Does where you live influence what you are made of? Spatial correlates of chemical traits across commonly occurring boreal plants
Bibliographic record
Abstract
Abstract Context Spatially explicit drivers of foliar chemical traits link plants to ecosystem processes to reveal landscape functionality. Specifically, foliar elemental, stoichiometric, and phytochemical (ESP) compositions represent key indicator traits. Objectives Here, we investigate the spatial drivers of foliar ESP at the species level and across species at the trait level for five commonly occurring boreal forest understory plants. Methods On the island of Newfoundland, Canada, we collected foliar material from four chronosequenced forest grids. Using response variables of foliar elemental (C, N, P, percent and quantity), stoichiometric (C:N, C:P, N:P), and phytochemical (terpenoids) composition, we tested multiple competing hypotheses using spatial predictors of land cover (e.g., coniferous, deciduous, mixedwood), productivity (e.g., enhanced vegetation index), biotic (e.g., stand age/height, canopy closure) and abiotic (e.g., elevation, aspect, slope) factors. Results We found evidence to support spatial relationships of foliar ESP for most species (mean R 2 = 0.22, max = 0.65). Spatial variation in elemental quantity traits of C, N, P were related to land cover along with biotic and abiotic factors for 2 of 5 focal species. Notably, foliar C, C:P, and sesquiterpene traits between different species were related to abiotic factors. Similarly, foliar terpenoid traits between different species were related to a combination of abiotic and biotic factors (mean R 2 = 0.26). Conclusions Spatial-trait relationships mainly occur at the species level, with some commonalities occurring at the trait level. By linking foliar ESP traits to spatial predictors, we can map plant chemical composition patterns that influence landscape-scale ecosystem processes.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".