Relationship between ecological stoichiometry and community diversity of plant ecosystems in the upper reaches of the Tarim River, China
Bibliographic record
Abstract
Abstract Aim Although it is commonly proposed that nutrient cycling can impact plant community diversity, this relationship has not been fully examined in arid and semi-arid zones. Here, we expand on the framework for evaluating the relationship between biodiversity and ecological stoichiometry by scaling up from the level of the community. Location The upper reaches of the Tarim River (Northwest China, 80°10’-84°36’E, 0°25’-41°10’N). Methods We used multivariate analysis of variance to compare the stoichiometric characteristics and species diversity indices of sampled plant communities. We also measured carbon (C), nitrogen (N), and phosphorous (P) content of plants. We then assessed correlations between community stoichiometry and species diversity through structural equation models (SEM) and redundancy analysis (RDA). Results We found that the differences between stoichiometric characteristics and community diversity indices were highly significant. The Margalef index was strongly related to C and P content. The Simpson’s index and Shannon-Weaner index were most strongly correlated with C content. Pielou’s index was closely related to C and N contents, and the C:N and C:P ratios were important at driving ecological dominance. Main Conclusions Our study highlights the importance of ecological stoichiometry in driving community assembly and diversity within a desert ecosystems in northwestern China. The relationship between eclogical stoichiometry in the desert plant community had an effect on species diversity, and it was a good indicator of plant community diversity.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".