Cyanobacteria Blooms In Lakes Research Articles

Nitrogen Limitation of Intense and Toxic Cyanobacteria Blooms in Lakes within Two of the Most Visited Parks in the USA: The Lake in Central Park and Prospect Park Lake.

The Lake in Central Park (LCP) and Prospect Park Lake (PPL) in New York City (NYC), USA, are lakes within two of the most visited parks in the USA. Five years of nearshore sampling of these systems revealed extremely elevated levels of cyanobacteria and the toxin, microcystin, with microcystin levels averaging 920 µg L−1 and chlorophyll a from cyanobacterial (cyano-chla) populations averaging 1.0 × 105 µg cyano-chla L−1. Both lakes displayed elevated levels of orthophosphate (DIP) relative to dissolved inorganic nitrogen (DIN) during summer months when DIN:DIP ratios were < 1. Nutrient addition and dilution experiments revealed that N consistently limited cyanobacterial populations but that green algae were rarely nutrient limited. Experimental additions of public drinking water that is rich in P and, to a lesser extent N, to lake water significantly enhanced cyanobacterial growth rates in experiments during which N additions also yielded growth enhancement. Collectively, this study demonstrates that the extreme microcystin levels during blooms in these highly trafficked lakes represent a potential human and animal health threat and that supplementation of these artificial lakes with public drinking water to maintain water levels during summer may promote the intensity and N limitation of blooms.

Using Bayesian hierarchical modelling to capture cyanobacteria dynamics in Northern European lakes

Cyanobacteria blooms in lakes and reservoirs currently threaten water security and affect the ecosystem services provided by these freshwater ecosystems, such as drinking water and recreational use. Climate change is expected to further exacerbate the situation in the future because of higher temperatures, extended droughts and nutrient enrichment, due to urbanisation and intensified agriculture. Nutrients are considered critical for the deterioration of water quality in lakes and reservoirs and responsible for the widespread increase in cyanobacterial blooms. We model the response of cyanobacteria abundance to variations in lake Total Phosphorus (TP) and Total Nitrogen (TN) concentrations, using a data set from 822 Northern European lakes. We divide lakes in ten groups based on their physico-chemical characteristics, following a modified lake typology defined for the Water Framework Directive (WFD). This classification is used in a Bayesian hierarchical linear model which employs a probabilistic approach, transforming uncertainty into probability thresholds. The hierarchical model is used to calculate probabilities of cyanobacterial concentrations exceeding risk levels for human health associated with the use of lakes for recreational activities, as defined by the World Health Organization (WHO). Different TN and TP concentration combinations result in variable probabilities to exceed pre-set thresholds. Our objective is to support lake managers in estimating acceptable nutrient concentrations and allow them to identify actions that would achieve compliance of cyanobacterial abundance risk levels with a given confidence level.

A multi-factor data-driven prediction model for cyanobacteria blooms in lakes and reservoirs

A multi-factor data-driven prediction model for cyanobacteria blooms in lakes and reservoirs

Impact of Urban Stormwater Runoff on Cyanobacteria Dynamics in A Tropical Urban Lake

Worldwide, eutrophication and cyanobacteria blooms in lakes and reservoirs are a great concern for water resources management. Coupling a catchment hydrological model and a lake model has been a strategy to assess the impact of land use, agricultural practices and climate change on water quality. However, research has mainly focused on large lakes, while urban reservoirs and their catchments, especially in tropical regions, are still poorly studied despite the wide range of ecosystem services they provide. An integrated modelling approach coupling the hydrological model Storm Water Management Model SWMM and the lake ecological model DYRESM-CAEDYM is proposed for Lake Pampulha (Brazil). Scenarios of increased imperviousness of the catchment and of reduction in the load of nutrients and total suspended solids (TSS) in dry weather inflow were simulated. Runoff water quality simulations presented a fair performance for TSS and ammonium (NH4+) while the dynamics of total phosphorus (TP) and nitrate (NO3−) were poorly captured. Phytoplankton dynamics in the lake were simulated with good accuracy (Normalized Mean Absolute Error, NMAE = 0.24 and r = 0.89 in calibration period; NMAE = 0.55 and r = 0.54 in validation period). The general trends of growth, decline and the magnitude of phytoplankton biomass were well represented most of the time. Scenario simulations suggest that TP reduction will decrease cyanobacteria biomass and delay its peaks as a consequence of orthophosphate (PO43−) concentration reduction in the lake surface layers. However, even decreasing TP load into Lake Pampulha by half would not be sufficient to achieve the water quality objective of a maximum concentration of 60 µg chla L−1. Increased imperviousness in the catchment will raise runoff volume, TSS, TP and NO3− loads into Lake Pampulha and promote greater cyanobacteria biomass, mainly in the beginning of the wet season, because of additional nutrient input from catchment runoff. Recovering Lake Pampulha water quality will require the improvement of the sanitation system. The lake water quality improvement will also require more sustainable and nature-based solutions for urban drainage in order to reduce non-point pollution through infiltration and retention of stormwater and to enhance natural processes, such as chemical sorption, biodegradation and phytoremediation. The integrated modelling approach here proposed can be applied for other urban reservoirs taking advantage of existing knowledge on Lake Pampulha.

