Population dynamics of <i>Podocarpus lambertii</i> in southern Brazilian forest–grassland mosaics
Bibliographic record
Abstract
The grasslands conversion to forests is occurring globally and modifying the population dynamics of species. Here, we characterized the population dynamics of Podocarpus lambertii Klotzsch ex Endl. over four years in southern Brazilian forest–grassland mosaics. We asked (i) if the studied P. lambertii population would decrease or increase over time and (ii) what the role of forest patches is in the growth and recruitment of a P. lambertii population. Thus, we studied forest–grassland mosaics, stratified the population into four demographic classes, evaluated the population dynamics, and estimated the correlation between canopy cover and average number of individuals. All individuals of P. Lambertii occurred in forest patches. Density was high but decreased from seedlings to the reproductive stage. The population growth rate was λ = 1.025, and the recruitment of individuals was high and variable among years. The transition and mortality rates showed a pattern of reduction from seedlings to the reproductive stage. Mortality rate for seedlings and juveniles was low and concentrated at the smaller heights. The correlations between canopy cover and the average number of individuals were positive and significant. The ecological characteristics of this species and specific conditions provided by forest patches allow population growth and species conservation in the southern Brazilian forest–grassland mosaics.
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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".