Phytoplankton Biomass In Lakes Research Articles

Bimodality and alternative equilibria do not help explain long-term patterns in shallow lake chlorophyll-a

Since its inception, the theory of alternative equilibria in shallow lakes has evolved and been applied to an ever wider range of ecological and socioecological systems. The theory posits the existence of two alternative stable states or equilibria, which in shallow lakes are characterised by either clear water with abundant plants or turbid water where phytoplankton dominate. Here, we used data simulations and real-world data sets from Denmark and north-eastern USA (902 lakes in total) to examine the relationship between shallow lake phytoplankton biomass (chlorophyll-a) and nutrient concentrations across a range of timescales. The data simulations demonstrated that three diagnostic tests could reliably identify the presence or absence of alternative equilibria. The real-world data accorded with data simulations where alternative equilibria were absent. Crucially, it was only as the temporal scale of observation increased (>3 years) that a predictable linear relationship between nutrient concentration and chlorophyll-a was evident. Thus, when a longer term perspective is taken, the notion of alternative equilibria is not required to explain the response of chlorophyll-a to nutrient enrichment which questions the utility of the theory for explaining shallow lake response to, and recovery from, eutrophication.

Identifying factors that affect mountain lake sensitivity to atmospheric nitrogen deposition across multiple scales

Increased nitrogen (N) deposition rates over the past century have affected both North American and European mountain lake ecosystems. Ecological sensitivity of mountain lakes to N deposition varies, however, because chemical and biological responses are modulated by local watershed and lake properties. We evaluated predictors of mountain lake sensitivity to atmospheric N deposition across North American and European mountain ranges and included as response variables dissolved inorganic N (DIN = NNH4+ + NNO3–) concentrations and phytoplankton biomass. Predictors of these responses were evaluated at three different spatial scales (hemispheric, regional, subregional) using regression tree, random forest, and generalized additive model (GAM) analysis. Analyses agreed that Northern Hemisphere mountain lake DIN was related to N deposition rates and smaller scale spatial variability (e.g., regional variability between North American and European lakes, and subregional variability between mountain ranges). Analyses suggested that DIN, N deposition, and subregional variability were important for Northern Hemisphere mountain lake phytoplankton biomass. Together, these findings highlight the need for finer-scale, subregional analyses (by mountain range) of lake sensitivity to N deposition. Subregional analyses revealed differences in predictor variables of lake sensitivity. In addition to N deposition rates, lake and watershed features such as land cover, bedrock geology, maximum lake depth (Zmax), and elevation were common modulators of lake DIN. Subregional phytoplankton biomass was consistently positively related with total phosphorus (TP) in Europe, while North American locations showed variable relationships with N or P. This study reveals scale-dependent watershed and lake characteristics modulate mountain lake ecological responses to atmospheric N deposition and provides important context to inform empirically based management strategies.

Construction and Application Optimization of the Chl-a Forecast Model ARIMA for Lake Taihu

As an important indicator of phytoplankton biomass in lakes, the chlorophyll-a (Chl-a) concentration reflects the abundance and variation of phytoplankton in the water. Based on the monthly monitoring data of Chl-a and environmental factors in Lake Taihu from December 1999 to August 2019, key environmental factors related to Chl-a and their relationships were found using the principal component analysis (PCA) method. A multiple linear stepwise regression model and an auto-regressive integrated moving average (ARIMA) model were developed to predict the monthly Chl-a concentrations. The results showed that the Chl-a concentrations in Lake Taihu exhibited clear seasonal change characteristics and an overall trend of a gradual increase. The changes in total phosphorus (TP), the permanganate index, monthly average temperature (MAT), and monthly rainfall (MR) matched the Chl-a concentrations relatively well, whereas the changes in total nitrogen (TN) and ammonium nitrogen (NH4+-N) lagged significantly. The PCA results showed that the increased phytoplankton biomass and consequent algae outbreaks in Lake Taihu were not limited to the effect of a single factor such as TN or TP, but were comprehensively affected by multiple factors such as TN, NH4+-N, TP, the permanganate index, MR, and MAT. Through further validation, the ARIMA model of Chl-a concentrations was proved to be significantly better than the multiple linear stepwise regression model, especially when considering the key environmental factors as independent variables and optimizing their values. The established ARIMA (0,1,1) (0,1,1) model would be helpful for forecasting algae blooms in Lake Taihu and provide useful suggestions for water environmental management, such as water resources dispatch and regulation.

