Signals Can Trump Rewards in Attracting Seed-Dispersing Ants
Bibliographic record
Abstract
Both rewards and signals are important in mutualisms. In myrmecochory, or seed dispersal by ants, the benefits to plants are relatively well studied, but less is known about why ants pick up and move seeds. We examined seed dispersal by the ant Aphaenogaster rudis of four co-occurring species of plants, and tested whether morphology, chemical signaling, or the nutritional quality of fatty seed appendages called elaiosomes influenced dispersal rates. In removal trials, ants quickly collected diaspores (seeds plus elaiosomes) of Asarum canadense, Trillium grandiflorum, and Sanguinaria canadensis, but largely neglected those of T. erectum. This discrepancy was not explained by differences in the bulk cost-benefit ratio, as assessed by the ratio of seed to elaiosome mass. We also provisioned colonies with diaspores from one of these four plant species or no diaspores as a control. Colonies performed best when fed S. canadensis diaspores, worst when fed T. grandiflorum, and intermediately when fed A. canadense, T. erectum, or no diaspores. Thus, the nutritional rewards in elaiosomes affected colony performance, but did not completely predict seed removal. Instead, high levels of oleic acid in T. grandiflorum elaiosomes may explain why ants disperse these diaspores even though they reduce ant colony performance. We show for the first time that different elaiosome-bearing plants provide rewards of different quality to ant colonies, but also that ants appear unable to accurately assess reward quality when encountering seeds. Instead, we suggest that signals can trump rewards as attractants of ants to seeds.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".