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19 pages, 6 figures, 4 tables, supporting information https://doi.org/10.1111/1462-2920.16153.-- Data Availability Statement: The data that support the findings of this study are openly available in GitHub at http://github.com/lanzen/Garate_et_al_2022_data. Raw sequences are available from the INSDC Sequence Read Archive with BioProject number PRJEB54766, Pico- and nanoplankton are key players in the marine ecosystems due to their implication in the biogeochemical cycles, nutrient recycling and the pelagic food webs. However, the specific dynamics and niches of most bacterial, archaeal and eukaryotic plankton remain unknown, as well as the interactions between them. Better characterization of these is critical for understanding and predicting ecosystem functioning under anthropogenic pressures. We used environmental DNA metabarcoding across a 6-year time series to explore the structure and seasonality of pico- and nanoplankton communities in two sites of the Bay of Biscay, one coastal and one offshore, and construct association networks to reveal potential keystone and connector taxa. Temporal trends in alpha diversity were similar between the two sites, and concurrent communities more similar than within the same site at different times. However, we found differences between the network topologies of the two sites, with both shared and site-specific keystones and connectors. For example, Micromonas, with lower abundance in the offshore site is a keystone here, indicating a stronger effect of associations such as resource competition. This study provides an example of how time series and association network analysis can reveal how similar communities may function differently despite being geographically close, This study was financed by a ‘Retos Investigación: Proyectos I + D + i 2018’ grant (RTI2018-101018-A-I00) from the Spanish government. Laura Alonso-Sáez was supported by a ‘Ramon y Cajal’ Grant from the Spanish Government. The environmental data for this study were obtained from the project ‘Program for the monitoring and assessment of the ecological status of transitional and coastal waters of the Basque Country’ funded by URA, the Basque Water Agency, through a convention with AZTI. Anders Lanzén is further supported by a Research Fellow contract from IKERBASQUE (Basque Foundation for Science), With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S)

Routine monitoring of benthic biodiversity is critical for managing and understanding the anthropogenic impacts on marine, transitional and freshwater ecosystems. However, traditional reliance on morphological identification generally makes it cost-prohibitive to increase the scale of monitoring programmes. Metabarcoding of environmental DNA has clear potential to overcome many of the problems associated with traditional monitoring, with prokaryotes and other microorganisms showing particular promise as bioindicators. However, due to the limited knowledge regarding the ecological roles and responses of environmental microorganisms to different types of pressure, the use of de novo approaches is necessary. Here, we use two such approaches for the prediction of multiple impacts present in estuaries and coastal areas of the Bay of Biscay based on microbial communities. The first (Random Forests) is a machine learning method while the second (Threshold Indicator Taxa Analysis and quantile regression splines) is based on de novo identification of bioindicators. Our results show that both methods overlap considerably in the indicator taxa identified, but less for sequence variants. Both methods also perform well in spite of the complexity of the studied ecosystem, providing predictive models with strong correlation to reference values and fair to good agreement with ecological status groups. The ability to predict several specific types of pressure is especially appealing. The cross-validated models and biotic indices developed can be directly applied to predict the environmental status of estuaries in the same geographical region, although more work is needed to evaluate and improve them for use in new regions or habitats.

Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2021.151783, Environmental DNA (eDNA) metabarcoding (parallel sequencing of DNA/RNA for identification of whole communities within a targeted group) is revolutionizing the field of aquatic biomonitoring. To date, most metabarcoding studies aiming to assess the ecological status of aquatic ecosystems have focused on water eDNA and macroinvertebrate bulk samples. However, the eDNA metabarcoding has also been applied to soft sediment samples, mainly for assessing microbial or meiofaunal biota. Compared to classical methodologies based on manual sorting and morphological identification of benthic taxa, eDNA metabarcoding offers potentially important advantages for assessing the environmental quality of sediments. The methods and protocols utilized for sediment eDNA metabarcoding can vary considerably among studies, and standardization efforts are needed to improve their robustness, comparability and use within regulatory frameworks. Here, we review the available information on eDNA metabarcoding applied to sediment samples, with a focus on sampling, preservation, and DNA extraction steps. We discuss challenges specific to sediment eDNA analysis, including the variety of different sources and states of eDNA and its persistence in the sediment. This paper aims to identify good-practice strategies and facilitate method harmonization for routine use of sediment eDNA in future benthic monitoring., The work is the result of the DNAqua-Net workshop held in Rome, on June 10–12, 2019, organised by IRSA-CNR and supported by European Cooperation in Science and Technology (COST) grant CA15219 (DNAqua-Net). It largely benefited from the discussions with DNAqua-Net members during this event as well as other DNAqua-Net conferences.This study was carried out within the UNIVPM VIRIDE Project (n. 2017EKFA98) funded by the Ministry of University and Research of Italy (MUR) and the Italian National Project on the use of environmental DNA for the identification of marine non-indigenous species funded by the Istituto Superiore per la Protezione e la Ricerca Ambientale (ISPRA). JP, TC, and LAPG were supported by the Swiss National Science Foundation (grant 31003A_179125) as well as FL with an Early Postdoc.Mobility grant (P2GEP3_171829). MS-M was supported by the Ministry of Education, Youth and Sports of the Czech Republic, European Regional Development Fund-Project CZ.02.1.01/0.0/0.0/16_019/0000845 and LTC 20063. KP was supported by the European Regional Development Fund within National Programme for Addressing Socio-Economic Challenges through R&D (L190003VLVI RITA1/02-60-05) and base financed project P190250PKKH of Estonian University of Life Sciences, IM was supported by the Slovenian Research Agency (ARRS) programme P4-0085. SD was supported by the Portuguese Foundation for Science and Technology (FCT) (CEECIND/00667/2017). LC was supported by the PON AIM 1854833 – PON Ricerca e Innovazione 2014–2020 – Azione I.2 – D.D. n. 407, 27/02/2018 – Attraction and International Mobility.VK was supported by grants PUT1389 (ETAg) and Institute of Technology, University of Tartu basic funding grant.

Estuarine and coastal ecosystems play a fundamental role in human activities. Since millions of people depend on the numerous resources they offer (i.e., fisheries, transportation, and recreational activities), these ecosystems are also exposed to a high anthropogenic pressure. In order to better monitor and regulate such pressures, it is critical to improve our understanding of the functioning of these ecosystems. This is especially important for microorganisms, considering, for instance, their important roles in biogeochemical cycles. In this study we investigate estuarine and coastal microbial communities along the coast of the Basque Country using eDNA metabarcoding of bacterioplankton and microbenthos samples along a time series, from locations that present different degrees of disturbance. Metabarcoding of the 16S and 18S rRNA gene was carried out, and the OTU and taxa tables generated were used to reconstruct ecological networks, representing potential biological interactions. These ecological networks were used to identify on one hand the species that play a major role in the maintenance of the whole community structure (keystones), and on the other, complex network modules affected by environmental impacts. Here, we define keystone taxa as those that presented the highest ratio between the degree of connectivity (number of associations they established within the network) and their relative abundance. The underlying aim of this study is to develop novel “bioindicators” based on taxa that are both sensitive to impacts and important for community structure. Results of two seawater communities, one from offshore waters and the other from the coast, showed similar bacterial composition at family level, dominated by Flavobacteraceae and Rhodobacteraceae. Nevertheless, their ecological network properties differed strongly: for a similar number of taxa that established associations, the conectance was similar in both communities, but the number of associations in the coastal community was twice as high as for the offshore one, resulting in a higher modularity. Identified keystone taxa were taxonomically different between both communities: those from the coastal community belonged to Protobacteria, Bacteroidetes, Firmicutes, and Fusobacteria phyla, while the keystones from the offshore community belonged to Proteobacteria, Actinobacteria, Chloroflexi, Thaumarchaeota, PAUC34f and Verrucomicrobia. Moreover, we identified “connector” taxa that presented the highest values of betweenness centrality. These taxa, without having a high degree of connectivity, may be important for the interaction structure because they connect modules, i.e., highly connected subnetworks within the whole network. Further, hierarchical clustering was performed to identify seasonal trends and to better understand the associations retrieved. Taxa with different seasonal preference often grouped together in the same modules, which indicates that modularity was not caused primarily by seasonality.

