From projected species distribution to food‐web structure under climate change
Bibliographic record
Abstract
Climate change is inducing deep modifications in species geographic ranges worldwide. However, the consequences of such changes on community structure are still poorly understood, particularly the impacts on food-web properties. Here, we propose a new framework, coupling species distribution and trophic models, to predict climate change impacts on food-web structure across the Mediterranean Sea. Sea surface temperature was used to determine the fish climate niches and their future distributions. Body size was used to infer trophic interactions between fish species. Our projections reveal that 54 fish species of 256 endemic and native species included in our analysis would disappear by 2080-2099 from the Mediterranean continental shelf. The number of feeding links between fish species would decrease on 73.4% of the continental shelf. However, the connectance of the overall fish web would increase on average, from 0.26 to 0.29, mainly due to a differential loss rate of feeding links and species richness. This result masks a systematic decrease in predator generality, estimated here as the number of prey species, from 30.0 to 25.4. Therefore, our study highlights large-scale impacts of climate change on marine food-web structure with potential deep consequences on ecosystem functioning. However, these impacts will likely be highly heterogeneous in space, challenging our current understanding of climate change impact on local marine ecosystems.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".