The Impact of Whaling on the Ocean Carbon Cycle: Why Bigger Was Better
Bibliographic record
Abstract
BACKGROUND: Humans have reduced the abundance of many large marine vertebrates, including whales, large fish, and sharks, to only a small percentage of their pre-exploitation levels. Industrial fishing and whaling also tended to preferentially harvest the largest species and largest individuals within a population. We consider the consequences of removing these animals on the ocean's ability to store carbon. METHODOLOGY/PRINCIPAL FINDINGS: Because body size is critical to our arguments, our analysis focuses on populations of baleen whales. Using reconstructions of pre-whaling and modern abundances, we consider the impact of whaling on the amount of carbon stored in living whales and on the amount of carbon exported to the deep sea by sinking whale carcasses. Populations of large baleen whales now store 9.1×10(6) tons less carbon than before whaling. Some of the lost storage has been offset by increases in smaller competitors; however, due to the relative metabolic efficiency of larger organisms, a shift toward smaller animals could decrease the total community biomass by 30% or more. Because of their large size and few predators, whales and other large marine vertebrates can efficiently export carbon from the surface waters to the deep sea. We estimate that rebuilding whale populations would remove 1.6×10(5) tons of carbon each year through sinking whale carcasses. CONCLUSIONS/SIGNIFICANCE: Even though fish and whales are only a small portion of the ocean's overall biomass, fishing and whaling have altered the ocean's ability to store and sequester carbon. Although these changes are small relative to the total ocean carbon sink, rebuilding populations of fish and whales would be comparable to other carbon management schemes, including ocean iron fertilization.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".