Eutrophication triggered changes in network structure and fluxes of the Comacchio Lagoon (Italy)
Bibliographic record
Abstract
Coastal lagoons, which cover about 13% of coastline, are among the most productive ecosystems worldwide. However, they are subject to significant stressors, both natural and anthropogenic, which can alter ecosystem services and functioning and food web structure. In the Comacchio Lagoon (Northern Italy), eutrophication, among other minor factors, transformed the ecosystem in the early 1980s. Here, we compiled available data for the lagoon into trophic networks (pre- and post-transformation), analyzed the ecosystem using local and global network analysis, and computed trophic fluxes of the two periods. For comparability, the networks of two periods (i.e., pre- and post- transformation) were aggregated into food webs with 23 nodes. We found differences in the trophic networks before and after eutrophication, resulting in some decrease in complexity, increase of flow diversity, and an overall shortening of the food chain. A crucial aspect of this change is the disappearance of submerged vegetation in the lagoon and the increased importance of cyanobacteria in the post-eutrophication period. We provide an approach to better understand ecosystem changes after severe disturbances which can be extended to biodiversity conservation and for the management of coastal resources in general.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".