BOTTOM-UP EFFECTS OF PLANT GENOTYPE ON APHIDS, ANTS, AND PREDATORS
Bibliographic record
Abstract
Theory predicts that bottom-up ecological forces can affect community dynamics, but whether this extends to the effects of heritable plant variation on tritrophic communities is poorly understood. In a field experiment, I contrasted the effects of plant genotype (28 genotypes; 1064 plants), aphid density, and the presence/absence of mutualistic ants in affecting the per capita population growth of a specialist aphid herbivore, as well as the effects of plant genotype on the third trophic level. Plant genotype strongly affected aphid population growth rate, explaining 29% of the total variation in growth rate, whereas aphid density and ant-aphid interactions explained substantially less variation (< 2%) in aphid population growth rate. Plant genotype also had direct and indirect effects on the third trophic level, affecting the abundance of aphid-tending ants and the richness of predators. Multiple regression identified several heritable plant traits that explained 49% of the variation in aphid growth rate and 30% of the variation in ant abundance among plant genotypes. These bottom-up effects of plant genotype on tritrophic interactions were independent of the effects of either initial aphid density or the presence/absence of mutualistic ants. This study shows that plant genotype can be one of the most important ecological factors shaping tritrophic communities.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".