Including foraging arena and top-down controls improves the modeling of trophic flows and fishing impacts in aquatic food webs
Bibliographic record
Abstract
Food web dynamics consist of processes that affect ecosystem structure and functioning. EcoTroph (ET) is a recently developed approach and software for modeling aquatic ecosystems, articulated entirely around the trophic level concept. Here, we used ET to investigate impacts of 2 trophic controls (i.e. foraging arena and top-down controls) on marine ecosystem trophic flows and associated fishing effects. A new version of the ET model accounting for the foraging arena theory was developed. Cross impacts of the 2 trophic controls and different fishing scenarios were analyzed using a virtual ecosystem. Results showed that foraging arena controls decreased the resistance and production of an ecosystem facing increasing fishing mortality. In contrast, the inclusion of top-down controls resulted in a more resistant ecosystem, with a decrease in the kinetics of trophic flows at lower trophic levels (TLs) when the abundance of higher TLs is reduced by fishing. These 2 controls increased the interactions between TLs, and, in part, shaped fishing impacts at the ecosystem scale. Then, we applied ET to 3 real ecosystems which have been previously modeled using Ecopath with Ecosim (EwE). EcoTroph and Ecosim predictions related to changes in fishing effort were compared, and showed that accounting for trophic controls enabled EcoTroph to mimic Ecosim models, and better reflect associated changes in trophic flows. The 3 case studies exhibited different behaviors: while the pelagic ecosystem had strong foraging arena controls but no top-down controls, the other ecosystems were characterized by weaker foraging arena controls but effective top-down controls.
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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) | 0.001 | 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".