Stoichiometric homeostasis: a test to predict tundra vascular plant species and community-level responses to climate change
Bibliographic record
Abstract
Climate change is having profound influences on Arctic tundra plant composition, community dynamics, and ecosystem processes. Stoichiometric homeostasis (H), the degree to which a plant maintains its internal nutrient concentrations independent of nutrient variations in its environment, may be a useful approach to predict the impacts of these influences. In this case study, we used fertilization manipulation data to calculate homeostasis indices based on nitrogen (HN), phosphorus (HP), and nitrogen to phosphorus ratios (HN:P) of aboveground tissues for seven common tundra vascular species belonging to three growth forms. We then analyzed species H relationships with dominance, spatial stability, and responsiveness to various experimental manipulations. Each of the H indices was correlated amongst tissue types within each species and was generally highest in ericoid mycorrhizal host species and lowest in the ectomycorrhizal birch. Species HP and HN:P were consistently positively correlated with aboveground biomass within the controls and across all manipulations. Furthermore, these same species were spatially stable across experimentally warmed field plots. Stoichiometric homeostasis theory has been successful in predicting grassland community dynamics. This first test of its applicability across a variety of Arctic plant growth forms highlights its considerable potential in predicting tundra plant community structure and responses to environmental change.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".