As global energy demand grows, our oceans are becoming increasingly industrialised. In the North Sea, one of the world's most developed marine regions, offshore infrastructure is shifting from isolated hydrocarbon platforms to large multi-turbine offshore wind farms (OWFs). These structures support diverse epibiotic assemblages which can influence structural integrity, alter ecological processes, and affect ecosystem service provisioning (e.g. water filtration). While epibiotic assemblage composition and zonation is well characterised on solitary structures, little is known about this varies at the intra-OWF scale. Our study explores this variation using ROV footage from a UK OWF; the foundations of two jacketed turbines situated at both the edge and centre of the OWF footprint were surveyed across all legs and depths, and the cover of five dominant epibenthic taxa quantified using a combination of structure-from-motion photogrammetry and machine-learning-based taxonomic segmentation. Epibiotic assemblages were dominated by anemones (Metridium senile) at intermediate depths, with increasing abundance of other target taxa at greater depths. While depth was the primary structuring factor, assemblage composition also appeared to vary with cardinal orientation and turbine position, with higher coverage of most target taxa at the OWF centre (notably soft corals (Alcyonium digitatum)). These patterns may be influenced by turbine-induced changes in downstream turbulence, stratification, and resource availability. While a limited sample size, our results suggest intra-OWF epibiotic heterogeneity, implying assemblage structure varies beyond depth zonation. As offshore wind development accelerates, research is needed to determine this variability's extent and drivers, helping to manage the consequences of an industrialised seascape.

Photo-identification (photo-ID) is commonly used in wildlife ecology and management, but its effectiveness depends on reliable determination of whether an observed individual is already contained within the database. This is typically done by trained human operators but becomes an increasingly demanding task as photo-ID databases expand, potentially limiting the method's applicability. Artificial intelligence approaches have been suggested as potential solutions to this problem. We present a case study involving a photo-ID database of flapper skate (Dipturus intermedius) from Scotland, which was used to train a multistage, deep learning model using a method similar to high-performing facial recognition system, FaceNet, to enable automatic assessment of the similarity between flapper skate newly submitted to the database and those pre-existing in the database. Evaluation using a blind test set of 100 images taken from the database and also a second, smaller test set of tagged animals of known identity to confirm the model's photo-ID performance for end users. When assessed against the previously unseen test set, the model achieved a mean average precision (mAP) of 84.1% and a top-1 accuracy of 80%. The resulting photo-ID model was integrated into the photo-ID database, which was further tested with images of tagged individuals of known identity. This integration significantly reduced the time required to confirm whether new skates were already included in the database. With a top-1 accuracy of 80%, the matching skate, if contained in the database, will likely be returned as a match, removing the need to check by eye against 2500+ individuals already in the database. Solution. The integration of machine-assisted image analysis into a photo-ID database of skate improved our ability to track individuals and understand movement and residency at far larger scales, essential for the continued management of the species. This approach is suitable for a wide range of research projects reliant on photo-ID and should be considered for new and legacy data sets.

ABSTRACT Environmental DNA is being increasingly used for research and regulatory purposes, but currently the lack of standardization is holding it back. There are gaps in our understanding of the eDNA analysis pipeline and the potential impacts of areas of variability within that. Standardization is necessary for all uses of eDNA to allow comparison between sample sets and ensure accuracy and reproducibility. Understanding sample stability is particularly important for planning fieldwork and writing practicable guidance for regulatory compliance monitoring. Samples collected for eDNA analysis are preserved as soon as possible for stability, but practicalities of sampling in the field can lead to delays where the sample temperature may be uncontrolled. We collected eDNA sediment samples along an organic enrichment impact gradient and incubated them at 10°C, 20°C, and 40°C for up to 48 h prior to preservation, then sequenced the bacterial 16S gene. We show that bacteria families responded differently to the incubation temperatures and times to an extent that affected ecological interpretation. Predictions of benthic health using a trained random forest machine learning model were tolerant of incubation up to 20°C, and showed sensitivity to temperature within 3 h of incubation at 40°C. We show that the influence of temperature can depend on the study aim, taxa involved, and analysis used, such that some situations may allow temporary storage up to 20°C but others will be affected by 10°C. We confirm that keeping sediment temperature low is critical for many applications, and that potential temperature deviations must be reported.

