Interaction between biotic and abiotic factors determines tadpole survival rate under natural conditions
Bibliographic record
Abstract
:The estimation of survival rates and the assessment of factors influencing variation in survival are essential to understanding population dynamics. However, in amphibians that alternate between an aquatic larval stage and a dispersing terrestrial stage, such understanding is limited due to the difficulty of estimating survival under field conditions. In this study, we obtained precise estimates of daily survival rates of tadpoles under field conditions using capture-mark-recapture (CMR) methods and assessed their temporal and spatial variation. Specifically, we assessed the effect of temperature, intra-specific density, and the presence of introduced bullfrogs (Rana catesbeiana) on the survival rate of Pacific treefrog (Pseudacris regilla) tadpoles in southern Vancouver Island, British Columbia, Canada. Daily survival rates of tadpoles were relatively constant within a season and were also similar between years. Survival rates in different ponds varied from 95.4 to 87.9 %·d-1. Among-pond differences in survival were best explained by the interaction of temperature and tadpole density. At low tadpole densities, survival increased with temperature, but at high densities, survival decreased with increasing temperature. It was not possible to detect the effect of introduced bullfrogs over the variation accounted for by differences in temperature and intra-specific density. As in terrestrial vertebrates, biotic and abiotic factors interacted strongly to determine survival rates in these tadpoles.
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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 | 0.000 | 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".