Cobalt induces the set-up of new structural networks in river biofilms: Impairment of autotrophic-heterotrophic coupling
Bibliographic record
Abstract
Biofilms are integral to the biogeochemical cycles of aquatic ecosystems, primarily through complex interactions among microorganism that mediate the transformation and movement of key elements. In a previous study, we reported that Cyanobacteria within biofilms grown in outdoor mesocosms exhibited sensitivity to cobalt (Co) when exposed to increasing Co concentrations (background concentrations, 0.1, 0.5, and 1 μM). Under exposure to Co, biofilms were found to adopt alternative carbon fixation pathways via non-photosynthetic prokaryotes, suggesting a disruption in phototrophic activity and raising questions about broader autotroph-heterotroph interactions that drive biofilm functions. In the present study, we extended this investigation to assess the impact of Co on microeukaryotes (primarily microalgae) and their interactions with prokaryotes, thereby encompassing the entire biofilm community. Eukaryotic abundance and diversity were quantified using qPCR and DNA metabarcoding, while microbial interactions were inferred through co-occurrence network analysis based on operational taxonomic units (OTUs). Our findings indicate that Co exposure significantly altered the composition of the microalgal and meiofaunal communities, with Bacillariophycea exhibiting pronounced sensitivity. At 1 μM Co, microbial networks were characterized by reduced OTU richness and fewer interactions, yet displayed stronger structural centrality around a limited number of taxa. Notably, in control conditions, 40 % of keystone taxa were affiliated with microalgae, whereas at 1 μM Co, keystone taxa were predominantly prokaryotic. These results indicate that Co disrupts autotroph-heterotroph coupling, driving a shift toward prokaryotic dominance in microbial interactions. The study highlights adaptive strategies employed by biofilms to mitigate metal-induced stress and maintain functional integrity in contaminated environments.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".