Benthic bacterial communities are increasingly recognized as sensitive indicators of aquaculture impacts, yet disentangling the effects of organic enrichment from natural variability remains a challenge, especially in salmon farming. We leveraged 16S-rRNA V3-V4 eDNA metabarcoding and ecological trajectory analysis across an enrichment gradient and two salmon production cycles, testing how cumulative disturbance and management history affect microbial dynamics. Our analyzes revealed that nutrient enrichment associated with aquaculture was the dominant factor structuring bacterial communities, while seasonal variability exerted only a modest influence. Diversity metrics consistently distinguished impacted from reference sites, with bacterial richness approximately 3.9 & times; higher and Shannon diversity 1.4 & times; higher at the reference site compared to the cage edge. Multivariate analyzes confirmed that organic enrichment explained 59% of the variation in benthic bacterial assemblage composition, surpassing smaller but significant contributions from temperature and production cycle, indicating that seasonal effects do not obscure enrichment-related patterns, including during peak biomass. Trajectory analysis further revealed contrasting temporal dynamics, where reference sites retained variable but resilient assemblages, while cage edge communities stabilized into enrichment-tolerant states with reduced microbial variability. Intermediate sites were most sensitive, shifting from stochastic to directional change in response to altered exposure following farm relocation and longer fallowing. Collectively, we showed that cumulative enrichment not only alters community composition, but also changes how stable and predictable microbial communities are through time. This has critical implications for biomonitoring, as trajectory-based approaches can help build time-aware bacterial indicators that separate natural variability from disturbance, giving managers clearer signals to guide sustainable aquaculture practices.

ABSTRACTThe marine aquaculture industry and regulators are in the process of implementing environmental DNA (eDNA) metabarcoding of microbial communities for compliance monitoring. This requires standardization of sampling, laboratory, and data analysis protocols. Towards this goal, we in this study completed two further milestones using samples collected from two Scottish salmon farms: (i) We tested the effect of using two different PCR protocols (i.e., different DNA polymerases, master mixes, and annealing temperatures), which are frequently being used in eDNA biomonitoring of aquaculture installations, for the amplification of the taxonomic marker gene (V3‐V4 hypervariable region of the bacterial 16S rRNA gene). (ii) We quantified sampling background noise obtained from eDNA samples and statistically compared results with the sampling bias observed in macrofaunal samples from the same source sediments. We detected differences in bacterial community structures resulting from the performance of different PCR protocols, profoundly influencing the interpretation of biomonitoring results. Furthermore, we found that sampling‐induced errors for eDNA samples were similar to errors for macrofaunal samples collected according to compliance monitoring protocol (~25% variability in both cases). Finally, we showed that within‐grab variances of microbial community structures were in the same order of magnitude (less than 10× difference in all cases) as the one obtained from replicate grabs collected from the same locale (impact category). Based on our findings, we suggest using a consistent PCR protocol for biomonitoring efforts to improve the comparability of results, especially when different service providers are conducting the biomonitoring. We propose a sampling scheme to be considered in eDNA biomonitoring that includes taking three replicate grabs at each locale, with one replicate sample from each grab. This minimizes sampling‐induced errors and makes upcoming eDNA‐based monitoring results comparable with previous compliance monitoring results obtained from macrofaunal data.

