Identification of Mountain Pine Beetle Fungal Associates Using Whole Genome Sequence Data
Bibliographic record
Abstract
The mountain pine beetle (MPB), Dendroctonus ponderosae, is a forest pest in western North America, infesting millions of hectares of pine forests and causing extensive tree mortality. As the MPB infests trees, it introduces fungal symbionts to the tree. These blue-stain fungi facilitate the beetle's colonization and reproduction within the tree. The blue stain fungi comprise multiple species, and our understanding of the diversity and prevalence of these individual species remains limited. This study aims to identify and characterize the fungal associates of the MPB, gain insight into fungal distribution, and aid in forest management strategies. The study was conducted by collecting 26 MPB specimens from various locations across North America and extracting DNA for whole-genome sequencing using next-generation Sequencing Illumina sequencing. A bioinformatic pipeline was developed to analyze the sequencing data and characterize the fungal community associated with individual MPB sequences. Here, we show that it is possible to use whole genome sequencing to identify the fungal associates transmitted by MPB. Our results align with previous literature characterizing the presence of Grosmannia clavigera and Ophiostoma montium as primary fungal partners in MPB populations across various regions. The analysis aims to understand the geographic distribution trends of these fungal species in different habitats. Furthermore, this project provides valuable insights into the role of these various fungi species in supporting MPB during range expansion.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".