Sapling leaf trait responses to light, tree height and soil nutrients for three conifer species of contrasting shade tolerance
Bibliographic record
Abstract
We developed models to describe the responses of four commonly examined leaf traits (mass per area, weight, area and nitrogen (N) concentration) to gradients of light, soil nutrients and tree height in three conifer species of contrasting shade tolerance. Our observational dataset from the sub-boreal spruce forests of British Columbia included subalpine fir (Abies lasioscarpa [Hook.] Nutt; high shade tolerance), interior spruce (Picea glauca × Picea engelmannii [Moench] Voss; intermediate shade tolerance) and lodgepole pine (Pinus contorta Dougl. ex Loud. var. latifolia; low shade tolerance) saplings from 0.18 to 4.87 m tall, in 8-98% of total incident light, from field sites with <17.6 kg ha(-1) to >46.8 kg ha(-1) total dissolved N. Leaf weights and areas showed strong positive responses to light and height, but little or no response to soil nutrients. Parameter estimates indicated that the shape of leaf weight and area responses to light corresponded with shade tolerance ranking for the three species; pine had the most linear response whereas spruce and fir had asymptotic responses. Leaf N concentration responded positively to soil nutrients, negatively to light and idiosyncratically to height. The negative effect of light was only apparent on sites of high soil nutrient availability, and parameter estimates for the shape of the negative response also corresponded to shade tolerance ranking (apine = -0.79, aspruce = -0.15, afir = -0.07). Of the traits we measured, leaf mass per area showed the least response to light, soil nutrient and height gradients. Although it is a common practice in comparisons across many species, characterizing these conifers by mean values of their leaf traits would miss important intraspecific variation across environmental and size gradients. In these forests, parameter estimates representing the intraspecific variability of leaf trait responses can be used to understand relative shade tolerances.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".