Integrating ecophysiology and plankton dynamics into projected maximum fisheries catch potential under climate change in the Northeast Atlantic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Cheung, W. W. L., Dunne, J., Sarmiento, J. L., and Pauly, D. 2011. Integrating ecophysiology and plankton dynamics into projected maximum fisheries catch potential under climate change in the Northeast Atlantic. – ICES Journal of Marine Science, 68: 1008–1018. Previous global analyses projected shifts in species distributions and maximum fisheries catch potential across ocean basins by 2050 under the Special Report on Emission Scenarios (SRES) A1B. However, these studies did not account for the effects of changes in ocean biogeochemistry and phytoplankton community structure that affect fish and invertebrate distribution and productivity. This paper uses a dynamic bioclimatic envelope model that incorporates these factors to project distribution and maximum catch potential of 120 species of exploited demersal fish and invertebrates in the Northeast Atlantic. Using projections from the US National Oceanic and Atmospheric Administration's (NOAA) Geophysical Fluid Dynamics Laboratory Earth System Model (ESM2.1) under the SRES A1B, we project an average rate of distribution-centroid shift of 52 km decade−1 northwards and 5.1 m decade−1 deeper from 2005 to 2050. Ocean acidification and reduction in oxygen content reduce growth performance, increase the rate of range shift, and lower the estimated catch potentials (10-year average of 2050 relative to 2005) by 20–30% relative to simulations without considering these factors. Consideration of phytoplankton community structure may further reduce projected catch potentials by ∼10%. These results highlight the sensitivity of marine ecosystems to biogeochemical changes and the need to incorporate likely hypotheses of their biological and ecological effects in assessing climate change impacts.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it