Short- and long-term effects of fire and vegetation cover on four lizard species in Amazonian savannas
Bibliographic record
Abstract
Ecological succession in tropical savannas is limited by seasonal fire, which affects habitat quality. Although fire may cause negligible or positive effects on animals occupying savannas, most short-term studies (months to a few years) are based on a single temporal sampling snapshot, and long-term studies (decades) are rare. We sampled four lizard species (Rainbow Whiptail, Cnemidophorus lemniscatus (Linnaeus, 1758); Striped Whiptail, Kentropyx striata (Daudin, 1802); Grass Anole, Norops auratus Duméril and Bibron, 1837 = Anolis auratus Daudin, 1802; Amazon Racerunner, Ameiva ameiva (Linnaeus, 1758)) in Amazonian savannas to test the effects of fire and vegetation cover on lizard densities at two temporal scales. In the short term, we use three sampling snapshots to test the effects of fire and vegetation cover on estimated lizard densities over the subsequent 1–5 years. In the long term, we test the effects of fire and changes in vegetation cover over 21 years on current lizard density differences. In the short term, species responses were usually consistent with foraging and thermoregulation modes. However, the results were not consistent among species and years, although the variances in species density explained by year as a random factor were generally low. In the long term, the main effects of fire and vegetation cover show that lizard densities may change spatially, but not necessarily temporarily. Wildfire is a natural resource of savannas and apparently have little impact on resident lizards of that ecosystem.
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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 | 0.001 | 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 | 0.001 | 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) | 0.000 | 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".