The influence of multi-stage predation on population growth and the distribution of the pond-breeding salamander, <i>Ambystoma jeffersonianum</i>
Bibliographic record
Abstract
Understanding the drivers of distributional patterns is a fundamental goal of ecology. For many organisms, distributions are determined by the habitats in which breeding occurs. Therefore, determining the factors that limit post-ovipositional success in specific habitats is critical to deciphering the factors that shape distributions. Using field surveys and laboratory, mesocosm, and field experiments, we conducted a study of the breeding effort of the salamander Ambystoma jeffersonianum (Green, 1827) in sites of varying predation pressure and the susceptibility of its embryos and larvae to predators. We then used these data to parameterize a matrix model examining the effects of predation on population growth. We found that A. jeffersonianum egg masses were less abundant in ponds with higher predation pressure. Moreover, A. jeffersonianum performance was negatively affected by both embryonic and larval predators. The results of the model suggest that only predation acting upon multiple life-history stages can limit population growth for A. jeffersonianum. These data provide support for the hypothesis that multi-stage predation can shape breeding distributions by imposing strong selective costs in specific environments. Furthermore, these data highlight the importance of conducting multi-stage studies and utilizing multiple ecological methodologies when addressing the factors that limit the distribution and abundance of organisms.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".