Incomplete recovery of plant diversity in restored prairie wetlands on agricultural landscapes
Bibliographic record
Abstract
Restoration efforts are being implemented globally to mitigate the degradation and loss of wetland habitat; however, the rate and success of wetland vegetation recovery post‐restoration is highly variable across wetland classes and geographies. Here, we measured the recovery of plant diversity along a chronosequence of restored temporary and seasonal prairie wetlands ranging from 0 to 23 years since restoration, including drained and natural wetlands embedded in agricultural and natural reserve landscapes in central Alberta, Canada. We assessed plant diversity using the following structural indicators: percent cover of hydrophytes, native and non‐native species, species richness, and community composition. Our findings indicate that plant diversity recovered to resemble reference wetlands in agricultural landscapes within 3–5 years of restoration; however, restored wetlands maintained significantly lower species richness and a distinct community composition compared to reference wetlands located within natural reserves. Early establishment of non‐native species during recovery, dispersal limitation, and depauperated native seed bank were probable barriers to complete recovery. Determining the success of vegetation recovery provides important knowledge that can be used to improve restoration strategies, especially considering projected future changes in land use and climate.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".