Abstract Accurate biomass estimates are key to understanding a wide variety of ecological functions. In marine systems, epibenthic biomass estimates have traditionally relied on either destructive/extractive methods that are limited to horizontal soft‐sediment environments, or simplistic geometry‐based biomass conversions that are unsuitable for more complex morphologies. Consequently, there is a requirement for non‐destructive, higher‐accuracy methods that can be used in an array of environments, targeting more morphologically diverse taxa, and at ecological relevant scales. We used a combination of 3D photogrammetry, convolutional neural network (CNN) automated taxonomic identification, and taxa‐specific biovolume:biomass calibrations to test the viability of estimating biomass of three species of morphologically complex epibenthic taxa from in situ stereo 2D source imagery. Our trained CNN produced accurate and reliable annotations of our target taxa across a wide range of conditions. When incorporated into photogrammetric 3D models of underwater surveys, we were able to automatically isolate our three target taxa from their environment, producing biovolume measurements that had respective mean similarities of 99%, 102% and 120% of those obtained from human annotators. When combined with taxa‐specific biovolume:biomass calibration values, we produced biomass estimates of 88%, 125% and 133% mean similarity to that of the ‘true’ biomass of the respective taxa. Our methodology provides a highly reliable and efficient method for estimating epibenthic biomass of morphologically complex taxa using non‐destructive 2D imagery. This approach can be applied to a variety of environments and photo/video survey approaches (e.g. SCUBA, ROV, AUV) and is especially valuable in spatially extensive surveys where manual approaches are prohibitively time‐consuming.

Thousands of artificial (‘human-made’) structures are present in the marine environment, many at or approaching end-of-life and requiring urgent decisions regarding their decommissioning. No consensus has been reached on which decommissioning option(s) result in optimal environmental and societal outcomes, in part, owing to a paucity of evidence from real-world decommissioning case studies. To address this significant challenge, we asked a worldwide panel of scientists to provide their expert opinion. They were asked to identify and characterise the ecosystem effects of artificial structures in the sea, their causes and consequences, and to identify which, if any, should be retained following decommissioning. Experts considered that most of the pressures driving ecological and societal effects from marine artificial structures (MAS) were of medium severity, occur frequently, and are dependent on spatial scale with local-scale effects of greater magnitude than regional effects. The duration of many effects following decommissioning were considered to be relatively short, in the order of days. Overall, environmental effects of structures were considered marginally undesirable, while societal effects marginally desirable. Experts therefore indicated that any decision to leave MAS in place at end-of-life to be more beneficial to society than the natural environment. However, some individual environmental effects were considered desirable and worthy of retention, especially in certain geographic locations, where structures can support improved trophic linkages, increases in tourism, habitat provision, and population size, and provide stability in population dynamics. The expert analysis consensus that the effects of MAS are both negative and positive for the environment and society, gives no strong support for policy change whether removal or retention is favoured until further empirical evidence is available to justify change to the status quo. The combination of desirable and undesirable effects associated with MAS present a significant challenge for policy- and decision-makers in their justification to implement decommissioning options. Decisions may need to be decided on a case-by-case basis accounting for the trade-off in costs and benefits at a local level.

Switching from fossil fuels to renewable energy is key to international energy transition efforts and the move toward net zero. For many nations, this requires decommissioning of hundreds of oil and gas infrastructure in the marine environment. Current international, regional and national legislation largely dictates that structures must be completely removed at end-of-life although, increasingly, alternative decommissioning options are being promoted and implemented. Yet, a paucity of real-world case studies describing the impacts of decommissioning on the environment make decision-making with respect to which option(s) might be optimal for meeting international and regional strategic environmental targets challenging. To address this gap, we draw together international expertise and judgment from marine environmental scientists on marine artificial structures as an alternative source of evidence that explores how different decommissioning options might ameliorate pressures that drive environmental status toward (or away) from environmental objectives. Synthesis reveals that for 37 United Nations and Oslo-Paris Commissions (OSPAR) global and regional environmental targets, experts consider repurposing or abandoning individual structures, or abandoning multiple structures across a region, as the options that would most strongly contribute toward targets. This collective view suggests complete removal may not be best for the environment or society. However, different decommissioning options act in different ways and make variable contributions toward environmental targets, such that policy makers and managers would likely need to prioritise some targets over others considering political, social, economic, and ecological contexts. Current policy may not result in optimal outcomes for the environment or society.

