Marine subsidy promotes spatial and dietary niche variation in an omnivore, the Keen’s mouse (<i>Peromyscus keeni</i>)
Bibliographic record
Abstract
Abstract Marine‐derived resource subsidies can generate intrapopulation variation in the behaviors and diets of terrestrial consumers. How omnivores respond, given their multiple trophic interactions, is not well understood. We sampled mice (Peromyscus keeni) and their food sources at five sites on three islands of the Central Coast of British Columbia, Canada, to test predictions regarding variation in the spatial behavior and consumption of marine‐subsidized foods among individuals. About 50% of detections (n = 27 recaptures) occurred at traps closest to shoreline (25 m), with capture frequencies declining significantly inland (up to 200 m). Stable isotope signatures (δ13C and δ15N), particularly δ15N, in plant foods, forest arthropod prey, and mouse feces were significantly enriched near shorelines compared with inland, while δ13C patterns were more variable. Bayesian isotope mixing models applied to isotope values in mouse hair indicated that over one‐third (35–37%) of diet was comprised of beach‐dwelling arthropods, a marine‐derived food source. Males were more abundant near the shoreline than females and consumed more marine‐derived prey, regardless of reproductive status or availability of other food sources. Our results identify how multiple pathways of marine nutrient transfer can subsidize terrestrial omnivores and how subsets of recipient populations can show variation in spatial and dietary response.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".