Modelling functional groups of phytoplankton in three lakes of different trophic state

Internal carbon recycling by heterotrophic prokaryotes compensates for mismatches between phytoplankton production and heterotrophic consumption

Molecular observational tools are useful for characterizing the composition and genetic endowment of microbial communities but cannot measure fluxes, which are critical for the understanding of ecosystems. To overcome these limitations, we used a mechanistic inference approach to estimate dissolved organic carbon (DOC) production and consumption by phytoplankton operational taxonomic units and heterotrophic prokaryotic amplicon sequence variants and inferred carbon fluxes between members of this microbial community from Western English Channel time-series data. Our analyses focused on phytoplankton spring and summer blooms, as well as bacteria summer blooms. In spring blooms, phytoplankton DOC production exceeds heterotrophic prokaryotic consumption, but in bacterial summer blooms heterotrophic prokaryotes consume three times more DOC than produced by the phytoplankton. This mismatch is compensated by heterotrophic prokaryotic DOC release by death, presumably from viral lysis. In both types of summer blooms, large amounts of the DOC liberated by heterotrophic prokaryotes are reused through internal recycling, with fluxes between different heterotrophic prokaryotes being at the same level as those between phytoplankton and heterotrophic prokaryotes. In context, internal recycling accounts for approximately 75% and 30% of the estimated net primary production (0.16 vs 0.22 and 0.08 vs 0.29 μmol l-1 d-1) in bacteria and phytoplankton summer blooms, respectively, and thus represents a major component of the Western English Channel carbon cycle. We have concluded that internal recycling compensates for mismatches between phytoplankton DOC production and heterotrophic prokaryotic consumption, and we encourage future analyses on aquatic carbon cycles to investigate fluxes between heterotrophic prokaryotes, specifically internal recycling.

Disruption of ecological networks in lakes by climate change and nutrient fluctuations

Climate change interacts with local processes to threaten biodiversity by disrupting the complex network of ecological interactions. While changes in network interactions drastically affect ecosystems, how ecological networks respond to climate change, in particular warming and nutrient supply fluctuations, is largely unknown. Here, using an equation-free modelling approach on monthly plankton community data in ten Swiss lakes, we show that the number and strength of plankton community interactions fluctuate and respond nonlinearly to water temperature and phosphorus. While lakes show system-specific responses, warming generally reduces network interactions, particularly under high phosphate levels. This network reorganization shifts trophic control of food webs, leading to consumers being controlled by resources. Small grazers and cyanobacteria emerge as sensitive indicators of changes in plankton networks. By exposing the outcomes of a complex interplay between environmental drivers, our results provide tools for studying and advancing our understanding of how climate change impacts entire ecological communities.

The dynamics of phytoplankton seasonal development and its horizontal distribution in Lake Sevan (Armenia)

High-altitude freshwater lakes are experiencing ever-increasing risk of harmful algal blooms (HABs) on the face of climate change and a growing demand on agricultural production. The biggest alpine lake of the Caucasus, Lake Sevan, has "blooming" recently almost every year. Thus, the study of phytoplankton community' development patterns in Lake Sevan is gaining urgency. The aim of the work has been the study of the seasonal dynamics of quantitative and qualitative characteristics of phytoplankton in Lake Sevan. Also, we have tried to determine the features of horizontal distribution of phytoplankton within different seasons with the focus on Cyanobacteria distribution to identify current spatial-temporal features of HABs in Lake Sevan. Seasonal ground data collected from the photic zone of 178 stations in 2016-2018 was analysed and spatially interpolated. The results of analysis of seasonal dynamics of phytoplankton groups abundance have revealed the increased role of Cyanobacteria in the algal "blooms". Particularly, HABs were led by the dominant species of the genus Dolichospermum/Anabaena that are potentially toxic. Univariate analysis of variance with the post hoc Tukey test has proved the significance of changes in the quantitative parameters of phytoplankton development within years with the peak in 2018. Some antagonistic relations between the groups of phytoplankton under the HAB events were also shown through factorial and correlation analysis. Spatial interpolations revealed very limited extents of HABs compared with "blooms" led by Bacillariophyta species. HABs were mainly occurring in the littoral zone in a close proximity to the estuaries of Lake Sevan major tributaries.