Man’s impacts on global ecosystems are increasing and there is a growing demand that these activities be properly monitored, frequently by looking activity-associated change in biological assemblages. Traditional monitoring requires taxonomic expertise, and this is expensive limiting the spatial and temporal scale of the programme. Monitoring is optimised by maximising the detected change-related signal to noise ratio in the data. Sample characterisation, via sequencing short sections of DNA (metabarcoding), offers considerable potential. Metabarcoding targets variously specific taxonomic groups via gene-marker choice, including prokaryotes (16S marker) and eukaryotes (18S and COI markers). Most environmental sequences cannot be identified to species level because they are not represented on databases but the sequences themselves have direct potential as indicators of environmental change. In monitoring applications, repeatability, and precision (low noise between replicates) are key criteria. We compared three commonly used markers (16S, 18S, COI) and, sampling along a steep environmental gradient, generated between 13.5 – 66K separate sequences. We showed that, in general, the unannotated 16S sequences were associated with the largest signal: noise ratio, as determined using non-parametric ANOVA (NPA), and COI the smallest signal: noise ratio. We trialled four separate, intuitive, data-filtration approaches and demonstrated that removing less frequent sequences improved the signal: noise ratio, partitioning an additional 25% from residual to explanatory factors in NPA and variously reduced multivariate dispersion in the data. For the 16S marker, retaining only the most frequently observed sequence, per sample, resulting in nine sequences across 150 samples, generated a near-maximal signal: noise ratio (95% of the variance explained in NPA). We recommend that NPA, combined with rigorous elimination of less frequent sequences, be used to pre-filter sequences/taxa being used in monitoring applications. Our approach will simplify downstream analysis for example the identification of key taxa and functional associations.

Man's impacts on global ecosystems are increasing and there is a growing demand that these activities be appropriately monitored. Monitoring requires measurement of a response metric ('signal') that changes maximally and consistently in response to the monitored activity irrespective of other factors ('noise'), thus maximising the signal-to-noise ratio. Indices derived from time-consuming morphology-based taxonomic identification of organisms are a core part of many monitoring programmes. Metabarcoding is an alternative to morphology-based identification and involves the sequencing of short fragments of DNA ('markers') from multiple taxa simultaneously. DNA suitable for metabarcoding includes that extracted from environmental samples (eDNA). Metabarcoding outputs DNA sequences that can be identified (annotated) by matching them against archived annotated sequences. However, sequences from most organisms are not archived - preventing annotation and potentially limiting metabarcoding in monitoring applications. Consequently, there is growing interest in using unannotated sequences as response metrics in monitoring programmes. We compared the sequences from three commonly used markers (16S (V3/V4 regions), 18S (V1/V2 regions) and COI) and, sampling along steep impact gradients, showed that the 16S and COI sequences were associated with the largest and smallest signal-to-noise ratio respectively. We trialled four separate, intuitive, noise-reduction approaches and demonstrated that removing less frequent sequences improved the signal-to-noise ratio, partitioning an additional 25 % from noise to explanatory factors in non-parametric ANOVA (NPA) and reducing dispersion in the data. For the 16S marker, retaining only the most frequently observed sequence, per sample, resulting in nine sequences across 150 samples, generated a near-maximal signal-to-noise ratio (95 % of the variance explained in NPA). We recommend that NPA, combined with rigorous elimination of less frequent sequences, be used to pre-filter sequences/taxa being used in monitoring applications. Our approach will simplify downstream analysis, for example the identification of key taxa and functional associations.

Environmental DNA metabarcoding is a powerful approach for use in biomonitoring and impact assessments. Amplicon-based eDNA sequence data are characteristically highly divergent in sequencing depth (total reads per sample) as influenced inter alia by the number of samples simultaneously analyzed per sequencing run. The random forest (RF) machine learning algorithm has been successfully employed to accurately classify unknown samples into monitoring categories. To employ RF to eDNA data, and avoid sequencing-depth artifacts, sequence data across samples are normalized using rarefaction, a process that inherently loses information. The aim of this study was to inform future sampling designs in terms of the relationship between sampling depth and RF accuracy. We analyzed three published and one new bacterial amplicon datasets, using a RF, based initially on the maximal rarefied data available (minimum mean of > 30,000 reads across all datasets) to give our baseline performance. We then evaluated the RF classification success based on increasingly rarefied datasets. We found that extreme to moderate rarefaction (50–5000 sequences per sample) was sufficient to achieve prediction performance commensurate to the full data, depending on the classification task. We did not find that the number of classification classes, data balance across classes, or the total number of sequences or samples, were associated with predictive accuracy. We identified the ability of the training data to adequately characterize the classes being mapped as the most important criterion and discuss how this finding can inform future sampling design for eDNA based biomonitoring to reduce costs and computation time.

