Rewiring the Carbon Cycle: A Theoretical Framework for Animal‐Driven Ecosystem Carbon Sequestration
Bibliographic record
Abstract
Abstract Most carbon cycle models do not consider animal‐mediated effects, focusing instead on carbon exchanges among plants, microbes, and the atmosphere. Yet, a growing body of empirical evidence from diverse ecosystems points to pervasive animal effects on ecosystem carbon cycling and shows that ignoring them could lead to misrepresentation of an ecosystem's carbon cycle. We develop a new theoretical framework to account for animal effects on ecosystem carbon cycling. We combine a classic ecosystem compartment modeling approach with a classic carbon model to account for carbon flux and storage among plant, animal, and soil microbial trophic compartments. We show, by way of numerical analyses of steady state conditions across three competing scenarios, that animal presence alters the dominant pathways of control over carbon storage and capture. This altered control arises via direct, consumptive effects and especially via indirect, non‐consumptive pathways by instigating faster nutrient recycling. This leads to a quantitative change in the ecosystem's carbon balance, enhancing the amount of carbon captured and stored in the ecosystem. Further, the indirect pathways appear especially important in enabling these effects because of their sensitivity to the structure of the ecosystem's food chain. The modeling shows that animals could play a larger role in ecosystem carbon cycle than previously thought. Our framework provides further guidance for empirical research aimed at quantifying animal‐mediated control of carbon cycling and to inform the development of nature‐based climate change solutions that leverage animal influence on the carbon cycle to help mitigate climate change.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".