Abundance, density and activity of<i>Salvator merianae</i>(Reptilia: Teiidae) and the effect of poaching on the site occupancy by the lizard in an Atlantic Forest Reserve, Brazil
Bibliographic record
Abstract
Abstract We aimed to estimate the density, occupancy and detectability ofSalvator merianae(Tegu) in one of the largest Atlantic rainforest remnants in Espírito Santo, Brazil, theVNR. Species patch occupancy was modelled and used to predict the response direction of six covariates based on prior knowledge of the Tegu's ecology.A priori, we expected that the covariates measured should represent key habitat features for the species (i.e. temperature, forest edge, open habitats) or elements possibly avoided by the species, based on the hypothesis that poaching would have a negative effect on patch occupancy. We used line‐transect surveys to estimate density and abundance. Camera‐traps were used to estimate patch occupancy by the Tegu. Estimated density forS. merianaewas 0.21 ± 0.02 Tegus/ha and estimated population size was 4990 ± 521 individuals. Patch occupancy was best described by two covariates: poaching intensity and distance to the forest edge. Detectability was affected by three covariates: poaching intensity, tree density and temperature. Our study presents robust information on abundance and density, habitat use, and activity ofS. merianaein theVNRand is the first study providing data on the effects that poaching has on patch occupancy of this lizard. The data indicated that the occupancy and detectability of this species were influenced by a set of factors, providing information that can be useful in management plans in areas where this species can potentially decline and in areas where it may be introduced.
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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 | 0.000 | 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".