Frog species richness, composition and beta-diversity in coastal Brazilian restinga habitats
Bibliographic record
Abstract
We studied the species richness and composition of frogs in 10 restinga habitats (sand dune environments dominated by herbaceous and shrubby vegetation) along approximately 1500 km of coastal areas of three Brazilian States: Rio de Janeiro (Grumari, Maricá, Massambaba, Jurubatiba and Grussaí), Espírito Santo (Praia das Neves and Setiba) and Bahia (Prado and Trancoso). We estimated beta-diversity and similarity among areas and related these parameters to geographic distance between areas. All areas were surveyed with a similar sampling procedure. We found 28 frog species belonging to the families Hylidae, Microhylidae, Leptodactylidae and Bufonidae. Frogs in restingas were in general nocturnal with no strictly diurnal species. The richest restinga was Praia das Neves (13 species), followed by Grussaí and Trancoso (eight species in each). The commonest species in the restingas was Scinax alter (found in eight restingas), followed by Aparasphenodon brunoi (seven areas). Our data shows that richness and composition of frog communities vary consistently along the eastern Brazilian coast and, in part, the rate of species turnover is affected by the distance among areas. Geographic distance explained approximately 12% of species turnover in restingas and about 9.5% of similarity among frog assemblages. Although geographic distance somewhat affects frog assemblages, other factors (e.g. historical factors, disturbances) seem to be also involved in explaining present frog assemblage composition in each area and species turnover among areas. The frog fauna along restinga habitats was significantly nested (matrix community temperature = 26.13 degrees; p = 0.007). Our data also showed that the most hospitable restinga was Praia das Neves and indicated that this area should be protected as a conservation unit. Frog assemblage of each area seems to partially represent a nested subset of the original assemblage, although we should not ignore the importance of historical factors. This nestedness pattern, in part, probably results from the intensive fragmentation of restinga habitats. Possibly, many frog species may have been lost in some studied areas as a result of the extensive habitat degradation to which restinga habitats are presently exposed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".