Low Inbreeding and High Pollen Dispersal Distances in Populations of Two Amazonian Forest Tree Species
Bibliographic record
Abstract
ABSTRACT Recent studies suggest that tropical tree species exhibit low inbreeding and high gene dispersal levels despite the typically low density of conspecifics in tropical forests. To examine this, we undertook a study of pollen gene dispersal and mating system of two Amazonian tree species. We analyzed 341 seeds from 33 trees at four microsatellite loci in a Carapa guianensis population from Brazil, and 212 seeds from 22 trees at four microsatellite loci in a Sextonia rubra population from French Guiana. Differentiation of allele frequencies among the pollen pool of individual trees was ΦFT= 0.053 (95% CI: 0.027–0.074) for C. guianensis and ΦFT= 0.064 (95% CI: 0.017–0.088) for S. rubra. The mean pollen dispersal distances were estimated at 69–355 m for C. guianensis, and 86–303 m for S. rubra, depending on the pollen dispersal model and the estimate of reproductive tree density used. The multi‐locus outcrossing rate was estimated at 0.918 and 0.945, and the correlation of paternity at 0.089 and 0.096, for C. guianensis and S. rubra, respectively, while no significant levels of biparental inbreeding were detected. Comparing trees with high and low local density of conspecifics, we found no evidence for differences in inbreeding levels. The results are discussed within the framework of the emerging picture of the reproductive biology of tropical forest trees.
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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 | 0.001 | 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) | 0.001 | 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".