Prairie wetland communities recover at different rates following hydrological restoration
Bibliographic record
Abstract
Summary Prairie pothole wetlands provide many ecosystem services, including supporting biodiversity and filtering water on the landscape. However, over half of these wetlands have been drained for agriculture, thereby requiring restoration to re‐establish ecosystem services. We assessed the recovery of hydrologically restored wetlands based on water chemistry and taxonomic shifts within and across five biological communities (phytoplankton, benthic diatoms, zooplankton, macroinvertebrates, submersed aquatic vegetation [SAV]). We sampled 24 wetlands in southeastern Saskatchewan, Canada, spanning three restoration states: recently restored (restored 1–3 years before the study; n = 8), older restored (restored 7–14 years before the study; n = 8) and natural (never drained; n = 8). Within approximately a decade of the re‐establishment of these previously drained wetlands, water chemistry, macroinvertebrate and SAV communities closely resembled those in natural wetlands. Here, total phosphorus and carbon dioxide concentrations declined, while salinity and pH increased, with time since restoration. No detectable differences in diatom and zooplankton communities persisted among the restored and natural sites; however, cyanobacteria were more representative of the restored wetlands Our findings suggest that hydrological restoration is an effective tool for re‐establishing baseline water quality and the capacity of prairie wetlands to support biodiversity across multiple trophic levels. However, given that there is a decadal lag in the re‐establishment of certain species, it is preferable to protect and retain intact wetlands on the landscape.
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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 | 0.000 |
| 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".