Trade-offs Between Light and Nutrient Availability Across Gradients of Dissolved Organic Carbon Lead to Spatially and Temporally Variable Responses of Lake Phytoplankton Biomass to Browning

Northern lakes are experiencing widespread increases in dissolved organic carbon (DOC) that are likely to lead to changes in pelagic phytoplankton biomass. Pelagic phytoplankton biomass responds to trade-offs between light and nutrient availability. However, the influence of DOC light absorbing properties and carbon–nutrient stoichiometry on phytoplankton biomass across seasonal or spatial gradients has not been assessed. Here, we analyzed data from almost 5000 lakes to examine how the carbon–phytoplankton biomass relationship is influenced by seasonal changes in light availability, DOC light absorbing properties (carbon-specific visual absorbance, SVA420), and DOC–nutrient [total nitrogen (TN) and total phosphorus (TP)] stoichiometry, using TOC as a proxy for DOC. We found evidence for trade-offs between light and nutrient availability in the relationship between DOC and phytoplankton biomass [chlorophyll (chl)-a], with the shape of the relationship varying with season. A clear unimodal relationship was found only in the fall, particularly in the subsets of lakes with the highest TOC:TP. Observed trends of increasing TOC:TP and decreasing TOC:TN suggest that the effects of future browning will be contingent on future changes in carbon–nutrient stoichiometry. If browning continues, phytoplankton biomass will likely increase in most northern lakes, with increases of up to 76% for a 1.7 mg L−1 increase in DOC expected in subarctic regions, where DOC, SVA420, DOC:TN, and DOC:TP are all low. In boreal regions with higher DOC and higher SVA420, and thus lower light availability, lakes may experience only moderate increases or even decreases in phytoplankton biomass with future browning.

Delineating the relative contribution of climate related variables to chlorophyll-a and phytoplankton biomass in lakes using the ERA5-Land climate reanalysis data

Understanding the climatic drivers of eutrophication is critical for lake management under the prism of the global change. Yet the complex interplay between climatic variables and lake processes makes prediction of phytoplankton biomass a rather difficult task. Quantifying the relative influence of climate-related variables on the regulation of phytoplankton biomass requires modelling approaches that use extensive field measurements paired with accurate meteorological observations. In this study we used climate and lake related variables obtained from the ERA5-Land reanalysis dataset combined with a large dataset of in-situ measurements of chlorophyll-a and phytoplankton biomass from 50 water bodies to develop models of phytoplankton related responses as functions of the climate reanalysis data. We used chlorophyll-a and phytoplankton biomass as response metrics of phytoplankton growth and we employed two different modelling techniques, boosted regression trees (BRT) and generalized additive models for location scale and shape (GAMLSS). According to our results, the fitted models had a relatively high explanatory power and predictive performance. Boosted regression trees had a high pseudo R2 with the type of the lake, the total layer temperature, and the mix-layer depth being the three predictors with the higher relative influence. The best GAMLSS model retained mix-layer depth, mix-layer temperature, total layer temperature, total runoff and 10-m wind speed as significant predictors (p<0.001). Regarding the phytoplankton biomass both modelling approaches had less explanatory power than those for chlorophyll-a. Concerning the predictive performance of the models both the BRT and GAMLSS models for chlorophyll-a outperformed those for phytoplankton biomass. Overall, we consider these findings promising for future limnological studies as they bring forth new perspectives in modelling ecosystem responses to a wide range of climate and lake variables. As a concluding remark, climate reanalysis can be an extremely useful asset for lake research and management.

