Enhanced Prediction of Seafloor Ecological State Using 16S Nanopore Sequencing

Abstract

Anthropogenic stress on benthic habitats, particularly from aquaculture, calls for accurate and efficient monitoring of the macrofauna ecological state. Recent advancements in Oxford Nanopore Technology (ONT) together with environmental DNA offers cost-effective and rapid, on-site monitoring of such ecosystems. Previous studies have demonstrated that Nanopore sequencing provides sufficient precision for predicting ecological state, despite reported challenges with sequencing accuracy. In this study, we aim to predict the seafloor ecological state with both Illumina and Nanopore 16S rRNA gene sequencing data and using a combination of machine learning and feature selection. We analyzed 88 seafloor samples from aquaculture sites located on a north-south gradient along the Norwegian coast. Both sequencing methods were evaluated in combination with various bioinformatic approaches in the context of predicting the normalized EQR index (nEQR, standard ecological index based on macroinvertebrate counting) as a metric of seafloor ecosystem status. Our results show that the predictive performance of Illumina and Nanopore sequencing platforms are comparable, establishing Nanopore as a feasible alternative to illumina sequencing. By employing a stabilized LASSO regression, the feature set (potential taxa) was efficiently optimized from thousands to 40-60 OTUs. The feature selection reduced prediction errors to less than half of what was obtained through full feature modeling. This feature set demonstrated strong predictive accuracy across both sequencing technologies, with a high correlation between observed and predicted nEQR values. The Pearson correlation coefficient of 0.98 for Illumina and 0.95 (mean prediction error: {+/-}0.04) for Nanopore data (mean prediction error: {+/-}0.06). This study demonstrates that continual improvements in Nanopore sequencing accuracy, in combination with optimized feature selection on a broader set of samples, provides a precise and cost-effective monitoring method for marine benthic environments.