Response of testate amoeba assemblages to peatland drain blocking
Bibliographic record
Abstract
Abstract Peatlands represent globally important habitats and carbon stores. However, human impacts and climate change leave peatlands with a substantial management challenge. Degradation of peatland habitats and their hydrological integrity is increasingly counteracted through the rehabilitation of peatlands including re-wetting and drain blocking. Research into how such management interventions affect peatland microbial assemblages is limited. Here, we investigate the response of testate amoebae (established unicellular amoeboid protist indicators of hydrological conditions in peatlands) to drain blocking on three small lowland raised bogs in Northern Ireland, UK. We sampled Sphagnum adjacent to areas of focused flow near sites of damming in addition to control sites away from dam blocking. These restoration measures show complex but meaningful results after restoration. We observe several key developments following dam blocking: (i) species diversity increases; (ii) unambiguous wet indicator taxa appear in increasing abundance at dammed sites; (iii) and transfer-function reconstructed water-table depths show wetter conditions in the dammed sites. These findings imply wetter conditions after restoration, where routine monitoring presented no clear trend in water-table depths. We found no statistically significant assemblage-level response to experimental or environmental variables, which may be related to antecedent conditions and significant periods of drought during the study period. Thus, caution is advised when utilising testate amoebae for bioindication until their assemblage-level response to restoration is better understood. Nevertheless, this study emphasises the potential of an indicator-taxa based approach to applying testate amoebae as contemporary bioindicators of peatland restoration—particularly on short-term timescales immediately following restoration.
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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 | 0.001 |
| 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 | 0.001 |
| 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".