Phytoplankton Populations Research Articles

Mineral supplementation in low saline culture of Pacific white shrimp—Effects on growth and water quality

As low saline inland shrimp culture is becoming more prevalent day by day, the application of mineral supplements in pond water has become a necessity to maintain the ionic concentration by fortifying the deficiency of key minerals like calcium (Ca), magnesium (Mg) and potassium (K). In this study, a 120-day trial was conducted in earthen ponds, wherein, the benefits of applying mineral supplement in low saline (>5 Practical Salinity Unit, PSU) was evaluated by measuring the animal performance parameters like feed conversion ratio (FCR), specific growth rate (SGR), survival rate, weight gain, mineral composition of experimental animals and the quality of rearing water like pH, dissolved oxygen (DO), salinity, hardness, alkalinity, biological oxygen demand (BOD), chemical oxygen demand (COD), Vibrio count and phytoplankton population in comparison with control. Our study showed that the application of mineral mixture improved the animal performance in terms of FCR, SGR and body weight. Despite, on supplementation, an increased mineral (Ca, Mg and K) content and improved phytoplankton levels was observed in rearing water whilst corresponding increase in minerals Ca, Mg and K was observed in animals as well. Indicating the improvement in water quality, mineral composition in water and animals and growth performance, affirming good absorption of minerals by animals.

A Bayesian approach to modeling phytoplankton population dynamics from size distribution time series.

The rates of cell growth, division, and carbon loss of microbial populations are key parameters for understanding how organisms interact with their environment and how they contribute to the carbon cycle. However, the invasive nature of current analytical methods has hindered efforts to reliably quantify these parameters. In recent years, size-structured matrix population models (MPMs) have gained popularity for estimating division rates of microbial populations by mechanistically describing changes in microbial cell size distributions over time. Motivated by the mechanistic structure of these models, we employ a Bayesian approach to extend size-structured MPMs to capture additional biological processes describing the dynamics of a marine phytoplankton population over the day-night cycle. Our Bayesian framework is able to take prior scientific knowledge into account and generate biologically interpretable results. Using data from an exponentially growing laboratory culture of the cyanobacterium Prochlorococcus, we isolate respiratory and exudative carbon losses as critical parameters for the modeling of their population dynamics. The results suggest that this modeling framework can provide deeper insights into microbial population dynamics provided by size distribution time-series data.

Alien toxic dinoflagellate Heterocapsa circularisquama from the Western Pacific in Kuwait, NW Indian Ocean

A bloom of non-indigenous dinoflagellate Heterocapsa circularisquama (Peridiniales, Dinophyceae) and bloom-associated shellfish mortality were detected for the first time in Kuwait's waters (Arabian/Persian Gulf, NW Indian Ocean) during June–July 2020. This species is endemic to the Asia Pacific region, and associated with shellfish kills, causing tremendous damage to bivalve aquaculture, and economic losses in western Japanese waters and Hong Kong. In Kuwait Bay, this dinoflagellate was found in bloom proportions up to 108 cells·l−1 and represented from 72 to 83% of the total phytoplankton population in red tide patches. Native bloom-forming small-sized diatoms, Thalassiosira delicatula, T. exigua, and Minutocellus polymorphus were also abundant at the time of the H. circularisquama bloom. Microalgal cell morphology was determined using light and electron microscopy for specimens from the field and cultures established by cell isolation from the bloom area. The molecular characterization of cultured Heterocapsa strains was based on the large subunit region of the ribosomal DNA sequences. The overall morphology and body scale structure of Heterocapsa from Kuwait Bay were very similar to that of the H. circularisquama described from Japanese waters. The sequences of the LSU rDNA of Kuwait's strains were identical to those of the H. circularisquama. This is the first record of H. circularisquama occurring at high abundance outside the Asia Pacific region, and it may be an indication this invasive and harmful species is now established in Kuwait's marine environment.

