Dissimilar bacterial and fungal decomposer communities across rich to poor fen peatlands exhibit functional redundancy
Bibliographic record
Abstract
Haynes, K. M., Preston, M. D., McLaughlin, J. W., Webster, K. and Basiliko, N. 2015. Dissimilar bacterial and fungal decomposer communities across rich to poor fen peatlands exhibit functional redundancy. Can. J. Soil Sci. 95: 219–230. Climatic and environmental changes can lead to shifts in the dominant vegetation communities present in northern peatland ecosystems, including from Sphagnum- to vascular-dominated systems. Such shifts in vegetation result in changes to the chemical quality of carbon substrates for soil microbial decomposers, with leaves and roots deposited in the peat surface and subsurface that potentially decompose faster. This study characterized the bacterial and fungal communities present along a nutrient gradient ranging from rich to poor fen peatlands and assessed the metabolic potential of these communities to mineralize a variety of organic matter substrates of varying chemical complexity using substrate-induced respiration (SIR) assays. Distinct microbial communities existed between rich, intermediate and poor fens, but SIR in each of the three sites exhibited the same pattern of carbon mineralization, providing support for the concept of functional redundancy, at least under standardized in vitro conditions. Preferential mineralization of simple organic substrates in the rich fen and complex compounds in the poor fen was not observed. Similarly, no preference was given to “native” organic matter extracts derived from each fen, with microbial communities opting for the most bioavailable substrate. This study suggests that soil bacteria and fungi might be able to respond relatively rapidly to shifts in vegetation communities and subsequent changes in the quality of carbon substrate additions to peatlands associated with environmental and climatic change.
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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 | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".