Assessing the time of emergence of global ocean fish biomass using ensemble climate to fish simulations
Bibliographic record
Abstract
Climate change is anticipated to considerably reduce global marine fish biomass, driving marine ecosystems into unprecedented states with no historical analogues. The Time of Emergence (ToE) marks the pivotal moment when climate conditions (i.e. signal) deviate from pre-industrial norms (i.e. noise). Leveraging ensemble climate-to-fish simulations, this study examines the ToE of epipelagic, migratory and mesopelagic fish biomass, alongside their main environmental drivers, for two contrasted climate-change scenarios. Globally-averaged biomass signals emerge over the historical period. Epipelagic biomass decline emerges earlier (1950) than mesozooplankton decline (2000) due to a stronger signal in the early 20th century, possibly related to trophic amplification induced by an early-emerging surface warming (1915). Trophic amplification is delayed for mesopelagic biomass due to postponed warming in the mesopelagic zone, resulting in a later emergence (2000). ToE displays strong size class dependence, with medium sizes (20 cm) experiencing delays compared to the largest (1 m) and smallest (1 cm) categories. Regional signal emergence lags behind the global average, with median ToE estimates of 2029, 2034 and 2033 for epipelagic, mesopelagic and migrant communities, respectively, due to systematically larger local noise compared to global one. These ToEs are also spatially heterogeneous, driven predominantly by the signal pattern, akin to mesozooplankton. Additionally, our findings underscore that mitigation efforts (i.e. transitioning from SSP5-8.5 to SSP1-2.6 scenario) have a potential to curtail emerging ocean surface signals by 40%.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".