Climatic and vegetational controls of Holocene wildfire regimes in the boreal forest of northern Fennoscandia
Bibliographic record
Abstract
Abstract Climate change is expected to increase wildfire activity in boreal ecosystems, thus threatening the carbon stocks of these forests, which are currently the largest terrestrial carbon sink in the world. Describing the ecological processes involved in fire regimes in terms of frequency, size, type (surface vs. crown) and severity (biomass burned) would allow better anticipation of the impact of climate change on these forests. In Fennoscandia, this objective is currently difficult to achieve due to the lack of knowledge of long‐term (centuries to millennia) relationships between climate, fire and vegetation. We investigated the causes and consequences of changes in fire regimes during the Holocene (last ~11,000 years) on vegetation trajectories in the boreal forest of northern Finland. We reconstructed fire histories from sedimentary charcoal at three sites, as well as vegetation dynamics from pollen, moisture changes from Sphagnum spore abundance at two sites, and complemented these analyses with published regional chironomid‐inferred July temperature reconstructions. Low‐frequency, large fires were recorded during the warm and dry mid‐Holocene period (8500–4500 cal. year BP), whereas high‐frequency, small fires were more characteristic of the cool and wet Neoglacial period (4500 cal. year BP onward). A higher proportion of charcoal particles with a woody aspect—characterizing crown fires—was recorded at one of the two sites at times of significant climatic and vegetational changes, when the abundance of Picea abies was higher. Synthesis. Our results show both a direct and an indirect effect of climate on fire regimes in northern Fennoscandia. Warm and dry periods are conducive to large surface fires, whereas cool and moist periods are associated with small fires, either crown or surface. Climate‐induced shifts in forest composition also affect fire regimes. Climatic instability can alter vegetation composition and structure and lead to fuel accumulation favouring stand‐replacing crown fires. Considering the ongoing climate warming and the projected increase in extreme climatic events, Fennoscandian forests could experience a return to a regime of large surface fires, but stand‐replacing crown fires will likely remain a key ecosystem process in areas affected by climatic and/or vegetational instability.
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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 | 0.001 | 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".