Phytoplankton Population Dynamics Research Articles

Local intraspecific aggregation in phytoplankton model communities: spatial scales of occurrence and implications for coexistence.

The coexistence of multiple phytoplankton species despite their reliance on similar resources is often explained with mean-field models assuming mixed populations. In reality, observations of phytoplankton indicate spatial aggregation at all scales, including at the scale of a few individuals. Local spatial aggregation can hinder competitive exclusion since individuals then interact mostly with other individuals of their own species, rather than competitors from different species. To evaluate how microscale spatial aggregation might explain phytoplankton diversity maintenance, an individual-based, multispecies representation of cells in a hydrodynamic environment is required. We formulate a three-dimensional and multispecies individual-based model of phytoplankton population dynamics at the Kolmogorov scale. The model is studied through both simulations and the derivation of spatial moment equations, in connection with point process theory. The spatial moment equations show a good match between theory and simulations. We parameterized the model based on phytoplankters' ecological and physical characteristics, for both large and small phytoplankton. Defining a zone of potential interactions as the overlap between nutrient depletion volumes, we show that local species composition-within the range of possible interactions-depends on the size class of phytoplankton. In small phytoplankton, individuals remain in mostly monospecific clusters. Spatial structure therefore favours intra- over inter-specific interactions for small phytoplankton, contributing to coexistence. Large phytoplankton cell neighbourhoods appear more mixed. Although some small-scale self-organizing spatial structure remains and could influence coexistence mechanisms, other factors may need to be explored to explain diversity maintenance in large phytoplankton.

Use of mesocosm and field studies to assess the effects of nutrient levels on phytoplankton population dynamics in Korean coastal waters

We integrated data from field observations during April and March with data from a 2-week mesocosm experiment to investigate changes in phytoplankton populations in southern Korean coastal waters (KCWs) following nutrient enrichment during early spring of 2021. The mesocosm experiments used 1000 L cylindrical plastic containers that had natural seawater (control), a low nutrient (LN) treatment, or a high nutrient (HN) treatment. The field observations showed that increased freshwater runoff following spring rainfall led to elevated levels of dissolved inorganic nitrogen and silicate and a significant increase in total phytoplankton abundance. In March, nutrient enrichment from water mixing and terrestrial runoff led to dominance of cryptophyte Cryptomonas spp. In April, higher nutrient levels than March (p< 0.05) resulting from increased terrestrial runoff after rainfall and dominant species were Skeletonema spp., and Cryptomonas spp. In the mesocosm experiment, a succession from E. zodiacus initially to Chaetoceros spp. in the middle stage, and then to Cylindrotheca closterium and Pseudo-nitzschia spp. finally was observed, depending on the species-specific nutrient availability after nutrient addition. In principal component analysis, the negative correlation between C. closterium and nutrient levels supports their nutrient availability, which is an adaptation to low-nutrient conditions. The combined data from the field observations and mesocosm experiments indicated that nutrient supplementation from terrestrial runoff and tidal mixing played a crucial role in determining the dynamics of phytoplankton populations during early spring in the KCWs.

Stability and Hopf Bifurcation Analysis of a Phytoplankton-Zooplankton Model Under Temperature Factor and Stage-Structure Population of Phytoplankton

In the paper, we built a predator-prey model to simulate and study the dynamics of zooplankton and phytoplankton populations under the temperature impact, in which the stage structure is considered in the zooplankton population. Our model is an ordinary differential system of three nonlinear equations with some parameters as temperature-dependent functions and uses the generalized Holling response function. The non-negative and boundedness of the model solutions have been proven. The behaviors of our system are shown by the local stability conditions of the equilibria, especially the co-existence case. The stage transformation of zooplankton was studied through the Hopf bifurcation results of varying the temperature. The analysis and simulation results indicate that the ideal temperature for the co-existence is about 12-21 degrees Celsius. The zooplankton's transformation decreases when the temperature increases, leading to an imbalance in the system. Besides that, we also provided simulation figures to illustrate the found theoretical results.