Abstract Mussels belonging to the Mytilus species complex (M. edulis, ME; M. galloprovincialis, MG; and M. trossulus, MT) often occur in sympatry, facilitating introgressive hybridization. This may be further promoted by mussel aquaculture practices, with MT introgression often resulting in commercially unfavourable traits such as low meat yield and weak shells. To investigate the relationship between genotype and shell phenotype, genetic and morphological variability was quantified across depth (1 m to 7 m) along a cultivation rope at a mussel farm on the West coast of Scotland. A single nuclear marker (Me15/16) and a novel panel of 33 MT‐diagnostic single nucleotide polymorphisms were used to evaluate stock structure and the extent of MT introgression across depth. Variation in shell strength, determined as the maximum compression force for shell puncture, and shell shape using geometric morphometric analysis were evaluated in relation to cultivation depth and the genetic profiles of the mussels. Overall, ME was the dominant genotype across depth, followed by ME × MG hybrids and smaller quantities of ME × MT hybrids and pure MT individuals. In parallel, we identified multiple individuals that were either predominantly homozygous or heterozygous for MT‐diagnostic alleles, likely representing pure MT and first‐generation ME × MT hybrids, respectively. Both the proportion of individuals carrying MT alleles and MT allele frequency declined with depth. Furthermore, MT‐introgressed individuals had significantly weaker and more elongate shells than nonintrogressed individuals. This study provides detailed insights into stock structure along a cultivation rope and suggests that practical methods to assess shell strength and shape of cultivated mussels may facilitate the rapid identification of MT, limiting the impact of this commercially damaging species.

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.

The analysis of benthic bacterial community structure has emerged as a powerful alternative to traditional microscopy-based taxonomic approaches to monitor aquaculture disturbance in coastal environments. However, local bacterial diversity and community composition vary with season, biogeographic region, hydrology, sediment texture, and aquafarm-specific parameters. Therefore, without an understanding of the inherent variation contained within community complexes, bacterial diversity surveys conducted at individual farms, countries, or specific seasons may not be able to infer global universal pictures of bacterial community diversity and composition at different degrees of aquaculture disturbance. We have analyzed environmental DNA (eDNA) metabarcodes (V3–V4 region of the hypervariable SSU rRNA gene) of 138 samples of different farms located in different major salmon-producing countries. For these samples, we identified universal bacterial core taxa that indicate high, moderate, and low aquaculture impact, regardless of sampling season, sampled country, seafloor substrate type, or local farming and environmental conditions. We also discuss bacterial taxon groups that are specific for individual local conditions. We then link the metabolic properties of the identified bacterial taxon groups to benthic processes, which provides a better understanding of universal benthic ecosystem function(ing) of coastal aquaculture sites. Our results may further guide the continuing development of a practical and generic bacterial eDNA-based environmental monitoring approach.

