Cascading effects of predation risk determine how marine predators become terrestrial prey on an oceanic island
Bibliographic record
Abstract
Apex predators can suppress the foraging activity of mesopredators, which may then result in cascading benefits for the prey of those mesopredators. We studied the interactions between a top predator, the Barn Owl (Tyto alba), and their primary prey, an island endemic deer mouse (Peromyscus maniculatus elusus), which in turn consumes the eggs of seabirds nesting on Santa Barbara Island in California. Scripps's Murrelets (Synthliboramphus scrippsi), a threatened nocturnal seabird, arrive annually to breed on this island, and whose first egg is particularly vulnerable to predation by mice. We took advantage of naturally occurring extreme variations in the density of mice and owls on the island over 3 years and predicted that (1) mouse foraging would decrease with increasing predation risk from owls and moonlight and (2) these decreases in foraging would reduce predation on murrelet eggs. We measured the giving up densities of mice with experimental foraging stations and found that mice were sensitive to predation risk and foraged less when owls were more abundant and less during the full moon compared to the new moon. We also monitored the fates of 151 murrelet eggs, and found that murrelet egg predation declined as owl abundance increased, and was lower during the full moon compared to the new moon. Moreover, high owl abundance suppressed egg predation even when mice were extremely abundant. We conclude that there is a behaviorally mediated cascade such that owls on the island had a positive indirect effect on murrelet egg survival. Our study adds to the wider recognition of the strength of risk effects to structure food webs, as well as highlighting the complex ways that marine and terrestrial food webs can intersect.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".