Carryover effects of phenotypic plasticity: embryonic environment and larval response to predation risk in Wood Frogs (<i>Lithobates sylvaticus</i>) and Northern Leopard Frogs (<i>Lithobates pipiens</i>)
Bibliographic record
Abstract
Limitations of phenotypic plasticity affect the success of individuals and populations in changing environments. We assessed the plasticity-history limitation on predator-induced defenses in anurans (Wood Frogs, Lithobates sylvaticus (LeConte, 1825), and Northern Leopard Frogs, Lithobates pipiens (Schreber, 1782)), predicting that plastic responses to predation risk by dragonfly larvae (family Aeshnidae) in the embryonic environment would limit the defensive response to predators in the larval environment. Predator-conditioned Wood Frog embryos increased relative tail depth in response to those same cues as larvae, whereas predator-naive tadpoles did not. However, no carryover effect was noted in the behavioural response of Wood Frog tadpoles to predation risk. Predator-naive Northern Leopard Frog tadpoles increased relative tail depth in response to predation risk in the larval environment. Predator-conditioned Northern Leopard Frog embryos hatched with, and maintained, a marginal increase in tail depth as larvae in the absence of predation risk. Predator-conditioned Northern Leopard Frog embryos exposed to predation risk as larvae showed no morphological response. While we find no strong support for the plasticity-history limitation per se, carryover effects across embryonic and larval life-history stages were noted in both Wood Frog and Northern Leopard Frog, suggesting that predation risk early in ontogeny can influence the outcome of future interactions with 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 | 0.000 | 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".