Modeling the pest‐pathogen threats in a warming world for the red turpentine beetle (<scp><i>Dendroctonus valens</i></scp>) and its symbiotic fungus (<scp><i>Leptographium procerum</i></scp>)
Bibliographic record
Abstract
BACKGROUND: Dendroctonus valens along with its symbiotic fungi have caused unprecedented damage to pines in China. Leptographium procerum, its primary symbiotic fungus, facilitates the invasion and colonization of the pest, thereby aggravating ecological threats. Assessing shifts in the niches and ranges of D. valens and its symbiotic fungus could provide a valuable basis for pest control. Here, we conducted niche comparisons between native and invasive populations of D. valens. Then, we employed standard ecological niche models and ensembles of small models to predict the potential distributions of D. valens and L. procerum under climate change conditions and to estimate areas of overlap. RESULTS: The niche of invasive population of D. valens in Chinese mainland only occupied a limited portion of the niche of native population in North America, leaving a substantial native niche unfilled and without any niche expansion. The suitable regions for D. valens are predicted in central and southern North America and central and northeastern Chinese mainland. The overlap with the suitable regions of L. procerum included eastern North America and the central and northeastern Chinese mainland under historical climatic scenarios. The regions susceptible to their symbiotic damage will shift northward in response to future climate change. CONCLUSIONS: Projected distributions of D. valens and its symbiotic fungus, along with areas vulnerable to their symbiotic damage, provide essential insights for devising strategies against this association. Additionally, our study contributes to comprehending how biogeographic approaches aid in estimating potential risks of pest-pathogen interactions in forests within a warming world. © 2024 Society of Chemical Industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".