Living in a same microhabitat should means eating the same food? Diet and trophic niche of sympatric leaf-litter frogs Ischnocnema henselii and Adenomera marmorata in a forest of Southern Brazil
Bibliographic record
Abstract
In this study we analyzed diet composition, niche breadth and overlap of the two leaf-litter frogs Ischnocnema henselii and Adenomera marmorata. Frogs were collected in an Atlantic Rainforest area in the Reserva Natural Salto Morato, in Paraná State, Southern Brazil, using plots of 16 m2 established on forest floor. Ischnocnema henselii consumed 18 different types of prey and the diet of this species was composed predominantly by Hymenoptera (Formicidae) (15.4%), Araneae (13.83%), Orthoptera (6.15%) and Opiliones (6.15%), whereas Adenomera marmorata consumed 15 different types of prey and its diet was composed mainly by Hymenoptera (Formicidae) (45.7%), Acari (31.8%) and Blattodea (14.8%). The niche breadth of I. henselii was BA = 0.43 and that of A. marmorata was BA = 0.19. The diet of the two sympatric species of leaf-litter frogs was basically composed by arthropods and the trophic niche overlap among them did not differ from expected at random. The differences in prey consumption should potentially facilitate the coexistence of two sympatric frogs on the forest floor. Possibly, this difference of prey consumption partly reflects differences in jaw width, species-specific body size of the two species and the period of activity of these two species.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".