The response of trophic interaction networks to multiple stressors along a large-scale latitudinal range in the Southern Hemisphere
Bibliographic record
Abstract
Ecological networks offer valuable insights into community structure, key species identification, and ecosystem management. Understanding how these networks respond to global change stressors is of increasing interest, especially along geographical gradients. This review summarizes potential stressor responses in marine food webs from the Southwest Atlantic to the Antarctic (45–78°S), encompassing areas such as San Jorge Gulf, Beagle Channel, Burdwood Bank, Scotia Sea, Potter Cove, and the Weddell Sea in Antarctica. The objectives are (1) to describe the structure of marine food webs along this latitudinal axis using a network approach; (2) to identify predominant global change-related stressors affecting each ecosystem; and (3) to summarize observed food web changes and hypothesize on stressor impacts. The effects of stressors were primarily reviewed at the species level. Alternative hypotheses for each study area were formulated considering (a) main stressors; (b) impacted parameters; (c) node-level species properties; and (d) network-level food web properties. Global warming emerges as the most common stressor among the studied areas across the latitudinal gradient, except in the Beagle Channel and Burdwood Bank, where alien species introduction and fisheries are more influential. We offer a series of alternative hypotheses on how warming may affect the food webs. This review emphasizes the benefits of using a network approach to understand and predict stressor effects in Southern Hemisphere marine ecosystems. This approach provides a holistic understanding of ecosystems, which enhances our ability to identify key species and their interactions, offering insights for ecosystem management and conservation in the face of global change stressors.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".