Longitudinal Variations in Physiochemical Conditions and Their Consequent Effect on Phytoplankton Functional Diversity Within a Subtropical System of Cascade Reservoirs

The social and environmental impacts of large dams are quantifiable and have been well documented, while small dams have often been presumed to be less environmentally damaging than large dams. The purpose of this study was to analyze longitudinal gradients in environmental, hydrodynamic variables and their impact on phytoplankton function, within a cascade of four reservoirs (XuanMiaoGuan, XMG; TianFuMiao, TFM; XiBeiKou, XBK; ShangJiaHe, SJH) and one reservoir bay (Huangbohe Bay, HBH), located from upstream to downstream in the Huangbo River, Hubei Province, China. Our results showed that water temperature, total nitrogen, and soluble silicate increased along the cascade reservoir system, while the concentration of dissolved oxygen and total phosphorus decreased. We identified 16 phytoplankton functional groups, and the predominant groups, including D (Synedra and Stephanodiscus hantzschii), E (Dinobryon divergens), Lo (Dinoflagellate: Peridinium bipes and Peridiniopsis), X2 (Chroomona), and Y (Cryptomonas), changed longitudinally from up to down in the cascade reservoirs. The number of dominant functional groups increased along the longitudinal gradient, indicating that the function of the phytoplankton community was more stable. Functional group D was the dominant phytoplankton functional group among the four reservoirs, and Lo group was dominant except SJH. The phytoplankton functional groups in the HBH have been completely changed due to the backwater jacking of the main stream of the Yangtze River. Euphotic depth, suspended solids, and nutrients were apparently the key factors driving variations in phytoplankton functional groups among the reservoirs. Notably, the patterns we observed were not all consistent with the cascading reservoir continuum concept (CRCC) that typically characterizes large rivers. Thus, our findings contribute to the further theoretical development of the CRCC, which may not apply widely to all cascade systems.

Dynamic carbon flux network of a diverse marine microbial community

The functioning of microbial ecosystems has important consequences from global climate to human health, but quantitative mechanistic understanding remains elusive. The components of microbial ecosystems can now be observed at high resolution, but interactions still have to be inferred e.g., a time-series may show a bloom of bacteria X followed by virus Y suggesting they interact. Existing inference approaches are mostly empirical, like correlation networks, which are not mechanistically constrained and do not provide quantitative mass fluxes, and thus have limited utility. We developed an inference method, where a mechanistic model with hundreds of species and thousands of parameters is calibrated to time series data. The large scale, nonlinearity and feedbacks pose a challenging optimization problem, which is overcome using a novel procedure that mimics natural speciation or diversification e.g., stepwise increase of bacteria species. The method allows for curation using species-level information from e.g., physiological experiments or genome sequences. The product is a mass-balancing, mechanistically-constrained, quantitative representation of the ecosystem. We apply the method to characterize phytoplankton-heterotrophic bacteria interactions via dissolved organic matter in a marine system. The resulting model predicts quantitative fluxes for each interaction and time point (e.g., 0.16 µmolC/L/d of chrysolaminarin to Polaribacter on April 16, 2009). At the system level, the flux network shows a strong correlation between the abundance of bacteria species and their carbon flux during blooms, with copiotrophs being relatively more important than oligotrophs. However, oligotrophs, like SAR11, are unexpectedly high carbon processors for weeks into blooms, due to their higher biomass. The fraction of exudates (vs. grazing/death products) in the DOM pool decreases during blooms, and they are preferentially consumed by oligotrophs. In addition, functional similarity of phytoplankton i.e., what they produce, decouples their association with heterotrophs. The methodology is applicable to other microbial ecosystems, like human microbiome or wastewater treatment plants.

