Ecological restoration of rich fens in Europe and North America: from trial and error to an evidence‐based approach
Bibliographic record
Abstract
Fens represent a large array of ecosystem services, including the highest biodiversity found among wetlands, hydrological services, water purification and carbon sequestration. Land-use change and drainage has severely damaged or annihilated these services in many parts of North America and Europe; restoration plans are urgently needed at the landscape level. We review the major constraints on the restoration of rich fens and fen water bodies in agricultural areas in Europe and disturbed landscapes in North America: (i) habitat quality problems: drought, eutrophication, acidification, and toxicity, and (ii) recolonization problems: species pools, ecosystem fragmentation and connectivity, genetic variability, and invasive species; and here provide possible solutions. We discuss both positive and negative consequences of restoration measures, and their causes. The restoration of wetland ecosystem functioning and services has, for a long time, been based on a trial-and-error approach. By presenting research and practice on the restoration of rich fen ecosystems within agricultural areas, we demonstrate the importance of biogeochemical and ecological knowledge at different spatial scales for the management and restoration of biodiversity, water quality, carbon sequestration and other ecosystem services, especially in a changing climate. We define target processes that enable scientists, nature managers, water managers and policy makers to choose between different measures and to predict restoration prospects for different types of deteriorated fens and their starting conditions.
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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 | 0.337 | 0.529 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.007 | 0.005 |
| 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".