An increasing number of pipelines and associated protective materials in the North Sea are reaching the end of their operational life and require decommissioning. Identifying the optimal decommissioning option from an environmental perspective requires an understanding of ecological interactions; currently there is little knowledge as to species associations with pipelines and associated protective materials. This study utilises industry ROV footage from the North Sea to quantify these interactions. A total of 58 taxa were identified, including 41 benthic taxa and 17 fish taxa. Taxa were grouped into seven groups for analysis including four groups for benthic epifauna: grazers, suspension/filter feeders, decapods, and colonial/encrusting taxa. Fish were organised into three groups: pollock, other fish, and other gadoids. Using zero-inflated generalised linear mixed models, we show that abundances of benthic epifauna and fish vary between types of protective structure (e.g., concrete mattresses, rock dump), depth, levels of fishing effort and proximity to oil and gas platforms. Six taxa groups exhibited higher abundances on concrete mattresses than bare pipelines with benthic epifaunal decapods showing the highest difference at 3.04 (1.83, 4.84, 95% CrI) times higher on mattresses compared to bare pipelines. Six groups were higher in abundance within the 500 m fisheries exclusion zone around platforms, compared to outside of the zone, with other gadoids showing the highest difference at 1.83 times (1.09, 2.89, 95% CrI) times higher inside zones. Five groups decreased in abundance with an increase in fishing effort, with the biggest effect observed on grazers which decreased in abundance by 28% (14 – 40, 95% CrI) per 50 h of fishing. We show that pipelines and protective materials are operating as artificial reefs, and our results suggest that removal of infrastructure could result in the loss of habitat and species.

ABSTRACTMost molluscs possess shells, constructed from a vast array of microstructures and architectures. The fully formed shell is composed of calcite or aragonite. These CaCO3 crystals form complex biocomposites with proteins, which although typically less than 5% of total shell mass, play significant roles in determining shell microstructure. Despite much research effort, large knowledge gaps remain in how molluscs construct and maintain their shells, and how they produce such a great diversity of forms. Here we synthesize results on how shell shape, microstructure, composition and organic content vary among, and within, species in response to numerous biotic and abiotic factors. At the local level, temperature, food supply and predation cues significantly affect shell morphology, whilst salinity has a much stronger influence across latitudes. Moreover, we emphasize how advances in genomic technologies [e.g. restriction site‐associated DNA sequencing (RAD‐Seq) and epigenetics] allow detailed examinations of whether morphological changes result from phenotypic plasticity or genetic adaptation, or a combination of these. RAD‐Seq has already identified single nucleotide polymorphisms associated with temperature and aquaculture practices, whilst epigenetic processes have been shown significantly to modify shell construction to local conditions in, for example, Antarctica and New Zealand. We also synthesize results on the costs of shell construction and explore how these affect energetic trade‐offs in animal metabolism. The cellular costs are still debated, with CaCO3 precipitation estimates ranging from 1–2 J/mg to 17–55 J/mg depending on experimental and environmental conditions. However, organic components are more expensive (~29 J/mg) and recent data indicate transmembrane calcium ion transporters can involve considerable costs. This review emphasizes the role that molecular analyses have played in demonstrating multiple evolutionary origins of biomineralization genes. Although these are characterized by lineage‐specific proteins and unique combinations of co‐opted genes, a small set of protein domains have been identified as a conserved biomineralization tool box. We further highlight the use of sequence data sets in providing candidate genes for in situ localization and protein function studies. The former has elucidated gene expression modularity in mantle tissue, improving understanding of the diversity of shell morphology synthesis. RNA interference (RNAi) and clustered regularly interspersed short palindromic repeats ‐ CRISPR‐associated protein 9 (CRISPR‐Cas9) experiments have provided proof of concept for use in the functional investigation of mollusc gene sequences, showing for example that Pif (aragonite‐binding) protein plays a significant role in structured nacre crystal growth and that the Lsdia1 gene sets shell chirality in Lymnaea stagnalis. Much research has focused on the impacts of ocean acidification on molluscs. Initial studies were predominantly pessimistic for future molluscan biodiversity. However, more sophisticated experiments incorporating selective breeding and multiple generations are identifying subtle effects and that variability within mollusc genomes has potential for adaption to future conditions. Furthermore, we highlight recent historical studies based on museum collections that demonstrate a greater resilience of molluscs to climate change compared with experimental data. The future of mollusc research lies not solely with ecological investigations into biodiversity, and this review synthesizes knowledge across disciplines to understand biomineralization. It spans research ranging from evolution and development, through predictions of biodiversity prospects and future‐proofing of aquaculture to identifying new biomimetic opportunities and societal benefits from recycling shell products.

