How is tree growth rate linked to root functional traits in phylogenetically related poplar hybrids?
Bibliographic record
Abstract
Fine roots play a crucial role in soil nutrient and water acquisition, significantly contributing to tree growth. Fine roots with a high specific root length (SRL) and small diameter are often considered to help trees grow fast. However, inconsistencies in the literature do not provide a clear basis on the effect of root functional traits, such as SRL or root mass density (RMD), on tree growth rate in phylogenetically related trees. Our aim was to examine relationships between tree growth rate and root functional traits, using clones displaying different growth rates in a hybrid poplar plantation located in New Liskeard, ON, Canada. Fine roots (diameter < 2 mm) samples were collected using soil cores at depths of 0-20, 20-40 and 40-60 cm, and analyzed for morphological, chemical and architectural traits. High SRL and thin fine roots were associated with the least productive clones, which is not consistent with the root economics spectrum (RES) theory. However, the most productive clone had larger fine root diameter and higher root lignin concentrations, probably reducing root construction and maintenance costs and carbon losses. Therefore, at the 0-20 and 20-40 cm depths, tree growth rates showed positive correlations with root diameter and root lignin concentrations, but negative correlations with SRL and root soluble compounds concentration. Increasing RMD at the 0-20 cm depth promoted tree growth rates, showing the importance of soil exploration in the topsoil for tree growth. We conclude that fine root variation does not always follow the RES hypothesis and argue that the rapid growth rate of trees may also be driven by fine root growth in diameter and mass in phylogenetically related trees.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".