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.

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.

Our ability to assess biodiversity at relevant spatial and temporal scales for informing management is of increasing importance given this is foundational to identify and mitigate the impacts of global change. Collecting baseline information and tracking ecological changes are particularly important for areas experiencing rapid changes and representing data gaps such as Arctic marine ecosystems. Environmental DNA has the potential to provide such data. We extracted environmental DNA from 90 surface sediment samples to assess eukaryote diversity around Greenland and Svalbard using two separate primer pairs amplifying different sections of the 18S rRNA gene. We detected 27 different phyla and 99 different orders and found that temperature and the change in temperature explained the most variation in the community in a single linear model, while latitude, sea ice cover and change in temperature explained the most variation in the community when assessed by individual non-linear models. We identified potential indicator taxa for Arctic climate change, including a terebellid annelid worm. In conclusion, our study demonstrates that environmental DNA offers a feasible method to assess biodiversity and identifies warming as a key driver of differences in biodiversity across these remote ecosystems.

Persistent oxygen-depleted zones in the ocean are known primarily from enclosed basins in temperate regions or the open ocean (including oxygen minimum and limiting zones) (1). However, little is known about the possibility of such zones forming in tropical coastal domains, even though the combination of warmer temperatures and complex geological features in some tropical regions makes their occurrence more likely (1, 2). Here, we report two subsurface oxygen-depleted zones within deep (>490 m) depressions of the Red Sea's Difaht Farasan—a carbonate platform hosting the world's third largest contiguous tropical coral reef system. One zone maintains suboxic oxygen levels (∼11–14 µmol O2 kg⁻¹), while the other sustains oxygen levels below detection (21 °C). Targeted exploration of deep tropical coastal environments is crucial for determining whether similar zones exist beyond the Red Sea and understanding their potential responses to climate change.

Pelagic particle‐associated bacterioplankton play crucial roles in marine ecosystems, influencing biogeochemical cycling and ecosystem functioning. However, their diversity, composition, and dynamics remain poorly understood, particularly in unique environments such as the Red Sea. In this study, we employed eDNA metabarcoding to comprehensively characterise bacterioplankton communities associated with pelagic particles in a three‐dimensional assessment spanning depths from the surface to a depth of 2300 m along the full length of the eastern Red Sea within the exclusive economic zone of the Kingdom of Saudi Arabia. Our results reveal a diverse assemblage of taxa, with Pseudomonadota, Cyanobacteriota, and Planctomycetota being the dominant phyla. We identified pronounced spatial variability in community composition among five major Red Sea geographical regions, with a third of all amplicon sequence variants being unique to the Southern Red Sea in contrast to a relatively homogenous distribution along the water column depth gradient. Our findings contribute to a deeper understanding of microbial ecology in the Red Sea and provide valuable insights into the factors governing pelagic particle‐associated bacterioplankton communities in this basin.