Interactions between fishing vessels and oil and gas infrastructure can result in damage to fishing gear, loss of fishing time/access, and risks to crew health and safety. The spatial and temporal patterns characterizing previous incidents (and subsequent losses) between fishers and oil and gas infrastructure were quantified and used to identify key risk factors associated with fisheries losses. Between the years 1989 and 2016, 1590 incidents that resulted in a financial loss, vessel abandonment, or an injury/fatality for UK commercial fishers were recorded. The annual number of recorded incidents decreased by 98.6% over a 27-year period. The majority of past incidences resulted in financial losses (rather than injuries or fatalities) and were associated with interactions between single otter trawlers and oil and gas production-related debris. The odds of an incidence occurring varied according to substrate type and fishing intensity. A risk-model for pipeline-fishing interactions in the Fladen Ground showed that there was significant spatial heterogeneity in the risk of an incident along a pipeline according to the angle and intensity of fishing. The results highlight the need to include the full spectrum of potential losses in fisheries impact assessments associated with the installation and decommissioning of oil and gas assets.

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.

Jennifer Dannheim *, Lena Bergström, Silvana N. R. Birchenough, Radosław Brzana, Arjen R. Boon, Joop W. P. Coolen , Jean-Claude Dauvin, Ilse De Mesel, Jozefien Derweduwen, Andrew B. Gill, Zoë L. Hutchison, Angus C. Jackson, Urszula Janas, Georg Martin, Aurore Raoux, Jan Reubens, Liis Rostin, Jan Vanaverbeke, Thomas A. Wilding, Dan Wilhelmsson, and Steven Degraer Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Science, Am Handelshafen 12, Bremerhaven 27570, Germany Helmholtz Institute for Functional Marine Biodiversity at the University of Oldenburg (HIFMB), Ammerländer Heerstraße 231, Oldenburg 26129, Germany Department of Aquatic Resources, Swedish University of Agricultural Sciences, Skolgatan 6, Öregrund 74242, Sweden Cefas Lowestoft Laboratory, Pakefield Road, Lowestoft, Suffolk NR33 0HT, UK Institute of Oceanography, University of Gdansk, Al. Marsz. J. Pilsudskiego 46, Gdynia 81-378, Poland Deltares, Unit Marine and Coastal Studies, P.O. Box 177, Delft 2600 MH, The Netherlands Wageningen Marine Research (Formerly IMARES), P.O. Box 57, Den Helder 1780 AB, The Netherlands Aquatic Ecology and Water Quality Management Group, Wageningen University, Droevendaalsesteeg 3a, Wageningen 6708 PD, The Netherlands Normandie Univ, UNICAEN, Laboratoire Morphodynamique Continentale et Côtière, CNRS, UMR 6143 M2C, 24 Rue des Tilleuls, Caen 14000, France Operational Directorate Natural Environment (OD Nature), Marine Ecology and Management (MARECO), Royal Belgian Institute of Natural Sciences, Vautierstraat 29, Brussels B-1000, Belgium Institute for Agricultural and Fisheries Research (ILVO), Ankerstraat 1, Oostende B-8400, Belgium PANGALIA Environmental, Ampthill, Bedfordshire, UK Graduate School of Oceanography, University of Rhode Island, Narragansett, RI 02882, USA Centre of Applied Zoology, Cornwall College Newquay, Wildflower Lane, Trenance Gardens, Newquay, Cornwall TR7 2LZ, UK Estonian Marine Institute, University of Tartu, Mäealuse 14, Tallinn 12618, Estonia Flanders Marine Institute, Wandelaarkaai 7, Oostende 8400, Belgium Scottish Association for Marine Science, Scottish Marine Institute, Oban, Argyll PA37 1QA, UK Swedish Secretariat for Environmental Earth System Science (SSEESS), Royal Swedish Academy of Science, Box 50005, Stockholm 104 05, Sweden *Corresponding author: tel: þ 49 471 4831 1734; e-mail: jennifer.dannheim@awi.de.