Factors shaping alternate successional trajectories in burned black spruce forests of Alaska
Bibliographic record
Abstract
Abstract Disturbances can interrupt feedbacks that maintain stable plant community structure and create windows of opportunity for vegetation to shift to alternative states. Boreal forests are dominated by tree species that overlap considerably in environmental niche, but there are few tests of what conditions initiate and sustain different forest states. Here, we examine patterns of post‐fire growth and density of tree seedlings in early succession and use structural equation models to estimate relative effects of environmental and pre‐fire conditions, fire characteristics, and biotic interactions. We surveyed tree seedling recruits for 13 yr across a broad range of environmental and fire conditions (n = 89) in Alaskan black spruce stands that burned in 2004. Densities of established seedlings at 13 yr were strongly determined by initial recruitment that occurred within 2 yr after fire. High proportional combustion of the soil organic layer (fire severity) led to increased densities of deciduous seedlings but not of black spruce and had a positive influence on aboveground biomass of all species. Biotic interactions such as mammalian herbivory or woody competition, potential mechanisms for relay floristic succession, had no detectable effects on tree seedling densities or biomass. Repeated surveys instead suggested persistent shifts in successional trajectories of tree communities from spruce to deciduous dominance at sites where high fire severity created positive conditions for deciduous seedling recruitment and growth. Unless future species interactions alter the deciduous dominance of tree seedling composition, the vegetation transformations that we observed in response to high fire severity are likely to persist over the short fire cycle that increasingly characterizes the fire regime of Interior Alaska.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".