Winter atmospheric nutrient and pollutant deposition on Western Sayan Mountain lakes (Siberia)

Abstract. The world map of anthropogenic atmospheric nitrogen deposition and its effects on natural ecosystems is not described with equal precision everywhere. In this paper, we report atmospheric nutrient, sulfate and spheroidal carbonaceous particle (SCP) deposition rates, based on snowpack analyses of a formerly unexplored Siberian mountain region. Then, we discuss their potential effects on lake phytoplankton biomass limitation. We estimate that the nutrient depositions observed in the late-season snowpack (40 ± 16 mg NO3-N m−2 and 0.58 ± 0.13 mg TP-P m−2; TP for total phosphorous) would correspond to yearly depositions lower than 119 ± 71 mg NO3-N m−2 yr−1 and higher than 1.71 ± 0.91 mg TP-P m−2 yr−1. These yearly deposition estimates would approximately fit the predictions of global deposition models and correspond to the very low nutrient deposition range, although they are still higher than world background values. In spite of the fact that such a low atmospheric nitrogen deposition rate would be enough to induce nitrogen limitation in unproductive mountain lakes, phosphorus deposition was also extremely low, and the resulting lake water N : P ratio was unaffected by atmospheric nutrient deposition. In the end, the studied lakes' phytoplankton appeared to be split between phosphorus and nitrogen limitation. We conclude that these pristine lakes are fragile sensitive systems exposed to the predicted climate warming, increased winter precipitation, enhanced forest fires and shifts in anthropogenic nitrogen emissions that could finally couple their water chemistry to that of atmospheric nutrient deposition and unlock temperature-inhibited responses of phytoplankton to nutrient shifts.

The role of phosphorus and nitrogen on chlorophyll a: Evidence from hundreds of lakes

The effect of nutrients on phytoplankton biomass in lakes continues to be a subject of debate by aquatic scientists. However, determining whether or not chlorophyll a (CHL) is limited by phosphorus (P) and/or nitrogen (N) is rarely considered using a probabilistic method in studies of hundreds of lakes across broad spatial extents. Several studies have applied a unified CHL-nutrient relationship to determine nutrient limitation, but pose a risk of ecological fallacy because they neglect spatial heterogeneity in ecological contexts. To examine whether or not CHL is limited by P, N, or both nutrients in hundreds of lakes and across diverse ecological settings, a probabilistic machine learning method, Bayesian Network, was applied. Spatial heterogeneity in ecological context was accommodated by the probabilistic nature of the results. We analyzed data from 1382 lakes in 17 US states to evaluate the cause-effect relationships between CHL and nutrients. Observations of CHL, total phosphorus (TP), and total nitrogen (TN) were discretized into three trophic states (oligo-mesotrophic, eutrophic, and hypereutrophic) to train the model. We found that although both nutrients were related to CHL trophic state, TP was more related to CHL than TN, especially under oligo-mesotrophic and eutrophic CHL conditions. However, when the CHL trophic state was hypereutrophic, both TP and TN were important. These results provide additional evidence that P-limitation is more likely under oligo-mesotrophic or eutrophic CHL conditions and that co-limitation of P and N occurs under hypereutrophic CHL conditions. We also found a decreasing pattern of the TN/TP ratio with increasing CHL concentrations, which might be a key driver for the role change of nutrients. Previous work performed at smaller scales support our findings, indicating potential for extension of our findings to other regions. Our findings enhance the understanding of nutrient limitation at macroscales and revealed that the current debate on the limiting nutrient might be caused by failure to consider CHL trophic state. Our findings also provide prior information for the site-specific eutrophication management of unsampled or data-limited lakes.

Inorganic carbon promotes photosynthesis, growth, and maximum biomass of phytoplankton in eutrophic water bodies

Abstract The traditional perception in limnology has been that phytoplankton biomass in lakes is limited by phosphorus, nitrogen, and light, but not by dissolved inorganic carbon (DIC) because CO2 can be supplied from the atmosphere. We tested the possibility of carbon limitation of photosynthesis, growth, and biomass accumulation of phytoplankton communities across an alkalinity and DIC gradient (0.15–3.26 mM) in nutrient‐rich freshwater. During 47‐day long experiments, we measured phytoplankton biomass, organic carbon, calcium, DIC, pH, and oxygen in indoor, constantly mixed mesocosms with either no removal or a 70% weekly removal of the biomass. Photosynthesis was measured in the morning and in the afternoon at high biomass. Maximum biomass and organic carbon production increased two‐ to four‐fold with DIC, which supported 7% of organic carbon production at low DIC and 53% at high DIC concentration, while atmospheric CO2 uptake supplied the remainder. Weekly biomass removal increased growth rates through improved light conditions leading to enhanced total phytoplankton biomass production at high DIC. Photosynthesis was significantly higher in the morning compared to afternoon due to daily DIC depletion. We conclude that phytoplankton photosynthesis, growth rate, maximum biomass, and organic carbon production can be markedly carbon limited in eutrophic lake waters. Consequently, lakes of high DIC and pH can support a faster primary production by greater DIC use and chemically enhanced atmospheric CO2 uptake.

