The ecological impacts of multiple environmental stressors on coastal biofilm bacteria
Bibliographic record
Abstract
Abstract Ecological communities are increasingly exposed to multiple interacting stressors. For example, warming directly affects the physiology of organisms, eutrophication stimulates the base of the food web, and harvesting larger organisms for human consumption dampens top‐down control. These stressors often combine in the natural environment with unpredictable results. Bacterial communities in coastal ecosystems underpin marine food webs and provide many important ecosystem services (e.g. nutrient cycling and carbon fixation). Yet, how microbial communities will respond to a changing climate remains uncertain. Thus, we used marine mesocosms to examine the impacts of warming, nutrient enrichment, and altered top‐predator population size structure (common shore crab) on coastal microbial biofilm communities in a crossed experimental design. Warming increased bacterial α‐diversity (18% increase in species richness and 67% increase in evenness), but this was countered by a decrease in α‐diversity with nutrient enrichment (14% and 21% decrease for species richness and evenness, respectively). Thus, we show some effects of these stressors could cancel each other out under climate change scenarios. Warming and top‐predator population size structure both affected bacterial biofilm community composition, with warming increasing the abundance of bacteria capable of increased mineralization of dissolved and particulate organic matter, such as Flavobacteriia, Sphingobacteriia, and Cytophagia. However, the community shifts observed with warming depended on top‐predator population size structure, with Sphingobacteriia increasing with smaller crabs and Cytophagia increasing with larger crabs. These changes could alter the balance between mineralization and carbon sequestration in coastal ecosystems, leading to a positive feedback loop between warming and CO2 production. Our results highlight the potential for warming to disrupt microbial communities and biogeochemical cycling in coastal ecosystems, and the importance of studying these effects in combination with other environmental stressors.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".