Data from: Predator-induced collapse of niche structure and coexistence on islands
Bibliographic record
Abstract
Biological invasions represent both a pressing environmental challenge and an opportunity to investigate fundamental ecological processes, such as the role of top predators in regulating species diversity and food-web structure. In whole-ecosystem manipulations of small Caribbean islands where brown anole lizards (Anolis sagrei) were the native top predator, we experimentally staged invasions by competitors (green anoles, A. smaragdinus) and/or novel top predators (curly-tailed lizards, Leiocephalus carinatus). We show that curly-tails destabilized coexistence of competing prey species, contrary to the classic idea of keystone predation. Fear-driven avoidance of predators collapsed the spatial and dietary niche structure that otherwise stabilized coexistence, intensifying interspecific competition within predator-free refuges and contributing to green-anole population extinctions. Moreover, whereas adding either green anoles or curly-tails lengthened food chains, adding both species reversed this effect, in part because apex predators were trophic omnivores. Our results underscore the importance of top-down control in community ecology, but show that its outcomes hinge on prey behavior, spatial structure, and omnivory. Diversity-enhancing effects of top predators cannot be assumed, and non-consumptive effects of predation risk may be a widespread constraint on species coexistence.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.071 | 0.045 |
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".