Temperature variation and life history mediate the degree of nonlinearity in fluctuations of global marine fish populations
Bibliographic record
Abstract
Abstract Nonlinear dynamics readily occur in natural ecosystems and can drive irregular population fluctuations through oscillations, chaos and alternative stable states. However, the effects of anthropogenic changes, such as to demography and the climate, on nonlinearity of population fluctuations is unknown. We evaluated the extent and magnitude of nonlinearity and its environmental and life history correlates in 243 recruitment and 267 spawner time series of 143 marine fish species, worldwide. Here we show that temperature variation amplifies nonlinearity in recruitment and spawner biomass, while life history mediates the degree of nonlinearity for the latter, dampening it in slow-lived species. Nonlinearity was displayed by 81% of populations and correlated with the magnitude of fluctuations. These nonlinear dynamics were low-dimensional and causally forced by temperature in 60% of populations with the probability of forcing increasing for recruits in variable temperature environments and fast-lived spawners. Our results challenge assumptions of stable dynamics and sustainable yield common to fisheries management, and suggests nonlinear fluctuations of fish populations are magnified by size-selective fisheries and environmental variability from global climate change.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".