Species composition, richness and nestedness of lizard assemblages from Restinga habitats along the brazilian coast
Bibliographic record
Abstract
Habitat fragmentation is well known to adversely affect species living in the remaining, relatively isolated, habitat patches, especially for those having small range size and low density. This negative effect has been critical in coastal resting habitats. We analysed the lizard composition and richness of restinga habitats in 16 restinga habitats encompassing three Brazilian states (Rio de Janeiro, Espírito Santo and Bahia) and more than 1500km of the Brazilian coast in order to evaluate if the loss of lizard species following habitat reduction occur in a nested pattern or at random, using the "Nestedness Temperature Calculator" to analyse the distribution pattern of lizard species among the restingas studied. We also estimated the potential capacity that each restinga has to maintain lizard species. Eleven lizard species were recorded in the restingas, although not all species occurred in all areas. The restinga with the richest lizard fauna was Guriri (eight species) whereas the restinga with the lowest richness was Praia do Sul (located at Ilha Grande, a large coastal island). Among the restingas analysed, Jurubatiba, Guriri, Maricá and Praia das Neves, were the most hospitable for lizards. The matrix community temperature of the lizard assemblages was 20.49° (= P <0.00001; 5000 randomisations; randomisation temperature = 51.45° ± 7.18° SD), indicating that lizard assemblages in the coastal restingas exhibited a considerable nested structure. The degree in which an area is hospitable for different assemblages could be used to suggest those with greater value of conservation. We concluded that lizard assemblages in coastal restingas occur at a considerable level of ordination in restinga habitats and that some restinga areas such as Jurubatiba, Guriri, Maricá and Praia das Neves are quite important to preserve lizard diversity of restinga environments.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".