Environmental drivers of mixotrophs in boreal lakes

AbstractMixotrophy is increasingly recognized as an important trophic pathway among phytoplankton, yet its underlying drivers remain largely unknown and unexplored. Here, we present a study utilizing 69 lake samplings in boreal Quebec, Canada, identifying variables driving the success of phytoplankton that have a capacity for mixotrophy and pointing to the underlying mechanisms. We found that the success of mixotrophs (% of total biomass) was positively influenced by both colored dissolved organic matter (cDOM) and dissolved CO2 concentration but limited by the abundance of crustacean zooplankton. The effect of cDOM manifested as a consequence of limited autotrophic phytoplankton biomass in lakes with reduced light penetration. We observed a nonlinear (u‐shaped) relation between CO2 and mixotrophs, with biomass favored at both low and high CO2 concentrations. A reduced fitness of mixotrophs at near‐atmospheric CO2 concentrations is likely owing to the costs of rapidly switching between or maintaining multiple trophic strategies. The abundance of zooplankton had a negative effect on mixotroph biomass but a positive effect on autotrophic phytoplankton. We also found that while the community composition of potentially mixotrophic phytoplankton was to some degree likely influenced by zooplankton biomass, composition was unaffected by the CO2 and cDOM gradients. Overall, this study highlights mixotrophy in boreal lake systems as a strategy of persistence, with the maintenance of a moderate but constant presence across a changing gradient of light and trophic conditions. The results of our study support the hypothesis that phytoplankton with a capacity for mixotrophy provide a superior and stable stoichiometric food source for zooplankton, implicating mixotrophs as a vital component of boreal lake food webs.

Changes in mixing depth reduce phytoplankton biomass in an Arctic lake: Results from a whole-lake experiment

ABSTRACTResearch has revealed contradictory responses of primary producers in Arctic lakes to increasing temperatures, making it unclear how future warming and climate change will alter lake productivity. We conducted a whole-lake manipulation to examine the effect of altered thermal structure on phytoplankton biomass in a lake in Greenland, one of the most rapidly warming regions of the world. Deepened lake mixing (from 4 to 8 m) in the experimental lake led to significant declines in phytoplankton biomass in the sediment traps despite warmer surface waters, indicating that changes in mixing depth may negate a positive, direct effect of warming on primary producer biomass. Light limitation (induced by deeper mixing) of phytoplankton played a greater role than temperature in structuring these Arctic lake phytoplankton communities. To put the manipulation in context, we surveyed twenty-four lakes across western Greenland to determine the strongest regional predictors of phytoplankton density. Across the landscape, lake chemistry and light attenuation in the epilimnion were the most important predictors of algal assemblages in the survey lakes. Though temperature can directly influence lake ecosystems, multiple factors will affect mixing depths of Arctic lakes, potentially leading to variable effects of warming on phytoplankton biomass and community structure.

Long-term dynamics and drivers of phytoplankton biomass in eutrophic Lake Taihu

Understanding the relative effect sizes of climate-related environmental variables and nutrients on the high annual variation in the phytoplankton biomass in eutrophic lakes is important for lake management efforts. In this study, we used a data set of phytoplankton dynamics in eutrophic Lake Taihu that cover more than two decades (1993–2015) to show the variation in and the drivers of phytoplankton biomass under complex, fluctuating environmental conditions. Our results showed that the phytoplankton biomass increased slowly over the studied period despite the recent decrease in nutrient levels. The distribution of the phytoplankton biomass expanded spatially towards the central lake region, and seasonally towards the autumn and winter. Nutrients were still the primary predictors of the long-term phytoplankton biomass trend. The effect size of climate-related variables was also high and close to the effect size of nutrients. Among the climate-related variables, wind speed and underwater available light were more important predictors than temperature. The biomass of the phytoplankton taxonomic groups showed different responses to the environmental variables based on their niches. However, the compensatory dynamics affecting biomass were weak at phylum level, and synchronous dynamics drove the variation in total biomass. Our findings highlight the effect of climate-related variables on the phytoplankton biomass in Lake Taihu, which has experienced high nutrient loadings and concentrations for more than two decades. Therefore, changes in climate-related variables, such as wind speed and underwater available light, should be considered when evaluating the amount that nutrients should be reduced in Lake Taihu for future lake management.

