Spatial genetic structure, population dynamics, and spatial patterns in the distribution of <i>Ocotea catharinensis</i> from southern Brazil: implications for conservation
Bibliographic record
Abstract
In this study, we employ an integrated demographic–genetic approach with the aim of informing efforts to conserve Ocotea catharinensis Mez., an endangered tree species from the Brazilian Atlantic Rainforest. After establishing two permanent plots (15 and 15.5 ha) within protected areas in Santa Catarina state, Brazil, we evaluated demographic aspects (density, recruitment, mortality, and growth), spatial pattern, genetic diversity, and spatial genetic structure (SGS) in three categories (seedlings, juveniles, and reproductive individuals) over 2 years. Studied populations presented low recruitment of individuals and low rates of increment in diameter and height. Aggregation was the main spatial pattern observed for both populations. High levels of genetic diversity were estimated for both populations, as well as high levels of fixation index, signaling the risk of losing genetic diversity over generations. Significant SGS was found for both populations, reflecting nonrandom distribution of the genotypes. Demographic and genetic surveys also allowed the estimation of minimum viable areas for genetic conservation (>170 ha), deme sizes (around 10 ha), and distances for seed collection (at least 60 m). Effective population size is restricted in studied populations, locally threatening the species perpetuation over generations. Further research can clarify how this condition will change in subsequent years.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".