What happens when ants fail at plant defence?<i><scp>C</scp>ordia nodosa</i>dynamically adjusts its investment in both direct and indirect resistance traits in response to herbivore damage
Bibliographic record
Abstract
Summary Ants provide variable protection against herbivores to ant‐plants (i.e. myrmecophytes and myrmecophiles). The ways in which ant‐plants dynamically adjust both their direct (chemical and physical) and indirect (biotic) defences in response to varying levels of herbivory are not well understood. We experimentally generated a broad range of ant‐attendance levels and herbivory pressures in a tropical myrmecophyte,Cordia nodosa,which allowed exploration of the inducibility of and interactions between direct and indirect resistance traits. In response to increased herbivory, host plants encouraged indirect (biotic) defence by increasing domatium volume, regardless of whether ants were present on the plant. When ants were present, larger domatia housed more workers, which in turn decreased herbivory on adjacent leaves. Independent of the presence of ants, plants responded to increased herbivory by inducing both chemical (phenolics) and structural (leaf toughness, trichomes) resistance traits; these traits were associated with reduced palatability to a folivorous beetle. Synthesis. Our results show that both direct and indirect defences are inducible inC. nodosa, which suggests thatC. nodosamay retain direct defences as insurance against varying levels of protection from its ant bodyguards. Thus, the predictions of optimal defence theory are not violated: althoughC. nodosainvests in multiple forms of defence, they are not redundant.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".