Patterns in intraspecific variation in root traits are species‐specific along an elevation gradient
Bibliographic record
Abstract
Abstract Intraspecific trait variation is an important driver of plant performance in different environments. Although roots acquire essential resources that vary with the environment, most studies have focused on intraspecific variation in leaf traits, and research on roots is often restricted to a few species. It remains largely unclear how and to what extent root traits vary with the environment and whether general intraspecific patterns exist across species. We compared intraspecific variation in specific root length (SRL), root diameter, root tissue density (RTD) and root branching density of 11 species along a 1,000 m elevation gradient in the French Alps. We tested (a) the extent of intraspecific versus interspecific root trait variation along the gradient, (b) whether intraspecific trait patterns with elevation were consistent among species and (c) whether environmental variables better explained intraspecific variation in root traits than elevation. Specifically, we hypothesised that within a species, root trait values would adjust to enhance resource acquisition (either through an increase in SRL or root diameter, and/or in branching density) and/or conservation (increased RTD) at higher elevations. Species identity explained most of the overall variation in root traits. Elevation explained only a minor proportion of intraspecific root trait variation, which was larger within than between elevations. Also, trait relationships with elevation rarely agreed with our hypotheses, varied strongly across species and were often differently related to environmental variation. Generally, climate, soil and vegetation properties better explained intraspecific root variation than elevation, but these relationships were highly species‐dependent. Along complex environmental gradients where multiple properties simultaneously change, roots of different species vary in different ways, leading to species‐specific patterns in intraspecific root trait variation. The lack of support for our hypotheses may be caused by the multiple interactions between environmental properties, small‐scale soil heterogeneity, species phylogeny and changing plant–plant interactions. Our findings suggest that, to enhance our understanding of the effects of environmental change on plant performance, we need to better integrate the multiple dimensions of plant responses to change and measure a broader set of root traits and environmental variables. A free Plain Language Summary can be found within the Supporting Information of this article.
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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 | 0.000 |
| 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) | 0.002 | 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".