Seasonal Dynamics of Phytoplankton Population of Ramadugu Lake in Nizamabad District Telangana, India

Phytoplanktons are one of the smallest and the most plentiful organisms ranging from unicellular to multicellular forms. These organisms play a vital role in aquatic ecosystems. Algae being photoautotrophs take part in photosynthesis process, they form the food base for other organisms living in lakes, reservoirs, ponds, streams and oceans. Lakes are very important water resources which are used in daily life for several purposes. Majority of fresh water lakes showed distinct seasonal variation in phytoplankton abundance. The primary objective of this paper is to highlight the current status and to provide a quantitative record of the seasonal changes of phytoplankton and to examine their distribution and diversity in relation to environmental gradients of the Ramadugu lake which is located in Ramadugu village of Nizamabad district, Telangana. The present work was carried out to identify the fresh water algal flora and its diversity for a period of one year from January 2018 to December 2019 in different seasons. Water samples were collected seasonally over three seasons – March to June(summer), July to october(Monsoon) and November to February(winter) from selected four sampling stations of lake. The algal forms were identified by using various keys. Mainly four major algal groups were identified in Ramadugu lake throughout the study period. They are Chlorophyceae, Bacillariophyceae, Cyanophyceae and Euglenophyceae. Seasonal variations were observed in the presence and density of various algae. The density of diatoms are high in winter season indicates that lake water is unpolluted and good to support life. The lake water is used for irrigation and pisciculture but not suitable for drinking purpose.

Algal cell viability assessment: The role of environmental factors in phytoplankton population dynamics

The viability of algal cells is one of the most fundamental issues in marine ecological research. In this work, a method was designed to identify algal cell viability based on digital holography and deep learning, which divided algal cells into three categories: active, weak, and dead cells. This method was applied to measure algal cells in surface waters of the East China Sea in spring, revealing about 4.34 %–23.29 % weak cells and 3.98 %–19.47 % dead cells. Levels of nitrate and chlorophyll a were the main factors affecting the viability of algal cells. Furthermore, algal viability changes during the heating and cooling were observed in laboratory experiments: high temperatures led to an increase in weak algal cells. This may provide an explanation for why most harmful algal blooms occur in warming months. This study provided a novel insight into how to identify the viability of algal cells and understand their significance in the ocean.

MODELING CELL POPULATIONS MEASURED BY FLOW CYTOMETRY WITH COVARIATES USING SPARSE MIXTURE OF REGRESSIONS.

The ocean is filled with microscopic microalgae, called phytoplankton, which together are responsible for as much photosynthesis as all plants on land combined. Our ability to predict their response to the warming ocean relies on understanding how the dynamics of phytoplankton populations is influenced by changes in environmental conditions. One powerful technique to study the dynamics of phytoplankton is flow cytometry which measures the optical properties of thousands of individual cells per second. Today, oceanographers are able to collect flow cytometry data in real time onboard a moving ship, providing them with fine-scale resolution of the distribution of phytoplankton across thousands of kilometers. One of the current challenges is to understand how these small- and large-scale variations relate to environmental conditions, such as nutrient availability, temperature, light and ocean currents. In this paper we propose a novel sparse mixture of multivariate regressions model to estimate the time-varying phytoplankton subpopulations while simultaneously identifying the specific environmental covariates that are predictive of the observed changes to these subpopulations. We demonstrate the usefulness and interpretability of the approach using both synthetic data and real observations collected on an oceanographic cruise conducted in the northeast Pacific in the spring of 2017.