Spatial and temporal variations reveal the response of zooplankton to cyanobacteria

The effects of cyanobacteria on zooplankton abundance, structure and diversity were investigated, based on a systematic study on spatial and temporal variations of cyanobacteria and zooplankton in Lake Taihu from 1998 to 2007. It was found that similar increasing trends of cyanobacteria/phytoplankton ratios were accompanied by different trends in biomass, composition and biodiversity of zooplankton in different regions of the lake; the cladocerans benefitted from the increase in cyanobacteria; however, rotifers and protozoans were negatively affected by cyanobacteria. The biomass-based biodiversity of phytoplankton and zooplankton was negatively affected by cyanobacteria as well, and the adverse effects were in proportion to the cyanobacteria/phytoplankton ratio. These results indicated interestingly that higher amounts of cyanobacteria do not necessarily reduce zooplankton biomass, as the biomass of larger zooplankton such as cladocerans was positively related to cyanobacteria. The findings are essential to understand the complex ecological effects of cyanobacteria blooms in lakes.

The Adaptive-Clustering and Error-Correction Method for Forecasting Cyanobacteria Blooms in Lakes and Reservoirs

Globally, cyanobacteria blooms frequently occur, and effective prediction of cyanobacteria blooms in lakes and reservoirs could constitute an essential proactive strategy for water-resource protection. However, cyanobacteria blooms are very complicated because of the internal stochastic nature of the system evolution and the external uncertainty of the observation data. In this study, an adaptive-clustering algorithm is introduced to obtain some typical operating intervals. In addition, the number of nearest neighbors used for modeling was optimized by particle swarm optimization. Finally, a fuzzy linear regression method based on error-correction was used to revise the model dynamically near the operating point. We found that the combined method can characterize the evolutionary track of cyanobacteria blooms in lakes and reservoirs. The model constructed in this paper is compared to other cyanobacteria-bloom forecasting methods (e.g., phase space reconstruction and traditional-clustering linear regression), and, then, the average relative error and average absolute error are used to compare the accuracies of these models. The results suggest that the proposed model is superior. As such, the newly developed approach achieves more precise predictions, which can be used to prevent the further deterioration of the water environment.

Reducing Phosphorus to Curb Lake Eutrophication is a Success

As human populations increase and land-use intensifies, toxic and unsightly nuisance blooms of algae are becoming larger and more frequent in freshwater lakes. In most cases, the blooms are predominantly blue-green algae (Cyanobacteria), which are favored by low ratios of nitrogen to phosphorus. In the past half century, aquatic scientists have devoted much effort to understanding the causes of such blooms and how they can be prevented or reduced. Here we review the evidence, finding that numerous long-term studies of lake ecosystems in Europe and North America show that controlling algal blooms and other symptoms of eutrophication depends on reducing inputs of a single nutrient: phosphorus. In contrast, small-scale experiments of short duration, where nutrients are added rather than removed, often give spurious and confusing results that bear little relevance to solving the problem of cyanobacteria blooms in lakes.

Identifying the factors determining blooms of cyanobacteria in a set of shallow lakes

There is a strong interest in developing a capacity to predict the occurrence of cyanobacteria blooms in lakes and to identify the measures to be taken to reduce water quality problems associated with the occurrence of potentially harmful taxa. Here we conducted a weekly to bi-weekly monitoring program on five shallow eutrophic lakes during two years, with the aim of gathering data on total cyanobacterial abundance, as estimated from marker pigments determined by HPLC analysis of phytoplankton extracts. We also determined bloom composition and measured weather and limnological variables. The most frequently identified taxa were Aphanizomenon flos-aquae, Microcystis aeruginosa, Planktothrix agardhii and Anabaena spp. We used the data base composed of a total of 306 observations and an adaptive regression trees method, the boosted regression tree (BRT), to develop predictive models of bloom occurrence and composition, based on environmental conditions. Data processing with BRT enabled the design of satisfactory prediction models of cyanobacterial abundance and of the occurrence of the main taxa. Phosphorus (total and soluble reactive phosphate), dissolved inorganic nitrogen, epilimnion temperature, photoperiod and euphotic depth were among the best predictive variables, contributing for at least 10% in the models, and their relative contribution varied in accordance with the ecological traits of the taxa considered. Meteorological factors (wind, rainfall, surface irradiance) had a significant role in species selection. Such results may contribute to designing measures for bloom management in shallow lakes.

Current Scope, Case Studies and Future Directions of Ecological Informatics

Ecological data management, analysis and synthesis as well as information processing and transfer in ecology are considered feature areas of ecological informatics. The paper presents case studies for analysis and synthesis of ecological data by means of unsupervised artificial neural networks and hybrid evolutionary algorithms to assist in computational bioindication of wetland water quality and early warning of cyanobacteria blooms in lakes and reservoirs. Integrated analysis and synthesis of ecological and genomics data, hybrid model libraries generic for ecosystem categories as well as internet-based data and model sharing are identified as key research areas of ecological informatics.

