The impact of domestication on resistance to two generalist herbivores across 29 independent domestication events
Bibliographic record
Abstract
The domestication of crops is among the most important innovations in human history. Here, we test the hypothesis that cultivation and artificial selection for increased productivity of crops reduced plant defenses against herbivores. We compared the performance of two economically important generalist herbivores - the leaf-chewing beet armyworm (Spodoptera exigua) and the phloem-feeding green peach aphid (Myzus persicae) - across 29 crop species and their closely related wild relatives. We also measured putative morphological and chemical defensive traits and correlated them with herbivore performance. We show that, on average, domestication significantly reduced resistance to S. exigua, but not M. persicae, and that most independent domestication events did not cause differences in resistance to either herbivore. In addition, we found that multiple plant traits predicted resistance to S. exigua and M. persicae, and that domestication frequently altered the strength and direction of correlations between these traits and herbivore performance. Our results show that domestication can alter plant defenses, but does not cause strong allocation tradeoffs as predicted by plant defense theory. These results have important implications for understanding the evolutionary ecology of species interactions and for the search for potential resistance traits to be targeted in crop breeding.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".