Cut‐over peatland regeneration assessment using organic matter and microbial indicators (bacteria and testate amoebae)
Bibliographic record
Abstract
Summary Cut‐over peatlands cover large surfaces of high potential value for enhancing biodiversity and carbon sequestration if successfully restored. Unfortunately, evaluation of restoration success is not straightforward. We assessed the bioindicator value of organic matter (OM), testate amoebae (protozoa) and bacteria in peat from two regeneration stages and a reference site of a cut‐over bog. Contrasting biochemical signatures of peat OM were observed along the regenerating profiles, allowing clear differentiation between the newly regenerated peat and the old peat. Where peat macrofossils were absent sugar biomarkers were used to infer peat botanical origin and OM alteration. Over the succession, the OM composition of the new peat differed. Peat from the more recent stage was dominated by Sphagnum‐derived tissues and characterized by lower carbohydrate preservation and higher bacterial biomass than the advanced regeneration stage. Surface testate amoeba communities also changed from the recent to the advanced stages of regeneration, indicating a shift from wet and moderately acidic conditions to drier and more acidic conditions. Over this regeneration sequence (i) the biomass and average size of species declined but were higher at the unexploited site and (ii) species richness and diversity increased but density declined. Synthesis and applications. Although secondary succession in the cut‐over bog led to an ecosystem similar to that of the reference site in terms of surface vegetation, OM and testate amoebae continued to reflect disturbances associated with peat harvesting. Nevertheless, the described dynamics of both microbial and biochemical variables over the succession showed similarities between the advanced stage and the reference site: a higher testate amoeba diversity was associated with better carbohydrate preservation and a more heterogeneous botanical composition of the peat. The inferred water table depth and pH based on testate amoebae indicators proved to be an alternative approach for assessing restoration processes, in contrast to labour‐intensive repeated measurements in the field. The botanical and biochemical composition of peat OM provided additional information on past anthropogenic perturbations of the bog and could be used for restoration monitoring. The combination of several indicators therefore provides a more complete assessment of ecological conditions that could be valuable for the management of cut‐over peatlands.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".