Variability in functional response curves among larval salamanders: comparisons across species and size classes
Bibliographic record
Abstract
Predator species and body size represent critical factors that have differential effects on prey populations, as well as overall community structure. However, investigations of how morphologically-similar predator species, simultaneous to variation in predator body size, influence lower trophic levels are infrequently performed. We tested whether predator species and body size influenced the functional response curve of three larval ambystomatid salamanders (Ringed Salamander, Ambystoma annulatum Cope, 1886; Spotted Salamander, Ambystoma maculatum (Shaw, 1802); Marbled Salamander, Ambystoma opacum (Gravenhorst, 1807)) while eating congeneric prey. We combined larval salamanders of varying body sizes with up to six prey densities within experimental microcosms. We tested for the shape of the functional response curve and obtained parameter estimates for attack rate and handling time for each predator size – species combination. We found variability among both species and size classes, with a combination of type I and type II functional response curves. Large size classes of predators had higher attack rates than smaller size classes, but equivalently-sized larvae of different species exhibited differences in attack rates and handling time. Our study shows that predation risk varies depending on the size structure and diversity of predators present in a food web, and that grouping predators by either species or size class may reduce the ability to predict changes in community structure resulting from such interactions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".