Testate amoeba functional traits and indicator taxa are important tools for tracking peatland restoration effectiveness
Bibliographic record
Abstract
Restoring degraded peatlands is vital for sustaining their capacity as carbon sinks and long-term carbon stores. Microbial assemblages serve as valuable indicators for monitoring environmental change and assessing the success of ecosystem restoration efforts. Testate amoebae are a group of unicellular shelled protists that are commonly used for Holocene palaeohydrological reconstruction in peatlands. While progress is being made, the use of testate amoebae for biomonitoring in peatland restoration is still in its early stages. The aim of this study is to assess testate amoebae response to restoration measures (drain blocking) across three lowland raised bogs in Northern Ireland. To accomplish this, Sphagnum samples were collected from each site using a before-after control-impact (BACI) experimental design. After peatland drainage ditches were blocked, subtle yet significant responses in testate amoebae were observed: (1) key unambiguous wet-indicator taxa became more abundant in samples adjacent to blocked dams; (2) a widespread increase in the abundance of taxa with sub-spherical tests was observed, most notably in samples near to blocked drains. The findings of this study demonstrate the reliable response of testate amoebae to wetter conditions across all sites after restoration. Functional trait analysis paired with an indicator-taxa based approach, demonstrate the value of testate amoebae as contemporary bioindicators for tracking peatland restoration success, even when detailed hydrological monitoring data is not available. However, testate amoebae should be used with some degree of caution for peatland biomonitoring until long-term assemblage-level response to restoration is better understood.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".