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Salmon aquaculture is one of the fastest growing food sectors in the world. Because aquaculture can have a significant impact on the benthic environment, frequent monitoring is required by law. Recently, eDNA (environmental DNA) metabarcoding of benthic bacterial communities emerged as a very powerful tool to infer ecological quality based on amplicon sequence variants (ASVs) and their analyses. To exploit this monitoring approach and enable implementation into routine monitoring programs and official regulations, a high reproducibility of a standardized protocol among different laboratories is a prerequisite. Therefore, sediment samples from a compliance monitoring of a Scottish salmon farm were split into aliquots and distributed to two different laboratories for the wet lab procedure prior to independent Illumina sequencing of the two datasets. We then conducted pattern matching analyses and used supervised machine learning to predict ecological quality for each sample in the two datasets. Results were compared among the two datasets and to the reference data (ecological quality status as obtained from traditional macrofauna-based benthic monitoring). The obtained results demonstrate the high degree of congruence between the ecological quality assessments from the two laboratories. We conclude that the standard wet lab protocol used in our study is sufficiently robust across different laboratories, which meets a milestone criterion for practical routine application in industry.

Marine coastal ecosystems offer numerous ecosystem services and are therefore subject to a variety of stressors from anthropogenic activities. Environmental biomonitoring programs for effective management and conservation of coastal marine ecosystems are therefore crucial. Traditional monitoring has been based on macrofauna indices which are laborious and require expert knowledge. Recently, eDNA metabarcoding has become increasingly popular as it does not involve macrofauna species identification and is therefore cost and time inexpensive. Studies have shown that ecosystem monitoring based on eDNA metabarcoding is feasible and random forest (RF) algorithms can predict various biological indices, and therefore ecosystem health. To propose adequate designs for future eDNA metabarcoding-based marine coastal monitoring surveys, the aim of the study is to find out (1) What is the lower limit of reads for accurate RF predictions in coastal marine monitoring using microbial communities? (2) Is this limit the same for different monitoring targets? To achieve this goal, we exploited four different Illumina amplicon datasets obtained from bacterial communities in different costal environments. From these datasets, we predicted different objectives relevant for biomonitoring. For each dataset, those corresponding prediction objectives (labels) were predicted using amplicon sequence variants (ASVs) as features. After construction of RF models using all available sequences of a dataset (full model, serving as benchmark for targeted prediction accuracy), we then successively down-sampled each dataset to lower sequence numbers. Prediction accuracies of the reduced models were then compared to the accuracies of the full models to assess the minimum number of features to obtain the targeted prediction accuracy. Our results show that there is no general answer to question (1) and that (2) the limit varies between different monitoring targets. We have identified the most informative criteria that are relevant to assess the sequencing depth required to predict a biomonitoring category using RF. This may guide future study designs and may help to estimate and control costs in applied routine DNA-based biomonitoring using RF to predict the biomonitoring target. In our contribution we will elucidate and discuss these criteria.

With an increasing demand for finfish from aquaculture, the importance of monitoring the impact of aquaculture installations on coastal ecosystems becomes more and more important. The traditional approach employs macroinvertebrates as bioindicators to assess the ecological quality (EQ) of near-field benthic ecosystem, which is costly and time consuming. Modern approaches using environmental DNA metabarcodes of microbial organisms (eukaryotes and prokaryotes) have shown to be a powerful alternative. It is crucial that the ecological quality determined by molecular bioindicator signatures mirrors the results of standard macroinvertebrate inventories. Previous studies looking for bioindicators of organic enrichment have so far only analyzed samples within a specific geographic location (e.g. one farm, farms from one country). To answer the question if there are global bioindicators (or core communities) across different biogeographic regions, we analyzed 328 samples from three salmon producing countries (Norway, New Zealand, Scotland) to find commonalities in bacterial and ciliate DNA metabarcodes communities within the same ecological quality groups. We used supervised machine learning (SML) to predict both country origin and ecological status for both bacterial and ciliate eDNA markers. We then investigated if prediction accuracy for ecological quality increases if the dataset is reduced to single-country or two-country subsets to eliminate country/region specific effects. Further, core communities including only cosmopolitan ASVs were tested to detect possible global bioindicators for each individual EQ class. Based on the obtained results, we discuss the potential of a globally applicable database and a global training of an SML algorithm to predict ecological quality in aquafarm sediments using eDNA metabarcodes from benthic bacterial and ciliate communities.

Increasing anthropogenic impact and global change effects on natural ecosystems has prompted the development of less expensive and more efficient bioassessments methodologies. One promising approach is the integration of DNA metabarcoding in environmental monitoring. A critical step in this process is the inference of ecological quality (EQ) status from identified molecular bioindicator signatures that mirror environmental classification based on standard macroinvertebrate surveys. The most promising approaches to infer EQ from biotic indices (BI) are supervised machine learning (SML) and the calculation of indicator values (IndVal). In this study we compared the performance of both approaches using DNA metabarcodes of bacteria and ciliates as bioindicators obtained from 152 samples collected from seven Norwegian salmon farms. Results from standard macroinvertebrate-monitoring of the same samples were used as reference to compare the accuracy of both approaches. First, SML outperformed the IndVal approach to infer EQ from eDNA metabarcodes. The Random Forest (RF) algorithm appeared to be less sensitive to noisy data (a typical feature of massive environmental sequence data sets) and uneven data coverage across EQ classes (a typical feature of environmental compliance monitoring scheme) compared to a widely used method to infer IndVals for the calculation of a BI. Second, bacteria allowed for a more accurate EQ assessment than ciliate eDNA metabarcodes. For the implementation of DNA metabarcoding into routine monitoring programmes to assess EQ around salmon aquaculture cages, we therefore recommend bacterial DNA metabarcodes in combination with SML to classify EQ categories based on molecular signatures.

