Warming effects on decomposition via trophic cascades vary across elevations in an alpine meadow ecosystem
Bibliographic record
Abstract
Abstract Warming increases the foraging rates of ectothermic predators, potentially resulting in increased predation pressure on detritivores through top‐down effects, thereby influencing decomposition. Trophic cascade effects under warming are shaped by many factors, including temperature, precipitation and trophic structure. Greater species diversity may weaken these cascades through intensified interspecific interactions and, in turn, shape how decomposition responds to warming (the vertical diversity hypothesis), but this process has seldom been examined in natural ecosystems. Here, we experimentally increased warming at three elevations (4650, 4950 and 5200 m) in an alpine meadow ecosystem to test the predator‐mediated effects of warming on decomposition, as well as the role of arthropod diversity in these processes. Among the three elevations, arthropod diversity and predator abundance were significantly greater at 4950 m than at 4650 and 5200 m. Warming increased predator abundance at all three elevations, but decreased detritivore abundance only at 4650 and 5200 m. Detritivore abundances at 4650 and 5200 m, but not at 4950 m, were correlated negatively with predator abundance under experimental warming. Based on multigroup structural equation models, warming primarily reduced litter decomposition directly during the cold season when arthropod activity was limited. In contrast, during the warm season and over the whole year, both periods with greater arthropod activity, warming predominantly reduced litter decomposition via predator‐driven top‐down effects, rather than through direct effects. Although warming increased predator abundance across all elevations, the resulting trophic cascade was observed only at 4650 and 5200 m, where elevated predator abundance suppressed detritivores and reduced litter decomposition. In network analysis, indicators of arthropod diversity had the most correlations with indicators of ecosystem function, suggesting that the warming‐driven decline in detritivore abundance could influence ecosystem functionality negatively. Synthesis: We conclude that warming‐induced increases in predator abundance can reduce detritivores and decomposition through strong top‐down effects, but these effects appear confined to elevations with low arthropod diversity. Our study provides a novel perspective on the factors shaping decomposition responses to warming. Large‐scale field studies and mesocosm‐based experiments are warranted to assess the generality of this effect.
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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 | 0.001 | 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) | 0.000 | 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".