Aquaculture is a rapidly expanding industry and is now one of the primary sources of all consumed seafood. Intensive aquaculture production is associated with organic enrichment, which occurs as organic material settles onto the seafloor, creating anoxic conditions which disrupt ecological processes. Bacteria are sensitive bioindicators of organic enrichment, and supervised classifiers using features derived from 16s rRNA gene sequences have shown potential to become useful in aquaculture environmental monitoring. Current taxonomy-based approaches, however, are time intensive and built upon emergent features which cannot easily be condensed into a monitoring pipeline. Here, we used a taxonomy-free approach to examine 16s rRNA gene sequences derived from flocculent matter underneath and in proximity to hard-bottom salmon aquaculture sites in Newfoundland, Canada. Tetranucleotide frequencies (k = 4) were tabulated from sample sequences and included as features in a machine learning pipeline using the random forest algorithm to predict 4 levels of benthic disturbance; resulting classifications were compared to those obtained using a published taxonomy-based approach. Our results show that k-mer count features can effectively be used to create highly accurate predictions of benthic disturbance and can resolve intermediate changes in seafloor condition. In addition, we present a robust assessment of model performance which accounts for the effect of randomness in model creation. This work outlines a flexible framework for environmental assessments at aquaculture sites that is highly reproducible and free of taxonomy-assignment bias.

中文翻译：

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细菌寡核苷酸频率的机器学习分析以评估水产养殖的底栖影响

水产养殖是一个快速发展的行业，现在是所有消费海产品的主要来源之一。集约化水产养殖生产与有机富集有关，当有机物质沉淀到海底时，就会产生破坏生态过程的缺氧条件。细菌是有机富集的敏感生物指标，使用源自 16s rRNA 基因序列的特征的监督分类器已显示出在水产养殖环境监测中有用的潜力。然而，当前基于分类法的方法是时间密集型的，并且建立在无法轻易浓缩到监控管道中的紧急特征上。这里，我们使用无分类方法来检查来自加拿大纽芬兰硬底鲑鱼养殖场下方和附近的絮状物质的 16s rRNA 基因序列。四核苷酸频率 (k = 4) 从样本序列中制成表格，并作为特征包含在机器学习管道中，使用随机森林算法来预测 4 个底栖干扰水平；结果分类与使用已发布的基于分类法的方法获得的分类进行了比较。我们的结果表明，k-mer 计数特征可以有效地用于创建对底栖干扰的高度准确的预测，并且可以解决海底条件的中间变化。此外，我们对模型性能进行了稳健的评估，该评估考虑了模型创建中随机性的影响。