Influence of environmental parameters on the community structure of phytoplankton from river confluence of Cuddalore, Tamil Nadu, India

River ecosystems are widespread across the landscape through series of channels ultimately draining into the sea. Several theoretical models have provided coalescing concepts about ecosystem and biodiversity. Yet, a comparative study of phytoplankton community between the rivers is far limited and challenging. The present exploration embarks on comprehending the seasonal succession of phytoplankton community from the four major rivers such as Thenpennai, Gadilam, Vellar and Kollidam of Cuddalore district, Tamil Nadu, India. A total of 298 microalgal cultures belonging to 13 orders were isolated from the rivers from 2018 to 2019. The relative distribution indicated the dominance of species from Chlorococcales (32%), Pennales (28%), Nostocales (15%), Chroococcales (15%), and Conjugales (7%). Diversity indices results revealed variability in Gadilam and Kollidam rivers. High fluctuation in occurrence especially during the monsoon season due to sporadic rainfall. During post-monsoon season, organic parameters like magnesium, calcium, chloride, and phosphate were found to impact the population. This study encompasses a pragmatic approach to evaluate the seasonal relationship between the environmental variables and microalgal population. Our findings include the first report of Klebsormidium nitens from India. River Kollidam (29.61%) and Vellar (24.32%) recorded high species diversity across all seasons and could be a source for more potent isolates. As evident from the results of multivariate statistics, variables like phosphate, calcium, and magnesium are the most important factors in influencing the seasonal pattern of the phytoplankton population. Overall, these results provide a comprehensive database on the algal diversity and its seasonal dynamics from the major rivers of India.

The ecological response of natural phytoplankton population and related metabolic rates to future ocean acidification

The ecological response of natural phytoplankton population and related metabolic rates to future ocean acidification

Effect of the Site and the Type of Fertilizer on the Taxonomic Composition and Abundance of Phytoplankton Population of Ponds in West Cameroon

Pond fertilization is an age-old practice aimed at optimizing yields in so-called production fish farming systems. With this in mind, a characterization study of phytoplankton, the basis of the trophic food chain leading to fish, was conducted from January to December 2021 in ponds in the West Cameroon regionand at the Research Unit of Ichthyology and Applied Hydrobiology of the University of Dschang. To do this, a total of fifteen (15) ponds including 03 in Bamendou fertilized with cowpat, 04 in Batie including 02 fertilized with pig manure and 02 with wheat bran, 01 in Dschang fertilized with chicken manure, 04 in Fokoue including 02 fertilized with pig manure and 02 unfertilized and 03 in Foumbot fertilized with cowpat, were considered. These ponds were chosen based on the availability of fish farmers and the type of fertilizer administered. Sampling of biotic characteristics including phytoplankton richness, relative abundance and density was done between 6 and 8 a.m. following a monthly frequency. The results showed that the highest specific richness (42.73% of total species richness) was observed in Batie ponds fertilized with pig manure and generic richness (81.53% of total genera richness) and families (84.21% oftotal of families richness) highest phytoplankton in Batie ponds fertilized with wheat bran. Regardless of the type of pond, Chlorophyta were more abundant (40.50%). With the exception of Chrysophyta who were absent in the ponds of Foumbot fertilized with cowpat, all other groups were listed in the different types of ponds. The significantly (p<0.05) highest phytoplankton density (71. 107 ind/L) was observed in ponds fertilized with chicken manure with high concentrations of nitrates and nitrites.