Role of nutrients and temperature in shaping distinct summer phytoplankton and microzooplankton population dynamics in the western North Pacific and Bering Sea

AbstractPhytoplankton growth and microzooplankton grazing are two critical processes in marine food webs, but they remain understudied in the vast area of the subarctic western Pacific and the Bering Sea. In this study, we measured phytoplankton growth and microzooplankton grazing rates in these less‐explored regions to demonstrate their spatial patterns and investigate underlying mechanisms driving the planktonic food web dynamics. Our results showed that the phytoplankton growth in these regions was determined by nutrient availability and temperature. In the high‐nutrient, low‐chlorophyll regions (HNLC), iron availability was the primary factor limiting phytoplankton growth. In contrast, phytoplankton growth in the Gulf of Anadyr and Kamchatka Strait was mainly limited by inorganic nitrogen exhausted by the summer blooms. We found that microzooplankton grazing rate was affected by temperature and prey availability, highlighting the positive effect of temperature. Strong top‐down control on phytoplankton by microzooplankton in the Gulf of Anadyr and Kamchatka Strait indicated an active microbial food web with high turnover rates. In contrast, the decoupling of phytoplankton growth and microzooplankton grazing in the HNLC regions illustrates a weak role of microzooplankton in the marine food web. These results indicated different food web structures in the areas with and without riverine iron input. By revealing the roles of temperature and nutrient or prey availability in regulating the spatial variability of plankton rates, we expect that the plankton will respond differently to ocean warming between the HNLC and coastal regions of the subarctic Pacific due to different nutrient conditions. Our study helps understand how marine plankton will respond to environmental changes at high latitudes.

Phytoplankton Population Dynamics in Relation to Environmental Variables at Paradip Port, East Coast of India

Phytoplankton Population Dynamics in Relation to Environmental Variables at Paradip Port, East Coast of India

Impacts of a changing environment on a stoichiometric producer-grazer system: a stochastic modelling approach

Internal and external factors on a producer-grazer system are important because these systems underpin many food chains and changes in them are therefore significant for many animal species. In particular, alterations between phytoplankton and zooplankton are important because they represent a vast source of nutriments for aquatic species. However, the impacts of different intrinsic and extrinsic noise on phytoplankton and zooplankton population dynamics are still missing. By applying stochastic methods to a popular stoichiometric model, the influence of internal and external factors on the system are explored, culminating with a proposition of how noise in the system can be used to examine the latitudinal spatial distribution of phytoplankton. This is achieved by expanding the stoichiometric model to account for macro environmental factors that induce noise in the system. First, the population dynamics under the influence of factors, such as predation, wind gusts and temperature, are individually documented in the paper. Thereafter, using the macro environmental factors such as climate change, stochastic simulations generate a spatial distribution in a latitudinal sense of phytoplankton where it is observed that the phytoplankton clusters are blooming at a higher than expected latitude. Therefore, the latitudinal distribution of phytoplankton of these clusters gives further evidence of the influence of arctic amplification.

Energy Transformation Through Primary Productivity and Nutrient Dynamics of Lentic Water

Some societies can cope, at least temporarily, with pollution and availability constraints. In most of the world, however, population increases continue without significant reduction rates. Growth will continue to impose increasing demands upon freshwater supplies. Control and reversal of degradation requires a proper economic and social valuation of fresh water. Human impact on freshwater ecosystems will increase until industrial growth is stabilized.
 Emotionalism and alarmist reactions to the momentum of exploitation by the technological system accomplish little. Understanding the metabolic responses of aquatic ecosystems is essential in order to confront and offset the effects of human alterations. Many small ponds in different parts of the world have been drained or reclaimed to extend arable land. This has caused unusual fluctuations in water land, the impact of which is tremendous. The biodiversity and dynamics of different phytoplankton population are one of the least explored areas in aquatic biology.
 Ecological studies give humans a deep insight into principles of life, its forms and levels of existence. F.A forel may be considered as the founder of modern limnology for his original work on Swiss Lakes. The primary social need of every sustainable society is to protect and utilize all their natural resources wisely.
 In India, such studies on the pattern of energy flow in aquatic ecosystem by various organisms at different trophic levels are very few. Odum suggested to bring out a comparative picture by investigating the physical, chemical and biological characteristic of lentic water bodies. It is clear that no detailed bioecological investigations have been done so far in this area.

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.

Mathematical modeling and analysis of Phytoplankton–Zooplankton–Nanoparticle dynamics

In this paper, we investigate the population dynamics of phytoplankton–zooplankton–nanoparticle model with diffusion and density dependent death rate of predator. The functional response of predator in this model is considered as Beddington–DeAngelis type. The stability analysis of the equilibrium points is observed by applying the Routh–Hurwitz criterion. Numerical simulations are given to illustrate the theoretical results.

