Resilience of the boreal forest in response to Holocene fire-frequency changes assessed by pollen diversity and population dynamics
Bibliographic record
Abstract
The hypothesis that changes in fire frequency control the long-term dynamics of boreal forests is tested on the basis of paleodata. Sites with different wildfire histories at the regional scale should exhibit different vegetation trajectories. Mean fire intervals and vegetation reconstructions are based respectively on sedimentary charcoal and pollen from two small lakes, one in the Mixedwood boreal forests and the second in the Coniferous boreal forests. The pollen-inferred vegetation exhibits different trajectories of boreal forest dynamics after afforestation, whereas mean fire intervals have no significant or a delayed impact on the pollen data, either in terms of diversity or trajectories. These boreal forests appear resilient to changes in fire regimes, although subtle modifications can be highlighted. Vegetation compositions have converged during the last 1200 years with the decrease in mean fire intervals, owing to an increasing abundance of boreal species at the southern site (Mixedwood), whereas changes are less pronounced at the northern site (Coniferous). Although wildfire is a natural property of boreal ecosystems, this study does not support the hypothesis that changes in mean fire intervals are the key process controlling long-term vegetation transformation. Fluctuations in mean fire intervals alone do not explain the historical and current distribution of vegetation, but they may have accelerated the climatic process of borealisation, likely resulting from orbital forcing.
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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 | 0.001 | 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 | 0.000 | 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".