Chlorophyll a as an indicator of microcystin: Short-term forecasting and risk assessment in Lake Erie

We developed a Bayesian hierarchical modeling framework to establish a short-term forecasting model of particulate cyanobacterial toxin concentrations in Western Lake Erie using chlorophyll a concentration as the predictor. The model evolves over time with additional data to reflect the changing dyn...

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Autores principales:	Song S. Qian, Craig A. Stow, Freya E. Rowland, Qianqian Liu, Mark D. Rowe, Eric J. Anderson, Richard P. Stumpf, Thomas H. Johengen
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Bayesian hierarchical model harmful algal blooms informative priors predictive model sequential updating Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/dd71d3ebfc8a4300ac82d79ff18dce93
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id	oai:doaj.org-article:dd71d3ebfc8a4300ac82d79ff18dce93
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spelling	oai:doaj.org-article:dd71d3ebfc8a4300ac82d79ff18dce932021-12-01T04:58:34ZChlorophyll a as an indicator of microcystin: Short-term forecasting and risk assessment in Lake Erie1470-160X10.1016/j.ecolind.2021.108055https://doaj.org/article/dd71d3ebfc8a4300ac82d79ff18dce932021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X21007202https://doaj.org/toc/1470-160XWe developed a Bayesian hierarchical modeling framework to establish a short-term forecasting model of particulate cyanobacterial toxin concentrations in Western Lake Erie using chlorophyll a concentration as the predictor. The model evolves over time with additional data to reflect the changing dynamics of cyanobacterial toxin production. Specifically, parameters of the empirical relationship between the cyanobacterial toxin microcystin and chlorophyll a concentrations are allowed to vary annually and seasonally under a hierarchical framework. As such, the model updated using the most recent sampling data is suited to provide short-term forecasts. The reduced model predictive uncertainty makes the model a viable tool for risk assessment. Using data from the long-term Western Lake Erie harmful algal bloom monitoring program (2008–2018), we illustrate the model-building and model-updating process and the application of the model for short-term risk assessment. The modeling process demonstrates the use of the Bayesian hierarchical modeling framework for developing informative priors in Bayesian modeling.Song S. QianCraig A. StowFreya E. RowlandQianqian LiuMark D. RoweEric J. AndersonRichard P. StumpfThomas H. JohengenElsevierarticleBayesian hierarchical modelharmful algal bloomsinformative priorspredictive modelsequential updatingEcologyQH540-549.5ENEcological Indicators, Vol 130, Iss , Pp 108055- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Bayesian hierarchical model harmful algal blooms informative priors predictive model sequential updating Ecology QH540-549.5
spellingShingle	Bayesian hierarchical model harmful algal blooms informative priors predictive model sequential updating Ecology QH540-549.5 Song S. Qian Craig A. Stow Freya E. Rowland Qianqian Liu Mark D. Rowe Eric J. Anderson Richard P. Stumpf Thomas H. Johengen Chlorophyll a as an indicator of microcystin: Short-term forecasting and risk assessment in Lake Erie
description	We developed a Bayesian hierarchical modeling framework to establish a short-term forecasting model of particulate cyanobacterial toxin concentrations in Western Lake Erie using chlorophyll a concentration as the predictor. The model evolves over time with additional data to reflect the changing dynamics of cyanobacterial toxin production. Specifically, parameters of the empirical relationship between the cyanobacterial toxin microcystin and chlorophyll a concentrations are allowed to vary annually and seasonally under a hierarchical framework. As such, the model updated using the most recent sampling data is suited to provide short-term forecasts. The reduced model predictive uncertainty makes the model a viable tool for risk assessment. Using data from the long-term Western Lake Erie harmful algal bloom monitoring program (2008–2018), we illustrate the model-building and model-updating process and the application of the model for short-term risk assessment. The modeling process demonstrates the use of the Bayesian hierarchical modeling framework for developing informative priors in Bayesian modeling.
format	article
author	Song S. Qian Craig A. Stow Freya E. Rowland Qianqian Liu Mark D. Rowe Eric J. Anderson Richard P. Stumpf Thomas H. Johengen
author_facet	Song S. Qian Craig A. Stow Freya E. Rowland Qianqian Liu Mark D. Rowe Eric J. Anderson Richard P. Stumpf Thomas H. Johengen
author_sort	Song S. Qian
title	Chlorophyll a as an indicator of microcystin: Short-term forecasting and risk assessment in Lake Erie
title_short	Chlorophyll a as an indicator of microcystin: Short-term forecasting and risk assessment in Lake Erie
title_full	Chlorophyll a as an indicator of microcystin: Short-term forecasting and risk assessment in Lake Erie
title_fullStr	Chlorophyll a as an indicator of microcystin: Short-term forecasting and risk assessment in Lake Erie
title_full_unstemmed	Chlorophyll a as an indicator of microcystin: Short-term forecasting and risk assessment in Lake Erie
title_sort	chlorophyll a as an indicator of microcystin: short-term forecasting and risk assessment in lake erie
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/dd71d3ebfc8a4300ac82d79ff18dce93
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Chlorophyll a as an indicator of microcystin: Short-term forecasting and risk assessment in Lake Erie

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