Spatial food webs in the Barents Sea: atlantification and the reorganization of the trophic structure
Bibliographic record
Abstract
Climate change affects ecosystems at several levels: by altering the spatial distribution of individual species, by locally rewiring interspecific interactions, and by reorganizing trophic networks at larger scales. The dynamics of marine food webs are becoming more and more sensitive to spatial processes and connections in the seascape. As a case study, we study the atlantification of the Barents Sea: we compare spatio-temporal subsystems at three levels: the identity of key organisms, critically important interactions and the entire food web. Network analysis offers quantitative measurements, including centrality indices, trophic similarity indices, a topological measure of interaction asymmetry and network-level measures. We found that atlantification alters the identity of key species (boreal demersals becoming hubs), results in strongly asymmetric interactions (dominated by haddock), changes the dominant regulation regime (from bottom-up to wasp-waist control) and makes the food web less modular. Since the results of food web analysis may be quite sensitive to network construction, the aggregation of food web data was explicitly studied to increase the robustness of food web analysis. We found that an alternative, mathematical aggregation algorithm better preserves some network properties (e.g. density) of the original, unaggregated network than the biologically inspired aggregation into functional groups. This article is part of the theme issue 'Connected interactions: enriching food web research by spatial and social interactions'.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".