Integrating Inland and Coastal Water Quality Data for Actionable Knowledge

Water quality measures for inland and coastal waters are available as discrete samples from professional and volunteer water quality monitoring programs and higher-frequency, near-continuous data from automated in situ sensors. Water quality parameters also are estimated from model outputs and remote sensing. The integration of these data, via data assimilation, can result in a more holistic characterization of these highly dynamic ecosystems, and consequently improve water resource management. It is becoming common to see combinations of these data applied to answer relevant scientific questions. Yet, methods for scaling water quality data across regions and beyond, to provide actionable knowledge for stakeholders, have emerged only recently, particularly with the availability of satellite data now providing global coverage at high spatial resolution. In this paper, data sources and existing data integration frameworks are reviewed to give an overview of the present status and identify the gaps in existing frameworks. We propose an integration framework to provide information to user communities through the the Group on Earth Observations (GEO) AquaWatch Initiative. This aims to develop and build the global capacity and utility of water quality data, products, and information to support equitable and inclusive access for water resource management, policy and decision making.

Phytoplankton morpho-functional trait dataset from French water-bodies

In lake ecosystems, phytoplankton communities can be studied by adopting taxonomic-based approaches. However, these approaches suffer from identification issues and are sometimes of limited ecosystem ecological value. The recent development of functional approaches may allow an evaluation of other aspects of ecosystem quality, functions and interactions with abiotic parameters or other communities. Here, our aim was to create a phytoplankton trait database at the French scale. This database will be relevant for the analysis of phytoplankton communities that will lead to a better understanding of phytoplankton functional ecology in lakes of France and other European countries possessing similar biological communities. We used a French national database of phytoplankton occurrences sampled from 384 lakes over the entire French metropolitan territory. A final list of 636 taxa was used to compile 53 morpho-functional traits associated with taxonomic information. The traits encompassed variables such as shape, biovolume, motility, toxin production and Reynolds groups. With this new database, we aim to provide data for morpho-functional analyses of phytoplankton assemblages from local to European scale.

Imaging Flow Cytometry for Phylogenetic and MorphologicallyBased Functional Group Clustering of a Natural Phytoplankton Community over 1 Year in an Urban Pond

Ponds are an insufficiently studied research object but represent a biodiversity hotspot and have a high value for ecosystem services like recreation, water retention, or angling. Especially urban ponds create a direct contact for citizens experiencing nature. But on the other side, these systems also suffer from several pressures caused by humans, for example, high nutrient and salt influxes or high temperatures. Phytoplankton organisms are a crucial part of ponds ecosystem and an understanding of community composition is crucial especially when eutrophication and high temperatures lead to dominance of unpleasant toxic cyanobacteria. With traditional microscopic methods for phytoplankton analysis, monitoring is not feasible with high spatial resolution and frequency. Therefore, a new approach of imaging flow cytometry to classify phytoplankton species in either taxonomic or morphologically based functional groups (MBFGs) is suggested. In this study, both classifications could be successfully applied to a natural phytoplankton community in an urban pond in Leipzig with minor modifications. Both classifications in combination provide a good mechanistic understanding of phytoplankton community dynamics. In addition, a great advantage of the measurements is the archivability of microscopic images allowing a comprehensive respective data analysis. Two examples of detailed trait and image analysis are demonstrated to investigate single-cell traits for cyanobacteria and chlorophytes/euglenophytes and to follow the fate of a cyanobacterial bloom affected by a fungal infection. © 2020 The Author. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

An integrated mechanistic modeling of a facultative pond: Parameter estimation and uncertainty analysis

