Rapid deep ocean deoxygenation and acidification threaten life on Northeast Pacific seamounts
Bibliographic record
Abstract
Anthropogenic climate change is causing our oceans to lose oxygen and become more acidic at an unprecedented rate, threatening marine ecosystems and their associated animals. In deep-sea environments, where conditions have typically changed over geological timescales, the associated animals, adapted to these stable conditions, are expected to be highly vulnerable to any change or direct human impact. Our study coalesces one of the longest deep-sea observational oceanographic time series, reaching back to the 1960s, with a modern visual survey that characterizes almost two vertical kilometers of benthic seamount ecosystems. Based on our new and rigorous analysis of the Line P oceanographic monitoring data, the upper 3,000 m of the Northeast Pacific (NEP) has lost 15% of its oxygen in the last 60 years. Over that time, the oxygen minimum zone (OMZ), ranging between approximately 480 and 1,700 m, has expanded at a rate of 3.0 ± 0.7 m/year (due to deepening at the bottom). Additionally, carbonate saturation horizons above the OMZ have been shoaling at a rate of 1-2 m/year since the 1980s. Based on our visual surveys of four NEP seamounts, these deep-sea features support ecologically important taxa typified by long life spans, slow growth rates, and limited mobility, including habitat-forming cold water corals and sponges, echinoderms, and fish. By examining the changing conditions within the narrow realized bathymetric niches for a subset of vulnerable populations, we resolve chemical trends that are rapid in comparison to the life span of the taxa and detrimental to their survival. If these trends continue as they have over the last three to six decades, they threaten to diminish regional seamount ecosystem diversity and cause local extinctions. This study highlights the importance of mitigating direct human impacts as species continue to suffer environmental changes beyond our immediate control.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".