Cyanotoxins in Bloom

At present, cyanobacteria and their toxins (also known as cyanotoxins) constitute a major threat for freshwater resources worldwide. Cyanotoxin occurrence in water bodies around the globe is constantly increasing, whereas emerging, less studied or completely new variants and congeners of various chemical classes of cyanotoxins, as well as their degradation/transformation products are often detected. In addition to planctic cyanobacteria, benthic cyanobacteria, in many cases, appear to be important toxin producers, although far less studied and more difficult to manage and control. This Special Issue highlights novel research results on the structural diversity of cyanotoxins from planktic and benthic cyanobacteria, as well as on their expanding global geographical spread in freshwaters

Simulating Behavioral Microcystin Impairment in Fish

Fish experiencing blooms of the cyanobacteria genera Microcystis and Anabaena acquire microcystin and saxitoxin through ingestion of contaminated food and absorption of dissolved toxin. Even low chronic doses induce sensory and motor impairment—the impact of which is unquantified in wild populations. Here, I introduce Lagrangian particle models for cyanobacteria and fish which test the hypotheses that impairment symptoms suppress movement and growth. This is implemented within the Finite-Volume Coastal Ocean Model (FVCOM). Cyanobacteria particles move vertically according to mixing and buoyancy (a function of cellular reservoirs). Fish navigate the horizontal domain, foraging in high growth areas, and fleeing when toxin increases. The framework is demonstrated here for the case of juvenile fish encountering Microcystis aeruginosa in an idealized Louisiana estuary. Self-shading reduces bloom growth, and causes algae to collect at the surface. Turbulent diffusivity is insufficient to break up this layer, so dissolved toxin becomes surface-intensified. Fish seek high growth areas in this environment, and dietary uptake increases. This triggers flight and swimming impairment. As cyanobacteria excrete microcystin, absorption forces fish to become intoxicated even in areas of lower toxic risk. Repeated flight means fish spend more time in suboptimal areas, with final growth reduced up to 6.6%. In vivo, this would be exacerbated by physiological stress and the metabolic cost of toxin removal. Collective movement (group diffusivity) is suppressed nearly 50% during wide-spread intoxication. Simulations show that within a certain parameter space, both movement and growth are suppressed relative to the control case as expected. However, additional experiments resulted in higher growth, indicating the methods are sensitive to model parameterization. Ultimately, these are sandbox cases, which will require carefully-designed lab and field experiments before predictive capability can be assumed

Advancing Knowledge on Cyanobacterial Blooms in Freshwaters

Cyanobacterial blooms are a water quality problem that is widely acknowledged to have detrimental ecological and economic effects in drinking and recreational water supplies and fisheries. There is increasing evidence that cyanobacterial blooms have increased globally and are likely to expand in water resources as a result of climate change. Of most concern are cyanotoxins, along with the mechanisms that induce their release and determine their fate in the aquatic environment. These secondary metabolites pose a potential hazard to human health and agricultural and aquaculture products that are intended for animal and human consumption; therefore, strict and reliable control of cyanotoxins is crucial for assessing risk. In this direction, a deeper understanding of the mechanisms that determine cyanobacterial bloom structure and toxin production has become the target of management practices. This Special Issue, entitled “Advancing Knowledge on Cyanobacterial Blooms in Freshwaters”, aims to bring together recent multi- and interdisciplinary research, from the field to the laboratory and back again, driven by working hypotheses based on any aspect of mitigating cyanobacterial blooms, from ecological theory to applied research

Abstracts of manuscripts submitted in 1989 for publication

This volume contains the abstracts of manuscripts submitted for publication during calendar year 1989 by the staff and students of the Woods Hole Oceanographic Institution. We identify the journal of those manuscripts which are in press or have been published. The volume is intended to be informative, but not a bibliography. The abstracts are listed by title in the Table of Contents and are grouped into one of our five deparments, marine policy, or the student category. An author index is presented in the back to facilitate locating specific papers

Estimating the concentration of physico chemical parameters in hydroelectric power plant reservoir

The United Nations Educational, Scientific and Cultural Organization (UNESCO) defines the amazon region and adjacent areas, such as the Pantanal, as world heritage territories, since they possess unique flora and fauna and great biodiversity. Unfortunately, these regions have increasingly been suffering from anthropogenic impacts. One of the main anthropogenic impacts in the last decades has been the construction of hydroelectric power plants. As a result, dramatic altering of these ecosystems has been observed, including changes in water levels, decreased oxygenation and loss of downstream organic matter, with consequent intense land use and population influxes after the filling and operation of these reservoirs. This, in turn, leads to extreme loss of biodiversity in these areas, due to the large-scale deforestation. The fishing industry in place before construction of dams and reservoirs, for example, has become much more intense, attracting large populations in search of work, employment and income. Environmental monitoring is fundamental for reservoir management, and several studies around the world have been performed in order to evaluate the water quality of these ecosystems. The Brazilian Amazon, in particular, goes through well defined annual hydrological cycles, which are very importante since their study aids in monitoring anthropogenic environmental impacts and can lead to policy and decision making with regard to environmental management of this area. The water quality of amazon reservoirs is greatly influenced by this defined hydrological cycle, which, in turn, causes variations of microbiological, physical and chemical characteristics. Eutrophication, one of the main processes leading to water deterioration in lentic environments, is mostly caused by anthropogenic activities, such as the releases of industrial and domestic effluents into water bodies. Physico-chemical water parameters typically related to eutrophication are, among others, chlorophyll-a levels, transparency and total suspended solids, which can, thus, be used to assess the eutrophic state of water bodies. Usually, these parameters must be investigated by going out to the field and manually measuring water transparency with the use of a Secchi disk, and taking water samples to the laboratory in order to obtain chlorophyll-a and total suspended solid concentrations. These processes are time- consuming and require trained personnel. However, we have proposed other techniques to environmental monitoring studies which do not require fieldwork, such as remote sensing and computational intelligence. Simulations in different reservoirs were performed to determine a relationship between these physico-chemical parameters and the spectral response. Based on the in situ measurements, empirical models were established to relate the reflectance of the reservoir measured by the satellites. The images were calibrated and corrected atmospherically. Statistical analysis using error estimation was used to evaluate the most accurate methodology. The Neural Networks were trained by hydrological cycle, and were useful to estimate the physicalchemical parameters of the water from the reflectance of visible bands and NIR of satellite images, with better results for the period with few clouds in the regions analyzed. The present study shows the application of wavelet neural network to estimate water quality parameters using concentration of the water samples collected in the Amazon reservoir and Cefni reservoir, UK. Sattelite imagens from Landsats and Sentinel-2 were used to train the ANN by hydrological cycle. The trained ANNs demonstrated good results between observed and estimated after Atmospheric corrections in satellites images. The ANNs showed in the results are useful to estimate these concentrations using remote sensing and wavelet transform for image processing. Therefore, the techniques proposed and applied in the present study are noteworthy since they can aid in evaluating important physico-chemical parameters, which, in turn, allows for identification of possible anthropogenic impacts, being relevant in environmental management and policy decision-making processes. The tests results showed that the predicted values have good accurate. Improving efficiency to monitor water quality parameters and confirm the reliability and accuracy of the approaches proposed for monitoring water reservoirs. This thesis contributes to the evaluation of the accuracy of different methods in the estimation of physical-chemical parameters, from satellite images and artificial neural networks. For future work, the accuracy of the results can be improved by adding more satellite images and testing new neural networks with applications in new water reservoirs