Cascading impacts of changes in subsidy quality on recipient ecosystem functioning
Bibliographic record
Abstract
Resource quantity and quality can differ between adjacent ecosystems, and these differences can impact subsidies exchanged between ecosystems. The quantity and quality of subsidies are rapidly changing in response to stressors associated with global environmental change, but while we have models to predict the effects of changes in subsidy quantity, we currently lack models to predict the effects of changes in subsidy quality on recipient ecosystem functioning. We developed a novel model to predict the effects of subsidy quality on recipient ecosystem biomass distribution, recycling, production, and efficiency. We parameterized the model for a case study of a riparian ecosystem subsidized by pulsed emergent aquatic insects. In this case study we focused on a common measure of subsidy quality that differs between riparian and aquatic ecosystems: the higher content of long-chain polyunsaturated fatty acids (PUFAs) in aquatic ecosystems. We analyzed how changes in the PUFA concentration of aquatic subsidies affect the dynamics in biomass stocks and functions of the riparian ecosystem. We also conducted a global sensitivity analysis to identify key drivers of subsidy impacts. Our analysis showed that subsidy quality increased the functioning of the recipient ecosystem. Recycling increased more strongly than production per unit subsidy quality increase, meaning there was a threshold where an increase in subsidy quality led to stronger effects of subsidies on recycling relative to the production of the recipient ecosystem. Our predictions were most sensitive to basal nutrient input, highlighting the relevance of recipient ecosystem nutrient levels to understanding the effects of ecosystem connections. We argue that recipient ecosystems that rely on high-quality subsidies, such as aquatic-terrestrial ecotones, are highly sensitive to changes in subsidy-recipient ecosystem connections. Our novel model unifies the subsidy hypothesis and food quality hypothesis and provides testable predictions to understand the effects of ecosystem connections on ecosystem functioning under global changes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".