Nutrient inputs versus piscivore biomass as the primary driver of reservoir food webs
Bibliographic record
Abstract
We used an ecosystem-based modeling approach, Ecopath with Ecosim, to explore the relative importance of a top-down biotic management lever (top predator introduction) versus a bottom-up abiotic management lever (alteration of nutrient inputs) in regulating biomass in reservoir food webs. To do so, we modeled three Ohio reservoirs that varied in ecosystem productivity. For each, we simulated five hybrid striped bass (Morone chrysops × Morone saxatilis) (introduced top predator) biomass levels at three nutrient input levels (n = 15 simulations per reservoir). Nutrient inputs influenced the food web more than introduced predators within each reservoir. Further, across all three reservoirs, the impact of stocked hybrid striped bass on the equilibrium biomass of phytoplankton, prey fish (gizzard shad, Dorosoma cepedianum), and native top predators (e.g., largemouth bass, Micropterus salmoides) was <3%, <14%, and <20%, respectively, of the maximum impact of changes in nutrient inputs on these components. Thus, in mesotrophic to hypereutrophic reservoirs that are dominated by omnivorous gizzard shad, manipulating allochthonous inputs of nutrients offers agencies a more powerful means to regulate food web structure than manipulation of top predator biomass.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".