENSO on long-term Chl-a trend estimation.

Development of an Algorithm for Semantic Segmentation of Earth Remote Sensing Data to Determine Phytoplankton Populations

Introduction. Computer vision is widely used for semantic segmentation of Earth remote sensing (ERS) data. The method allows monitoring ecosystems, including aquatic ones. Algorithms that maintain the quality of semantic segmentation of ERS images are in demand, specifically, to identify areas with phytoplankton, where water blooms— the cause of suffocation — are possible. The objective of the study is to create an algorithm that processes satellite data as input information for the formation and checking of mathematical models of hydrodynamics, which are used to monitor the state of water bodies. Various algorithms for semantic segmentation are described in the literature. New research focuses on enhancing the reliability of recognition — often using neural networks. This approach is modified in the presented work. To develop the direction, a new set of information from open sources and synthetic data are proposed. They are aimed at improving the generalization ability of the model. For the first time, the contour area of the phytoplankton population is compared to the database — and thus the boundary conditions are formed for the implementation of mathematical models and the construction of boundary-adaptive grids.Materials and Methods. The set of remote sensing images was supplemented with the author's augmentation algorithm in Python. Computer vision segmented areas of phytoplankton populations in the images. The U-Net convolutional neural network (CNN) was trained on the basis of NVIDIA Tesla T4 computing accelerators.Results. To automate the detection of phytoplankton distribution areas, a computer vision algorithm based on the U-Net CNN was developed. The model was evaluated by the calculated values of the main quality metrics related to segmentation tasks. The following metric values were obtained: Precision = 0.89, Recall = 0.88, F1 = 0.87, Dice = 0.87, and IoU = 0.79. Graphical visualization of the results of CNN learning on the training and validation sets showed good quality of model learning. This is evidenced by small changes in the loss function at the end of training. The segmentation performed by the model turned out to be close to manual marking, which indicated the high quality of the proposed solution. The area of the segmented region of the phytoplankton population was calculated by the area of one pixel. The result obtained for the original image was 51202.5 (based on information about the number of pixels related to the bloom of blue-green algae). The corresponding result of the modeling was 51312.Discussion and Conclusion. The study expands theoretical and practical knowledge on the use of convolutional neural networks for semantic segmentation of space imagery data. Given the results of the work, it is possible to assess the potential for automating the process of semantic segmentation of remote sensing data to determine the boundaries of phytoplankton populations using artificial intelligence. The use of the proposed computer vision model to obtain contours of water bloom due to phytoplankton will provide for the creation of databases — the basis for environmental monitoring of water resources and predictive modeling of hydrobiological processes.

Supervised machine learning for understanding and predicting the status of bistable eukaryotic plankton community in urbanized rivers

Understanding and predicting the ecological status of urbanized rivers is crucial for their restoration and management. However, the complex and nonlinear nature of ecological responses poses a challenge to the development of predictive models. Here, the study investigated and predicted the status of eukaryotic plankton communities in urbanized rivers by coupling environmental DNA metabarcoding, the alternative stable states theory, and supervised machine learning (SML) models. The results revealed two distinct states of eukaryotic plankton communities under similar environmental conditions: one state was characterized by the enrichment of a diverse phytoplankton population and the high relative abundance of protozoa, whereas the alternative state was characterized by abundant phytoplankton and fungi with an associated risk of algal blooms. Turbidity was identified as a key driver based on the SML model and Mantel test. Potential analysis demonstrated that the response pattern of eukaryotic plankton communities to turbidity was thresholds with hysteresis (Threshold1 = 17 NTU, Threshold2 = 24 NTU). A reduction in turbidity induced a regime shift in the eukaryotic plankton community toward an alternative state associated with a risk of algal blooms. In the prediction of ecological status, both SML models showed excellent performance (R2 > 0.80, RMSE < 0.1, Kappa > 0.70). Additionally, SHapley Additive exPlanations analysis identified turbidity, chlorophyll-a, chemical oxygen demand (COD), ammonia nitrogen and green algae's amplicon sequence variants as crucial features for prediction, with turbidity and COD showing a synergistic effect on ecological status. A framework was further proposed to enhance the understanding and prediction of ecological status in urbanized rivers. The obtained results of this study demonstrated the feasibility of using SML models to predict and explain the ecological status of urbanized rivers with alternative stable states. This provides valuable insights for the application of SML models in the restoration and management of urbanized rivers.

