Plant litter effects on soil carbon stabilization and nitrogen availability: A trade‐off and some versatile species
Bibliographic record
Abstract
Abstract Globally, the constrained stoichiometry of living organisms is responsible for the coupling of carbon (C) and nitrogen (N) elements in living organic matter, with afterlife effects in dead organic matter. This coupling has long been thought to foster trade‐offs among several key functions of ecosystems, such as soil fertility and C sequestration. However, while there is evidence for a general coupling of C and N cycling in ecosystems, there are many ways in which these cycles also diverge, both temporally and spatially, under the influence of multiple biotic and abiotic drivers. Here, focusing on the role of plant residues in feeding and steering the C and N cycles in soil, we examine how 24 leaf and root litters with contrasting chemistry differentially influence the temporal release of compounds with varying C:N stoichiometry to soil, and how C and N elements end up as stabilized (mineral‐associated organic matter, MAOM) or more bioavailable organic (particulate organic matter, POM) or mineral forms (e.g. CO 2 , NO 3 − , NH 4 + ). There were major differences in the C:N stoichiometry of compounds released during decomposition, from low C:N early on to very high C:N at later stages. We observed a trade‐off in the role of litters towards increasing soil N availability (i.e. N in the soil solution) versus soil C stabilization (i.e. C in MAOM). Slow‐decomposing litters (with high lignin and low N concentrations, C‐poor leachates), particularly roots, favoured soil C stabilization over N availability. For each gram of litter decomposed, roots contributed 33% more C to the MAOM fraction of the soil, whereas leaves contributed 87% more N to the soil solution. This pattern was strongly driven (44% of variance explained) by the contrasting biochemistry of leaf versus root litters. Synthesis . These results suggest that leaf and root litters are highly complementary in the way they contribute to soil C stabilization and N availability. As such, global changes that influence the production and turnover of above and below‐ground litter inputs will likely have cascading effects on the balance between these functions. Our results also reveal substantial variation around the trade‐off between soil C stabilization and N availability, suggesting a continuum from ‘underachieving species’ to ‘versatile species’ contributing more to both functions. This opens perspectives for selecting versatile species capable of influencing positively several agro‐ecosystem functions.
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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 | 0.000 | 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".