Impact of selective logging on inbreeding and gene dispersal in an Amazonian tree population of <i>Carapa guianensis</i> Aubl.
Bibliographic record
Abstract
Selective logging may impact patterns of genetic diversity within populations of harvested forest tree species by increasing distances separating conspecific trees, and modifying physical and biotic features of the forest habitat. We measured levels of gene diversity, inbreeding, pollen dispersal and spatial genetic structure (SGS) of an Amazonian insect-pollinated Carapa guianensis population before and after commercial selective logging. Similar levels of gene diversity and allelic richness were found before and after logging in both the adult and the seed generations. Pre- and post-harvest outcrossing rates were high, and not significantly different from one another. We found no significant levels of biparental inbreeding either before or after logging. Low levels of pollen pool differentiation were found, and the pre- vs. post-harvest difference was not significant. Pollen dispersal distance estimates averaged between 75 m and 265 m before logging, and between 76 m and 268 m after logging, depending on the value of tree density and the dispersal model used. There were weak and similar levels of differentiation of allele frequencies in the adults and in the pollen pool, before and after logging occurred, as well as weak and similar pre- and post-harvest levels of SGS among adult trees. The large neighbourhood sizes estimated suggest high historical levels of gene flow. Overall our results indicate that there is no clear short-term genetic impact of selective logging on this population of C. guianensis.
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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 | 0.000 |
| 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".