Biogeographic features mediate marine subsidies to island food webs
Bibliographic record
Abstract
Abstract Although marine subsidies often enrich terrestrial ecosystems, their influence is known to be context‐dependent. Additionally, the multitrophic impact of marine subsidies has not been traced through food webs across physically diverse islands. Here, we test predictions about how island characteristics can affect marine enrichment of food web constituents and how nutrients flow through island food webs. To evaluate enrichment and trace marine nutrients across food webs, we used stable isotopes of soil, flora, and fauna (n = 4752 samples) collected from 97 islands in British Columbia, Canada. Island area was the strongest predictor of enrichment across taxa; we found that samples were more 15N‐rich on smaller islands. Enrichment declined with distance from shore but less so on small islands, implying a higher per‐unit‐area subsidy effect. These area and distance‐to‐shore effects were taxon‐specific, and nearly twice as strong in basal food web groups. We also found that increases in δ15N correlated with increases in %N in basal trophic groups, as well as in songbirds, implying biologically relevant uptake of a potentially limiting nutrient. Path analysis demonstrated that subsidies in soil flow through plants and detritivores, and into upper‐level consumers. Our results reveal an interplay between island biogeography and marine subsidies in shaping island food webs through bottom‐up processes.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".