Storm impacts on phytoplankton community dynamics in lakes

In many regions across the globe, extreme weather events such as storms have increased in frequency, intensity, and duration due to climate change. Ecological theory predicts that such extreme events should have large impacts on ecosystem structure and function. High winds and precipitation associated with storms can affect lakes via short‐term runoff events from watersheds and physical mixing of the water column. In addition, lakes connected to rivers and streams will also experience flushing due to high flow rates. Although we have a well‐developed understanding of how wind and precipitation events can alter lake physical processes and some aspects of biogeochemical cycling, our mechanistic understanding of the emergent responses of phytoplankton communities is poor. Here we provide a comprehensive synthesis that identifies how storms interact with lake and watershed attributes and their antecedent conditions to generate changes in lake physical and chemical environments. Such changes can restructure phytoplankton communities and their dynamics, as well as result in altered ecological function (e.g., carbon, nutrient and energy cycling) in the short‐ and long‐term. We summarize the current understanding of storm‐induced phytoplankton dynamics, identify knowledge gaps with a systematic review of the literature, and suggest future research directions across a gradient of lake types and environmental conditions

Storm impacts on phytoplankton community dynamics in lakes

In many regions across the globe, extreme weather events such as storms have increased in frequency, intensity, and duration due to climate change. Ecological theory predicts that such extreme events should have large impacts on ecosystem structure and function. High winds and precipitation associated with storms can affect lakes via short-term runoff events from watersheds and physical mixing of the water column. In addition, lakes connected to rivers and streams will also experience flushing due to high flow rates. Although we have a well-developed understanding of how wind and precipitation events can alter lake physical processes and some aspects of biogeochemical cycling, our mechanistic understanding of the emergent responses of phytoplankton communities is poor. Here we provide a comprehensive synthesis that identifies how storms interact with lake and watershed attributes and their antecedent conditions to generate changes in lake physical and chemical environments. Such changes can restructure phytoplankton communities and their dynamics, as well as result in altered ecological function (e.g., carbon, nutrient and energy cycling) in the short- and long-term. We summarize the current understanding of storm-induced phytoplankton dynamics, identify knowledge gaps with a systematic review of the literature, and suggest future research directions across a gradient of lake types and environmental conditions.Peer reviewe

A Multiscale Analysis of the Factors Controlling Nutrient Dynamics and Cyanobacteria Blooms in Lake Champlain

Cyanobacteria blooms have increased in Lake Champlain due to excessive nutrient loading, resulting in negative impacts on the local economy and environmental health. While climate warming is expected to promote increasingly severe cyanobacteria blooms globally, predicting the impacts of complex climate changes on individual lakes is complicated by the many physical, chemical, and biological processes which mediate nutrient dynamics and cyanobacteria growth across time and space. Furthermore, processes influencing bloom development operate on a variety of temporal scales (hourly, daily, seasonal, decadal, episodic), making it difficult to identify important factors controlling bloom development using traditional methods or coarse temporal resolution datasets. To resolve these inherent problems of scale, I use 4 years of high-frequency biological, hydrodynamic, and biogeochemical data from Missisquoi Bay, Lake Champlain; 23 years of lake-wide monitoring data; and integrated process-based climate-watershed-lake models driven by regional climate projections to answer the following research questions: 1) To what extent do external nutrient inputs or internal nutrient processing control nutrient concentrations and cyanobacteria blooms in Lake Champlain; 2) how do internal and external nutrient inputs interact with meteorological drivers to promote or suppress bloom development; and 3) how is climate change likely to impact these drivers and the risk of cyanobacteria blooms in the future? I find that cyanobacteria blooms are driven by specific combinations of meteorological and biogeochemical conditions in different areas of the lake, and that in the absence of strong management actions cyanobacteria blooms are likely to become more severe in the future due to climate change

Phytoplankton production in relation to simulated hydro- and thermodynamics during a hydrological wet year – Goczałkowice reservoir (Poland) case study