Imitating natural lakes, pond treatment systems inherit a high complexity with interconnected web of biochemical reactions and complex hydraulic processes. As such, its simulation requires a large and integrated model, which has been a challenge for pond engineers. In this study, we develop an all-encompassing model to gain a quantitative and comprehensive understanding of the hydraulic, physicochemical and microbiological conversion processes in the most common pond, a facultative pond. Moreover, to deal with an evitable issue of large mechanistic models as overparameterization leading to poor identifiability, a systematic parameter estimation was implemented. The application of sensitivity analysis reveals the most influential parameters on pond performance. Particularly, physical parameters, such as vertical eddy diffusivity, water temperature, and maximum growth rate of heterotrophs induce the most changes of organic matters while microbial assimilation and ammonia volatilization appear to be main processes for nutrient removal. In contrast, the efficiency of phosphate precipitation and nutrient biological removal via polyphosphate accumulating organisms and denitrifying bacteria is limited. Identifiability problems are addressed mainly by the characterization of light dependence of algal growth, interaction between water temperature and its coefficient, and the growth of autotrophic bacteria while based on the determinant measures, the most important parameter subsets affecting model outputs are related to physical processes and algal activity. After the establishment of the influential and identifiable parameter subset, an automatic calibration with the data collected from Ucubamba pond system (Ecuador) demonstrates the effect of high-altitude climatic conditions on pond behaviors. An aerobic prevailing condition is observed as a result of high light intensity causing accelerated algal activities, hence, leading to the limitation of hydrolysis, anaerobic processes, and the growth of anoxic heterotrophs for denitrification. Furthermore, the output of uncertainty analysis indicates that a large avoidable uncertainty as a result of vast complexity of the applied model can be reduced greatly via a systematic approach for parameter estimation.

Modelling eutrophication in lake ecosystems: A review

Eutrophication is one of the main causes of the degradation of lake ecosystems. Its intensification during the last decades has led the stakeholders to seek for water management and restoration solutions, including those based on modelling approaches. This paper presents a review of lake eutrophication modelling, on the basis of a scientific appraisal performed by researchers for the French ministries of Environment and Agriculture. After a brief introduction presenting the scientific context, a bibliography analysis is presented. Then the main results obtained with process-based models are summarized. A synthesis of the scientist recommendations in order to improve the lake eutrophication modelling is finally given before the conclusion.

Model-Based Analysis of Increased Loads on the Performance of Activated Sludge and Waste Stabilization Ponds

In a way to counter criticism on low cost-effective conventional activated sludge (AS) technology, waste stabilization ponds (WSPs) offer a valid alternative for wastewater treatment due to their simple and inexpensive operation. To evaluate this alternative with respect to its robustness and resilience capacity, we perform in silico experiments of different peak-load scenarios in two mathematical models representing the two systems. A systematic process of quality assurance for these virtual experiments is implemented, including sensitivity and identifiability analysis, with non-linear error propagation. Moreover, model calibration of a 210-day real experiment with 31 days of increased load was added to the evaluation. Generally speaking, increased-load scenarios run in silico showed that WSP systems are more resilient towards intermediate disturbances, hence, are suitable to treat not only municipal wastewater, but also industrial wastewater, such as poultry wastewater, and paperboard wastewater. However, when disturbances are extreme (over 7000 mg COD·L−1), the common design of the natural system fails to perform better than AS. Besides, the application of sensitivity analysis reveals the most influential parameters on the performance of the two systems. In the AS system, parameters related to autotrophic bacteria have the highest influence on the dynamics of particulate organic matter, while nitrogen removal is largely driven by nitrification and denitrification. Conversely, with an insignificant contribution of heterotrophs, the nutrient removal in the pond system is mostly done by algal assimilation. Furthermore, this systematic model-based analysis proved to be a suitable means for investigating the maximum load of wastewater treatment systems, and from that avoiding environmental problems and high economic costs for cleaning surface waters after severe overload events.