Effect of Hydraulic Flow on Phytoplankton Population Structure in Tajan River

The aim of present study was to investigate the relationship between hydraulic and ecological variables of the studied span and to investigate changes in living conditions of phytoplankton (Amphora ovsalis and Navicula placentula). According to the measurements and analysis of habitat fit curves, it can be concluded that the highest frequency of Amphora ovsalis and Navicula placentula occurred and specified at flow rate of 15.82 and 14.942 m3/s, respectively. As can be seen, the lowest frequency of Amphora ovsalis and Navicula placentula in this measurement occurred at flow rate of 15.355 and 5.289 m3/s, respectively. The best habitat conditions for Amphora ovsalis and Navicula placentula are in the range of 0.18 -0.31 and 0.18 - 0.265 of Froude number range respectively. The highest density and abundance of Amphora ovsalis and Navicula placentula was observed in the range of 0.017 - 0.055 and 0.017 -0.039 mg/l of phosphate demonstrating that the best growth and living conditions are predisposed in this range for these species had suitable habitat conditions for life in this range. The highest frequency of Amphora ovsalis and Navicula placentula is in the range of 0.019 - 0.071 and 0.047-0.071 mg/l nitrite, respectively. Amphora ovsalis and Navicula placentula had the highest frequency when nitrate ranges 4.48 – 5.16 and 4.48- 5.37 mg/l. The best habitat conditions for Amphora ovsalis and Navicula placentula were between the mean velocity values in the range of 1 -1.2 m/s.

Distribution and abundance of dinoflagellates from the coastal waters of Karachi, Pakistan, northern part of the Arabian Sea.

The present study reports on seasonal and spatial variations in diversity, distribution and abundance of dinoflegellates and indicates the presence of HAB species in Pakistan waters. A total of 179 taxa, recorded in this study from offshore and near-shore waters, belong to 41 genera in 26 families and 10 orders. The high species count (149 species) was recorded from Manora Island offshore station (MI-1) and 105 spp, 109 spp and 115 spp were encountered from the Mubarak village offshore station (MV-1), Manora near shore station (MI-2) and Mubarak Village near-shore station (MV-2) respectively. Tripos furca was the dominant and frequently occurring species (> 1 x103 to > 25 x103 cells L-1 from coastal and >1x 105 cells L-l from near-shore stations) in addition to less abundant Alexandrium catenella, Alexandrium sp., Alexandrium minutum, and Prorocentrum micans (>103 to 25x 103cells/L). Another 44 species occurred in relatively low numbers (<103 cell L-l). Seventy species were found throughout the study period at all four stations. High number of species in three genera (Tripos (38), Protoperidinium (34) and Prorocentrum (20) was recorded. Potently toxic (16 genera 43 species) and HAB related (19 genera and 30 species) dinoflagellate taxa were also recorded. The percent contribution of dinoflagellates in total phytoplankton population generally remained below 20% except for a few instances. Manora Island stations had comparatively higher Shannon index and equitability and slightly lower dominance index. The PCA plot showed strong positive correlation among chlorophyll-a concentration, dissolved oxygen, total number of phytoplankton and dinoflagellates.

Mangrove Phytoplankton Quality Index Evolving Sustainability

The growth of Mangroves along backwaters of Ennore creek, Tamil Nadu, India was selected for the present study. Study was undertaken every month for the period of one year from January to December 2021. Four decisive stations were identified for survey to bring out seasonal consequences. The phytoplankton community was investigated by estimating population through adopting qualitative and quantitative analysis such as species composition, percentage composition, density, diversity, richness, evenness. Supportive parameters viz. nitrate, silicate and phosphate were also examined. Meanwhile, regulating parameters such as temperature, salinity, pH, dissolved oxygen were also investigated. Three groups such as Bacillariophyceae, Cyanophyceae and Chlorophyceae were identified with 37, 31 and 18 (nos.) species, respectively. The phytoplankton density was observed as 280 to 8050 cells/l. Recorded density was lower compared to other ecosystems and back waters, the community structure of phytoplankton population still thriving due to supportive parameters as documented in the presented study. Despite, the recorded high level of BOD, COD, heavy metals and regulating parameters not conducive, the population of phytoplankton community has affluently sustained and made sustain other biota in vicinity, which can still regulate the carrying capacity of Ennore back waters by acting effectively as primary producers in the system.

