Host association, environment, and geography underlie genomic differentiation in a major forest pest
Bibliographic record
Abstract
Abstract Diverse geographic, environmental, and ecological factors affect gene flow and adaptive genomic variation within species. With recent advances in landscape ecological modelling and high‐throughput DNA sequencing, it is now possible to effectively quantify and partition their relative contributions. Here, we use landscape genomics to identify determinants of genomic differentiation in the forest tent caterpillar,Malacosoma disstria, a widespread and irruptive pest of numerous deciduous tree species in North America. We collected larvae from multiple populations across Eastern Canada, where the species experiences a diversity of environmental gradients and feeds on a number of different host tree species, including trembling aspen (Populus tremuloides), sugar maple (Acer saccharum), red oak (Quercus rubra), and white birch (Betula papyrifera). Using a combination of reciprocal causal modelling (RCM) and distance‐based redundancy analyses (dbRDA), we show that differentiation of thousands of genome‐wide single nucleotide polymorphisms (SNPs) among individuals is best explained by a combination of isolation by distance, isolation by environment (spatial variation in summer temperatures and length of the growing season), and differences in host association. Configuration of suitable habitat inferred from ecological niche models was not significantly related to genomic differentiation, suggesting thatM. disstriadispersal is agnostic with respect to habitat quality. Although population structure was not discretely related to host association, our modelling framework provides the first molecular evidence of host‐associated differentiation inM. disstria, congruent with previous documentation of reduced growth and survival of larvae moved between natal host species. We conclude that ecologically mediated selection is contributing to variation withinM. disstria, and that divergent adaptation related to both environmental conditions and host association should be considered in ongoing research and management of this important forest pest.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".