Exploration of an urban lake management model to simulate chlorine interference based on the ecological relationships among aquatic species

In eutrophic lakes, algae are known to be sensitive to chlorine, but the impact of chlorine on the wider ecosystem has not been investigated. To quantitatively investigate the effects of chlorine on the urban lake ecosystem and analyze the changes in the aquatic ecosystem structure, a dynamic response model of aquatic species to chlorine was constructed based on the biomass density dynamics of aquatic species of submerged macrophytes, phytoplankton, zooplankton, periphyton, and benthos. The parameters were calibrated using data from the literature and two simulative experiments. The model was then validated using field data from an urban lake with a surface area of approximately 8000 m² located in the downtown area of Guangzhou, South China. The correlation coefficient (R), root mean square error-observations standard deviation ratio (RSR) and index of agreement (IOA) were used to evaluate the accuracy and reliability of the model and the results were consistent with the observations (0.446 R < 0.985, RSR < 0.7, IOA > 0.6). Comparisons between the simulated and observed trends confirmed the feasibility of using this model to investigate the dynamics of aquatic species under chlorine interference. The model can help managers apply a modest amount of chlorine to control eutrophication and provides scientific support for the management of urban lakes.

On the successful use of a simplified model to simulate the succession of toxic cyanobacteria in a hypereutrophic reservoir with a highly fluctuating water level

Many freshwater bodies worldwide that suffer from harmful algal blooms would benefit for their management from a simple ecological model that requires few field data, e.g. for early warning systems. Beyond a certain degree, adding processes to ecological models can reduce model predictive capabilities. In this work, we assess whether a simple ecological model without nutrients is able to describe the succession of cyanobacterial blooms of different species in a hypereutrophic reservoir and help understand the factors that determine these blooms. In our study site, Karaoun Reservoir, Lebanon, cyanobacteria Aphanizomenon ovalisporum and Microcystis aeruginosa alternatively bloom. A simple configuration of the model DYRESM-CAEDYM was used; both cyanobacteria were simulated, with constant vertical migration velocity for A. ovalisporum, with vertical migration velocity dependent on light for M. aeruginosa and with growth limited by light and temperature and not by nutrients for both species. The model was calibrated on two successive years with contrasted bloom patterns and high variations in water level. It was able to reproduce the measurements; it showed a good performance for the water level (root-mean-square error (RMSE) lower than 1 m, annual variation of 25 m), water temperature profiles (RMSE of 0.22-1.41 °C, range 13-28 °C) and cyanobacteria biomass (RMSE of 1-57 μg Chl a L^-1, range 0-206 μg Chl a L^-1). The model also helped understand the succession of blooms in both years. The model results suggest that the higher growth rate of M. aeruginosa during favourable temperature and light conditions allowed it to outgrow A. ovalisporum. Our results show that simple model configurations can be sufficient not only for theoretical works when few major processes can be identified but also for operational applications. This approach could be transposed on other hypereutrophic lakes and reservoirs to describe the competition between dominant phytoplankton species, contribute to early warning systems or be used for management scenarios.

Modeling phytoplankton community in reservoirs. A comparison between taxonomic and functional groups-based models

In this paper we address the formulation of two mechanistic water quality models that differ in the way the phytoplankton community is described. We carry out parameter estimation subject to differential-algebraic constraints and validation for each model and comparison between models performance. The first approach aggregates phytoplankton species based on their phylogenetic characteristics (Taxonomic group model) and the second one, on their morpho-functional properties following Reynolds' classification (Functional group model). The latter approach takes into account tolerance and sensitivity to environmental conditions. The constrained parameter estimation problems are formulated within an equation oriented framework, with a maximum likelihood objective function. The study site is Paso de las Piedras Reservoir (Argentina), which supplies water for consumption for 450,000 population. Numerical results show that phytoplankton morpho-functional groups more closely represent each species growth requirements within the group. Each model performance is quantitatively assessed by three diagnostic measures. Parameter estimation results for seasonal dynamics of the phytoplankton community and main biogeochemical variables for a one-year time horizon are presented and compared for both models, showing the functional group model enhanced performance. Finally, we explore increasing nutrient loading scenarios and predict their effect on phytoplankton dynamics throughout a one-year time horizon.

