Legacy Effects of Plant Community Structure Are Manifested in Microbial Biofilm Development With Consequences for Ecosystem <scp>CO<sub>2</sub></scp> Emissions
Bibliographic record
Abstract
ABSTRACT To better understand linkages between hydrology and ecosystem carbon flux in northern aquatic ecosystems, we evaluated the relationship between plant communities, biofilm development, and carbon dioxide (CO2) exchange following long‐term changes in hydrology in an Alaskan fen. We quantified seasonal variation in biofilm composition and CO2 exchange in response to lowered and raised water table position (relative to a control) during years with varying levels of background dissolved organic carbon (DOC). We then used nutrient‐diffusing substrates (NDS) to evaluate cause–effect relationships between changes in plant subsidies (i.e., leachates) and biofilm composition among water table treatments. We found that background DOC concentration determined whether plant subsidies promoted net autotrophy or heterotrophy on NDS. In conditions where background DOC was ≤ 40 mg L−1, plant subsidies promoted an autotrophic biofilm. Conversely, when background DOC concentration was ≥ 50 mg L−1, plant subsidies promoted heterotrophy. Greater light attenuation associated with elevated levels of DOC may have overwhelmed the stimulatory effect of nutrients on autotrophic microbes by constraining photosynthesis while simultaneously allowing heterotrophs to outcompete autotrophs for available nutrients. At the ecosystem level, conditions that favored an autotrophic biofilm resulted in net CO2 uptake among all water table treatments, whereas the site was a net source of CO2 to the atmosphere in conditions that supported greater heterotrophy. Taken together, these findings show that hydrologic history interacts with changes in dominant plant functional groups to alter biofilm composition, which has consequences for ecosystem CO2 exchange.
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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".