Wave‐Coupled Effects on Oceanic Biogeochemistry: Insights From a Global Ocean Biogeochemical Model in the Southern Ocean

AbstractOceanic biogeochemistry plays a pivotal role in regulating Earth's climate system by governing the cycling of key elements such as carbon, oxygen, and nutrients. Various metocean processes including wind, tides, currents, waves, and eddies significantly influence the dynamics of this system. In particular, ocean surface waves contribute to this intricate interplay by facilitating the exchange of heat, gas, and momentum between the atmosphere and the ocean. Although wave‐coupled effects are substantial, studies on their impacts on oceanic biogeochemistry, particularly on phytoplankton abundance are missing in present‐day research. Additionally, wave‐coupled effects cannot be disregarded in regions like the Southern Ocean (SO), where wind and waves activities are prominent. Addressing this gap, we incorporated a parameterization of surface wave mixing into a global ocean biogeochemical model to investigate its effects on upper ocean and biogeochemical parameters. Our results show that surface wave mixing has significant impacts on sea surface temperature (SST), mixed layer depth (MLD), and nutrient distribution—key factors that influence phytoplankton growth. Additionally, we observed significant improvements in model biases against the observations. During austral summer, additional mixing from surface waves can significantly lower SST by 0.5°C, deepen MLD by 13 m, and enhance Chlorophyll‐a (Chl‐a) concentration, an index of phytoplankton population, by 8% in the SO. This observed increase in Chl‐a concentration is mainly driven by enhanced dissolved iron levels resulting from wave‐induced mixing. Our findings underscore the significance of incorporating surface wave mixing in ocean biogeochemistry studies, an aspect that is currently overlooked.

Analysis of the potential of marine bioblue carbon storage and their regional contributions in Shanghai marine areas

Shanghai's extensive coastline and offshore marine areas feature diverse ecosystems. This study aimed to determine the current status, spatial distribution, and total capacity of marine carbon storage in Shanghai. Surveys were conducted on oyster reefs, phytoplankton, and fish populations from August to November 2022, with samples collected to quantify biomass and carbon content. The carbon storage of oyster reefs, phytoplankton, and fish was found to be 2.045 × 105 tC, 5113.19 kgC, and 56.6014 tC, respectively. The spatial distributions exhibited significant heterogeneity, influenced by substrate type, nutrient concentrations, and fishing activities. The total marine carbon storage capacity in Shanghai's offshore waters was estimated at 2.045 × 105 tC, highlighting a pathway for achieving regional carbon neutrality goals. This study enriches baseline data, elucidates carbon sequestration functions and spatial patterns, and provides scientific support for marine ecological protection and blue carbon resource utilization. Future research should investigate spatiotemporal variation mechanisms and potential regulation pathways.

Physiological impact of perfluorooctanoic acid (PFOA) on the cyanobacterium Microcystis aeruginosa

ABSTRACT Perfluorooctanoic acid (PFOA) is a surfactant that is detected in small quantities across various environments away from its manufacturing zones. Despite the widespread detection of PFOA in aquatic ecosystems, the impact of this compound on phytoplankton populations remains poorly understood. This study aimed to determine the physiological effects of PFOA on Microcystis aeruginosa. The cyanobacterium was exposed to 1‒10 000 μg l‒1 PFOA for 7 days under controlled laboratory conditions. The EC50 of PFOA for the test species after 7 days of exposure was 30.03 μg l‒1. Cell density significantly decreased at all PFOA concentrations (1‒10 000 μg l‒1). Chlorophyll a and carotenoid concentrations increased on day 1 (except for 1 μg l‒1, the lowest PFOA treatment) but then decreased across all treatment concentrations on day 7 of the experiment. When M. aeruginosa was exposed to PFOA, the total lipid, protein and carbohydrate content generally increased across all treatment concentrations. The concentration of intracellular hydrogen peroxide increased with increasing surfactant concentrations. In different treatments, lipid peroxidation increased, with 100 μg l‒1 PFOA producing the highest malondialdehyde content. Peroxidase and glutathione S-transferase activities increased across all treatment concentrations, demonstrating progressive changes consistent with the increase in reactive oxygen species observed. Total microcystin concentration increased in cultures exposed to 1000 and 10 000 µg l‒1 PFOA. These findings suggest that PFOA can adversely affect the survival of M. aeruginosa populations in contaminated aquatic ecosystems, with potential cascading effects on the general health of the environment.

