The blooms of the toxic dinoflagellate Karenia selliformis can be predicted with accuracy derived from knowledge of the main forcing variables. A naive Bayes classifier modeling framework, a member of the Bayesian network family, is used to identify the phytoplankton community and the physical and meteorological parameters involving K. selliformis blooms in the eutrophic Boughrara Lagoon (BL), Tunisia. The proposed model takes into account the physical environment parameters (salinity, water temperature, tide amplitude), meteorological constraints (evaporation, air temperature, insolation, rainfall, atmospheric pressure, and humidity), phytoplankton groups (diatoms, dinoflagellates, cyanobacteria, Euglenophyceae), and the sampling months on K. selliformis blooms. The shift to highest salinity and atmospheric pressure, associated with reduced tide, are the most favorable conditions for K. selliformis blooms in BL. The results show that K. selliformis formed nearly monospecific blooms. A shift in species composition was pointed out between the bloom and non-bloom conditions. The Euglenophycea and some dinoflagellates like Peridinium minimum, Prorocentrum minimum, Prorocentrum micans, Prorocentrum gracile, Protoperidinium minutum, and Scrippsiella trochoidea appeared during blooms, whereas diatoms, diazotrophic cyanobacteria, and dinoflagellates (Akaskiwo sanguinea, Karlodinium veneficum, Gyrodinium spirale, Oxyrrhis marina, Polykrikos kofoidii) were observed under non-bloom conditions. This study highlighted the most favorable physical and meteorological conditions for K. selliformis bloom occurrences and also pointed out species indicators for bloom establishment and others for non-bloom conditions. Monitoring the dynamics of these species with the associated physical and meteorological variability offers valuable information to plan for the best options for prediction of potentially toxic blooms of K. selliformis and associated dystrophic consequences.

中文翻译：

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使用朴素的贝叶斯分类器探索导致东南地中海泻湖中有害鞭毛藻粉蝶（Karenia selliformis）开花的因素

可以从主要强迫变量的知识中准确地预测出有毒的鞭毛藻类沙雷氏菌的开花。一个朴素的贝叶斯分类器建模框架是贝叶斯网络家族的成员，用于识别突尼斯富营养化的Boughrara泻湖（BL）中浮游植物群落以及涉及K. selliformis开花的物理和气象参数。所提出的模型考虑了物理环境参数（盐度，水温，潮汐幅度），气象限制因素（蒸发，空气温度，日照，降雨，大气压力和湿度），浮游植物群（硅藻，鞭毛藻，蓝藻，大叶藻科） ，以及K. selliformis的采样月份绽放。向潮汐降低导致的最高盐度和大气压的转变，是BL中沙雷氏菌开花的最有利条件。结果表明，K。selliformis几乎形成了单特异性花。指出了在开花和非开花条件之间物种组成的转变。该Euglenophycea有的像甲藻多甲藻最低，微小原甲藻，具齿原甲藻，具齿原甲竹叶，Protoperidinium藻和锥状斯氏藻大量繁殖时出现，而硅藻，蓝藻固氮和甲藻（Akaskiwo荪，Karlodinium veneficum，环沟SPIRALE，Oxyrrhis码头，Polykrikos kofoidii）的非花条件下观察到。这项研究着重介绍了K. selliformis开花发生的最有利的物理和气象条件，并指出了开花建立和其他非开花条件的物种指标。监测这些物种的动态以及相关的物理和气象变异性，可提供有价值的信息，以计划最佳的预测方法，以预测蝶sell的潜在毒性繁殖和相关的营养不良后果。