Holocene fire frequency and links to climate and vegetation history on Pender Island, British Columbia, Canada
Bibliographic record
Abstract
Contiguous macroscopic charcoal analyses were performed on a 9.03 m long lake sediment core from Roe Lake on Pender Island in the Gulf Islands National Park Reserve of British Columbia, Canada to reconstruct the island’s fire history over the last 10,000 years. Charcoal particles >150μm were counted to quantify charcoal concentrations, charcoal accumulation rates and mean fire return intervals. Results show that the early Holocene was characterized by high charcoal accumulation rates and frequent low-severity fire with a mean fire return interval of 100 ± 29 years. Forests at the time were dominated by Pseudotsuga menziesii with an open canopy and fern taxa, particularly Pteridium aquilinum, being common in the understorey. This open vegetation, coupled with warm and dry summer climate, likely created conditions conducive to this fire regime. Charcoal accumulation rates decreased in the middle to late Holocene, and fire frequency decreased, resulting in a mean fire return interval of 167 ± 43 years. Climate cooled and moistened along with a decrease in seasonality during this time and the canopy closed, establishing closed-canopy Pseudotsuga menziesii forests. Climate appears to be the primary factor controlling fire regimes near Roe Lake for most of the Holocene. At times, shifts in the fire regime cannot be explained by changes in climate. Fire frequency increased between 7000-5000 cal yr BP, coincident with a peak in Quercus garryana pollen, despite cooling and moistening climate. Fire likely maintained patches of Q. garryana savanna during this time. Fire again became more common contrary to trends in climate after ~2500 cal yr BP. This late Holocene increase in fire is also seen elsewhere in the Pacific Northwest and may be a reflection of increased climate variability due to more frequent El Niño events or an increase in human-lit fires. Indigenous populations on southern Vancouver Island commonly used fire as a resource management tool and it is likely that people on Pender Island did as well. As fire management practices shift from fire suppression to more sustainable practices, this study offers the Gulf Islands National Park Reserve important baseline information on the area’s natural fire regime to help guide future conservation efforts.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".