Recovery of lake vegetation following reduced eutrophication and acidification

Abstract In recent decades, many aquatic ecosystems in Europe and North America have experienced reduced inputs of nutrients and acidifying substances because of improved sewage treatment and reduced emission of sulphur oxides. We evaluated the consequences of these efforts to changes in water chemistry, species richness and community composition of aquatic macrophytes in 56 lakes in Denmark around 1990 and again around 2010. Reductions in lake water concentrations of phosphorus and nitrogen were strongest in eutrophic and hypertrophic lakes, for example, lakes which had been heavily affected by domestic sewage. These changes translated into decreased algal biomass in the most eutrophied lakes. Oligo‐ and mesotrophic lakes did not change significantly in terms of nutrients or algal biomass. Water clarity increased across all lakes but not significantly in specific trophic lake groups. Alkalinity and pH increased significantly (up to 2 pH‐units) in low‐alkaline lakes, while well‐buffered high‐alkaline lakes (&gt;0.5 meq/L) did not show any change. Macrophyte species richness per lake increased, on average, by 13% during the 20‐year study period. The increase was strongest in species preferring nutrient‐rich conditions and could be directly attributed to reductions in phytoplankton biomass in lakes of medium water clarity. The similarity among all lakes in terms of species composition increased over the study period. This development was closely related to higher average species richness and was mainly caused by recolonisation of lakes, recovering from past eutrophication, by relatively common species (e.g., Lemna trisulca, Sparganium emersum and Potamogeton berchtoldii). Higher pH in low‐alkaline lakes was accompanied by a shift from acid‐tolerant to more acid‐sensitive species. Our results demonstrate that investment in pollution control has been successful in terms of markedly improving water quality of lakes and, with a time lag, macrophyte species richness. Although relatively common species have spread across lakes and resulted in homogenised macrophyte communities, continued efforts to reduce pollution could ensure the survival of rare specialist species and perhaps even increase their abundance in the future.

Reconciling the opposing effects of warming on phytoplankton biomass in 188 large lakes

Lake ecosystems are deeply integrated into local and regional economies through recreation, tourism, and as sources of food and drinking water. Shifts in lake phytoplankton biomass, which are mediated by climate warming will alter these benefits with potential cascading effects on human well-being. The metabolic theory of ecology suggests that warming reduces lake phytoplankton biomass as basal metabolic costs increase, but this hypothesis has not been tested at the global scale. We use satellite-based estimates of lake surface temperature (LST) and lake surface chlorophyll-a concentration (chl-a; as a proxy for phytoplankton biomass) in 188 of the world’s largest lakes from 2002-2016 to test for interannual associations between chl-a and LST. In contrast to predictions from metabolic ecology, we found that LST and chl-a were positively correlated in 46% of lakes (p < 0.05). The associations between LST and chl-a depended on lake trophic state; warming tended to increase chl-a in phytoplankton-rich lakes and decrease chl-a in phytoplankton-poor lakes. We attribute the opposing responses of chl-a to LST to the effects of temperature on trophic interactions, and the availability of resources to phytoplankton. These patterns provide insights into how climate warming alters lake ecosystems on which millions of people depend for their livelihoods.

