Taxonomic and functional stability of sedimentary microbial communities in a pristine upwelling-influenced coastal lagoon
Bibliographic record
Abstract
Coastal lagoons are dynamic transitional ecosystems shaped by complex hydrodynamic and biogeochemical processes. Their sediments host diverse microbial communities essential for nutrient cycling, organic matter sequestration, and pollutant degradation. However, the taxonomic and functional profiles of these communities remain poorly understood, especially in pristine systems. Here, shotgun metagenomics was used to investigate microbial diversity and functional potential in a seagrass-dominated coastal lagoon on the Mexican Pacific coast, influenced by seasonal upwelling and with minimal anthropogenic impact. Despite pronounced physicochemical gradients and oceanographic variability, these sediments harbored a diverse and taxonomically conserved microbial community. 60% of genera and 38% of species (with relative abundance >0.1%) were consistently shared across sites and the two upwelling seasons, with Gammaproteobacteria, Deltaproteobacteria, Alphaproteobacteria, Flavobacteria, and Actinobacteria as dominant taxa. Genes associated with nitrogen and sulfur metabolic pathways were consistently detected, suggesting the presence of a conserved functional core supporting key biogeochemical processes. In contrast, genes related to antibiotic resistance and virulence factors exhibited more heterogeneous distributions. Among measured physicochemical variables, only nitrate and ferric iron significantly influenced microbial community structure and its functional repertoire, suggesting that additional factors likely contribute to the broader distribution of these communities. These findings reveal a high degree of taxonomic and functional stability of microbial communities in a minimally impacted lagoon, providing a valuable baseline for understanding microbial dynamics in coastal sediments primarily shaped by oceanographic processes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".