Legacy effects control root elemental composition and stoichiometry in subtropical forests: Empirical support for the biogeochemical niche hypothesis
Bibliographic record
Abstract
Abstract Under biogeochemical niche (BN) theory, plant allocation of elements to organs to maintain fundamental biological processes varies with species, leading to the formation of species‐specific BNs. However, empirical support for the BN theory is largely restricted to plant leaf elemental composition and stoichiometry, with a lack of clarity about the contribution of fine root element content. Here, we analysed fine root concentrations and stoichiometry of 9 elements including carbon (C), nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), iron (Fe), aluminium (Al) and manganese (Mn) in 137 tree species of a subtropical forest to quantify BN and test the theoretical framework of the BN hypothesis. Our study showed phylogenetic signals of fine root elemental composition and stoichiometry. Fine root elemental composition of the 21 most abundant co‐existing species tends to be unique and primarily driven by root N content, as indicated by canonical discriminant analysis. Legacy effects (phylogeny and species) explained 23.3%–70.7% of the variation across the different variables used to characterize fine root elemental composition, stoichiometry and BNs, whereas combined effects of soil property, mycorrhizal association type and topography factors explained 3.9%–17.7%. Synthesis : These results indicate that phylogenetic and taxonomic distance (calculated as distance metrics) represent a proxy for species‐specific evolution and achievement of optimal function linked to bio‐element use. Thus, our study provides new empirical evidence in support of the BN hypothesis, based on plant fine root elemental composition and stoichiometry, and improves our mechanistic understanding of species coexistence dynamics.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".