Non-rainfall moisture: a key driver of carbon flux from standing litter in arid, semiarid, and mesic grasslands
Bibliographic record
Abstract
Abstract Models assume that rainfall is the major source of moisture driving decomposition. Non-rainfall moisture (NRM: high humidity, dew, and fog) can also induce standing litter decomposition, but there have been few standard measurements of NRM-mediated decompositions across sites, and no efforts to extrapolate the contribution of NRM to larger scales to assess whether this mechanism can improve model predictions. Here we show that NRM is an important, year-round source of moisture in grassland sites with contrasting moisture regimes using field measurements and modeling. We first characterized NRM frequency and measured NRM-mediated decomposition in sites on the extreme dry and wet end of grassland systems: at two sites in the Namib Desert, Namibia (hyperarid desert) and at one site in Iowa, USA (tallgrass prairie). NRM was frequent at all sites (85-99% of hours that litter was likely to be wet were attributed to NRM) and tended to occur in cool, high-humidity periods for several hours or more at a time. NRM also caused respiration of standing litter at all sites when litter became sufficiently wet (>5% for fine litter and >13% for coarse), and contributed to mass loss, even in the Namib West site that had almost no rain. When we modeled annual mass loss induced by NRM and rain, and extrapolated our characterization of NRM decomposition to a final site with intermediate rainfall (Sevilleta, New Mexico, semiarid grassland), we found that models driven by rainfall alone underestimated mass loss, while including NRM produced estimates within the range of observed mass loss. Together these findings suggest that NRM is an important missing component in quantitative and conceptual models of litter decomposition, but there is nuance involved in modeling NRM at larger scales. Specifically, temperature and physical features of the substrate emerge as factors that affect the common microbial response to litter wetting under NRM across grasslands sites, and require further study. Hourly humidity can provide an adequate proxy of NRM frequency, but site-specific calibration with litter wetness is needed to accurately attribute decomposition to periods when NRM wets litter. Greater recognition of NRM-driven decomposition and its interaction with other processes (e.g. photodegradation) is needed, especially since fog, dew, and humidity are likely to shift under future climates. Manuscript highlights Non-rainfall moisture (NRM; humidity, fog, dew) induces decomposition in grasslands NRM decomposition depends on substrate type, and occurs at colder times than rain Including NRM (instead of rain alone) improved predictions of litter decomposition
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 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 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".