Historic variability in fire-generated landscape heterogeneity of subalpine forests in the Canadian Rockies
Bibliographic record
Abstract
Question: This study evaluates historical changes in landscape structure and heterogeneity in subalpine forests. We use response to severe fires in 2001 and 2003, along with historical reconstructions to examine crown-fire effects on landscape heterogeneity and to assess, comparatively, effects of fire exclusion management in the 20th century. Location: Subalpine forests of Kootenay National Park (KNP), Canadian Rockies. Methods: Using a landscape-level model based on a fire-origin stand age map, we reconstructed decadal burned areas within the landscape for 1750-2000 (forming reconstructed landscapes). Landscape pattern was analysed for each reconstructed landscape map, and we compared landscape pattern indices (total area, number of patches, mean patch area, patch area variation, largest patch index, edge density, perimeter–area ratio, landscape shape index) with those in 2005 after recent large fires. Results: After large fires in 1926, connectivity of the KNP landscape increased and its diversity was quite low. After 2001 and 2003 fires, the post-fire landscape of 2005 was highly heterogeneous in terms of size, variation, edge density and perimeter–area ratio of the remnant forest patches. Since the decline in occurrence of large fires after 1926 reflected a period of wet weather, fuel build-up resulting from landscape homogenization within the 20th century landscape could not be attributed solely to fire exclusion. This period without fires greatly enhanced connectivity of late-successional forests that finally burned in 2001/2003, but connectivity was within the historical range for these forests. The gradual increase in stand connectivity before recent large fires may indicate that fire exclusion was less responsible than often believed for fuel build-up in these fire-susceptible older forests. Conclusions: The large fires at the beginning of the 21st century are within the natural range of disturbances for this landscape, and do not stand out as “human-induced disasters” in their effects on landscape patterns. Such stochastic large disturbances contribute to maintenance of highly heterogeneous landscape structure, which is important for many taxa and natural ecological processes. Identifying future probability of such large disturbances and their ecological roles should be incorporated into management of these dynamic, disturbance-prone systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".