Fire and whitebark pine recovery strategies: drivers of post-fire natural regeneration
Bibliographic record
Abstract
Whitebark pine (Pinus albicaulis Engelmann), a tree species of high elevation forests in western North America, is listed as an endangered species in Canada. Prescribed burns have been employed by conservation agencies as a recovery strategy to create open habitats free of competition and to increase regeneration opportunities. However, questions remain with respect to the success of prescribed burns for the restoration of whitebark pine and best practices of this technique, as well as to what role wildfire plays in whitebark pine communities at the northern limits of its range. Understanding what drives whitebark pine post-fire regeneration and how it responds to fire severity is important for guiding future burn prescriptions and managing wildfire to effectively implement Alberta’s provincial recovery plan at a landscape scale. Therefore, this research project aimed to better understand how: (i) site, stand and plot level factors, and (ii) fire severity influences the natural regeneration occurrence and abundance of whitebark pine in post-fire environments. Five prescribed burns and four wildfires across the federal and provincial mountain parks in western Alberta were sampled and information on environmental variables and whitebark pine regeneration was collected. Generalized mixed effect models were used to test individual predictors and perform model selection. Whitebark pine post-fire regeneration was shown to be a complex process linked to a variety of biological processes at multiple spatial scales. Regeneration occurrence increased in the first 18 years after fire, mainly at stands with larger whitebark pine basal area. Seedling density increased up to 18 years on wildfires, while it declined after 10 years on prescribed burns, indicating that regeneration abundance was probably driven by the existence of favourable seedbeds and understory conditions at smaller scales. This creates a challenge in predicting regeneration abundance because of the multitude of factors that can influence post-fire conditions, such as fire severity, burning season, post-fire weather and pre-forest composition. At a plot level, decaying wood cover and litter cover up to 25 % and 9 cm depth, respectively, and medium shrub cover up to 30% were positively correlated with seedling density. Fire was not a requirement for regeneration to occur as post-fire seedling densities in the unburned plots (320.8 seedlings/ha) were higher than in the burned plots at 50 m from forest edge (288.5 seedlings/ha). We observed both beneficial and detrimental effects of fire on whitebark pine regeneration. The lower post-fire and advanced seedling densities in the burned plots may suggest that fire is not beneficial for regeneration, while the colonization of burned stands that had no mature whitebark pine trees pre-fire may suggests that fire creates new habitats for regeneration. Proximal seed sources were important as they increased the probability of regeneration occurrence. However, the current increase in tree mortality caused by white pine blister rust and mountain pine beetle threatens remaining whitebark pine stands and raises the question for how long seed sources will remain viable to sustain natural regeneration. After 18 years post-fire, regeneration densities were lower than in previous studies that looked at recent and advanced regeneration in undisturbed stands (463 – 1082 seedlings/ha) or similar to fires up to 60 years old (0 – 406 seedlings/ha). If conservation agencies are to use those densities as reference values during restoration efforts, long term post-fire occupancy surveys and artificial planting will likely be necessary to complement lack of natural regeneration in burned areas and achieve restoration goals, particularly at stands experiencing high tree mortality caused by blister rust and mountain pine beetle.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".