Genetic and genomic evidence of niche partitioning and adaptive radiation in mountain pine beetle fungal symbionts
Bibliographic record
Abstract
Bark beetles form multipartite symbiotic associations with blue stain fungi (Ophiostomatales, Ascomycota). These fungal symbionts play an important role during the beetle's life cycle by providing nutritional supplementation, overcoming tree defences and modifying host tissues to favour brood development. The maintenance of stable multipartite symbioses with seemingly less competitive symbionts in similar habitats is of fundamental interest to ecology and evolution. We tested the hypothesis that the coexistence of three fungal species associated with the mountain pine beetle is the result of niche partitioning and adaptive radiation using SNP genotyping coupled with genotype-environment association analysis and phenotypic characterization of growth rate under different temperatures. We found that genetic variation and population structure within each species is best explained by distinct spatial and environmental variables. We observed both common (temperature seasonality and the host species) and distinct (drought, cold stress, precipitation) environmental and spatial factors that shaped the genomes of these fungi resulting in contrasting outcomes. Phenotypic intraspecific variations in Grosmannia clavigera and Leptographium longiclavatum, together with high heritability, suggest potential for adaptive selection in these species. By contrast, Ophiostoma montium displayed narrower intraspecific variation but greater tolerance to extreme high temperatures. Our study highlights unique phenotypic and genotypic characteristics in these symbionts that are consistent with our hypothesis. By maintaining this multipartite relationship, the bark beetles have a greater likelihood of obtaining the benefits afforded by the fungi and reduce the risk of being left aposymbiotic. Complementarity among species could facilitate colonization of new habitats and survival under adverse conditions.
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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 | 0.001 |
| 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) | 0.001 | 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".