Microbial communities inhabiting soil-water-sediment continuum in coastal areas provide important ecosystem services. Their adaptation in response to environmental stressors, particularly mitigating the impact of pollutants discharged from human activities, has been considered for the development of microbial biomonitoring tools, but their use is still in the infancy. Here, chemical and molecular (16S rRNA gene metabarcoding) approaches were combined in order to determine the impact of pollutants on microbial assemblages inhabiting the aquatic network of a soil-water-sediment continuum around the Ichkeul Lake (Tunisia), an area highly impacted by human activities. Samples were collected within the soil-river-lake continuum at three stations in dry (summer) and wet (winter) seasons. The contaminant pressure index (PI), which integrates Polycyclic aromatic hydrocarbons (PAHs), alkanes, Organochlorine pesticides (OCPs) and metal contents, and the microbial pressure index microgAMBI, based on bacterial community structure, showed significant correlation with contamination level and differences between seasons. The comparison of prokaryotic communities further revealed specific assemblages for soil, river and lake sediments. Correlation analyses identified potential "specialist" genera for the different compartments, whose abundances were correlated with the pollutant type found. Additionally, PICRUSt analysis revealed the metabolic potential for pollutant transformation or degradation of the identified "specialist" species, providing information to estimate the recovery capacity of the ecosystem. Such findings offer the possibility to define a relevant set of microbial indicators for assessing the effects of human activities on aquatic ecosystems. Microbial indicators, including the detection of “specialist” and sensitive taxa, and their functional capacity, might be useful, in combination with integrative microbial indices, to constitute accurate biomonitoring tools for the management and restoration of complex coastal aquatic systems.

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.

International audience; A decade after environmental scientists integrated high-throughput sequencing technologies in their toolbox, the genomics-based monitoring of anthropogenic impacts on the biodiversity and functioning of ecosystems is yet to be implemented by regulatory frameworks. Despite the broadly acknowledged potential of environmental genomics to this end, technical limitations and conceptual issues still stand in the way of its broad application by end-users. In addition, the multiplicity of potential implementation strategies may contribute to a perception that the routine application of this methodology is premature or "in development", hence restraining regulators from binding these tools into legal frameworks. Here, we review recent implementations of environmental genomics-based methods, applied to the biomonitoring of ecosystems. By taking a general overview, without narrowing our perspective to particular habitats or groups of organisms, this paper aims to compare, review and discuss the strengths and limitations of four general implementation strategies of environmental genomics for monitoring: (A) Taxonomy-based analyses focused on identification of known bioindicators or described taxa; (B) De novo bioindicator analyses; (C) Structural community metrics including inferred ecological networks; and (D) Functional community metrics (metagenomics or metatranscriptomics). We emphasise the utility of the three latter strategies to integrate meiofauna and microorganisms that are not traditionally utilised in biomonitoring because of difficult taxonomic identification. Finally, we propose a roadmap for the implementation of environmental genomics into routine monitoring programs that leverage recent analytical advancements, while pointing out current limitations and future research needs.

Increased exploitation of resources in sensitive marine ecosystems emphasizes the importance of knowledge regarding ecological impacts. However, current bio-monitoring practices are limited in terms of target-organisms and temporal resolution. Hence, developing new technologies is vital for enhanced ecosystem understanding. In this study, we have applied a prototype version of a phylogenetic microarray to assess the eukaryote community structures of marine sediments from an area with ongoing oil and gas drilling activity. The results were compared with data from both sequencing (metabarcoding) and morphology-based monitoring to evaluate whether microarrays were capable of detecting ecosystem disturbances. A significant correlation between microarray data and chemical pollution indicators, as well as sequencing-based results, was demonstrated, and several potential indicator organisms for pollution-associated parameters were identified, among them a large fraction of microorganisms not covered by traditional morphology-based monitoring. This suggests that microarrays have a potential in future environmental monitoring. acceptedVersion

