Environmental and spatial factors affect the composition and morphology of tadpole assemblages
Bibliographic record
Abstract
Understanding how community compositions are affected by environmental and spatial factors are essential to provide knowledge about the distributions of species. Furthermore, these factors can play a role in species morphological variation. Tadpoles can be found in different types of aquatic microhabitats, showing a considerable amount of morphological diversity. We hypothesized that tadpole morphological diversity is controlled by ecological and spatial factors other than assemblage attributes, and that tadpole assemblage composition is affected by spatial factors. To test these hypotheses, we recorded the abundance of tadpoles from different ponds, identified eight environmental variables that represented local and landscape descriptors of the ponds, recorded the spatial coordinates of the ponds, and measured the morphological variation of assemblages. Spatial factors significantly affected the composition of tadpole assemblages, while both spatial and environmental factors affected morphological variation. The ability of tadpoles to alter their morphology in response to environmental factors might be a result of poor oviposition site choice, and this probably interacts with spatial factors to control the assemblage composition of tadpoles. Morphological variation is advantageous for tadpoles because it allows them to adjust their morphology to environmental conditions. This study has demonstrated how factors that control the assemblage composition of tadpoles also drive their morphological diversity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".