Algorithms and programs based on neural networks and local binary patterns approaches for monitoring plankton populations in sea systems

The work is devoted to the method of multispectral space images analyzing of aquatic coastal systems for identifying phytoplankton populations of complicated structures: determining their boundaries, distributing color gradations and, based on this, determining the distribution of phytoplankton concentrations within patches and the location of the "mass" center. A combination of local binary patterns (LBP) and neuralnetworks methods is considered. Due to these characteristics it is possible, basing on a series of processed images of the same water area for different time points (dates), to determine the changing rate in the spots boundaries and their concentrations, the shift of the mass center which are influenced by the aquatic environment movement and the processes of phytoplankton growth and death. The results of the work allow us to determine the Azov Sea state. Experimental data of the program are given in confirmation part.

Intelligent analysis of Earth remote sensing data on the distribution of phytoplankton and pollutants in coastal systems

Currently, one of the topical areas of application of artificial intelligence methods in ensuring environmental monitoring of water resources is the analysis of Earth remote sensing images in order to control and prevent potentially dangerous changes in the environment. In the future, algorithms with elements of artificial intelligence form the basis of forecasting and decision-making systems. Systems for ensuring high-quality environmental monitoring can be improved using artificial intelligence methods, in particular, the development and application of special algorithms to prevent emergencies. The aim of the study is to develop an algorithm using artificial intelligence to detect spots of substances of various origins on the water surface. It has been established that the YOLOv4 convolutional neural network is applicable for high-quality detection of oil spots and bloom spots of phytoplankton populations. The developed algorithm was tested on real satellite images and showed an accuracy of 84-94%.

Single Phytoplankton Species Growth with Nonlocal Crowding Effect Caused by Chemosensory Aggregation in a Water Column

In this paper, we study the existence of positive steady states for a nonlocal reaction‐diffusion model which describes the growth of a single phytoplankton species with nonlocal crowding effect caused by chemosensory aggregation in a water column. Our result shows that under some conditions, the nonlocal crowding effect is effective to the existence of steady states of phytoplankton populations. Based on a key boundedness Lemma, the main result is proved by fixed point index theory and some analytical techniques.

Numerical investigation of fractional model of phytoplankton–toxic phytoplankton–zooplankton system with convergence analysis

In this paper, a fractional order model of the phytoplankton–toxic phytoplankton–zooplankton system with Caputo fractional derivative is investigated via three computational methods, namely, residual power series method (RPSM), homotopy perturbation Sumudu transform method (HPSTM) and the homotopy analysis Sumudu transform method (HASTM). This model is constituted by three components: phytoplankton, toxic phytoplankton and zooplankton. Phytoplankton species are self-feeding members of the plankton community and play a very significant role in ecosystems. A wide range of sea creatures get food through phytoplankton. This paper focuses on the implementation of the three above-mentioned computational methods for a nonlinear time-fractional phytoplankton–toxic phytoplankton–zooplankton (PTPZ) model with a perception to study the dynamics of a model. This study shows that the solutions obtained by employing the suggested computational methods are in good agreement with each other. The computational procedures reveal that the HASTM solution generates a more general solution as compared to RPSM and HPSTM and incorporates their results as a special case. The numerical results presented in the form of graphs authenticate the accuracy of computational schemes. Hence, the implemented methods are very appropriate and relevant to handle nonlinear fractional models. In addition, the effect of variation of fractional order of a time derivative and time [Formula: see text] on populations of phytoplankton, toxic–phytoplankton and zooplankton has also been studied through graphical presentations. Moreover, the uniqueness and convergence analyses of HASTM solution have also been discussed in view of the Banach fixed-point theory.

