Xylem porosity shapes sapwood characteristics and stem water use of temperate and boreal tree species
Bibliographic record
Abstract
Sapwood characteristics, such as sapwood area as well as thermal and hydraulic conductivity, are linked to species-specific hydraulic function and resource allocation to water transport tissues (xylem). These characteristics are often unknown and thus a major source of uncertainty in sap flow data processing and transpiration estimates because bulk rather than species-specific values are usually applied. Here, we analyzed the sapwood characteristics of fifteen common tree species in eastern North America from different taxa (i.e., angiosperms and gymnosperms) and xylem porosity groups (i.e., tracheid-bearing, diffuse- or ring-porous species). We quantified their sapwood area changes with stem diameter (allometric scaling) and thermal conductivity. We combined these measurements with species-specific values of wood density and hydraulic conductivity found in literature and assessed the role of wood anatomy in orchestrating their covariation. Angiosperms (ring- and diffuse-porous species), with specialized vessels for water transport, showed steeper relation (scaling) between tree size and sapwood area in comparison to gymnosperms (tracheid-bearing species). Despite the variability in thermal conductivity between species, gymnosperms (angiosperms) were characterized by lower (higher) wood density and higher (lower) sapwood moisture content, resulting in non-significant differences in sapwood thermal conductivity between taxa and xylem porosity groups. Clustering of species sapwood characteristics based on taxa or xylem porosity could facilitate more accurate parameterizations of these attributes. When combined with an increasing number of sap flow observations, these findings can improve tree- and landscape-level transpiration estimates, leading to more robust partitioning of terrestrial water fluxes.
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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 | 0.000 | 0.000 |
| 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".