Temporal variation and its drivers in the elemental traits of four boreal plant species
Bibliographic record
Abstract
Abstract Aims Intraspecific variation in plant traits has important consequences for individual fitness and herbivore foraging. For plants, trait variability across spatial dimensions is well documented. However, temporal dimensions of trait variability are less well known, and may be influenced by seasonal differences in growing degree days (GDD), temperature and precipitation. Here, we aim to quantify intraspecific temporal variation in traits and the underlying drivers for four commonly occurring boreal plant species. Methods We sampled the elemental and stoichiometric traits (%C, %N, %P, C:N, C:P, N:P) of four common browse species’ foliage across 2 years. Using a two-step approach, we first fitted generalized linear models (GzLM, n = 24) to the species’ elemental and stoichiometric traits, and tested if they varied across years. When we observed evidence for temporal variability, we fitted a second set of GzLMs (n = 8) with temperature, productivity and moisture as explanatory variables. Important Findings We found no evidence of temporal variation for most of the elemental and stoichiometric traits of our four boreal plants, with two exceptions. Year was an important predictor for percent carbon across all four species (R2 = 0.47–0.67) and for multiple elemental and stoichiometric traits in balsam fir (5/8, R2 = 0.29–0.67). Thus, variation in percent carbon was related to interannual differences, more so than nitrogen and phosphorus, which are limiting nutrients in the boreal forest. These results also indicate that year may explain more variation in conifers’ stoichiometry than for deciduous plants due to life history differences. GDD was the most frequently occurring variable in the second round of models (8/8 times, R2 = 0.21–0.41), suggesting that temperature is an important driver of temporal variation in these traits.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".