Removal of<scp>N</scp>‐fixing vs.<scp>non‐N</scp>‐fixing herbs in postfire chaparral: Competition and contributions to soil N and C cycling
Bibliographic record
Abstract
Abstract As climate change increases fire frequency in Mediterranean‐type shrublands, it is essential to understand the links between common postfire plant assemblages and soil nitrogen (N) and carbon (C) cycling during succession. In California chaparral, periodic fire removes shrub cover, deposits ammonium (NH4+‐N) on soils, and allows herbaceous assemblages to dominate for 3–5 years. Herbs influence soil biogeochemistry through several mechanisms, including nutrient uptake, litter decomposition, and rhizodeposition. Controlled experimental removal of select plant groups from wild assemblages can demonstrate interactions between plant groups and how plant traits influence belowground processes. In a two‐year herb‐removal experiment, we investigated the impact of N‐fixing and non‐N‐fixing herbs on soil N and C cycling. Treatments were (1) all herbs, (2) only non‐N‐fixing species, (3) only N‐fixing species, and (4) no herbs. In high‐N environments, N‐fixers were predicted to compete poorly against non‐N‐fixing neighbors. N‐fixers doubled in abundance when non‐N‐fixers were removed, but non‐N‐fixers were unaffected by N‐fixer removal. Two years after fire, no‐herbs plots had the lowest soil microbial respiration rates, and total accumulated C and N were lower than all‐herb plots. Two treatments, no‐herb and N‐fixer plots, had elevated mineral N concentrations, net N mineralization, and net nitrification in the second year of the experiment. Our findings underscore the importance of fire‐following herbs for postfire N retention and organic matter accumulation. A combination of both N‐fixing and non‐N‐fixing herbs maximized total soil C and N, although the accumulation of TC and TN in all‐herb plots was not significantly higher than in non‐N‐fixer plots. Results demonstrated the key role of non‐N‐fixing herbs in accumulating soil C and herbaceous communities for retaining N. Elevated soil nutrient availability two years postfire may contribute to the long‐term recovery of shrubs, even after herbs are no longer dominant. Future investigations should also consider the magnitude of soil microbial N retention in plots with different herb functional groups, along with the species‐specific contribution of non‐N‐fixing herbs to postfire C and N cycling.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".