The importance of litter traits and decomposers for litter decomposition: a comparison of aquatic and terrestrial ecosystems within and across biomes
Bibliographic record
Abstract
Summary Plant leaf litter comprises the major common source of energy and nutrients in forested soil and freshwater ecosystems world‐wide. However, despite the similarity of physical and biochemical processes, generalizations across aquatic and terrestrial ecosystems regarding litter decomposition drivers remain elusive. We re‐analysed data from a published field decomposition experiment conducted in two ecosystems (forest floors and streams) across five biomes (from the tropics to subarctic) with increasing decomposer community complexity (microbes, microbes and mesofauna, microbes and meso‐ and macrofauna). Using a wide litter quality gradient (15 litter combinations), we aimed to disentangle the roles of decomposer community complexity from that of leaf litter traits (18 traits encompassing four broad trait categories: nutrients, C quality, physical structure and stoichiometry) on litter C and N loss. Comparisons of decomposition drivers between ecosystems were evaluated across and within biomes. Differences in environmental conditions (e.g. climate, soil/water fertility) and litter nutrients – with a particular focus on Mg and Ca – across biomes were the major drivers of litter C loss in both ecosystems, but decomposer complexity also played a prominent role in streams. Within biomes, we observed consistent effects of litter nutrients and stoichiometry on litter C and N loss between ecosystems, but the effects of decomposer complexity differed between streams and forest floors in the temperate, Mediterranean and tropical biomes. Our results highlight that, beyond the litter traits commonly identified as controlling decomposition (e.g. C, N and lignin), micronutrients (e.g. Mg and Ca) can also play an important, and globally consistent, role in both aquatic and terrestrial ecosystems. In addition, in forest streams the complexity of decomposer communities had similar importance as litter traits for predicting litter C and N turnover across all five biomes. The identification of common drivers in our large‐scale ecosystem comparison suggests a basis to develop common models across aquatic and terrestrial ecosystems for C and N dynamics during decomposition. Future modelling efforts should account for the global similarities (litter micronutrient effects) and biome‐level differences (contingent decomposer effects) found between 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".