Persistence of pyrophilous insects in fire‐driven boreal forests: population dynamics in burned and unburned habitats
Bibliographic record
Abstract
ABSTRACT Several boreal insect species respond to smoke and heat generated by forest fires and use recent burns to reproduce in high numbers. Some of these species are rare or uncommon in undisturbed forests, and the contribution of recently burned habitats to their population dynamics has been deemed crucial by some to their long‐term persistence. Consequently, the severe decline seen in some species in Fennoscandia has been frequently linked with fire suppression. In this paper, we explore some aspects of the spatial dynamics of pyrophilous insect populations in relation to the expected relative contribution of burned and unburned habitats to their global population dynamics. Forest fires are, throughout the boreal forest biome, generally highly aggregated in some years while rare in most other years. The low connectivity between fire events and the typical life cycle seen in these species make it improbable that recent burns act as significant population sources. This leads us to suggest that populations of pyrophilous species may be more limited by the adequacy of the unburned matrix than by the occurrence of fire events. Moreover, by combining an age‐class distribution model and a dead wood availability model, we show that the quality of the unburned matrix increases in landscapes with longer fire cycles, in which pyrophilous insects should persist at higher population levels. We conclude that the degradation of the unburned habitat better explains the decline of pyrophilous insects than fire suppression alone.
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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 | 0.000 |
| 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".