An Extended Ecosystem Model for Understanding EE2 Indirect Effects on a Freshwater Food Web and its Ecosystem Function Resilience
Bibliographic record
Abstract
Freshwater species are highly impacted by human activities and the consequences on ecosystem functioning are still not well understood. In the literature, a multitrophic perspective appears to be key to advance future biodiversity and ecosystem functioning (BEF) research. This paper aims at studying indirect effects of the synthetic hormone 17α-ethinylestradiol (EE2) on a freshwater food web by creating BEF links, through the interpretation of seasonal cycles and multitrophic interactions. An ecosystem model previously developed using experimental data from a unique whole-ecosystem study on EE2 was extended with the addition of Chaoborus, an omnivorous insect. During the experimental study, a collapse of fathead minnow was measured after one year of exposure. The simulation results showed that EE2 indirect effects on other fishes (horizontal diversity) and lower trophic levels (vertical diversity) were connected to multitrophic interactions with a top-down cascade effect. The results also demonstrated that adding an omnivorous, mid-trophic level group such as Chaoborus enhances resilience. Conversely, missing such a species means that the actual resilience of an ecosystem and its functioning cannot be properly simulated. Thus, the extended ecosystem model offers a tool that can help better understand what is happening after environmental perturbations, such as with EE2.
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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 | 0.001 | 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".