Belowground biomass of Spartina alterniflora: seasonal variability and response to nutrients
Bibliographic record
Abstract
Spartina alterniflora is a salt marsh macrophyte found from Canada to the Gulf of Mexico which often provides the dominant plant cover. Although S. alterniflora is well known for its high aboveground productivity, fifty to ninety percent of the total plant production occurs belowground. No previous studies address the seasonal variation of belowground biomass or the response of above-and belowground biomass to nutrients at the southern limits of its U. S. range. The objectives of this study were to: 1) document the seasonal variability of its above- and belowground biomass and test for responses to various combinations of N, P, and Fe supplements, 2) test the usefulness and variability of three functional indicators of nutrient use efficiency, resorption efficiency, resorption proficiency, and, 3) compare nutrient limitation controls in East coast and Gulf of Mexico salt marshes. Various combinations of N additions resulted in more aboveground biomass, higher stem densities and longer stem lengths, but had no effect on the amount of belowground biomass. No change in the aboveground biomass observed when P was added, but there was a decrease in the live belowground biomass. The average N : P molar ratios in the above- and belowground tissues, and three resorption indices supported the hypothesis that the accumulation of biomass aboveground was limited by N, and by P belowground. Higher soil respiration and a lower Eh are anticipated additional soil property changes with nutrient enrichment. The observations from these field trials formed a unified conclusion, which is that the widespread effects of coastal eutrophication leads to lower root and rhizome biomass, belowground production, and organic matter accumulation. The cumulative effects of increased nutrient loadings to salt marshes may be to decrease soil elevation and accelerate the conversion of emergent plant habitat to open water, particularly at the lower elevation range of the plant. These results support management actions supporting coastal marsh conservation through: 1) reducing nutrient loading to coastal zones and not diverting more nutrients to coastal marshes, 2) solving water quality problems with a multiple nutrient approach, and, 3) choosing monitoring metrics based on both belowground and aboveground plant production.
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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 | 0.000 |
| 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 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".