Prediction Of Harmful Algal Blooms Research Articles

Adsorption of marine phycotoxin okadaic acid on a covalent organic framework

Phycotoxins, compounds produced by some marine microalgal species, can reach high concentrations in the sea when a massive proliferation occurs, the so-called harmful algal bloom. These compounds are especially dangerous to human health when concentrated in the digestive glands of seafood. In order to generate an early warning system to alert for approaching toxic outbreaks, it is very important to improve monitoring methods of phycotoxins in aquatic ecosystems. Solid-phase adsorption toxin tracking devices reported thus far based on polymeric resins have not been able to provide an efficient harmful algal bloom prediction system due to their low adsorption capabilities.In this work, a water-stable covalent organic framework (COF) was evaluated as adsorbent for the hydrophobic toxin okadaic acid, one of the most relevant marine toxins and the parental compound of the most common group of toxins responsible for the diarrhetic shellfish poisoning. Adsorption kinetics of okadaic acid onto the COF in seawater showed that equilibrium concentration was reached in only 60min, with a maximum experimental adsorption of 61mgg−1. Desorption of okadaic acid from the COF was successful with both 70% ethanol and acetonitrile as solvent, and the COF material could be reused with minor losses in adsorption capacity for three cycles. The results demonstrate that COF materials are promising candidates for solid-phase adsorption in water monitoring devices.

Water quality assessment with hierarchical cluster analysis based on Mahalanobis distance.

Water quality assessment is crucial for assessment of marine eutrophication, prediction of harmful algal blooms, and environment protection. Previous studies have developed many numeric modeling methods and data driven approaches for water quality assessment. The cluster analysis, an approach widely used for grouping data, has also been employed. However, there are complex correlations between water quality variables, which play important roles in water quality assessment but have always been overlooked. In this paper, we analyze correlations between water quality variables and propose an alternative method for water quality assessment with hierarchical cluster analysis based on Mahalanobis distance. Further, we cluster water quality data collected form coastal water of Bohai Sea and North Yellow Sea of China, and apply clustering results to evaluate its water quality. To evaluate the validity, we also cluster the water quality data with cluster analysis based on Euclidean distance, which are widely adopted by previous studies. The results show that our method is more suitable for water quality assessment with many correlated water quality variables. To our knowledge, it is the first attempt to apply Mahalanobis distance for coastal water quality assessment.

Initial skill assessment of the California Harmful Algae Risk Mapping (C-HARM) system

Toxic algal events are an annual burden on aquaculture and coastal ecosystems of California. The threat of domoic acid (DA) toxicity to human and wildlife health is the dominant harmful algal bloom (HAB) concern for the region, leading to a strong focus on prediction and mitigation of these blooms and their toxic effects. This paper describes the initial development of the California Harmful Algae Risk Mapping (C-HARM) system that predicts the spatial likelihood of blooms and dangerous levels of DA using a unique blend of numerical models, ecological forecast models of the target group, Pseudo-nitzschia, and satellite ocean color imagery. Data interpolating empirical orthogonal functions (DINEOF) are applied to ocean color imagery to fill in missing data and then used in a multivariate mode with other modeled variables to forecast biogeochemical parameters. Daily predictions (nowcast and forecast maps) are run routinely at the Central and Northern California Ocean Observing System (CeNCOOS) and posted on its public website. Skill assessment of model output for the nowcast data is restricted to nearshore pixels that overlap with routine pier monitoring of HABs in California from 2014 to 2015. Model lead times are best correlated with DA measured with solid phase adsorption toxin tracking (SPATT) and marine mammal strandings from DA toxicosis, suggesting long-term benefits of the HAB predictions to decision-making. Over the next three years, the C-HARM application system will be incorporated into the NOAA operational HAB forecasting system and HAB Bulletin.

Development of a mobile phone application for the prediction of harmful algal blooms in inland lakes

Development of a mobile phone application for the prediction of harmful algal blooms in inland lakes

Hindcasts of potential harmful algal bloom transport pathways on the Pacific Northwest coast

Harmful algal blooms (HABs) pose a significant threat to human and marine organism health, and negatively impact coastal economies around the world. An improved understanding of HAB formation and transport is required to improve forecasting skill. A realistic numerical simulation of the US Pacific Northwest region is used to investigate transport pathways from known HAB formation hot spots, specifically for Pseudo-nitzschia (Pn), to the coast. We show that transport pathways are seasonal, with transport to the Washington (WA) coast from a northern source (the Juan de Fuca Eddy) during the summer/fall upwelling season and from a southern source (Heceta Bank) during the winter/early spring due to the predominant wind-driven currents. Interannual variability in transport from the northern source is related to the degree of wind intermittency with more transport during years with more frequent relaxation/downwelling events. The Columbia River plume acts to mitigate transport to the coast as the plume front blocks onshore transport. The plume's influence on alongshore transport is variable although critical in aiding transport from the southern source to the WA coast via plume entrainment. Overall transport from our simulations captures most observed Pn HAB beach events from 2004 to 2007 (characterized by Pseudo-nitzschia cell abundance); however, numerous false positives occur. We show that incorporating phytoplankton biomass results from a coupled biogeochemical model reduces the number of false positives significantly and thus improves our Pn HAB predictions. Key Points Potential PNW HAB transport is seasonal, consistent with regional currents Transport is blocked by the Columbia River plume unless entrainment occurs A coupled hydrodynamic-biological model can predict PNW Pn HAB transport paths © 2014. American Geophysical Union. All Rights Reserved.

