Linking metagenomics to aquatic microbial ecology and biogeochemical cycles
Bibliographic record
Abstract
Abstract Microbial communities are essential components of aquatic ecosystems through their contribution to food web dynamics and biogeochemical processes. Aquatic microbial diversity is immense and a general challenge is to understand how metabolism and interactions of single organisms shape microbial community dynamics and ecosystem‐scale biogeochemical transformations. Metagenomic approaches have developed rapidly, and proven to be powerful in linking microbial community dynamics to biogeochemical processes. In this review, we provide an overview of metagenomic approaches, followed by a discussion on some recent insights they have provided, including those in this special issue. These include the discovery of new taxa and metabolisms in aquatic microbiomes, insights into community assembly and functional ecology as well as evolutionary processes shaping microbial genomes and microbiomes, and the influence of human activities on aquatic microbiomes. Given that metagenomics can now be considered a mature technology where data generation and descriptive analyses are relatively routine and informative, we then discuss metagenomic‐enabled research avenues to further link microbial dynamics to biogeochemical processes. These include the integration of metagenomics into well‐designed ecological experiments, the use of metagenomics to inform and validate metabolic and biogeochemical models, and the pressing need for ecologically relevant model organisms and simple microbial systems to better interpret the taxonomic and functional information integrated in metagenomes. These research avenues will contribute to a more mechanistic and predictive understanding of links between microbial dynamics and biogeochemical cycles. Owing to rapid climate change and human impacts on aquatic ecosystems, the urgency of such an understanding has never been greater.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".