Do macrophytes support harmful cyanobacteria? Interactions with a green alga reverse the inhibiting effects of macrophyte allelochemicals on Microcystis aeruginosa

Plant-released allelopathic substances are increasingly suggested as potential natural measure to control cyanobacteria blooms in lakes. We tested whether the common cyanobacterium Microcystis aeruginosa, known to be sensitive to allelochemicals, remains suppressed when interacting with a less sensitive green alga. We investigated the effects of polyphenolic allelochemicals on single and mixed cultures of the common green alga Desmodesmus armatus and M. aeruginosa with three different inoculum compositions. Polyphenols were added as tannic acid or released by a macrophyte (Myriophyllum verticillatum L.) in coexistence experiments with dialysis bags. Interaction with the green alga turned the inhibiting effect of allelochemicals on the cyanobacterium into an enhancement resulting in increased growth rates. The ratio between M. aeruginosa and the green alga consequently became significantly higher in treatments with allelochemicals as compared to controls after three days. When initial growth rates of M. aeruginosa and the green alga were similar, this resulted in an increasing abundance of the cyanobacterium under allelochemical presence. Interactions between phytoplankton species thus need to be taken into account when investigating the ecological relevance of macrophyte allelopathy and its potential to suppress cyanobacteria blooms.

Sediment fingerprinting as an environmental forensics tool explaining cyanobacteria blooms in lakes

Cyanobacteria (blue-green algae) blooms in water bodies present serious public health issues with attendant economic and ecological impacts. Llyn Tegid (Lake Bala) is an important conservation and amenity asset within Snowdonia National Park, Wales which since the mid-1990s has experienced multiple toxic cyanobacteria blooms threatening the ecology and tourism-dependent local economy. Multiple working hypotheses explain the emergence of this problem, including climate change, land management linked to increased nutrient flux, hydromorphological alterations or changing trophic structure - any of which may operate individually or cumulatively to impair lake function. This paper reports the findings of a sediment fingerprinting study using dated lake cores to explore the linkages between catchment and lake management practices and the emergence of the algal blooms problem. Since 1900 AD lake bed sedimentation rates have varied from 0.06 to 1.07 g cm −2 yr −1, with a pronounced acceleration since the early 1980s. Geochemical analysis revealed increases in the concentrations of total phosphorus (TP), calcium and heavy metals such as zinc and lead consistent with eutrophication and a rising pollution burden, particularly since the late 1970s. An uncertainty-inclusive sediment fingerprinting approach was used to apportion the relative fluxes from the major catchment land cover types of improved pasture, rough grazing, forestry and channel banks. This showed improved pasture and channel banks are the dominant diffuse sources of sediment in the catchment, though forestry sources were important historically. Conversion of rough grazing to improved grassland, coupled with intensified land management and year-round livestock grazing, is concluded to provide the principal source of rising TP levels. Lake Habitat Survey and particle size analysis of lake cores demonstrate the hydromorphological impact of the River Dee Regulation Scheme, which controls water level and periodically diverts flow into Llyn Tegid from the adjacent Afon Tryweryn catchment. This hydromorphological impact has also been most pronounced since the late 1970s. It is concluded that an integrated approach combining land management to reduce agricultural runoff allied to improved water level regulation enabling recovery of littoral macrophytes offers the greatest chance halting the on-going cyanobacteria issue in Llyn Tegid.

The pathway to noxious cyanobacteria blooms in lakes: the food web as the final turn

1. Cyanobacteria blooms have long been the focus of limnological research as they often represent the ‘end member’ of limnological deterioration under various human impacts. Research over the past several decades has greatly illuminated the ecological factors promoting cyanobacteria blooms but controversy and confusion surround the successful integration of this diverse body of work.2. In this opinion article I attempt to integrate well‐known aspects of cyanobacteria bloom ecology (such as the roles of nutrient loading, N:P ratio, and mixing conditions) with more recent developments that highlight the importance of feedbacks within the food web in regulating cyanobacteria blooms. Food‐web feedbacks involving stoichiometric mechanisms appear to be particularly important, as accumulating data indicate that the food web influences cyanobacteria not just by regulating the rate of grazing mortality. Rather, trophic interactions may also regulate cyanobacteria dynamics by altering the consumer‐driven nutrient recycling regime in a way that shifts the competitive advantage away from cyanobacteria.3. Viewed in this way, cyanobacteria blooms can be seen as probabilistic events that are the end result of a series of key mechanisms involving nutrient loading, physical mixing conditions, and trophic interactions. To successfully manage lake water quality we should take advantage of each node of contingency leading to undesirable blooms. In doing so we will also have a more coherent scientific message to communicate with those directly involved with the socioeconomic politics of water quality decision making.