Connectivity mediates the spatial ecological impacts of a glyphosate-based herbicide in experimental metaecosystems

Metacommunity ecology has shown that connectivity is important for the persistence of a species locally and across connected ecosystems, however we do not know if ecological effects in freshwater ecosystems exposed to biocides leaking from agriculture depend on metaecosystem connectivity. We experimentally replicated metaecosystems in the laboratory using gradostats as a model system. We tested the effects of connectivity, in terms of node distance from the pollutant-source, flow rate, and a glyphosate-based herbicide, on phytoplankton productivity, diversity and stability. Gradostats were composed of interconnected equally spaced nodes where resources and phytoplankton move directionally along a gradient of increasing distance from the source of the polluting herbicide. We hypothesised that ecological effects would be stronger in the node situated closer to the point of herbicide input, but that flow would suppress phytoplankton populations in distant nodes. Overall, RoundUp impacted phytoplankton productivity and stability by reducing algal biomass and abundances. This occurred especially in the node closest to the diluted herbicide point-source and under high flow, where species abundances were heavily suppressed by the effects of the rapidly flowing herbicide. At low flow on the other hand, distant nodes where buffered from the effects of the slow-moving herbicide. No differences in beta and gamma diversity among replicate metaecosystems was found; however, a significant loss of alpha diversity in all metaecosystems occurred through time until the end of the experiment. Together, these results point to the importance of considering aquatic connectivity in management plans for monitoring and mitigating unintended ecological consequences of agrochemical runoff.

Image-derived indicators of phytoplankton community responses to Pseudo-nitzschia blooms

Phytoplankton populations in the natural environment interact with each other. Despite rising global concern with Pseudo-nitzschia blooms, which can produce the potent neurotoxin domoic acid, we still do not fully understand how other phytoplankton genera respond to the presence of Pseudo-nitzschia. Here, we used a 4-year high-resolution imaging dataset for 9 commonly found phytoplankton genera in Narragansett Bay, alongside environmental data, to identify potential interactions between phytoplankton genera and their response to elevated Pseudo-nitzschia abundance. Our results indicate that Pseudo-nitzschia tends to bloom either concurrently with or right after other phytoplankton genera. Such bloom periods coincide with higher water temperatures and lower salinity. Pseudo-nitzschia image abundance tends to increase the most from March-May and peaks during May-Jun, whereas the image-derived biovolume and width of Pseudo-nitzschia chains increase the most during Jan-Feb. For most phytoplankton genera, their relationship with Pseudo-nitzschia abundance is noticeably different from their relationship with Pseudo-nitzschia image features. Despite the complexity in the phytoplankton community, our analysis suggests several ecological indicators that may be used to determine the risk of harmful algal blooms.

Zinc stimulation of phytoplankton in a low carbon dioxide, coastal Antarctic environment.

Zinc (Zn) is a key micronutrient used by phytoplankton for carbon (C) acquisition, yet there have been few observations of its influence on natural oceanic phytoplankton populations. In this study, we observed Zn limitation of growth in the natural phytoplankton community of Terra Nova Bay, Antarctica, due to low (~220 μatm) pCO2 conditions, in addition to primary iron (Fe) limitation. Shipboard incubation experiments amended with Zn and Fe resulted in significantly higher chlorophyll a content and dissolved inorganic carbon drawdown compared to Fe addition alone. Zn and Fe response proteins detected in incubation and environmental biomass provided independent verification of algal co-stress for these micronutrients. These observations of Zn limitation under low pCO2 conditions demonstrate Zn can influence coastal primary productivity. Yet, as surface ocean pCO2 rises with continued anthropogenic emissions, the occurrence of Zn/C co-limitation will become rarer, impacting the biogeochemical cycling of Zn and other trace metal micronutrients.