Understanding harmful algae in stratified systems: Review of progress and future directions

The Global Ecology and Oceanography of Harmful Algal Blooms (GEOHAB) program of the Scientific Committee on Oceanic Research (SCOR) and the Intergovernmental Oceanographic Commission (IOC) of UNESCO, was created in 1999 to foster research on the ecological and oceanographic mechanisms underlying the population dynamics of harmful algal blooms (HABs). The ultimate goal of this research is to develop observational systems and models that will eventually enable the prediction of HABs and thereby minimize their impact on marine ecosystems, human health and economic activities. In August of 2012, a workshop was held under the umbrella of the GEOHAB program at the Monterey Bay Aquarium Research Institute (MBARI). The over arching goal of this workshop was to review the current understanding of the processes governing the structure and dynamics of HABs in stratified systems, and to identify how best to couple physical/chemical and biological measurements at appropriate spatial and temporal scales to quantify the dynamics of HABs in these systems, paying particular attention to thin layers. This contribution provides a review of recent progress in the field of HAB research in stratified systems including thin layers, and identifies the gaps in knowledge that our scientific community should strive to understand in the next decade.

Spatial and temporal variations in algal blooms in the coastal waters of the western South China Sea

Algal blooms occur frequently in the coastal waters of the western South China Sea (SCS). This paper reports spatial and temporal variations of algal bloom events in these waters from 1993 to 2007. Twenty-five algal bloom events occurred in summer in the coastal waters of South and Central Vietnam where they were associated with wind-induced, coastal, nutrient upwelling and river discharges; a further eight events occurred in the coastal waters of North Vietnam. A greater number of algal bloom events were observed in 1999 and 2002, and were accompanied by several previously unobserved species for the study period. These events may be related to the El Niño events of 1998 and 2002. Furthermore, the bloom-causative species Trichodesmium erythraeum (Cyanophyta) entirely dominated the phytoplankton community of algal blooms during 1993–1999 whereas the species Phaeocystis globosa (Haptophyta) dominated blooms after 2002. This study establishes a basis for further long-term research of algal bloom event variations, and provides a compiled scientific reference that may be used for later prediction of Harmful algal blooms (HABs).

Bayesian model averaging for harmful algal bloom prediction

Harmful algal blooms (HABs) are a worldwide problem that have been increasing in frequency and extent over the past several decades. HABs severely damage aquatic ecosystems by destroying benthic habitat, reducing invertebrate and fish populations, and affecting larger species such as dugong that rely on seagrasses for food. Few statistical models for predicting HAB occurrences have been developed, and in common with most predictive models in ecology, those that have been developed do not fully account for uncertainties in parameters and model structure. This makes management decisions based on these predictions more risky than might be supposed. We used a probit time series model and Bayesian model averaging (BMA) to predict occurrences of blooms of Lyngbya majuscula, a toxic cyanophyte, in Deception Bay, Queensland, Australia. We found a suite of useful predictors for HAB occurrence, with temperature figuring prominently in models with the majority of posterior support, and a model consisting of the single covariate, average monthly minimum temperature, showed by far the greatest posterior support. A comparison of alternative model averaging strategies was made with one strategy using the full posterior distribution and a simpler approach that utilized the majority of the posterior distribution for predictions but with vastly fewer models. Both BMA approaches showed excellent predictive performance with little difference in their predictive capacity. Applications of BMA are still rare in ecology, particularly in management settings. This study demonstrates the power of BMA as an important management tool that is capable of high predictive performance while fully accounting for both parameter and model uncertainty.

The value of harmful algal bloom predictions to the nearshore commercial shellfish fishery in the Gulf of Maine

In this study, we develop a framework for measuring the value of harmful algal bloom (HAB) predictions. The framework captures the effects of both private and public responses to HABs. Using data from the New England nearshore commercial shellfish fishery and impact estimates for a large-scale HAB event in 2005, we illustrate how the potential value of HAB forecasts may be estimated. The results of our study suggest that the long-term value of a HAB prediction and tracking system for the Gulf of Maine is sensitive to the frequency of HAB events, the accuracy of predictions, the choice of HAB impact measures, and the effectiveness of public and private responses.