Ecological impacts of shortening fire return intervals on boreal peatlands and transition zones using integrated in situ field sampling and lidar approaches
Bibliographic record
Abstract
Abstract Aridity associated with rising air temperatures in northern latitudes is expected to contribute to increased frequency of wildland fires. Here, we examined regenerating vegetation following short return interval (SRI) fire (56 years post‐fire) compared to long return interval (LRI) fire (>80 years post‐fire) in boreal peatlands and their adjacent transitional areas. The objectives of this study were to quantify if differences exist between (1) peatland and transitional soil characteristics in LRI versus SRI areas and (2) regenerating vegetation species, structural characteristics and diversity. We also determined if patterns of vegetation structural characteristics observed using field data also occur across the broader landscape using airborne lidar data. The Utikuma Region Study Area (URSA) is located in central Alberta, Canada. Here, 19 peatlands were sampled, coincident with an airborne lidar survey of the broader region, where 120 peatlands in short and long fire return intervals were identified. We found that SRI transitional areas had significantly deeper organic soil deposits than those found in LRI (p < 0.0001). Proportions of regenerating species differed significantly between peatlands and transitional areas in SRI versus LRI, where greater proportion of coniferous species were observed in LRI. Deciduous transitional–upland species and taller post‐fire vegetation heights were more commonly found SRI peatlands compared with LRI. This suggest that fires with SRIs in this region may result in enhanced deciduous succession, which may transition boreal peatlands into ecosystems that have some characteristics of transitional and upland forests.
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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 | 0.001 |
| 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) | 0.000 | 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".