Quantifying the potential for marine reserves or harvest reductions to buffer temporal mismatches caused by climate change
Bibliographic record
Abstract
Climate change has caused shifts in seasonal timing of climatic events such as the onset of spring upwelling, which can lead to temporal mismatches between fish spawning and production of zoopklankton prey. Fishing may exacerbate mismatches through age truncation, particularly when offspring survival is dependent on maternal age, but no-take reserves or harvest reductions might buffer this effect. To quantify the potential for management to buffer synergistic interactions between fishing and climate, we developed a dynamic population model of a harvested species where larval survival depended on spring transition timing and maternal age. We applied this model to rockfishes (Sebastes spp.) after verifying empirically that spring transition timing affects their recruitment success. We found that yield and persistence changed more with maternal-age-dependent larval provisioning than maternal-age-dependent spawning timing across a range of spring transition timings, especially with environmental stochasticity. Either implementing reserves or reducing fishing can mitigate impacts on larval survival, but reserves convey the added benefit of decreased sensitivity of yield and persistence to fishing. However, reserve buffering effects decreased with the inclusion of environmental stochasticity.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".