Competition among Eggs Shifts to Cooperation along a Sperm Supply Gradient in an External Fertilizer
Bibliographic record
Abstract
Competition among gametes for fertilization imposes strong selection. For external fertilizers, this selective pressure extends to eggs for which spawning conditions can range from sperm limitation (competition among eggs) to sexual conflict (overabundance of competing sperm toxic to eggs). Yet existing fertilization models ignore dynamics that can alter the functional nature of gamete interactions. These factors include attraction of sperm to eggs, egg crowding effects, or other nonlinearities in per capita rates of sperm-egg interaction. Such processes potentially allow egg concentrations to drastically affect viable fertilization probabilities. I experimentally tested whether such egg effects occur, using the urchin Strongylocentrotus purpuratus, and parameterized a newly derived model of fertilization dynamics and existing models modified to include such interactions. The experiments revealed that at low sperm concentrations, eggs compete for sperm, while at high sperm concentrations, eggs cooperatively reduce abnormal fertilization (a proxy for polyspermy). I show that these observations are consistent with declines in the per capita rate at which sperm and eggs interact as eggs increase in density. The results suggest a fitness trade-off of egg release during spawning: as sperm range from scarce to superabundant, interactions among eggs transition from highly competitive to cooperative in terms of viable fertilization probabilities.
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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 | 0.001 |
| Research integrity | 0.001 | 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".