Outbreaks of invasive phloem feeding Toumeyella parvicornis modified fire behaviour and canopy surface reflectance in Mediterranean Pinus pinea forests
Bibliographic record
Abstract
The continued introduction of non-native insect species, coupled with the rising threat of extreme wildfire events, poses significant risks to terrestrial ecosystems and the services they offer globally. However, the impact of invasive phloem-feeding insect species on fire severity is not well understood, particularly in terms of how they influence fire behaviour and the likelihood of crown fire ignition. Two experimental designs were set up to investigate how the alien tortoise scale (Toumeyella parvicornis) outbreaks have influenced fire behaviour dynamics and canopy surface reflectance in the Mediterranean P. pinea stands severely burnt in the summer of 2017. We combined Rothermel’s model for fire surface spread and Van Wagner’s crown ignition model to simulate fire behaviour and employed data from the Landsat 8 collection to detect canopy wilt symptoms related to T. parvicornis outbreaks. Simulating fire behaviour in single-storied P. pinea stands indicated that all predicted fires were surface fires. An uncertainty analysis concerning the inputs of the canopy fuel attributes model revealed that fires in thinned stands were entirely classified as surface fires. In contrast, in unthinned stands, only 62.7% were surface fires, with 37.3% categorised as conditional fire types. Among the Landsat 8 reflectance bands, only NIR, Green, and SWIR 2 were sensitive to the abundance of T. parvicornis. Based on these sensitive bands, two-band NIR-multiplied vegetation indexes were significantly associated with the abundance of T. parvicornis from the fall generation onward, when sooty mould consistently covered canopy needles. The divergence between observed and predicted fire behaviour underscores the need to investigate the processes and variables linked to T. parvicornis feeding activity on the trees to improve fire behaviour prediction. Understanding how insect outbreaks can modify fire behaviour in Mediterranean stands is crucial for effective management at stand and landscape levels. The satellite vegetation indexes based on sensitive reflectance bands represent an essential tool for an early recognition of insect outbreak distribution on large spatial 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 | 0.000 |
| 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 | 0.000 | 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 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".