Effects of near‐future ocean acidification, fishing, and marine protection on a temperate coastal ecosystem
Bibliographic record
Abstract
Understanding ecosystem responses to global and local anthropogenic impacts is paramount to predicting future ecosystem states. We used an ecosystem modeling approach to investigate the independent and cumulative effects of fishing, marine protection, and ocean acidification on a coastal ecosystem. To quantify the effects of ocean acidification at the ecosystem level, we used information from the peer-reviewed literature on the effects of ocean acidification. Using an Ecopath with Ecosim ecosystem model for the Wellington south coast, including the Taputeranga Marine Reserve (MR), New Zealand, we predicted ecosystem responses under 4 scenarios: ocean acidification + fishing; ocean acidification + MR (no fishing); no ocean acidification + fishing; no ocean acidification + MR for the year 2050. Fishing had a larger effect on trophic group biomasses and trophic structure than ocean acidification, whereas the effects of ocean acidification were only large in the absence of fishing. Mortality by fishing had large, negative effects on trophic group biomasses. These effects were similar regardless of the presence of ocean acidification. Ocean acidification was predicted to indirectly benefit certain species in the MR scenario. This was because lobster (Jasus edwardsii) only recovered to 58% of the MR biomass in the ocean acidification + MR scenario, a situation that benefited the trophic groups lobsters prey on. Most trophic groups responded antagonistically to the interactive effects of ocean acidification and marine protection (46%; reduced response); however, many groups responded synergistically (33%; amplified response). Conservation and fisheries management strategies need to account for the reduced recovery potential of some exploited species under ocean acidification, nonadditive interactions of multiple factors, and indirect responses of species to ocean acidification caused by declines in calcareous predators.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".