As global exploitation of available resources increases, operations extend towards sensitive and previously protected ecosystems. It is important to monitor such areas in order to detect, understand and remediate environmental responses to stressors. The natural heterogeneity and complexity of communities means that accurate monitoring requires high resolution, both temporally and spatially, as well as more complete assessments of taxa. Increased resolution and taxonomic coverage is economically challenging using current microscopy-based monitoring practices. Alternatively, DNA sequencing-based methods have been suggested for cost-efficient monitoring, offering additional insights into ecosystem function and disturbance. Here, we applied DNA metabarcoding of eukaryotic communities in marine sediments, in areas of offshore drilling on the Norwegian continental shelf. Forty-five samples, collected from seven drilling sites in the Troll/Oseberg region, were assessed, using the small subunit ribosomal RNA gene as a taxonomic marker. In agreement with results based on classical morphology-based monitoring, we were able to identify changes in sediment communities surrounding oil platforms. In addition to overall changes in community structure, we identified several potential indicator taxa, responding to pollutants associated with drilling fluids. These included the metazoan orders Macrodasyida, Macrostomida and Ceriantharia, as well as several ciliates and other protist taxa, typically not targeted by environmental monitoring programmes. Analysis of a co-occurrence network to study the distribution of taxa across samples provided a framework for better understanding the impact of anthropogenic activities on the benthic food web, generating novel, testable hypotheses of trophic interactions structuring benthic communities. publishedVersion

Genomics is fast becoming a routine tool in medical diagnostics and cutting-edge biotechnologies. Yet, its use for environmental biomonitoring is still considered a futuristic ideal. Until now, environmental genomics was mainly used as a replacement of the burdensome morphological identification, to screen known morphologically distinguishable bioindicator taxa. While prokaryotic and eukaryotic microbial diversity is of key importance in ecosystem functioning, its implementation in biomonitoring programs is still largely unappreciated, mainly because of difficulties in identifying microbes and limited knowledge of their ecological functions. Here, we argue that the combination of massive environmental genomics microbial data with machine learning algorithms can be extremely powerful for biomonitoring programs and pave the way to fill important gaps in our understanding of microbial ecology.

Mountain grasslands in the Iberian Peninsula are the result of extensive grazing. However, a progressive abandonment of grazing activity is currently observed in the study region. The objective of this work was to evaluate the short-term (2 years) effects of non-grazing on the diversity and composition of plants, soil microorganisms (prokaryotes, fungi, arbuscular mycorrhiza), mesofauna, macrofauna and aboveground-belowground links, through the study of 16 grazed vs. non-grazed areas in Atlantic grasslands located in the Basque Country (Spain). Sites were divided between 4 habitat types with different elevation, pasture productivity, vegetation type and parent material. Herbivores appeared to influence plant community composition, contributing to increase aboveground diversity, while having unequal effects on belowground communities depending on the organisms analysed. This may be explained by the different habitat and trophic level of each soil organism, which may be more or less affected by the predominating negative effects of grazing, such as soil compaction, and only partially compensated by other positive effects. Finally, habitat type appeared to be the strongest influence on both above- and belowground communities, also influencing the effect of the absence of grazing.

Toxicity of metals released from mine tailings may cause severe damage to ecosystems. A diversity of microorganisms, however, have successfully adapted to such sites. In this study, our objective was to advance the understanding of the indigenous microbial communities of mining-impacted soils. To this end, a metatranscriptomic approach was used to study a heavily metal-contaminated site along a metal concentration gradient (up to 3220 000 and 97 000 mg kg(-1) of Cd, Pb and Zn, respectively) resulting from previous mining. Metal concentration, soil pH and amount of clay were the most important factors determining the structure of soil microbial communities. Interestingly, evenness of the microbial communities, but not its richness, increased with contamination level. Taxa with high metabolic plasticity like Ktedonobacteria and Chloroflexi were found with higher relative abundance in more contaminated samples. However, several taxa belonging to the phyla Actinobacteria and Acidobacteria followed opposite trends in relation to metal pollution. Besides, functional transcripts related to transposition or transfer of genetic material and membrane transport, potentially involved in metal resistance mechanisms, had a higher expression in more contaminated samples. Our results provide an insight into microbial communities in long-term metal-contaminated environments and how they contrast to nearby sites with lower contamination.

