Breeding-site selection by the poison frog Ranitomeya biolat in Amazonian bamboo forests: an experimental approach
Bibliographic record
Abstract
Habitat selection in amphibians has typically been investigated using species that breed in medium-sized to large bodies of water. So far, few studies have focused on tropical, phytotelm-breeding species. We examined habitat selection in the context of reproductive resource use by Ranitomeya biolat (Morales, 1992), a poison frog that uses bamboo internodes as breeding sites. We conducted field observations and experiments using bamboo and PVC sections to test the effect of physical and biotic factors on tadpole deposition. Our field observations indicated that water volume, as well as internode length, height, and angle, may be important for tadpole deposition. We predicted that adult R. biolat would deposit tadpoles in pools that are close to the ground, pools with high water volume, pools contained in long structures, and pools without conspecific tadpoles or heterospecific predators. Our experiments demonstrated that water volume and the length of the structure containing the pool affect the pattern of tadpole deposition. Tadpoles were also deposited more frequently in experimental pools containing no other tadpoles or no predators. Our results support the prediction that phytotelm-breeding species, to maximize their reproductive success, should deposit their tadpoles in pools with water volumes that maximize nutrient content and that present no competitors or predators.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".