Paralytic shellfish poison toxins (PSTs) produced by the dinoflagellate in the genus Alexandrium are a threat to human health and subsistence lifestyles in Southeast Alaska. It is important to understand the drivers of Alexandrium blooms to inform shellfish management and aquaculture, as well as to predict trends of PST in a changing climate. In this study, we aggregate environmental data sets from multiple agencies and tribal partners to model and predict concentrations of PSTs in Southeast Alaska from 2016 to 2019. We used daily PST concentrations interpolated from regularly sampled blue mussels (Mytilus trossulus) analyzed for total PSTs using a receptor binding assay. We then created random forest models to classify shellfish above and below a threshold of toxicity (80 µg 100 g⁻¹) and used two methods to determine variable importance. We obtained a multivariate model with key variables being sea surface temperature, salinity, freshwater discharge, and air temperature. We then used a similar model trained using lagged environmental variables to hindcast out-of-sample (OOS) shellfish toxicities during April-October in 2017, 2018, and 2019. Hindcast OOS accuracies were low (37–50%); however, we found forecasting using environmental variables may be useful in predicting the timing of early summer blooms. This study reinforces the efficacy of machine learning to determine important drivers of harmful algal blooms, although more complex models incorporating other parameters such as toxicokinetics are likely needed for accurate regional forecasts.

亚历山大藻中甲鞭毛藻产生的麻痹性贝类毒素毒素（PSTs）威胁着阿拉斯加东南部的人类健康和生活。重要的是要了解亚历山大藻绽放的动因，以便为贝类管理和水产养殖提供信息，并预测气候变化中的PST趋势。在这项研究中，我们汇总了多个机构和部落伙伴的环境数据集，以建模和预测2016年至2019年阿拉斯加东南部的PST浓度。我们使用从定期采样的蓝贻贝（Mytilus trossulus）使用受体结合测定法分析了总PST。然后，我们创建了随机森林模型，对高于和低于毒性阈值（80 µg 100 g ^-1），并使用两种方法确定变量的重要性。我们获得了一个多变量模型，其关键变量是海面温度，盐度，淡水排放量和气温。然后，我们使用了经过滞后的环境变量训练的相似模型，以在2017年，2018年和2019年4月至10月的后期播出样本外（OOS）贝类毒性。后播OOS准确性较低（37％至50％）。但是，我们发现使用环境变量进行预测可能有助于预测初夏的开花时间。这项研究增强了机器学习确定有害藻华的重要驱动力的有效性，尽管可能需要更复杂的模型并入其他参数，例如毒物动力学，才能进行准确的区域预测。