Modeling Dinophysis in Western Andalucia using a autoregressive hidden Markov model

被引:0
作者
Aron, Jordan [1 ]
Albert, Paul S. [1 ]
Gribble, Matthew O. [2 ]
机构
[1] NCI, Biostat Branch, Div Canc & Epidemiol, Rockville, MD 20850 USA
[2] Univ Alabama Birmingham, Sch Publ Hlth, Dept Epidemiol, Birmingham, AL 35294 USA
关键词
Autoregressive; EM algorithm; Harmful algal bloom; Missing data; Toxins; OKADAIC ACID; BLOOMS;
D O I
10.1007/s10651-022-00534-7
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Dinophysis spp. can produce diarrhetic shellfish toxins (DST) including okadaic acid and dinophysistoxins, and some strains can also produce non-diarrheic pectenotoxins. Although DSTs are of human health concern and have motivated environmental monitoring programs in many locations, these monitoring programs often have temporal data gaps (e.g., days without measurements). This paper presents a model for the historical time-series, on a daily basis, of DST-producing toxigenic Dinophysis in 8 monitored locations in western Andalucia over 2015-2020, incorporating measurements of algae counts and DST levels. We fitted a bivariate hidden Markov Model (HMM) incorporating an autoregressive correlation among the observed DST measurements to account for environmental persistence of DST. We then reconstruct the maximum-likelihood profile of algae presence in the water column at daily intervals using the Viterbi algorithm. Using historical monitoring data from Andalucia, the model estimated that potentially toxigenic Dinophysis algae is present at greater than or equal to 250 cells/L between< 1% and>10% of the year depending on the site and year. The historical time-series reconstruction enabled by this method may facilitate future investigations into temporal dynamics of toxigenic Dinophysis blooms.
引用
收藏
页码:557 / 585
页数:29
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