Intraspecific variability rivals interspecific differences in root traits of temperate tree seedlings
Bibliographic record
Abstract
Abstract Global change and associated disturbances are increasing the risk of regeneration failure for tree species in temperate forests. Seedlings are particularly vulnerable to water stress due to their shallow root systems, making belowground plasticity a potentially key component of species adaptive capacity. Quantifying root trait variability and its drivers can improve our understanding of regeneration success under increasingly warm and dry conditions. We quantified between species variation (BTV) and intraspecific variation (ITV) in seven root traits linked to water uptake—root-to-shoot ratio, maximum rooting depth, proportion of absorptive roots, specific root length, root tissue density, average absorptive root diameter, and root branching density—for seedlings of seven common, co-occurring tree species in forests of northeastern North America. We sampled seedlings under contrasting climate and light conditions, and assessed the influence of abiotic (climate, light conditions, soil properties) and biotic drivers (neighboring vegetation) as well as seedling characteristics (species identity, age, spermatophyte type) on root ITV at local and regional scales. Species differed significantly for some traits but differed even more strongly in multivariate trait syndromes, suggesting distinct belowground strategies. ITV was substantial but trait-dependent, with maximum rooting depth and root to shoot ratio being the most variable (coefficient of variation > 45%) and branching density the least variable. BTV was the primary driver of overall trait variation for three traits, explaining more than 60% of variation, whereas within-plot ITV accounted for more than 50% of variation in the remaining four traits. Local drivers did not outweigh regional factors, and the overall explanatory power of measured drivers was limited, suggesting that fine-scale heterogeneity, not captured in our study, may strongly influence root ITV. High ITV in most traits suggests substantial plasticity in roots, which may contribute to the adaptive capacity of seedlings facing climate change. Integrating this plasticity into mechanistic models is critical for predicting regeneration dynamics or root-mediated ecosystem processes. We propose a set of guidelines for integrating root traits into comparative studies and models based on trait measurability and extent of ITV. We further highlight the need to account for the scale- and gradient-intensity dependence of ITV-environment relationships.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".