Beaver Damming Alters Sedge Phenology Through Water Table and Temperature Feedbacks in a Rocky Mountain Peatland
Bibliographic record
Abstract
ABSTRACT Beaver dams substantially reshape peatland hydrology, yet their influence on plant phenology, a key driver of ecosystem carbon dynamics, remains poorly understood. We used UAV‐based RGB imagery to quantify seasonal changes in greenness (GCC) of sedge ( Carex spp.) across three hydrological treatments in a Canadian Rocky Mountain peatland: flooded beaver pond, drained beaver pond and unimpacted fen. Repeat imagery captured from May to September 2023 revealed that beaver damming, whether current or legacy, significantly altered sedge phenology. Phenology in the flooded beaver pond followed a similar trajectory as the unimpacted fen but delayed green‐up by 2.5 weeks. Interestingly, the drained beaver pond exhibited the earliest green‐up, beginning 12 days earlier and reached a 12% higher peak greenness while having a similar length of season as the unimpacted fen, likely due to warmer peat and later‐season water stress. The flooded beaver pond maintained a high, stable water table which delayed senescence and extended the growing season by 6 weeks. These hydrological legacies created a patchwork of phenological responses across the peatland. Our findings highlight how beaver engineering via manipulation of water table elevation controls plant phenology, with potential indirect downstream effects on carbon cycling and forage availability in montane peatlands.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".