Tropicalization of temperate reef fish communities facilitated by urchin grazing and diversity of thermal affinities
Bibliographic record
Abstract
Abstract Aim Global declines in structurally complex habitats are reshaping both land‐ and seascapes in directions that affect the responses of biological communities to warming. Here, we test whether widespread loss of kelp habitats through sea urchin overgrazing systematically changes the sensitivity of fish communities to warming. Location Global temperate latitudes. Time period Modern. Major taxa studied Fishes. Methods Community shifts in thermal affinity related to habitat were assessed by simulating and comparing fish communities from 2271 surveys across 15 ecoregions. Results We found that fishes in kelp and urchin barrens differed in realized thermal affinities and range sizes, but only in regions where species pools had high variability in the thermal affinities of species. Barrens on warm temperate reefs host relatively more warm‐affinity fish species than neighbouring kelp beds, highlighting the acceleration of tropicalization processes facilitated by urchin grazing. In contrast, proportionally more cool‐affinity fishes colonize barrens at high temperate latitudes, contributing to community lags with ocean warming in these regions. Main conclusions Our findings implicate urchins as drivers of ecological change, in part by affecting ecological resilience to warming.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".