To avoid loss of genetic information in environmental DNA (eDNA) field samples, the preservation of nucleic acids during field sampling is a critical step. In the development of standard operating procedures (SOPs) for eDNA-based compliance monitoring, the effect of different routinely used sediment preservations on biological community structures serving as bioindicators has gone untested. We compared eDNA metabarcoding results of marine bacterial communities from sample aliquots that were treated with a nucleic acid preservation solution (treated samples) and aliquots that were frozen without further treatment (non-treated samples). Sediment samples were obtained from coastal locations subjected to different stressors (aquaculture, urbanization, industry). DNA extraction efficiency, bacterial community profiles, and measures of alpha- and beta-diversity were highly congruent between treated and non-treated samples. As both preservation methods provide the same relevant information to environmental managers and regulators, we recommend the inclusion of both methods into SOPs for biomonitoring in marine coastal environments.

This report presents the outcome of the joint work of PhD students and senior researchers working with DNA-based biodiversity assessment approaches with the goal to facilitate others the access to definitions and explanations about novel DNA-based methods. The work was performed during a PhD course (SLU PNS0169) at the Swedish University of Agricultural Sciences (SLU) in Uppsala, Sweden. The course was co-organized by the EU COST research network DNAqua-Net and the SLU Research Schools Focus on Soils and Water (FoSW) and Ecology - basics and applications. DNAqua-Net (COST Action CA15219, 2016-2020) is a network connecting researchers, water managers, politicians and other stakeholders with the aim to develop new genetic tools for bioassessment of aquatic ecosystems in Europe and beyond. The PhD course offered a comprehensive overview of the paradigm shift from traditional morphology-based species identification to novel identification approaches based on molecular markers. We covered the use of molecular tools in both basic research and applied use with a focus on aquatic ecosystem assessment, from species collection to the use of diversity in environmental legislation. The focus of the course was on DNA (meta)barcoding and aquatic organisms. The knowledge gained was shared with the general public by creating Wikipedia pages and through this collaborative Open Access publication, co-authored by all course participants.

Environmental DNA metabarcoding of benthic bacterial communities emerged as a very powerful technology to assess environmental disturbance effects of coastal salmon aquaculture. A prerequisite for the implementation of this approach into compliance monitoring regulations is its robustness and reproducibility of obtained results. In the framework of regular compliance monitoring of a Scottish salmon farm, we therefore collected sediment samples along a transect from the salmon cages to reference sites in duplicates (biological replicates). Aliquots of both biological replicate samples were then processed by two different laboratories using independently the same eDNA metabarcoding protocol (technical replicates). Measures of alpha diversity and beta diversity, as well as taxonomic profiles of benthic bacterial communities were highly congruent among technical replicates, which even showed less variations then the biological replicates that were processed within each laboratory. Both technical replicate datasets identified the same bacterial indicator taxon groups and ASVs that are characteristic for the environmental quality (EQ) categories to which each of the samples was assigned based on traditional macroinvertebrate biomonitoring of the same samples. In a supervised machine learning (SML) approach, we could classify all individual samples from one technical replicate dataset into the correct EQ category using all samples of the other technical replicate dataset as a training dataset for the SML algorithm. We conclude that eDNA metabarcoding is sufficiently robust that different laboratories come to the same conclusions regarding officially regulated action criteria for environmental impact assessments in salmon aquaculture.

Knowledge-driven management for wastewater treatment plant (WWTP) performance becomes increasingly important considering the globally growing production of wastewater and the rising demand of clean water supply. Even though the potential of microbial organisms (bacteria and protists) as bioindicators for WWTP performance is well known, it is far from being fully exploited for routine monitoring programs. Therefore, we here used massive sequencing of environmental (e)DNA metabarcodes from bacterial (V3-V4 region of the SSU rRNA gene) and eukaryote (V9 region of the SSU rRNA gene) communities in 21 activated sludge samples obtained from full-scale municipal WWTPs in Germany. Microbial community patterns were compared to standard WWTP operating parameters and two traditionally used WWTP performance indicators (Sludge Biotic Index and Sludge Index). Both indices showed low concordance and hardly correlated with chemical WWTP performance parameters nor did they correlate with microbial community structures. In contrast, microbial community profiles significantly correlated with WWTP performance parameters and operating conditions of the plants under study. Therefore, eDNA metabarcode profiles of whole microbial communities indicate the performance of WWTP and can provide useful information for management strategies. We here suggest a strategy for the development of an eDNA metabarcode based bioindicator system, which can be implemented in future standard monitoring programs for WWTP performance and effluent quality.