The interacting effects of diversity and propagule pressure on early colonization and population size
Bibliographic record
Abstract
We are now beginning to understand the role of intraspecific diversity on fundamental ecological phenomena. There exists a paucity of knowledge, however, regarding how intraspecific, or genetic diversity, may covary with other important factors such as propagule pressure. A combination of theoretical modelling and experimentation was used to explore the way propagule pressure and genetic richness may interact. We compare colonization rates of the Australian bivalve Saccostrea glomerata (Gould 1885). We cross propagule size and genetic richness in a factorial design in order to examine the generalities of our theoretical model. Modelling showed that diversity and propagule pressure should generally interact synergistically when positive feedbacks occur (e.g. aggregation). The strength of genotype effects depended on propagule size, or the numerical abundance of arriving individuals. When propagule size was very small (<4 individuals), however, greater genetic richness unexpectedly reduced colonization. The probability of S. glomerata colonization was 76% in genetically rich, larger propagules, almost 39 percentage points higher than in genetically poor propagules of similar size. This pattern was not observed in less dense, smaller propagules. We predict that density-dependent interactions between larvae in the water column may explain this pattern.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".