Long-term warming manipulations reveal complex decomposition responses across different tundra vegetation types
Bibliographic record
Abstract
In a rapidly warming tundra, ecosystems will undergo major environmental changes that are predicted to significantly alter belowground processes such as decomposition of plant litter. Making use of International Tundra Experiment sites (ITEX), which were established approximately two decades ago, we examined the long-term impacts of warming on decomposition. We used the Tea Bag Index (TBI) methodology to measure the annual mass loss (%) of two tea types as a proxy for potential decomposition rates, across five tundra vegetation types. Direct effects of warming were assessed by comparing mass loss within and outside warming manipulations. Indirect effects of warming, such as those caused by warming-induced changes in plant community composition, were assessed through the relationship between mass loss of tea and local biotic and abiotic conditions. We found positive effects of warming on decomposition, although the responses varied between vegetation and tea types. Interestingly, we found support for the indirect influence of long-term warming on decomposition through warming-induced changes in the composition of plant communities. Our findings demonstrate the complexity in decomposition responses to warming across different vegetation types and highlight the importance of long-term legacies of warming in decomposition responses across the Arctic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".