Spatial Genetic Structure of a Symbiotic Beetle-Fungal System: Toward Multi-Taxa Integrated Landscape Genetics
Bibliographic record
Abstract
Spatial patterns of genetic variation in interacting species can identify shared features that are important to gene flow and can elucidate co-evolutionary relationships. We assessed concordance in spatial genetic variation between the mountain pine beetle (Dendroctonus ponderosae) and one of its fungal symbionts, Grosmanniaclavigera, in western Canada using neutral genetic markers. We examined how spatial heterogeneity affects genetic variation within beetles and fungi and developed a novel integrated landscape genetics approach to assess reciprocal genetic influences between species using constrained ordination. We also compared landscape genetic models built using Euclidean distances based on allele frequencies to traditional pair-wise Fst. Both beetles and fungi exhibited moderate levels of genetic structure over the total study area, low levels of structure in the south, and more pronounced fungal structure in the north. Beetle genetic variation was associated with geographic location while that of the fungus was not. Pinevolume and climate explained beetle genetic variation in the northern region of recent outbreak expansion. Reciprocal genetic relationships were only detectedin the south where there has been alonger history of beetle infestations. The Euclidean distance and Fst-based analyses resulted in similar models in the north and over the entire study area, but differences between methods in the south suggest that genetic distances measures should be selected based on ecological and evolutionary contexts. The integrated landscape genetics framework we present is powerful, general, and can be applied to other systems to quantify the biotic and abiotic determinants of spatial genetic variation within and among taxa.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".