Naphthalene, the smallest polycyclic aromatic hydrocarbon (PAH), is found in abundance in crude oil, its major source in marine environments. PAH removal occurs via biodegradation, a key process determining their fate in the sea. Adequate estimation of PAH biodegradation rates is essential for environmental risk assessment and response planning using numerical models such as the oil spill contingency and response (OSCAR) model. Using naphthalene as a model compound, biodegradation rate, temperature response and bacterial community composition of seawaters from two climatically different areas (North Sea and Arctic Ocean) were studied and compared. Naphthalene degradation was followed by measuring oxygen consumption in closed bottles using the OxiTop(®) system. Microbial communities of untreated and naphthalene exposed samples were analysed by polymerase chain reaction denaturing gradient gel electrophoresis (PCR-DGGE) and pyrosequencing. Three times higher naphthalene degradation rate coefficients were observed in arctic seawater samples compared to temperate, at all incubation temperatures. Rate coefficients at in situ temperatures were however, similar (0.048 day(-1) for temperate and 0.068 day(-1) for arctic). Naphthalene biodegradation rates decreased with similar Q10 ratios (3.3 and 3.5) in both seawaters. Using the temperature compensation method implemented in the OSCAR model, Q10 = 2, biodegradation in arctic seawater was underestimated when calculated from the measured temperate k1 value, showing that temperature difference alone could not predict biodegradation rates adequately. Temperate and arctic untreated seawater communities were different as revealed by pyrosequencing. Geographic origin of seawater affected the community composition of exposed samples.

Deep-sea hydrothermal vents are unique environments on Earth, as they host chemosynthetic ecosystems fuelled by geochemical energy with chemolithoautotrophic microorganisms at the basis of the food webs. Whereas discrete high-temperature venting systems have been studied extensively, the microbiotas associated with low-temperature diffuse venting are not well understood. We analysed the structure and functioning of microbial communities in two diffuse venting sediments from the Jan Mayen vent fields in the Norwegian-Greenland Sea, applying an integrated 'omics' approach combining metatranscriptomics, metaproteomics and metagenomics. Polymerase chain reaction-independent three-domain community profiling showed that the two sediments hosted highly similar communities dominated by Epsilonproteobacteria, Deltaproteobacteria and Gammaproteobacteria, besides ciliates, nematodes and various archaeal taxa. Active metabolic pathways were identified through transcripts and peptides, with genes of sulphur and methane oxidation, and carbon fixation pathways highly expressed, in addition to genes of aerobic and anaerobic (nitrate and sulphate) respiratory chains. High expression of chemotaxis and flagella genes reflected a lifestyle in a dynamic habitat rich in physico-chemical gradients. The major metabolic pathways could be assigned to distinct taxonomic groups, thus enabling hypotheses about the function of the different prokaryotic and eukaryotic taxa. This study advances our understanding of the functioning of microbial communities in diffuse hydrothermal venting sediments.

Kelp forests worldwide are known as hotspots for macroscopic biodiversity and primary production, yet very little is known about the biodiversity and roles of microorganisms in these ecosystems. Secondary production by heterotrophic bacteria associated to kelp is important in the food web as a link between kelp primary production and kelp forest consumers. The aim of this study was to investigate the relationship between bacterial diversity and two important processes in this ecosystem; bacterial secondary production and primary succession on kelp surfaces. To address this, kelp, Laminaria hyperborea, from southwestern Norway was sampled at different geographical locations and during an annual cycle. Pyrosequencing (454-sequencing) of amplicons of the 16S rRNA gene of bacteria was used to study bacterial diversity. Incorporation of tritiated thymidine was used as a measure of bacterial production. Our data show that bacterial diversity (richness and evenness) increases with the age of the kelp surface, which corresponds to the primary succession of its bacterial communities. Higher evenness of bacterial operational taxonomical units (OTUs) is linked to higher bacterial production. Owing to the dominance of a few abundant OTUs, kelp surface biofilm communities may be characterized as low-diversity habitats. This is the first detailed study of kelp-associated bacterial communities using high-throughput sequencing and it extends current knowledge on microbial community assembly and dynamics on living surfaces.

