Getting ready for invasions: can background level of risk predict the ability of naïve prey to survive novel predators?
Bibliographic record
Abstract
Factors predicting the outcome of predator invasions on native prey communities are critical to our understanding of invasion ecology. Here, we tested whether background level of risk affected the survival of prey to novel predators, both native and invasive, predicting that high-risk environments would better prepare prey for invasions. We used naïve woodfrog as our prey and exposed them to a high or low risk regime either as embryos (prenatal exposure) or as larvae (recent exposure). Tadpoles were then tested for their survival in the presence of 4 novel predators: two dytiscid beetles, crayfish and trout. Survival was affected by both risk level and predator type. High risk was beneficial to prey exposed to the dytiscids larvae (ambush predators), but detrimental to prey exposed to crayfish or trout (pursuit predators). No effect of ontogeny of risk was found. We further documented that high-risk tadpoles were overall more active than their low-risk counterparts, explaining the patterns found with survival. Our results provide insights into the relationship between risk and resilience to predator invasions.
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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 | 0.000 |
| 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".