Summer litter decomposition is moderated by scale‐dependent microenvironmental variation in tundra ecosystems
Bibliographic record
Abstract
Tundra soils are one of the world's largest organic carbon stores, yet this carbon is vulnerable to accelerated decomposition as climate warming progresses. The landscape‐scale controls of litter decomposition are poorly understood in tundra ecosystems, which hinders our understanding of the global carbon cycle. We examined the extent to which the thermal sum of surface air temperature, soil moisture and permafrost thaw depth influenced litter mass loss and decomposition rates ( k ), and at which spatial thresholds an environmental variable becomes a reliable predictor of decomposition, using the Tea Bag Index protocol across a heterogeneous tundra landscape on Qikiqtaruk–Herschel Island, Yukon, Canada. We found greater green tea litter mass loss and faster decomposition rates ( k ) in wetter areas within the landscape, and to a lesser extent in areas with deeper permafrost active layer thickness and higher surface thermal sums. We also found higher decomposition rates ( k ) on north‐facing relative to south‐facing aspects at microsites that were wetter rather than warmer. Spatially heterogeneous belowground conditions (soil moisture and active layer depth) explained variation in decomposition metrics at local scales (< 50 m 2 ) better than thermal sum. Surprisingly, there was no strong control of elevation or slope on litter decomposition. Our results reveal that there is considerable scale dependency in the environmental controls of tundra litter decomposition, with moisture playing a greater role than the thermal sum at < 50 m 2 scales. Our findings highlight the importance and complexity of microenvironmental controls on litter decomposition in estimates of carbon cycling in a rapidly warming tundra biome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.011 | 0.005 |
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; both teacher heads agree on what is shown here.
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".