Do trophic cascades affect the storage and flux of atmospheric carbon? An analysis of sea otters and kelp forests
Bibliographic record
Abstract
We combine data collected from the past 40 years to estimate the indirect effects of sea otters ( Enhydra lutris ) on ecosystem carbon (C) production and storage across their North American range, from Vancouver Island to the western edge of Alaska's Aleutian Islands. We find that sea otters, by suppressing sea urchin ( Strongylocentrotus spp) populations, allow kelp (Order Laminariales) ecosystems to develop with a net primary productivity (NPP) of 313–900 grams C per square meter per year (g C m −2 yr −1 ) and biomass density of 101–180 grams C per square meter (g C m −2 ). In the absence of sea otters, these areas would have an NPP of 25–70 g C m −2 yr −1 and biomass density of 8–14 g C m −2 . Over an ecosystem area of approximately 5.1 × 10 10 m 2 , the effect of sea otter predation on living kelp biomass alone represents a 4.4‐to 8.7‐teragram increase in C storage. At 2012 prices (US$47 per ton of C), this stored C would be valued at US$205 million–$408 million on the European Carbon Exchange. Although questions remain concerning the pathways and compartments of kelp C flux and storage, sea otters undoubtedly have a strong influence on these elements of the C cycle. Predator‐induced trophic cascades likely influence the rates of C flux and storage in many other species and ecosystems.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".