Paleoecological Investigation of Vegetation, Climate and Fire History in, and Adjacent to, Kootenay National Park, Southeastern British Columbia, Canada
Bibliographic record
Abstract
Paleoecological investigation of two montane lakes in the Kootenay region of southeast British Columbia, Canada, reveal changes in vegetation in response to climate and fire throughout the Holocene. Pollen, charcoal, and lake sediment carbon accumulation rate analyses show seven distinct zones at Marion Lake, presently in the subalpine Engelmann Spruce-Subalpine Fir (ESSF) biogeoclimatic (BEC) zone of Kootenay Valley, British Columbia. Comparison of these records to nearby Dog Lake of Kootenay National Park of Canada in the Montane Spruce (MS) BEC zone of Kootenay Valley, British Columbia reveals unique responses of ecosystems in topographically complex regions. The two most dramatic shifts in vegetation at Marion Lake occur firstly in the early Holocene/late Pleistocene in ML Zone 3 (11,010–10,180 cal. yr. B.P.) possibly reflecting Younger Dryas Chronozone cooling followed by early Holocene xerothermic warming noted by the increased presence of the dry adapted conifer, Douglas-fir ( Pseudotsuga menziesii ) and increasing fire frequency. The second most prominent change occurred at the transition from ML Zone 5 through 6a (∼2,500 cal. yr. B.P.). This zone transitions from a warmer to a cooler/wetter climate as indicated by the increase in western hemlock ( Tsuga heterophylla ) and subsequent drop in fire frequency. The overall cooling trend and reduction in fire frequency appears to have occurred ∼700 years later than at Dog Lake (∼43 km to the south and 80 m lower in elevation), resulting in a closed montane spruce forest, whereas Marion Lake developed into a subalpine ecosystem. The temporal and ecological differences between the two study sites likely reflects the particular climate threshold needed to move these ecosystems from developed forests to subalpine conditions, as well as local site climate and fire conditions. These paleoecological records indicate future warming may result in the MS transitioning into an Interior Douglas Fir (IDF) dominated landscape, while the ESSF may become more forested, similar to the modern MS, or develop into a grassland-like landscape dependent on fire frequency. These results indicate that climate and disturbance over a regional area can dictate very different localized vegetative states. Local management implications of these dynamic landscapes will need to understand how ecosystems respond to climate and disturbance at the local or ecosystem/habitat scale.
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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 | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".