Disentangling tropicalization and deborealization in marine ecosystems under climate change
Bibliographic record
Abstract
As climate change accelerates, species are shifting poleward and subtropical and tropical species are colonizing temperate environments. 1–3 A popular approach for characterizing such responses is the community temperature index (CTI), which tracks the mean thermal affinity of a community. Studies in marine, 4 freshwater, 5 and terrestrial 6 ecosystems have documented increasing CTI under global warming. However, most studies have only linked increasing CTI to increases in warm-affinity species. Here, using long-term monitoring of marine fishes across the Northern Hemisphere, we decomposed CTI changes into four underlying processes—tropicalization (increasing warm-affinity), deborealization (decreasing cold-affinity), borealization (increasing cold-affinity), and detropicalization (decreasing warm-affinity)—for which we examined spatial variability and drivers. CTI closely tracked changes in sea surface temperature, increasing in 72% of locations. However, 31% of these increases were primarily due to decreases in cold-affinity species, i.e., deborealization. Thus, increases in warm-affinity species were prevalent, but not ubiquitous. Tropicalization was stronger in areas that were initially warmer, experienced greater warming, or were deeper, while deborealization was stronger in areas that were closer to human population centers or that had higher community thermal diversity. When CTI (and temperature) increased, species that decreased were more likely to be living closer to their upper thermal limits or to be commercially fished. Additionally, warm-affinity species that increased had smaller body sizes than those that decreased. Our results show that CTI changes arise from a variety of underlying community responses that are linked to environmental conditions, human impacts, community structure, and species characteristics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".