Phytoplankton is one of the crucial components of water body ecosystems. Its presence and development depend on biological, physical and chemical factors and in consequence it is an important indicator of ecosystem condition. Monitoring of phytoplankton production, measured as chlorophyll a concentration, is a useful tool for assessing the status of dam reservoirs. Modeled chlorophyll a concentrations are used as water quality indicators in locations not included in monitoring systems, in situations when the temporal resolution of the monitoring is not enough, and in assessments of the impacts of future activities. Therefore, the aim of this study was to find correlations between hydro- and thermodynamics and the chlorophyll a concentration for possible application in reservoir monitoring and management, using an ELCOM-CAEDYM model. The analysis included summer and fall which are most prone to algal blooms, and four phytoplankton groups identified as dominant in the reservoir based on periodic observations. Comparisons of simulated water temperature and both observed and simulated chlorophyll a concentrations confirmed that these variables are significantly correlated (correlation of hourly chlorophyll a and water temperature was 0.70, ranging from 0.55 to 0.81 in the bottom and surface water layers, respectively, while for daily outputs it was 0.74, ranging from 0.60 to 0.83). This relation was stronger than that of chlorophyll a to nutrient (N, P and Si) concentrations. What is more, the method used allowed the assessment of a much more detailed spatial and temporal distribution of phytoplankton groups compared with conventional monitoring techniques. The study indicated that the phytoplankton community was dominated by Chlorophytes and Diatoms with a larger share of Chlorophytes in shallow parts of the reservoir. This domination was weaker after short water mixing events in summer and especially after the fall turnover. The increase in phytoplankton diversity was estimated to occur mainly near the surface and in shallow parts of the reservoir. Most of the observed concentrations of individual phytoplankton groups differed from simulation results by less than 25% and the model reflected accurately 74% of observed trends in concentrations. Calculated chlorophyll a concentration was well matched to hourly monitoring data (mean squared error = 5.6, Nash–Sutcliffe model efficiency coefficient = 0.51, Pearson correlation coefficient = 0.72 and p-value = 0.0007). High compatibility of the model to the values measured in the reservoir make it a promising tool for the prediction and planning of actions aimed at maintaining good functioning of the reservoir

Modélisation et prédiction des assemblages de phytoplancton à l’aval de la rivière des Perles, en Chine

