HOLOCENE FIRE HISTORY OF A COASTAL TEMPERATE RAIN FOREST BASED ON SOIL CHARCOAL RADIOCARBON DATES
Bibliographic record
Abstract
The long-term role of fire in coastal temperate rain forest is poorly understood. To determine the historical role of fire on western Vancouver Island (British Columbia, Canada), we constructed a long-term spatially explicit fire history and examined the spatial and temporal distribution of fire during the Holocene. Two fire-history parameters (time-since-fire [TSF] and fire extent) were related to three landscape parameters (landform [hill slope or terrace], aspect, and forest composition) at 83 sites in a 730-ha low-elevation (less than ∼200 m) area of a mountainous watershed. We dated fires using tree rings (18 sites) and 120 soil-charcoal radiocarbon dates (65 sites). Comparisons among multiple radiocarbon dates indicated a high probability that the charcoal dated at each site represented the most recent fire, though we expect greater error in TSF estimates at sites where charcoal was very old (>6000 yr) and was restricted to mineral soil horizons. TSF estimates ranged from 64 to ∼12 220 yr; 45% of the sites have burned in the last 1000 yr, whereas 20% of the sites have not burned for over 6000 yr. Differences in median TSF were more significant between landform types or across aspects than among forest types. Median TSF was significantly greater on terraces (4410 yr) than on hill slopes (740 yr). On hill slopes, all south-facing and southwest-facing sites have burned within the last 1000 yr compared to only 27% of north- and east-facing sites burning over the same period. Comparison of fire dates among neighboring sites indicated that fires rarely extended >250 m. During the late Holocene, landform controls have been strong, resulting in the bias of fires to south-facing hillslopes and thus allowing late-successional forest structure to persist for thousands of years in a large portion of the watershed. In contrast, the early Holocene regional climate and forest composition likely resulted in larger landscape fires that were not strongly controlled by landform factors. The millennial-scale TSF detected in this study supports the distinction of coastal temperate rain forest as being under a fundamentally different disturbance regime than other Pacific Northwest forests to the east and south. Corresponding Editor: S. T. Jackson.
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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 | 0.000 | 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".