Spatio-temporal Assessment of Phytoplankton and Physicochemical Parameters of Dangana Lake, Lapai, Niger State Nigeria

Dangana Lake was constructed primarily to provide water for drinking, domestic activities and control flood. Phytoplankton assemblage and physicochemical parameters of Dangana Lake, Lapai, Niger State Nigeria were investigated monthly between March and October 2019 using standard methods and procedures. Water samples were collected from 3 different stations in the Lake. The result of the lake shows water temperature (23.2-29.5ºC), Biochemical oxygen Demand (2.30-3.91mg/l), Dissolved oxygen (3.40-4.70mg/l), water pH (6.10-7.5), Electrical conductivity (60.00-121µS/cm), Nitrates (0.30-1.05mg/l) and Phosphate (0.53-1.75mg/l). Temperature, DO, BOD5, and pH shows no significant difference (p&gt;0.05) among sampling sites, however, Electrical conductivity, Nitrate and Phosphate differs significantly among sampling sites (p&lt;0.05). Seasonally there was a significant difference (p&lt;0.05) among sampling months in all the physicochemical parameter measures. A total of Seventeen (17) species of phytoplankton in six (6) families were encountered. The percentage abundant of phytoplankton revealed Bacillariophyta (36.84%), Dinophyta (33.38%), Chlorophyta (17.05%), Heterokontophyta (6.93%) Cyanophyta, (4.76%), and Euglenophyta (1.02%). Phytoplankton population was significantly higher during the dry season than in the raining season. The Canonical Correspondence analysis (CCA) correlated positively between the measured physicochemical parameters and the observed organisms. The nutrient status of the lake shows a medium range as indicated by the measured environmental parameters.

Variations in Seasonal Phytoplankton Assemblages as a Response to Environmental Changes in the Surface Waters of Minicoy Island, Lakshadweep

Phytoplankton, the primary producers in all aquatic systems, play an important key role in biogeochemical processes that are linked to higher trophic levels and climate variability. The spatio temporal composition and abundance of micro phytoplankton species was examined in relation to physical and chemical surface water variables (i.e., Salinity, temperature and nutrients). The primary objectives of the study were to observe the variations in phytoplankton species assemblages as a response to environmental variables. Hierarchiel cluster analysis and non-metric multidimensional scaling were used to find out distinct seasonal groups based on the composition of phytoplankton. The results indicate that several key environmental factors like temperature, salinity and nutrients like nitrate, nitrite, phosphate and silicate influenced seasonal phytoplankton assemblages within the lagoon waters. The distribution of phytoplankton population showed main groups where the blue green algae populations favored the warmer conditions of pre-monsoon period, whereas the diatom population primarily flourished in the post monsoon and monsoon period. The present study deals with the phytoplankton community structure in the Indian Ocean, Lakshadweep Archipelago, Minicoy Island, particularly in the surface water with respect to environmental variables to understand the region-specific dominant community and its governing environmental settings.

Relationship of Spatial Phytoplankton Variability during Spring with Eutrophic Inshore and Oligotrophic Offshore Waters in the East Sea, Including Dokdo, Korea

The area near the subpolar front of the East Sea has high primary productivity during the spring season. We conducted two surveys, one in early spring and another in late spring, to assess environmental factors that influence phytoplankton community structure during these times. During early spring, vertical mixing supplied abundant nutrients to the surface. Due to the well-mixed water column, there were high nutrient levels, but total phytoplankton abundances and diversity were relatively low and were dominated by the diatom Chaetoceros spp. During late spring, the water column gradually stratified, with relatively high levels of nutrients in the surface layers near the coastal areas. Phytoplankton abundance and diversity at that time were higher, and there were diatoms (Pseudo-nitzschia spp. and Chaetoceros spp.), cryptophytes, and small flagellates. Pseudo-nitzschia spp. were especially abundant in re-sampled areas. The presence of a stratified and stable water mass and sufficient nitrate led to high phytoplankton growth, even in the open sea during late spring. These findings provide a better understanding of how phytoplankton population dynamics in the East Sea depend on water column stability during both early and late spring seasons.