Modeling the effects of environmental variables on short-term spatial changes in phytoplankton biomass in a large shallow lake, Lake Taihu

Understanding the short-term response of phy- toplankton biomass on environmental variables is needed for issuing early warnings of harmful algal blooms in aquatic ecosystems. Predicting harmful algal blooms are particularly challenging in large shallow lakes due to their complex mixing patterns. This study used a two-dimen- sional hydrodynamic-phytoplankton model to evaluate the effects of environmental variables on short-term changes in the horizontal distribution of phytoplankton biomass in a large shallow lake, Lake Taihu, China. Two simulations were performed using daily and hourly average wind condition and water temperature data collected in 2009. Other model inputs were identical for these two simula- tions. The response of phytoplankton to wind conditions, light intensity, water temperature, and total dissolved phosphorus and nitrogen concentrations were examined based on a sensitivity analysis using the hourly data. Hourly simulation achieved a more realistic distribution of phytoplankton biomass than the daily simulation. This finding implies that data with a higher temporal resolution are more useful for short-term prediction of phytoplankton biomass in this lake. Sensitivity analysis indicated that water temperature and light intensity dominate short-term changes in phytoplankton biomass in this lake. Wind conditions also affect phytoplankton biomass distribution by causing advective water movement.

Relationships between Chlorophyll-a, Phosphorus and Nitrogen as Fundamentals for Controlling Phytoplankton Biomass in Lakes

It is valuable for the control of water quality in lakes to grasp the global functional relationship between phytoplankton biomass (Chlorophyll-a; Chl-a), total phosphorous (TP) and total nitrogen (TN) in lake ecosystems. This paper aims at developing a comprehensive model that explains the relationship between Chl-a, TP and TN in lakes under a wide range of environments. Three regression models, the conventional Ordinary Least Squares (OLS) model, random effect panel model and dynamic panel model are compared. Estimation based on water quality data from 396 lakes in 18 European countries reveals that OLS estimation gives comparable parameters to those of many earlier studies, in which both TP and TN are significant determinants of Chl-a. Application of the non-conventional estimation methods alters this parameter structure radically. Station-specific effects being controlled, TN/TP is not a significant factor. The inclusion of auto-regressive effects makes TN insignificant. These results suggest that the concentration of Chl-a could be controlled by reducing the concentration of TP, rather than TN, the effect of which might however be offset by the past concentration of Chl-a. These models were tested by applying them to simulate the relationship for 9 Japanese lakes to show the superior performance of the non-conventional dynamic model.

Which factors affect phytoplankton biomass in shallow eutrophic lakes?

The restoration and management of shallow, pond-like systems are hindered by limitations in the applicability of the well-known models describing the relationship between nutrients and lake phytoplankton biomass in higher ranges of nutrient concentration. Trophic models for naturally eutrophic small, shallow, endorheic lakes have not yet been developed, even though these are the most frequent standing waters in continental lowlands. The aim of this study was to identify variables that can be considered as main drivers of phytoplankton biomass and to build a predictive model. The influence of potential drivers of phytoplankton biomass (nutrients, other chemical variables, land use, lake use and lake depth) from 24 shallow eutrophic lakes was tested using data in the Pannonian ecoregion (Hungary and Romania). By incorporating lake depth, TP, TN and lake use as independent and Chl-a as dependent variables into different models (multiple regression model, GLM and multilayer perception model) predictive models were built. These models explained >50% of the variance. Although phytoplankton biomass in small, shallow, enriched lakes is strongly influenced by stochastic effects, our results suggest that phytoplankton biomass can be predicted by applying a multiple stressor approach, and that the model results can be used for management purposes.

Increases in lake phytoplankton biomass caused by future climate-driven changes to seasonal river flow

For the many lakes world-wide with short residence times, changes to the rate of water throughput may have important effects on lake ecology. We studied relationships between current and predicted residence times and phytoplankton biomass using a eutrophic lake in the north-west of England with an annual residence time averaging about 20 days, as a test case. Using 32 years of recent hydrological flow data for Bassenthwaite Lake, multiple sets of scaled flow for each year, and the process-based phytoplankton response model, PROTECH, we modelled the effects of changing river flow on phytoplankton biomass in the lake. The impact on biomass was shown to depend on seasonal changes in flow rather than annual changes. Furthermore, there was a qualitative difference in impact depending on whether the nutrient loading to the lake came principally from flow-independent sources, or from flow-dependent ones. Predictions for changes in river flow under future climate scenarios in the north-west of England have suggested that, despite little change in the annual flow magnitude, there will be a shift to greater flow in the winter and lesser flow in the summer. Applying these flow predictions to our modelling of Bassenthwaite Lake revealed that, with flow-independent nutrient loading, and no overall increase in nutrient load, phytoplankton abundance in the summer could increase by up to 70%, including an increased proportion of Cyanobacteria. Conversely, were the loading completely dependent on the flow, the biomass would fall. In many parts of the world, river flow is expected to decrease in the summer even more than in England, suggesting these areas may expect substantial changes to seasonal phytoplankton biomass as a result of climate-driven changes to seasonal river flow. Such changes would be in addition to any other changes owing to warming effects or eutrophication.

