Re‐evaluating the role of selective abscission in moth/yucca mutualisms
Bibliographic record
Abstract
Conflicts of interest are common to mutualisms, particularly those derived from exploitative interactions. Conflicts of interest are particularly pronounced in pollination/seed predation mutualisms, such as moth/yucca interactions, where consumption of seeds by larvae of a plant's pollinator will raise the fitness of the pollinator but lower the fitness of the plant. A central question in these mutualisms is, therefore, “what limits seed predation?” If plants with excess flowers selectively abscise flowers containing many eggs, they may reduce seed predation and overall increase their fecundity. If eggs in abscised flowers die, selective abscission may additionally contribute to the limitation or regulation of pollinator populations, thereby decreasing the probability of future overexploitation. We examined the effect of selective abscission in the mutualism between Yucca kanabensis and one of its pollinating moths, Tegeticula altiplanella . Per capita mortality of moth eggs due to abscission was high (95.5%), but did not increase on inflorescences with more ovipositions per flower. Overall mortality was partitioned into two components based upon the proportion of visited flowers abscised (i.e. resource‐limitation) and additional mortality (=selective abscission). Resource‐limitation per se inflicted 93.9% egg mortality, or most of the mortality due to abscission. But, the average number of eggs in fruit was lower than the average number of eggs in flowers, indicating that there was some selectivity of abscission. However, neither source of mortality increased on inflorescences with more ovipositions per visited flower. Egg mortality resulting from selective abscission was not as high as possible, because the yuccas appeared to use oviposition‐damaged ovules as a cue for selective abscission, and there was considerable variation in the relationship between oviposition number and damaged ovules. However, even if yuccas had retained the flowers containing the fewest eggs, selective abscission still would not have been higher on inflorescences with more ovipositions per flower. Considering also that, 1) number of ovipositions is a poor predictor of the number of larvae that hatch and feed on the developing seeds in a fruit and that, 2) there are several moth/yucca interactions in which selective abscission does not occur, we conclude that abscission, and particular selective abscission, may have density‐limiting effects on moth populations, but will fail as general explanations for regulating the dynamics of moth populations.
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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".