Harmful algal blooms and their impact on fish mortalities in Lampung Bay: an overview

The first disaster caused by harmful algal blooms in Lampung Bay was reported in 1991, where mass mortality of cultivated shrimp occurred in the brackish water ponds due to a Trichodesmium bloom. After this incident, the phenomenon reoccured in the following years continuously. Around nine species bloom makers in this bay are namely Pyrodinium sp., Noctiluca sp., Phaeocystis sp., Dinophysis sp., Trichodesmium sp., Ceratium sp., Prorocentrum sp., Pseudonitzhia sp., and Cochlodinium sp. The most frequent causative species, such as green Noctiluca and Trichodesmium, co-occurring during blooms and causing fish mortalities in the fish farming floating nets (KJA). Two species are known as the most potentially harmful species, namely Pyrodinium sp. and Cochlodinium sp. Cochlodinium blooms happened at the end of 2012, and since then, this species has continuously reappeared in the following years. The outbreak of Cochlodinium sp. still appeared in 2017 and 2018, but no fish-killing occurred. Phytoplankton bloom events occur at specific locations, mainly at fish farming floating nets on the west side of the bay, next to Hurun Cove. This paper discusses the occurrence of algal blooms in Lampung Bay and the triggering factors for increasing phytoplankton populations that cause harmful algal blooms.

Algorithm for monitoring the plankton population dynamics based on satellite sensing data

The scientific work describes the algorithms for processing the multispectral water coastal imagery from satellite sensing data with the aim of identifying the phytoplankton population of a spotted structure: determining the contour, distributing color gradation and as a result - determining the concentration of phytoplankton distribution inside the zones and mass centers. Such characteristics let determine the speed of changing contours spots and their concentration, the mass center shift as a consequence of the water masses movement and the processes of phytoplankton growing and dying. All these may be done on the base of the processed image series of the same water area over different time (different dates). The combination of LBP and neural network methods are observed as algorithms for image processing and the results of computer experiments are presented.

Image processing algorithms application for the modeling biogeochemical cycles of the Azov Sea

In the article biogeochemical processes of the Azov Sea were researched. Mathematical non-stationary 3D model is proposed which describes the development dynamics of the two most common species of phytoplankton populations in the summer, the growth of which is limited by a single biogenic element, is proposed the linearization of continuous mathematical model on a uniform temporal grid is made. For a continuous model, a discrete analogue is constructed and an optimal method for grid equations solving is selected. To determine the boundary of the considered computational domain of a complex shape an image processing algorithm has been developed, implemented as a software module on Python, which makes it possible to obtain a dynamically changing contour of the Azov Sea from satellite images.

Mathematical Modeling of the Phytoplankton Populations Geographic Dynamics for Possible Scenarios of Changes in the Azov Sea Hydrological Regime

Increased influence of abiotic and anthropogenic factors on the ecological state of coastal systems leads to uncontrollable changes in the overall ecosystem. This paper considers the crucial problem of studying the effect of an increase in the water’s salinity in the Azov Sea and the Taganrog Bay on hydrobiological processes. The main aim of the research is the diagnostic and predictive modeling of the geographic dynamics of the general phytoplankton populations. A mathematical model that describes the dynamics of three types of phytoplankton is proposed, considering the influence of salinity and nutrients on algae development. Discretization is carried out based on a linear combination of Upwind Leapfrog difference schemes and a central difference scheme, which makes it possible to increase the accuracy of solving the biological kinetics problem at large values of the grid Péclet number (Peh &gt; 2). A software package has been developed that implements interrelated models of hydrodynamics and biogeochemical cycles. A modified alternating-triangular method was used to solve large-dimensional systems of linear algebraic equations (SLAE). Based on the scenario approach, several numerical experiments were carried out to simulate the dynamics of the main species of phytoplankton populations at different levels of water salinity in coastal systems. It is shown that with an increase in the salinity of waters, the habitats of phytoplankton populations shift, and marine species invasively replace freshwater species of algae.