META-ANALYSIS: TROPHIC LEVEL, HABITAT, AND PRODUCTIVITY SHAPE THE FOOD WEB EFFECTS OF RESOURCE SUBSIDIES
Bibliographic record
Abstract
Studies of the effects of cross-habitat resource subsidies have been a feature of food web ecology over the past decade. To date, most studies have focused on demonstrating the magnitude of a subsidy or documenting its effect in the recipient habitat. Ecologists have yet to develop a satisfactory framework for predicting the magnitude of these effects. We used 115 data sets from 32 studies to compare consumer responses to resource subsidies across recipient habitat type, trophic level, and functional group. Changes in consumer density or biomass in response to subsidies were inconsistent across habitats, trophic, and functional groups. Responses in stream cobble bar and coastline habitats were larger than in other habitats. Contrary to expectation, the magnitude of consumer response was not affected by recipient habitat productivity or the ratio of productivity between donor and recipient habitats. However, consumer response was significantly related to the ratio of subsidy resources to equivalent resources in the recipient habitat. Broad contrasts in productivity are modified by subsidy type, vector, and the physical and biotic characteristics of both donor and recipient habitats. For this reason, the ratio of subsidy to equivalent resources is a more useful tool for predicting the possible effect of a subsidy than coarser contrasts of in situ productivity. The commonness of subsidy effects suggests that many ecosystems need to be studied as open systems.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".