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.

Biophysical controls on seasonal changes in the structure, growth, and grazing of the size-fractionated phytoplankton community in the northern South China Sea

Abstract. The size-fractionated phytoplankton growth and microzooplankton grazing are crucial for the temporal change of community size structure, regulating not only trophic transfer but also the carbon cycle of the ocean. However, the size-dependent growth and grazing dynamics on a monthly or an annual basis are less addressed in the coastal ocean. In this paper, the seasonal responses of the size-fractionated phytoplankton growth and grazing to environmental change were examined over 1 year at a coastal site of the northern South China Sea. We found a nanophytoplankton-dominated community with strong seasonal variations in all size classes. Phytoplankton community growth rate was positively correlated to nutrients, with community grazing rate correlating to the total chlorophyll a at the station, reflecting a combined bottom-up and top-down effect on phytoplankton population dynamics. Further analyses suggested that the specific growth rate of microphytoplankton was significantly influenced by phosphate, and that of nanophytoplankton was influenced by light, although picophytoplankton growth was controlled by both nitrate and temperature. In addition, the specific grazing rate of nanophytoplankton was well correlated to phytoplankton standing stock, while that of micro- and pico-compartments was negatively influenced by ciliate abundance and salinity. Finally, a lower grazing impact for micro-cells (38 %) than nano- and pico-cells (72 % and 60 %, respectively) may support size-selective grazing of microzooplankton on small cells at this eutrophic system.

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 > 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.

Linking population dynamics models with empirically derived models through phytoplankton primary production.

There are alternative methods for estimation of phytoplankton primary production (PP) that are fundamentally different in the calculation approach. The process‐oriented PP model is a mechanistic, empirically derived method based on the photosynthesis–light relationships. The population dynamics‐based PP calculation, which is a synthetic method, provides a production estimate based on population dynamics of phytoplankton. These alternative methods were here compared with regard to production estimates and linked to enhance the performance of the existing models of population dynamics applied to a wide variety of lakes worldwide in terms of morphometry, nutrient status, and light environments. Estimates of PP were shown to be sensitive to changes in phytoplankton sinking and zooplankton grazing rates in both methods. Production estimates in the process‐oriented PP model were also sensitive to light‐associated parameters such as day length. Although the production estimated from the population dynamics‐based PP calculation tended to be lower than that from the process‐oriented PP model irrespective of lake morphometry, production estimates calculated from both methods with standard parameterization were comparable when production was estimated on an annual timescale. However, it was also shown that the alternative methods could produce different production estimates when estimated on shorter timescales such as cyanobacterial blooms in summer. Cyanobacteria with low mortality due to grazing and sinking losses have been considered as trophic bottlenecks, but there is increasing evidence that their mortality is, to a considerable extent, due to parasitic pathogens. In the case of cyanobacterial blooms, an addition of parasite‐related loss term (19%–33% of standing stock) resulted in a resolution of the difference in production estimates between the methods. These analyses theoretically support the critical role of parasitism and resolve the bottleneck problem in aquatic ecosystem metabolism.

Intraseasonal predictability of natural phytoplankton population dynamics.

It is difficult to make skillful predictions about the future dynamics of marine phytoplankton populations. Here, we use a 22‐year time series of monthly average abundances for 198 phytoplankton taxa from Station L4 in the Western English Channel (1992–2014) to test whether and how aggregating phytoplankton into multi‐species assemblages can improve predictability of their temporal dynamics. Using a non‐parametric framework to assess predictability, we demonstrate that the prediction skill is significantly affected by how species data are grouped into assemblages, the presence of noise, and stochastic behavior within species. Overall, we find that predictability one month into the future increases when species are aggregated together into assemblages with more species, compared with the predictability of individual taxa. However, predictability within dinoflagellates and larger phytoplankton (>12 μm cell radius) is low overall and does not increase by aggregating similar species together. High variability in the data, due to observational error (noise) or stochasticity in population growth rates, reduces the predictability of individual species more than the predictability of assemblages. These findings show that there is greater potential for univariate prediction of species assemblages or whole‐community metrics, such as total chlorophyll or biomass, than for the individual dynamics of phytoplankton species.