Three-dimensional lake water quality modeling: sensitivity and uncertainty analyses

Two sensitivity and uncertainty analysis methods are applied to a three-dimensional coupled hydrodynamic-ecological model (ELCOM-CAEDYM) of a morphologically complex lake. The primary goals of the analyses are to increase confidence in the model predictions, identify influential model parameters, quantify the uncertainty of model prediction, and explore the spatial and temporal variabilities of model predictions. The influence of model parameters on four model-predicted variables (model output) and the contributions of each of the model-predicted variables to the total variations in model output are presented. The contributions of predicted water temperature, dissolved oxygen, total phosphorus, and algal biomass contributed 3, 13, 26, and 58% of total model output variance, respectively. The fraction of variance resulting from model parameter uncertainty was calculated by two methods and used for evaluation and ranking of the most influential model parameters. Nine out of the top 10 parameters identified by each method agreed, but their ranks were different. Spatial and temporal changes of model uncertainty were investigated and visualized. Model uncertainty appeared to be concentrated around specific water depths and dates that corresponded to significant storm events. The results suggest that spatial and temporal variations in the predicted water quality variables are sensitive to the hydrodynamics of physical perturbations such as those caused by stream inflows generated by storm events. The sensitivity and uncertainty analyses identified the mineralization of dissolved organic carbon, sediment phosphorus release rate, algal metabolic loss rate, internal phosphorus concentration, and phosphorus uptake rate as the most influential model parameters.

Functional groups of marine ciliated protozoa and their relationships to water quality

Ciliated protozoa (ciliates) play important ecological roles in coastal waters, especially regarding their interaction with environmental parameters. In order to increase our knowledge and understanding on the functional structure of ciliate communities and their relationships to environmental conditions in marine ecosystems, a 12-month study was carried out in a semi-enclosed bay in northern China. Samples were collected biweekly at five sampling stations with differing levels of pollution/eutrophication, giving a total of 120 samples. Thirteen functional groups of ciliates (A-M) were defined based on their specific spatio-temporal distribution and relationships to physico-chemical parameters. Six of these groups (H-M) were the primary contributors to the ciliate communities in the polluted/eutrophic areas, whereas the other seven groups (A-G) dominated the communities in less polluted areas. Six groups (A, D, G, H, I and K) dominated during the warm seasons (summer and autumn), with the other seven (B, C, E, F, J, L and M) dominating in the cold seasons (spring and winter). Of these, groups B (mainly aloricate ciliates), I (aloricate ciliates) and L (mainly loricate tintinnids) were the primary contributors to the communities. It was also shown that aloricate ciliates and tintinnids represented different roles in structuring and functioning of the communities. The results suggest that the ciliate communities may be constructed by several functional groups in response to the environmental conditions. Thus, we conclude that these functional groups might be potentially useful bioindicators for bioassessment and conservation in marine habitats.

An automated platform for phytoplankton ecology and aquatic ecosystem monitoring

High quality monitoring data are vital for tracking and understanding the causes of ecosystem change. We present a potentially powerful approach for phytoplankton and aquatic ecosystem monitoring, based on integration of scanning flow-cytometry for the characterization and counting of algal cells with multiparametric vertical water profiling. This approach affords high-frequency data on phytoplankton abundance, functional traits and diversity, coupled with the characterization of environmental conditions for growth over the vertical structure of a deep water body. Data from a pilot study revealed effects of an environmental disturbance event on the phytoplankton community in Lake Lugano (Switzerland), characterized by a reduction in cytometry-based functional diversity and by a period of cyanobacterial dominance. These changes were missed by traditional limnological methods, employed in parallel to high-frequency monitoring. Modeling of phytoplankton functional diversity revealed the importance of integrated spatiotemporal data, including circadian time-lags and variability over the water column, to understand the drivers of diversity and dynamic processes. The approach described represents progress toward an automated and trait-based analysis of phytoplankton natural communities. Streamlining of high-frequency measurements may represent a resource for understanding, modeling and managing aquatic ecosystems under impact of environmental change, yielding insight into processes governing phytoplankton community resistance and resilience.