Reptiles of the Serra das Torres Natural Monument: using the Rapid Assessment method to fill a knowledge gap in the Atlantic Forest of southeastern Brazil
Bibliographic record
Abstract
Abstract: Data on the composition of local reptile assemblages in several Brazilian ecosystems can still be considered relatively restricted in scope in most cases. In this study, we conducted surveys in the Serra das Torres Natural Monument, located in the municipalities of Atílio Vivacqua, Muqui, and Mimoso do Sul, using the Rapid Assessments method (RAP) during 30 days in the rainy season of 2018. We sampled actively for approximately 1320 hours with a 6-10 person crew, supplemented by 720 hours of passive sampling (30 bucket-days) using pitfall traps with drift fences. We recorded 34 reptile species during our sampling method (2 amphisbaenid, 11 lizards, and 21 snakes) and an occasional encounter, after the end of sampling, that added a chelonian species to the list, Hydromedusa maximiliani, totaling 35 reptile species. The Dipsadidae was the family with the greatest snake species richness and, the Gymnophtalmidae had the greatest lizard species richness. The species richness recorded in the Serra das Torres Natural Monument (Ntotal = 35) represents ca. 27% of all reptile species found in the state of Espírito Santo (N = 130). The most abundant lizard species was Leposoma scincoides followed by Ecpleopus gaudichaudii and, the most abundant snake species was Bothrops jararaca being markedly higher than that recorded in similar studies. Twenty-seven percent of the reptile species recorded in our study are endemic to the Atlantic Forest and 30% (N = 10) have been recorded less than five times previously in the Brazilian state of Espírito Santo. Our study reinforces the need for the conservation of the Serra das Torres Natural Monument because of its importance as a reservoir of a considerable portion of the reptile biodiversity of Espírito Santo state, and of the Atlantic Forest biome as a whole.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 | 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 teacher head, 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".