A structured coagulation-fragmentation equation in the space of radon measures: Unifying discrete and continuous models

We present a structured coagulation-fragmentation model which describes the population dynamics of oceanic phytoplankton. This model is formulated on the space of Radon measures equipped with the bounded Lipschitz norm and unifies the study of the discrete and continuous coagulation-fragmentation models. We prove that the model is well-posed and show it can reduce down to the classic discrete and continuous coagulation-fragmentation models. To understand the interplay between the physical processes of coagulation and fragmentation and the biological processes of growth, reproduction, and death, we establish a regularity result for the solutions and use it to show that stationary solutions are absolutely continuous under some conditions on model parameters. We develop a semi-discrete approximation scheme which conserves mass and prove its convergence to the unique weak solution. We then use the scheme to perform numerical simulations for the model.

Moment approximation of individual-based models. Application to the study of the spatial dynamics of phytoplankton populations

The objective of this paper is to analyze the efficiency of a spatial moments model (SMM) in approximating an individual-based model (IBM) dedicated to the study of the aggregation phenomenon in a phytoplankton population. The dynamic system of spatial moments consists of a system of integro-differential equations derived from a phytoplankton IBM. The later is built on the basis of stochastic processes describing the dynamics of phytoplankton cells and their interactions. These processes are: (1) the movement of cells, which takes into account the random dispersion of cells in water and the attraction between cells due to their chemosensory abilities, (2) the demographical process (cell division or cell death) in which, the effect of local competition for nutrient resources on the cell’s division process is taken into account through the use of density dependent division rate.We solve numerically the SMM and then simulate the two models (IBM and SMM), for a set of scenarios chosen to analyze the aggregation and the different interactions in the population. The numerical results have led to the conclusion that in contrast to the mean-field model (MFM), the method of spatial moments is efficient to capture the dynamics of the IBM. Further, the SMM easily permits the prediction of long term behavior of phytoplankton cells and their spatial structure, namely that the aggregation behavior of the cells is very much related to their cellular diffusion and the perceptual radius.Concerning competition on resources, this process has a short and medium-term effect on aggregation, since regardless of its intensity, in the long term the population tends to be stable with formation of aggregates, as a result of the equilibrium between different mechanisms, including reproduction, competition, diffusion and cells attractions due to chemosensory abilities.

Impact of Lagrangian Sea Surface Temperature Variability on Southern Ocean Phytoplankton Community Growth Rates

AbstractOcean phytoplankton play a critical role in the global carbon cycle, contributing ∼50% of global photosynthesis. As planktonic organisms, phytoplankton encounter significant environmental variability as they are advected throughout the ocean. How this variability impacts phytoplankton growth rates and population dynamics remains unclear. Here, we systematically investigated the impact of different rates and magnitudes of sea surface temperature (SST) variability on phytoplankton community growth rates using surface drifter observations from the Southern Ocean (>30°S) and a phenotype‐based ecosystem model. Short‐term SST variability (<7 days) had a minimal impact on phytoplankton community growth rates. Moderate SST changes of 3–4°C over 7–45 days produced a large time lag between the temperature change and the biological response. The impact of SST variability on community growth rates was nonlinear and a function of the rate and magnitude of change. Additionally, the nature of variability generated in a Lagrangian reference frame (following trajectories of surface water parcels) was larger than that within an Eulerian reference frame (fixed point), which initiated different phytoplankton responses between the two reference frames. Finally, we found that these dynamics were not captured by the Eppley growth model commonly used in global biogeochemical models and resulted in an overestimation of community growth rates, particularly in dynamic, strong frontal regions of the Southern Ocean. This work demonstrates that the timescale for environmental selection (community replacement) is a critical factor in determining community composition and takes a first step towards including the impact of variability and biological response times into biogeochemical models.