Edge effects of linear disturbances on plant functional traits in boreal fens of northern Alberta, Canada
Bibliographic record
Abstract
Global resource development has resulted in numerous disturbances that have a myriad of consequences on peatlands. We examined edge effects from one such disturbance, seismic lines, on plant species percent cover and select functional traits in Alberta, Canada. We tested various hypotheses that seismic lines and their edge effects influence percent cover and functional traits for eight dominant fen plant species: Larix laricina, Picea mariana, Betula glandulosa , Salix pedicellaris, Andromeda polifolia, Menyanthes trifoliata, Carex aquatilis , and Sphagnum warnstorfii . We analyzed species percent cover, plant height, leaf dry matter content, and tissue nitrogen (N), phosphorus (P), potassium (K), and carbon (C) content on the seismic lines and at various distances from the seismic line edge. Our findings suggest plant stress resilience to environmental variation through functional trait expression. This was most evident on species percent cover; P. mariana, M. trifoliata, C. aquatilis, and S. warnstorfii showed significant and unique responses across measured distances, with generally decreased plant cover on seismic lines. Plant height was significantly different across measured distances for B. glandulosa and C. aquatilis . Generally, tissue N, P, K, and C increased on the seismic line, consistent with a lasting release of nutrients during and following the disturbance. Connecting plant species abundance to functional traits provided insight into why recovery is lacking on seismic lines, even close to 30 years after the initial disturbance. We show that functional trait variation signals a pathway of plant stress resilience to disturbance that must be considered in ongoing forest management practices.
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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 | 0.001 | 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".