Eastern Fence Lizards (<i>Sceloporus undulatus</i>) display an ontogenetic shift in relative consumption of native and invasive prey
Bibliographic record
Abstract
Interactions between invasive prey and native predators can provide an opportunity to better understand predator–prey dynamics and how these may change through ontogeny. Eastern Fence Lizards (Sceloporus undulatus (Bosc and Daudin in Sonnini and Latreille, 1801)) are ant specialist, particularly as juveniles. Invasive red imported fire ants (Solenopsis invicta Buren, 1972) pose a lethal risk to S. undulatus that eat them, especially smaller-bodied juveniles. We examine ontogenetic shifts in S. undulatus consumption of toxic invasive fire ants versus palatable native pyramid ants (Dorymyrmex bureni (Trager, 1988)). We predicted that hatchlings should avoid eating fire ants in favor of native ants, whereas less-vulnerable adults should take advantage of both prey sources. However, when given the choice between fire ants and native ants, hatchlings consumed similar numbers of these species, whereas adults consumed nearly three times as many native ants as invasive fire ants. Increased consumption of fire ants in adulthood could be the result of lifetime experience, strategies to safely consume fire ants, ontogenetic shifts in the ability to distinguish between ants, or reduced costs to adults of eating venomous ants. Future research should aim to distinguish these alternative mechanisms and examine the long-term consequences of native species incorporating toxic invasive prey into their diets.
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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".