Strong floristic distinctiveness across Neotropical successional forests
Bibliographic record
Abstract
Forests that regrow naturally on abandoned fields are important for restoring biodiversity and ecosystem services, but can they also preserve the distinct regional tree floras? Using the floristic composition of 1215 early successional forests (≤20 years) in 75 human-modified landscapes across the Neotropic realm, we identified 14 distinct floristic groups, with a between-group dissimilarity of 0.97. Floristic groups were associated with location, bioregions, soil pH, temperature seasonality, and water availability. Hence, there is large continental-scale variation in the species composition of early successional forests, which is mainly associated with biogeographic and environmental factors but not with human disturbance indicators. This floristic distinctiveness is partially driven by regionally restricted species belonging to widespread genera. Early secondary forests contribute therefore to restoring and conserving the distinctiveness of bioregions across the Neotropical realm, and forest restoration initiatives should use local species to assure that these distinct floras are maintained.
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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 | 0.001 |
| 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".