Post-fire vegetation regeneration during abnormally dry years following severe montane fire: Southern Alberta, Canada
Bibliographic record
Abstract
Fire regimes across montane regions of western Canada are changing resulting in longer fire seasons, higher intensity fires, and shortening fire return intervals. The implications of high severity fire and warmer, drier early post-fire conditions on herbaceous understory vegetation regeneration and seedling recruitment in the southern Canadian Rockies are not well known. The overall objective of this study is to quantify trajectories of vegetation recovery (species, structural characteristics, and biomass) during early years of abnormally warm, dry conditions following a high severity fire in two moisture endmember sites Waterton Lakes National Park, Alberta, Canada. Here, we compare the within and between year spatial and temporal variability of vegetation growth and species density and how these change over time and across the broader area as an indicator of ecosystem resilience within these endmember sites. Moderate to extreme drought occurred during the years following fire at Waterton, where 2021 was ranked as the 2nd driest year in 26 years. Despite this, the moist site was characterised by greater herbaceous vegetation recovery with few lodgepole pine seedlings (average biomass = 335 g m −2 ), while a drier site had greater seedling recruitment over a period of 5 years. Variations in site environmental conditions were more impactful than differences between years (drought) on post-fire vegetation recovery. Use of remotely piloted aircraft system (RPAS) remotely sensed data provided an effective means for quantifying variability in regenerating vegetation height (structure from motion), cover (green chromatic coordinate), and biomass when compared at plot level (R 2 = 0.53, 0.53, and 0.30 respectively) using optical photogrammetric methods. The research presented has implications for forest and fuel management in Canada as national parks and forest agencies consider historic use of heterogeneous species patches. High density of lodgepole pine seedling recruitment in mineral soils and under very dry conditions indicate resilience to drought. This will require continued and expanded monitoring as other tree species recruits populate the post-fire environment. • Moist riparian montane valley has greater early post-fire biodiversity than drier areas. • Multi-year drought during early post-fire period results in seedling mortality, reduced biomass. • Site characteristics were more important for vegetation regeneration than climate variability. • Photogrammetry from remotely piloted aircraft systems can be used to monitor post-fire vegetation structure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| 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".