Les écosystèmes aquatiques sont soumis à des pressions croissantes dues aux changements climatiques et aux activités anthropiques. Les rivières sont considérées comme la voie la plus importante pour la circulation de l'énergie, de la matière et des organismes. Le phytoplancton constitue le niveau de base de la chaîne alimentaire aquatique, et en liaison avec son cycle de vie, il a en plus une réponse rapide à des facteurs environnementaux qui régulent l'activité biologique et la qualité de l'eau. Les phytoplanctons ont été étudiés essentiellement en milieux lentiques tels que les lacs et les réservoirs, mais encore peu d'études ont été menées en écosystèmes lotiques. La rivière des Perles est le plus grand fleuve de plaine de Chine du Sud, mais les études pertinentes ont été interrompues au cours des trois dernières décennies. Ainsi, dans la présente étude, nous cherchons à mettre en évidence les patrons d'assemblages de phytoplancton de ce grand fleuve, par des approches de modélisation. Premièrement, nous faisons la synthèse des tendances scientifiques des études phytoplanctoniques entre 1991 et 2013 à l'aide d'une analyse bibliométrique. Le nombre de publications annuelles sur les phytoplanctons a montré une croissance rapide au cours des deux dernières décennies, sa contribution au total des articles scientifiques est toujours restée en dessous de 10%. Dans le cadre du développement rapide de la recherche scientifique, les publications dépendantes (en termes d'écosystèmes multi-aquatiques et des collaborations internationales) montrent une tendance à la hausse. Les variations de mots clés associés à des régions de recherche sont principalement impactées par les zones géographiques adjacentes aux différents pays, qui sont généralement les top-contributeurs. Les tendances des variations des mots-clés relatifs aux méthodes de recherche, le contenu de la recherche et des facteurs environnementaux indiquent que les études de phytoplancton menées à grande échelle et à long terme sont en significative augmentation, tandis que les études traditionnelles et à l'échelle locale sont en décroissance. Deuxièmement, les caractéristiques temporelles des assemblages de phytoplancton ont été analysées dans la partie avale de la rivière des Perles, grâce à un échantillonnage de séries chronologiques quotidien pendant toute l'année 2009. Les conditions excessives d'éléments nutritifs conduisent à une dominance de diatomées dans la communauté de phytoplancton. Alors que les algues vertes contribuent plutôt à la diversité spécifique. En utilisant la carte d'auto-organisation (SOM), des échantillons de phytoplancton ont été classés en quatre groupes sur la base de similitudes d'espèces. Ces groupes étaient bien différenciés par la richesse spécifique, la biomasse et les espèces indicatrices. En outre, le modèle LDA montre que ces groupes peuvent être facilement prédits par des facteurs environnementaux tels que la température de l'eau, le débit et la précipitation. Concernant les éléments nutritifs, seul le phosphate pourrait avoir un impact sur les assemblages de phytoplancton. Le score global de prédiction des assemblages était de 64,2%. Troisièmement, la distribution spatiale du phytoplancton a été analysée dans le delta de la rivière de Perle, en s'appuyant sur un échantillonnage saisonnier en 2012. La richesse en teneur d'éléments nutritifs et l'excellence des échanges d'eau ont abouti à une communauté de phytoplancton dominée par la diversité des Bacillariophyceae et Chlorophyceae et la biomasse de Bacillariophyceae. Par utilisation des méthodes d'ordination NMDS et la classification hiérarchique, les échantillons de phytoplancton ont pu être groupés en 5 clusters. Ces groupes étaient nettement différents, en termes de richesse spécifique, de biomasse et des espèces indicatrices, mais les différences entre les groupes ne sont significatives que dans la dimension spatiale. Le modèle prédictif LDA a indiqué que la répartition spatiale des assemblages de communautés de phytoplancton pourrait facilement être différenciée par des variables associées à la qualité de l'eau (TP, Si, Do et transparence). Le score global de prédiction des assemblages était de 75%. Enfin, la variabilité morphologique des espèces de diatomées prédominantes, Aulacoseira granulata (Ehrenberg) Simonsen, a été étudiée dans la partie avale de la rivière des Perles. On observe une grande cohérence entre les paramètres morphologiques, en particulier la taille de la cellule. En outre, les angles de phases des ondelettes-croisées illustrent bien que le diamètre des cellules est le paramètre le plus sensible aux variations de l'environnement et que par là les variations de taille des cellules et des filaments pourraient y être liées. La température de l'eau a des impacts sur les taux d'occurrence des algues et la taille au cours de la période printemps-hiver. Le cycle de vie des algues pourrait être affecté par le débit, tout comme la longueur de filament, dans la sélection de chaînes avec la flottabilité optimale. Les réponses de la taille des algues à des nutriments, en particulier la silicate, l'azote total et le phosphate, ont été associées avec le début et à la fin d'un cycle de vie. Ces corrélations entre la taille et les nutriments ont été démontrées à la fois par l'analyse par ondelettes et par la RDA. En outre, les valeurs extrêmement élevées à la fin de l'année ont été expliquées comme le recrutement d'algues au niveau du benthos. Notre présente étude dessine les tendances scientifiques du monde entier dans les études de phytoplancton en utilisant l'analyse bibliométrique, en démontrant les tendances temporelles et spatiales des assemblages de phytoplancton en réponse à des environnements dans un grand fleuve tropical en Chine. Nos résultats ont contribués ainsi à la compréhension de la dynamique du phytoplancton dans les écosystèmes d'eau douce, ainsi que dans les grands fleuves du monde entier.Freshwater ecosystems throughout the world are experiencing increasing pressures from both climate changes and anthropogenic activities. Rivers, the typical lotic freshwater ecosystems, are regarded as important pathways for the flow of energy, matter, and organisms through the landscape. Phytoplankton constitutes the base level of the aquatic food web, and it has quick response to environmental factors that regulate biological activity and water quality. Studies on phytoplankton have been extensive in lentic fresh-waters such as lakes and reservoirs, but still less in lotic ecosystems. The Pearl River is the largest lowland river of South China, but relevant studies were interrupted during the last three decades. Consequently in the present study, we contribute to highlight the patterns of the phytoplankton assemblages of this large river, with the approach of several ecological modeling. Firstly, we summarize the scientific trends in phytoplankton studies between 1991 and 2013 based on bibliometric analysis. Although the annual publication output of phytoplankton demonstrated a rapid linear increasing tendency during the last two decades, its contribution to total scientific articles always kept below 10%. Under the background of fast scientific research development, dependent publications (in terms of multi-aquatic ecosystems and international collaborations) indicate linear increasing trend. The variations of keywords associated with research regions are mostly impacted by the geographic adjacent countries, which are generally the top contributors. Variation trends of all the keywords relating to research methods, research contents and environmental factors indicate that phytoplankton studies carried out in large scale and long term are in significant ascending trend, while traditional and local scale studies are in descending trend. Secondly, temporal patterns of phytoplankton assemblages were analyzed within the downstream region of the Pearl River (China), through time-series sampling during the whole of 2009. The excessive nutrient conditions resulted in a diatom dominant phytoplankton community. While green algae only contributed more in species diversity. Phytoplankton samples were classified into four clusters using a self-organizing map (SOM) based on species similarities. These clusters were clearly different, with respect to species richness, biomass and indicators. Moreover, the LDA predicting model indicated that these clusters could easily be differentiated by physical factors such as water temperature, discharge and precipitation. As for nutrients, only phosphate could have an occasional impact on phytoplankton assemblages. The global score for predicting the assemblages was 64.2%. Thirdly, spatial patterns of phytoplankton were analyzed within the Pearl River delta system (China), through seasonal sampling during 2012. The excessive nutrient conditions and well water exchanges resulted in a phytoplankton community that Bacillariophyceae and Chlorophyceae dominated in diversity and Bacillariophyceae dominated in biomass. Phytoplankton samples were revealed by the ordination method using a NMDS and five groups were determined by using hclust. These groups were clearly different, with respect to species richness, biomass and indicators, but differences between the patterning groups were only significant in spatial dimension. The LDA predicting model indicated that the spatial patterns of phytoplankton community assemblages could easily be differentiated by variables (TP, Si, DO and transparency) associated with water quality. The global score for predicting the assemblages was 75%. Lastly, the morphological variability of the predominant diatom species, Aulacoseira granulata (Ehrenberg) Simonsen, was observed within the downstream region of the Pearl River (China). High coherence between morphological parameters, especially cell size, was confirmed. Moreover, phase angles in wavelet figures also illustrated that cell diameter was the most sensitive parameter to environmental variations and through this way cell and filament size variations could be related. Water temperature impacted algal occurrence rates and size values during the spring-winter period. Algal life cycle could be affected by discharge, as well as filament length by allowing for selection of chains with optimum buoyancy. The responses of algae sizes to nutrients, especially silicate, total nitrogen and phosphate, were associated with the start and end of a life cycle. These correlations between size and nutrients were supported by both wavelet analysis and RDA. Moreover, the extremely high values at the end of the year were explained as algal recruitment from benthos. Our present study have introduced the worldwide scientific trends in phytoplankton studies using bibliometric analysis, demonstrated the temporal and spatial patterns of phytoplankton assemblages in response to environments within the downstream region of a large subtropical river in China. Our results will benefit the understanding of phytoplankton dynamics in freshwater ecosystems, as well as the large rivers all over the world

