Top-down and bottom-up coupling effects of subsidies on recipient ecosystems
Bibliographic record
Abstract
Abstract A subsidy can directly enter a recipient ecosystem by either being consumed or being recycled to the nutrient pool with both pathways causing multiple indirect and potentially conflicting effects. Subsidy pathways are prone to anthropogenic stressors, which can impact the relative strength of the recycling and direct consumption fluxes. Current studies have mainly focused on a single coupling pathway, limiting our knowledge of subsidy effects on recipient ecosystems. We derive a series of models to predict the relative and interactive effects between consumption and recycling subsidy coupling pathways on the biomass and functions of a recipient ecosystem. We solved the models for analytical equilibria, and compared model predictions with and without each subsidy pathway. Our results show that the direct consumption and recycling coupling pathways of subsidies interact antagonistically, as the feedbacks between both pathways led to lower stocks and functions of the recipient ecosystem than a model which omits these feedbacks. These subsidy effects are consistent for each trophic level of the recipient ecosystem. Our models also predicted that consumption and recycling pathways of subsidies had differential effects on the recipient ecosystem. Recycling coupling pathway always led to equal or higher stocks and functions across recipient ecosystem trophic levels, whereas consumption couplings had alternating positive and negative effects depending on trophic level and characteristic of a trophic cascade. We suggest that future empirical and theoretical research on subsidies should consider various coupling pathways to provide a more mechanistic understanding of the effects of subsidies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".