Effects of fire return rates on traversability of lodgepole pine forests for mountain pine beetle (Coleoptera: Scolytidae) and the use of patch metrics to estimate traversability
Bibliographic record
Abstract
Abstract Monte-Carlo simulation was used to examine the effects of fire return rates on the equilibrium age structure of a one-million-hectare lodgepole pine forest ( Pinus contorta var. latifolia Engelm. ex S. Wats.; Pinaceae) and yielded a mosaic of ages over the one million hectares for each fire regime modelled. These mosaics were used to generate mosaics of susceptibility to mountain pine beetle (MPB) ( Dendroctonus ponderosae Hopkins, 1902) attack. This susceptibility was related to the age distribution to calculate the mean susceptibility of the forest. Susceptibility maps were produced for two timber supply areas in British Columbia, as well as for the whole of B.C. In addition, we defined a quality, called traversability, that describes the ability of a beetle population to disperse across a landscape according to defined rules of susceptibility and maximum distance for dispersal through unsuitable habitat. Using each of 40 combinations of susceptibility classifications and dispersal limits, the landscape was categorized as traversable or non-traversable. This represents the suitability of a landscape to the unimpeded spread of an incipient beetle population. It was found that ( i ) long fire cycles yield an age structure highly susceptible to beetle attack; ( ii ) fire suppression reduces the frequency of fires and yields an age structure highly susceptible to beetle attack; and ( iii ) harvesting one age class reduces the mean susceptibility to MPB attack, and this reduction decreases with increasing harvest age and increasing fire cycle length. When fires were limited in size to less than 100 ha, the area was always traversable. For larger fires, traversability declined, and for the largest fires (up to one million hectares), the area was often not traversable. Harvesting reduced the mean susceptibility and traversability, often substantially. Traversability was calculated for the whole of B.C. in blocks of about one million hectares using B.C. Ministry of Forests and Range inventory data for the year 2000. The area most traversable was the area around Tweedsmuir Park and the Lakes Timber Supply Area, where most of the present outbreak of MPB is centred. FRAGSTATS patch metrics were calculated for each of the simulations and were related to traversability using discriminant analysis. This analysis was then applied to the B.C. inventory; the concordance was high, with 93.3% of conditions being correctly classified.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".