Allelopathy of extracellular chemicals released by Karlodinium veneficum on photosynthesis of Prorocentrum donghaiense

Dinoflagellates Prorocentrum donghaiense and Karlodinium veneficum are the dominant species of harmful algal blooms in the East China Sea. The role of their allelopathy on the succession of marine phytoplankton populations is a subject of ongoing debate, particularly concerning the formation of blooms. To explore the allelopathy of K. veneficum on P. donghaiense, an investigation was conducted into photosynthetic performance (including PSII functional activities, photosynthetic electron transport chain, energy flux, photosynthetic different genes and photosynthetic performance) and photosynthetic damage-induced oxidative stress (MDA, SOD, and CAT activity). The growth of P. donghaiense was strongly restrained during the initial four days (1–6 folds, CK/CP), but the cells gradually resumed activity at low filtrate concentrations from the eighth day. On the fourth day of the strongest inhibition, allelochemicals reduced representative photosynthetic performance parameters PI and ΦPSII, disrupted related processes of photosynthesis, and elevated the levels of MDA content in P. donghaiense. Simultaneously, P. donghaiense repairs these impairments by up-regulating the expression of 13 photosynthetic genes, modifying photosynthetic processes, and activating antioxidant enzyme activities from the eighth day onward. Overall, this study provides an in-depth overview of allelopathic photosynthetic damage, the relationship between genes and photosynthesis, and the causes of oxidative damage induced by photosynthesis. Environmental implicationsAs a typical HAB species, Karlodinium veneficum is associated with numerous fish poisoning events, which have negative impacts on aquatic ecosystems and human health. Allelochemicals produced by K. veneficum can provide a competitive advantage by interfering with the survival, reproduction and growth of competing species. This study primarily investigated the effects of K. veneficum allelochemicals on the photosynthesis and photosynthetic genes of Prorocentrum donghaiense. Grasping the mechanism of allelochemicals inhibiting microalgae is helpful to better understand the succession process of algal blooms and provide a new scientific basis for effective prevention and control of harmful algal blooms.

A novel optimal control strategy for nutrient–phytoplankton–zooplankton model with viral infection in plankton

This paper presents a mathematical model that explores the intricate dynamics between nutrients, phytoplankton, and zooplankton populations, incorporating viral infection phenomena in the frame of the Caputo fractional derivative. The conceptual properties such as the existence, uniqueness, and stability of multiple equilibria are analyzed under specific conditions. We have defined some threshold parameters whose biological meaning helps in strengthening the stability conditions. Furthermore, an optimal control strategy is proposed for the suggested model and the impacts of the optimal control strategy on both commensurate and non-commensurate systems are investigated. A dynamic framework is developed to investigate the optimal use of resources, stock sustainability, and resource benefits. The control variables considered are concerned with the infection of the susceptible phytoplankton and the nutrient consumption of infected phytoplankton. Pontryagin’s maximum principle is employed to determine the best control strategy. Using the Adam-Bashforth-Moulton predictor–corrector method we have performed the numerical simulation that provides the justification of the theoretical findings.

Aquaculture Ponds Identification Based on Multi-Feature Combination Strategy and Machine Learning from Landsat-5/8 in a Typical Inland Lake of China

Inland aquaculture ponds, as an important land use type, have brought great economic benefits to local people but at the same time have caused many environmental problems threatening regional ecology security. Therefore, understanding the spatiotemporal pattern of aquaculture ponds and its potential influence on water quality is vital for the sustainable development of inland lakes. In this study, based on Landsat5/8 images, three types of land features, namely spectral features, index features, and texture features, and five machine learning algorithms, namely random forest (RF), extreme gradient boosting (XGBoost), artificial neural network (ANN), k-nearest neighbor (KNN), and Gaussian naive Bayes (GNB), were combined to identify aquaculture ponds and some other primary land use types around a typical inland lake of China. The results demonstrated that the XGBoost algorithm that integrated the three features performed the best among all groups of the five machine learning algorithms and the three features, with an overall accuracy of up to 96.15%. In particular, the texture features provided additional useful information besides the spectral features to allow more accurately separation of aquaculture ponds from other land use types and thus improve the land use mapping ability in complex inland lakes. Next, this study examined the tendency of aquaculture ponds and found a segmented increase mode, namely sharp increase during 1984–2003 and then slow elevation since 2003. Further positive correlation detected between the area of aquaculture ponds and the phytoplankton population dynamics suggest a likely influence of aquaculture activity on the lake water quality. This study provides an important scientific basis for the sustainable management and ecological protection of inland lakes.

