Potential impacts of climate change on Northeast Pacific marine foodwebs and fisheries
Bibliographic record
Abstract
Abstract Ainsworth, C. H., Samhouri, J. F., Busch, D. S., Cheung, W. W. L., Dunne, J., and Okey, T. A. 2011. Potential impacts of climate change on Northeast Pacific marine foodwebs and fisheries. – ICES Journal of Marine Science, 68: 1217–1229. Although there has been considerable research on the impacts of individual changes in water temperature, carbonate chemistry, and other variables on species, cumulative impacts of these effects have rarely been studied. Here, we simulate changes in (i) primary productivity, (ii) species range shifts, (iii) zooplankton community size structure, (iv) ocean acidification, and (v) ocean deoxygenation both individually and together using five Ecopath with Ecosim models of the northeast Pacific Ocean. We used a standardized method to represent climate effects that relied on time-series forcing functions: annual multipliers of species productivity. We focused on changes in fisheries landings, biomass, and ecosystem characteristics (diversity and trophic indices). Fisheries landings generally declined in response to cumulative effects and often to a greater degree than would have been predicted based on individual climate effects, indicating possible synergies. Total biomass of fished and unfished functional groups displayed a decline, though unfished groups were affected less negatively. Some functional groups (e.g. pelagic and demersal invertebrates) were predicted to respond favourably under cumulative effects in some regions. The challenge of predicting climate change impacts must be met if we are to adapt and manage rapidly changing marine ecosystems in the 21st century.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".