Investigating summer thermal stratification in Lake Ontario

Summer thermal stratification in Lake Ontario is simulated using the 3D hydrodynamic model Environmental Fluid Dynamics Code (EFDC). Summer temperature differences establish strong vertical density gradients (thermocline) between the epilimnion and hypolimnion. Capturing the stratification and thermocline formation has been a challenge in modeling Great Lakes. Deviating from EFDC's original Mellor-Yamada (1982) vertical mixing scheme, we have implemented an unidimensional vertical model that uses different eddy diffusivity formulations above and below the thermocline (Vincon-Leite, 1991; Vincon-Leite et al., 2014). The model is forced with the hourly meteorological data from weather stations around the lake, flow data for Niagara and St. Lawrence rivers; and lake bathymetry is interpolated on a 2-km grid. The model has 20 vertical layers following sigma vertical coordinates. Sensitivity of the model to vertical layers' spacing is thoroughly investigated. The model has been calibrated for appropriate solar radiation coefficients and horizontal mixing coefficients. Overall the new implemented diffusivity algorithm shows some successes in capturing the thermal stratification with RMSE values between 2-3°C. Calibration of vertical mixing coefficients is under investigation to capture the improved thermal stratification

Hydro-Ecological Modeling

Water is not only an interesting object to be studied on its own, it also is an important component driving almost all ecological processes occurring in our landscapes. Plant growth depends on soil water content, as well is nutrient turnover by microbes. Water shapes the environment by erosion and sedimentation. Species occur or are lost depending on hydrological conditions, and many infectious diseases are water-borne. Modeling the complex interactions of water and ecosystem processes requires the prediction of hydrological fluxes and stages on the one side and the coupling of the ecosystem process model on the other. While much effort has been given to the development of the hydrological model theory in recent decades, we have just begun to explore the difficulties that occur when coupled model applications are being set up