Invasion by <i>Hyptis suaveolens</i> modifies the effects of altered rainfall variability on nutrient cycling across seasons in a dry tropical grassland experiment
Bibliographic record
Abstract
Ecosystems often recover rapidly when changes in climatic conditions are moderate, but extreme changes may push the ecosystem beyond its biological threshold, resulting in rather profound changes in its functioning and species composition. We experimentally evaluated how the ecosystem functioning of tropical grassland may change under changing precipitation variability by investigating shifts in soil properties and their relation to plant invasions. We found that soil moisture, soil pH, inorganic N content (NO3 - N + NH4 - N), N mineralization rate, and soil CO2 flux increase with a rise in rainfall. Moreover, the grassland plots invaded by Hyptis suaveolens, particularly those with increased precipitation, demonstrated elevated mineralization rates, substantial nutrient accumulation, and a reduced microbial biomass in comparison to the uninvaded plots. Our study highlighted that, following soil moisture (SM) and soil temperature (ST), N mineralization emerged as the third primary driver of soil CO2 flux. Enhanced precipitation led to increased N mineralization and subsequent CO2 emissions. The results indicate that escalated CO2 flux in invaded plots could be linked to invasive H. suaveolens adverse effects on soil processes, potentially leading to short-term inefficient nutrient cycling and elevated CO2 emissions, with potential consequences for the overall stability of the ecosystem.
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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) | 0.000 | 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 | 0.001 |
| 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".