Paleovegetation Simulations of Lowland Amazonia and Implications for Neotropical Allopatry and Speciation
Bibliographic record
Abstract
Abstract Paleovegetation modeling simulations of the lowland Amazon basin were made to assess the relative importance of glacial climate and atmospheric CO2 for altering vegetation type and structure, as well as to explore the potential physiological mechanisms underlying these ecosystem-level responses. Modeling results support the view that widespread invasion of grasslands into the Amazon lowlands during the last glaciation was not likely. Glacial cooling was probably responsible for maintaining glacial forest cover via its effects in reducing photorespiration and decreasing evapotranspiration, which collectively improve plant carbon and water relations. Modeling results confirm that leaf area index (LAI), a common proxy for canopy density, is highly sensitive to independent and interactive changes in climate and low concentration of atmospheric CO2, and the results show considerable region-to-region variation during the last glaciation. Heterogeneous variations in glacial vegetation LAI may have promoted allopatric speciation by geographically isolating species (called vicariance) in the forest (sub)canopy. The proposed vicariance hypothesis incorporating spatial variations in canopy density conforms to many of the essential tenets addressed by previous neotropical speciation models, but also helps to overcome some of their inconsistencies.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".