Diversity and dynamics of phytoplankton in lakes Licheńskie and Ślesińskie in 2004-2005

The aim of the study was to identify the similarities and differences in the phytoplankton in lakes Lichenskie and Œlesinskie based on the phytoplankton species composition and biomass. Quantitative and qualitative studies of phytoplankton from two heated lakes were conducted in 2004-2005. A total of 280 and 240 taxa were identified belonging to six phyla: Chlorophyta, Cyanoprokaryota, Cryptophyta, Dinophyta, Euglenophyta, and Heterokontophyta. Most of the taxa were incidental species with frequencies of less than 25%. The phytoplankton biomass in Lake Lichenskie ranged from 0.2 to 8.6 mg dm -3 . Throughout the growing season of 2004, the phytoplankton observed was comprised of diatoms and green algae, while in 2005 it was comprised of diatoms, green algae, and cryptophytes. The phytoplankton biomass in Lake Œlesinskie ranged from 0.4 to 13.2 mg dm -3 . Diatoms dominated in this lake in the spring and fall of both years (54-90%), while in summer 2004 diatoms and green algae dominated and in summer 2005 the dominants were cryptophytes, dinophytes, and cyanophytes.

Control of Lacustrine Phytoplankton by Nutrients: Erosion of the Phosphorus Paradigm

AbstractControl of lacustrine phytoplankton biomass by phosphorus is one of the oldest and most stable paradigms in modern limnology. Even so, evidence from bioassays conducted by multiple investigators at numerous sites over the last three decades shows that N is at least as likely as P to be limiting to phytoplankton growth. A number of important flaws in the evidence supporting the phosphorus paradigm have contributed to an unrealistic degree of focus on phosphorus as a controlling element. These include insufficient skeptism in interpretation of: 1) the phosphorus: chlorophyll correlation in lakes, 2) the results of whole‐lake fertilization experiments, and 3) stoichiometric arguments based on total N:total P ratios for inland waters. A new paradigm based on parity between N and P control of phytoplankton biomass in lakes seems more viable than the P paradigm. The new paradigm renews interest in the degree to which plankton communities are molded in composition by small differences in relative availability of N and P, the mechanisms that lead to a high frequency of N limitation in oligotrophic lakes, and the failure of aquatic N‐fixers to compensate significantly for N deficiency under most conditions. A new N/P paradigm still must acknowledge that suppression of P loading often will be the most effective means of reducing phytoplankton biomass in eutrophic lakes, even if N is initially limiting.From error to error one discovers the entire truth Sigmund Freud (© 2008 WILEY‐VCH Verlag GmbH &amp; Co. KGaA, Weinheim)

Application of a nutrient-saturation concept to the control of algal growth in lakes

Either phosphorus or nitrogen can be responsible for nutrient limitation of algae in lakes. Nitrogen limitation can be defeated by heterocystous cyanobacteria through nitrogen fixation, but no comparable mechanism exists for P. Therefore, P is considered the predominant factor limiting phytoplankton biomass in lakes. Even so, increasing numbers of studies show that many lakes are limited by N deficiency because heterocystous cyanobacteria do not become sufficiently abundant to offset N deficiency. Where N limitation prevails, P control over phytoplankton populations can be achieved only if P concentrations are first reduced to a saturation threshold that is determined by the amount of available N. The extent of this reduction, which will typically occur without any suppression of phytoplankton biomass, can be estimated from nutrient chemistry, nutrient enrichment experiments, and information on the stoi-chiometry of phytoplankton, as illustrated with data for a Colorado reservoir in which a reduction of N of about 50% would be necessary to induce P limitation. Analysis based on stoichiometry could allow managers of water quality in lakes to anticipate the implications of N limitation for P-based management of water quality.