DNA sequencing technology has proven very valuable for analysing the microbiota of poorly accessible ecosystems such as hydrothermal vents. Using a combination of amplicon and shotgun sequencing of small-subunit rRNA and its gene, we examined the composition and diversity of microbial communities from the recently discovered Jan Mayen vent field, located on Mohn's Ridge in the Norwegian-Greenland Sea. The communities were dominated by the epsilonproteobacterial genera Sulfurimonas and Sulfurovum. These are mesophiles involved in sulphur metabolism and typically found in vent fluid mixing zones. Composition and diversity predictions differed systematically between extracted DNA and RNA samples as well as between amplicon and shotgun sequencing. These differences were more substantial than those between two biological replicates. Amplicon vs. shotgun sequencing differences could be explained to a large extent by bias introduced during PCR, caused by preferential primer-template annealing, while DNA vs. RNA differences were thought to be caused by differences between the activity levels of taxa. Further, predicted diversity from RNA samples was consistently lower than that from DNA. In summary, this study illustrates how different methods can provide complementary ecological insights.

Background: Soil ecosystems harbor the most complex prokaryotic and eukaryotic microbial communities on Earth. Experimental approaches studying these systems usually focus on either the soil community’s taxonomic structure or its functional characteristics. Many methods target DNA as marker molecule and use PCR for amplification. Methodology/Principal Findings: Here we apply an RNA-centered meta-transcriptomic approach to simultaneously obtain information on both structure and function of a soil community. Total community RNA is random reversely transcribed into cDNA without any PCR or cloning step. Direct pyrosequencing produces large numbers of cDNA rRNA-tags; these are taxonomically profiled in a binning approach using the MEGAN software and two specifically compiled rRNA reference databases containing small and large subunit rRNA sequences. The pyrosequencing also produces mRNA-tags; these provide a sequence-based transcriptome of the community. One soil dataset of 258,411 RNA-tags of ~98 bp length contained 193,219 rRNA-tags with valid taxonomic information, together with 21,133 mRNA-tags. Quantitative information about the relative abundance of organisms from all three domains of life and from different trophic levels was obtained in a single experiment. Less frequent taxa, such as soil Crenarchaeota, were well represented in the data set. These were identified by more than 2,000 rRNA-tags; furthermore, their activity in situ was revealed through the presence of mRNA-tags specific for enzymes involved in ammonia oxidation and CO² fixation. Conclusions/Significance: This approach could be widely applied in microbial ecology by efficiently linking community structure and function in a single experiment while avoiding biases inherent in other methods. publishedVersion

Soda lakes are intriguing ecosystems harboring extremely productive microbial communities in spite of their extreme environmental conditions. This makes them valuable model systems for studying the connection between community structure and abiotic parameters such as pH and salinity. For the first time, we apply high-throughput sequencing to accurately estimate phylogenetic richness and composition in five soda lakes, located in the Ethiopian Rift Valley. The lakes were selected for their contrasting pH, salinities and stratification and several depths or spatial positions were covered in each lake. DNA was extracted and analyzed from all lakes at various depths and RNA extracted from two of the lakes, analyzed using both amplicon- and shotgun sequencing. We reveal a surprisingly high biodiversity in all of the studied lakes, similar to that of freshwater lakes. Interestingly, diversity appeared uncorrelated or positively correlated to pH and salinity, with the most “extreme” lakes showing the highest richness. Together, pH, dissolved oxygen, sodium- and potassium concentration explained approximately 30% of the compositional variation between samples. A diversity of prokaryotic and eukaryotic taxa could be identified, including several putatively involved in carbon-, sulfur- or nitrogen cycling. Key processes like methane oxidation, ammonia oxidation and ‘nitrifier denitrification’ were also confirmed by mRNA transcript analyses.