Potential of prey size and type to affect foraging asymmetries in tiger salamander (<i>Ambystoma tigrinum nebulosum</i>) larvae
Bibliographic record
Abstract
Although competitive interactions within predator populations are known to depend on their size structure, we understand less about how these interactions are influenced by prey characteristics. Most studies of such interactions for tiger salamander (Ambystoma tigrinum nebulosum) larvae have used small zooplankton prey. We investigate the potential of exploitation and interference competition to influence the success of tiger salamander larvae feeding on relatively large prey, mayfly and damselfly larvae. We measured salamander foraging efficiency for a range of salamander and prey sizes and observed aggression levels of salamanders of varying size housed together. Exploitative foraging efficiency (captures per attempts) increased with salamander size but was better predicted by relative prey size (prey size as a percentage of salamander snout–vent length) than by salamander size alone; it also depended significantly on prey type. Aggression (interference) levels were higher when prey were present, and larger salamanders were more aggressive than smaller ones but did not consume more mayfly prey. Our results suggest that investigating the environmental conditions, particularly the prey characteristics, that influence size-based competitive advantages will lead to a better understanding of predator population dynamics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".