Differences exist in bird communities using restored and natural wetlands in the Parkland region, Alberta, Canada
Bibliographic record
Abstract
Wetland restoration is used to compensate for historic and ongoing wetland losses. We compared bird community composition in 24 restored wetlands and 36 natural wetlands in the Parkland region of Alberta. Natural wetlands ranged in exposure to agricultural activity and were binned into three classes (low, medium, and high disturbance). Although the abundance and average species richness of birds were similar between restored and natural wetlands (analysis of variance: p > 0.22), the avian community composition differed significantly among wetland types (multiresponse permutation procedure [MRPP]: A = 0.05, p < 0.001). The avifauna using restored wetlands was distinct from the avifauna using natural wetlands spanning a range of disturbance levels (A = 0.02–0.06; p ≤ 0.006). Notably, restored wetlands were surrounded by less shrub/forest cover and more open water than low‐disturbance, natural wetlands. The majority (58%) of species using the surveyed wetlands were not classified as wetland‐dependent. Interestingly, if only wetland‐dependent species are considered, the avifauna using restored wetlands is no longer distinctive (MRPP: A < 0.01, p = 0.187), although the abundance of wetland‐dependent birds was marginally higher in restored wetlands (n = 24) than in low‐disturbance, natural wetlands (n = 10; Tukey's honestly significant difference test: p = 0.041). Overall, restored wetlands had reduced beta diversity compared to natural wetlands, regardless of whether the avifauna were restricted to wetland‐dependent species or considered comprehensively. This draws into question the legitimacy of the assumption that restoration can fully offset continued losses of natural wetlands.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".