Cyanobacteria

Burkholder, JoAnn M.

doi:10.1002/0471263397.env197

Cited by 8 publications

(3 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While strong relationships have been shown for many years in freshwater ecosystems between phosphorus loadings and harmful cyanobacteria blooms (Schindler, 1977 and Burkholder 2002), increasing linkages between nutrient loading and estuarine/coastal marine HABs have more recently been recognized (Smayda, 1990, Smayda, 1997, Anderson et al, 2002, Glibert et al 2005a, and Gilbert and Burkholder, 2006). For example, in the Gulf of Mexico, the sedimentary record of concentration of potentially toxic diatoms, Pseudo-nitzcschia spp ., increased in parallel to increased nitrate loading over the past several decades (Fig.…”

Section: The Consensus Statementsmentioning

confidence: 99%

Eutrophication and harmful algal blooms: A scientific consensus

et al. 2008

View full text Add to dashboard Cite

In January 2003, the US Environmental Protection Agency sponsored a "roundtable discussion" to develop a consensus on the relationship between eutrophication and harmful algal blooms (HABs), specifically targeting those relationships for which management actions may be appropriate. Academic, federal, and state agency representatives were in attendance. The following seven statements were unanimously adopted by attendees based on review and analysis of current as well as pertinent previous data: 1) Degraded water quality from increased nutrient pollution promotes the development and persistence of many HABs and is one of the reasons for their expansion in the U.S. and the world; 2) The composition -not just the total quantity -of the nutrient pool impacts HABs; 3) High biomass blooms must have exogenous nutrients to be sustained; 4) Both chronic and episodic nutrient delivery promote HAB development; 5) Recently developed tools and techniques are already improving the detection of some HABs, and emerging technologies are rapidly advancing toward operational status for the prediction of HABs and their toxins; 6) Experimental studies are critical to further the understanding of the role of nutrients in HAB expression, and will strengthen prediction and mitigation of HABs; and 7) Management of nutrient inputs to the watershed can lead to significant reduction in HABs. Supporting evidence and pertinent examples for each consensus statement is provided herein.

show abstract

Section: The Consensus Statementsmentioning

confidence: 99%

Eutrophication and harmful algal blooms: A scientific consensus

et al. 2008

View full text Add to dashboard Cite

show abstract

“…These disruptive nutrient fluxes lead to eutrophication by promoting the development of toxic algae, causing harmful algal blooms (HAB) (Camargo and Alonso 2006;). The size and intensity of these blooms has been increasing for over 20 years (Burkholder 2003;Glibert et al 2005).…”

Section: Introductionmentioning

confidence: 99%

Benefits of machine learning and sampling frequency on phytoplankton bloom forecasts in coastal areas

Derot

Yajima

Schmitt

2020

Ecological Informatics

View full text Add to dashboard Cite

In aquatic ecosystems, anthropogenic activities disrupt nutrient fluxes, thereby promoting harmful algal blooms that could directly impact economies and human health. Within this framework, the forecasting of the proxy of chlorophyll a in coastal areas is the first step to managing these algal blooms. The primary goal was to analyze how phytoplankton bloom forecasts are impacted by different sampling frequencies, by using a machine learning model. The database used in this study was sourced from an automated system located in the English Channel. This device has a sampling frequency of 20 minutes. We considered 12 physicochemical parameters over a six-year period. Our forecast methodology is based on the random forest (RF) model and a sliding window strategy. The lag times for these sliding windows ranged from 12 hours to 3 months with four different sampling times until 1 day.The results indicate that the optimal forecast was obtained for a 20 minutes time step, with an average R² of 0.62. Moreover, the highest values of fluorescence were predicted when the water temperature was approximately 11.8°C. Consequently, we demonstrated that the sampling frequency directly impacts the forecast performance of an RF model. Furthermore, this kind of model can recreate interactions that closely resemble biological processes. Our study suggests that the RF model can utilize the additional information contained in high-frequency datasets. The methodology presented here lays the foundation for the development of a numerical decision-making tool that could help mitigate the impact of these algal blooms.

show abstract

“…Several studies discuss HAB and eutrophication, and upon examining the linkages some empirical relations have been found. Special nutrients tend to evoke algal blooms (NH 3 and NO 2 + NO 3 : Herndon & Cochlan, ; as well as phosphate (PO 4 ‐P), silicate: Tomas & Smayda, ; and phosphorus loadings: Burkholder, ). Some papers have also determined the relations between the variables, the majority of which have focused on inorganic nutrient ratios (N/Si: Anderson, Brzenzinski, Washburn, & Kudela, ; Bates, ; Parsons, Dortch, & Turner, ; Radach, Berg, & Hagmeier, ; P/Si: Anderson et al, ; N/CHL, P/CHL: Hodgkiss & Ho, ; N/P: Conley, ; Granéli, Weberg, & Salomon, ; Rijssel, Alderkamp, Nejstgaard, Sazhin, & Verity, ).…”

Section: Description Of Datamentioning

confidence: 99%

A Bayesian structural model for predicting algal blooms

Sun

Liu

Wang

2019

Journal of Forecasting

View full text Add to dashboard Cite

A Bayesian structural model with two components is proposed to forecast the occurrence of algal blooms, multivariate mean‐reverting diffusion process (MMRD), and a binary probit model with latent Markov regime‐switching process (BPMRS). The model has three features: (a) forecast of the occurrence probability of algal bloom is directly based on oceanographic parameters, not the forecasting of special indicators in traditional approaches, such as phytoplankton or chlorophyll‐a; (b) augmentation of daily oceanographic parameters from the data collected every 2 weeks is based on MMRD. The proposed method solves the problem of unavailability of daily oceanographic parameters in practice; (c) BPMRS captures the unobservable factors which affect algal bloom occurrence and therefore improve forecast accuracy. We use panel data collected in Tolo Harbour, Hong Kong, to validate the model. The model demonstrates good forecasting for out‐of‐sample rolling forecasts, especially for algal bloom appearing for a longer period, which severely damages fisheries and the marine environment.

show abstract

Cyanobacteria

Abstract: Taxonomy, Systematics, and Phylogeny Habitats Light, Photosynthesis, and Organic Carbon Uptake Other Nutritional Ecology Biological Interactions Future Directions

Cited by 8 publications

References 67 publications

Eutrophication and harmful algal blooms: A scientific consensus

Eutrophication and harmful algal blooms: A scientific consensus

Benefits of machine learning and sampling frequency on phytoplankton bloom forecasts in coastal areas

A Bayesian structural model for predicting algal blooms

Contact Info

Product

Resources

About