Frontalini Fabrizio, Greco Mattia, Semprucci Federica, Cermakova Kristina, Merzi Thomas, Pawlowski Jan
Department of Pure and Applied Sciences, University of Urbino, Campus Scientifico Enrico Mattei, Località Crocicchia, 61029, Urbino, Italy.
Institute of Marine Sciences (ICM), CSIC, Barcelona, Catalonia, 08003, Spain.
Chemosphere. 2025 Feb;370:143992. doi: 10.1016/j.chemosphere.2024.143992. Epub 2024 Dec 24.
Nematodes are the most diverse and dominant group of marine meiofauna with high potential as bioindicators of the ecological quality status (EcoQS). The present study explores, for the first time, the applicability of the nematode metabarcoding to infer EcoQS index based on the calibration of ecological behaviors of nematodes Amplicon Sequence Variants (ASVs). To achieve this, we analyzed the nematode community in sediment eDNA samples collected in 2018 and 2021 in areas around three offshore oil platforms in the Danish west coast of the North Sea. One training dataset based on eDNA and environmental data from the three platforms in 2021 covering a wide range of environmental gradients has been used as a training dataset to assign the nematodes ASVs to Ecological Groups. These assignments then allowed us to infer the EcoQS both around these three platforms and in an independent dataset (one of the platforms sampled in 2018). The EcoQS inferred from the nema-gAMBI is perfectly in line with the pollution gradient of the platforms. In fact, stations located close to the platforms (i.e., 100 m and 250 m) show a relatively lower EcoQS than those at greater distance (i.e., reference or 3000 m). The nema-gAMBI seems to capture well the EcoQS variability around platforms and correlates well with the environmental parameters (e.g., trace element and hydrocarbon pollution). Indeed, the nema-gAMBI is positively and significantly correlated with the traditional macrofauna-based AMBI. The present proof of concept strongly advocates for the application of the nematode eDNA-based index in the evaluation of EcoQS.
线虫是海洋小型底栖生物中种类最多、占主导地位的类群,作为生态质量状况(EcoQS)的生物指示物具有很高的潜力。本研究首次探索了基于线虫扩增子序列变体(ASVs)生态行为校准的线虫宏条形码技术在推断EcoQS指数方面的适用性。为此,我们分析了2018年和2021年在北海丹麦西海岸三个近海石油平台周边区域采集的沉积物环境DNA样本中的线虫群落。一个基于2021年来自这三个平台的环境DNA和环境数据的训练数据集,涵盖了广泛的环境梯度,已被用作训练数据集,将线虫ASVs分配到生态组。这些分配随后使我们能够推断这三个平台周围以及一个独立数据集(2018年采样的其中一个平台)中的EcoQS。从线虫-综合多生物指数(nema-gAMBI)推断出的EcoQS与平台的污染梯度完全一致。事实上,位于平台附近(即100米和250米处)的站点显示出的EcoQS相对低于距离较远的站点(即参考站点或3000米处)。线虫-综合多生物指数似乎很好地捕捉到了平台周围EcoQS的变化,并且与环境参数(如微量元素和碳氢化合物污染)有很好的相关性。确实,线虫-综合多生物指数与基于传统大型底栖生物的综合多生物指数呈正相关且显著相关。本概念验证有力地支持了基于线虫环境DNA的指数在EcoQS评估中的应用。