Taxis-driven complex patterns of a plankton model.

This paper reports an important conclusion that self-diffusion is not a necessary condition for inducing Turing patterns, while taxis could establish complex pattern phenomena. We investigate pattern formation in a zooplankton-phytoplankton model incorporating phytoplankton-taxis, where phytoplankton-taxis describes the zooplankton that tends to move toward the high-densities region of the phytoplankton population. By using the phytoplankton-taxis sensitivity coefficient as the Turing instability threshold, one shows that the model exhibits Turing instability only when repulsive phytoplankton-taxis is added into the system, while the attractive-type phytoplankton-taxis cannot induce Turing instability of the system. In addition, the system does not exhibit Turing instability when the phytoplankton-taxis disappears. Numerically, we display the complex patterns in 1D, 2D domains and on spherical and zebra surfaces, respectively. In summary, our results indicate that the phytoplankton-taxis plays a pivotal role in giving rise to the Turing pattern formation of the model. Additionally, these theoretical and numerical results contribute to our understanding of the complex interaction dynamics between zooplankton and phytoplankton populations.

A “trapezoidal” relationship between solar radiation and chlorophyll concentrations at the center of the South Pacific Gyre

Understanding the driving mechanism of phytoplankton dynamics is key to forecasting future changes in the ocean. Here, we report an apparent “trapezoidal” relationship between chlorophyll concentrations (Chl) and surface photosynthetically available radiation (PAR(0)) at the center of the South Pacific Gyre (cSPG) based on 18 years of MODIS Aqua measurements. A comparison of Chl with a photoacclimation model revealed that photoacclimation alone could not explain the temporal dynamics of Chl. Instead, the Chl dynamics were explained by a combination of photoacclimation, nutrient limitation, and the grazing pressure of zooplankton at different times throughout the year. An annual “trapezoidal” spiral relationship between Chl and PAR(0) suggested that the steady state of phytoplankton populations at the cSPG could be influenced by the alternation of co-regulation mechanisms during a year. Because this same pattern occurs in other subtropical gyres, this understanding of the underlying mechanisms not only facilitates simulating and forecasting phytoplankton dynamics but also provides a new perspective on how multiple stressors may impact phytoplankton communities in a warmer climate.

Intraspecific genetic diversity and coexistence in phytoplankton populations

AbstractThe past two decades have seen a drastic increase in the availability and use of genetic techniques to study phytoplankton communities. As a result, it is now well documented that phytoplankton populations are genetically diverse, despite predominantly asexual reproduction and minute morphological variation. Genetic variation can lead to variation also in phenotype, and some traits vary more among genotypes than between species. Trait‐based approaches tackle this by focusing on traits rather than on species. However, trait‐based models often have difficulty predicting and explaining the huge trait‐diversity among coexisting individuals competing for the same few resources. Thus, we ask the question: How do hundreds, if not thousands, of genotypes coexist in a highly competitive environment? In this review, we gather information on genetic and phenotypic variations in coexisting genotypes and elaborate on three mechanisms by which broad intraspecific genetic diversity may be possible: neutral mutations, environmental fluctuations, and trade‐offs among traits. These have all been applied on an interspecies level, and we discuss their use also among coexisting genotypes. We find that genetic diversity to be almost exclusively studied in blooming species and that clonal diversity frequently measure above 0.95 (i.e., 95% of individuals sampled are genetically different). Genetic diversity seems stable throughout blooms, suggesting that competitive exclusion is low or that new genetic material is frequently being introduced into populations. Further, we find high intraspecific trait‐variation in several key traits among coexisting strains but also that trait‐variation is often neglected in studies on phytoplankton, making coexistence difficult to predict.