Context‐dependent disturbance synergies: Subcortical competitors may constrain bark beetle outbreaks following wildfires
Bibliographic record
Abstract
Wildfires and bark beetles have historically interacted to create complex and resilient forests. However, recent record-breaking wildfires in western North America raise concerns that the large areas of injured and dead trees could facilitate increases in insect populations that respond to resource pulses. Populations of Douglas-fir beetle (Dendroctonus pseudotsugae), the primary mortality agent of Douglas-fir (Pseudotsuga menziesii), often irrupt following fires due to the resultant ephemeral pulses of defensively compromised hosts. Other subcortical phloeophagous insects are also attracted to fire (e.g., woodboring Coleoptera: Buprestidae, Cerambycidae) and similarly colonize damaged trees. Although Douglas-fir beetle and woodboring beetle species are known to colonize the phloem of injured trees, the potential for interactions among them following fire is relatively unknown. Rapid colonization by woodborers of the bark beetle niche following fires could constrain bark beetle population growth, potentially suppressing population irruptions through subcortical competition. To evaluate this hypothesis, we studied three wildfire complexes in mature Douglas-fir forests that burned in British Columbia in 2017. We found that Douglas-fir beetle preferentially colonized mature stands containing large-diameter trees with moderate fire injury and that these trees were frequently co-colonized by woodborers. In the absence of woodborers, we found that potential rates of increase in Douglas-fir beetle populations (i.e., offspring per female) were sufficient to lead to a local population irruption. Conversely, when woodborers were common (>50% of trees infested per stand), potential rates of increase in Douglas-fir beetle populations fell below replacement. These findings suggest that competition by woodboring beetles may suppress irruptions of Douglas-fir beetle in fire-injured forests. Our results reveal complex, context-dependent interactions among disturbance agents and indicate that population irruptions by resource pulse-driven bark beetles following fire may depend upon the response of local subcortical competitors. Forest management practices that enhance the diversity and abundance of non-irruptive phloeophagous insects such as many woodboring beetle species may limit the potential for wildfires to contribute to subsequent bark beetle outbreaks.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".