Integrated Hydraulic and Biomechanical Strategies of Grapevine Fine Roots for Adaptation to Aridity and Salinity
Bibliographic record
Abstract
Grapevines from the hyper-arid Atacama Desert possess unique hydraulic and biomechanical root adaptations that confer resilience to extreme drought and salinity. Here, we provide insights into root hydraulic properties, tissue–water relations, and mechanical traits to investigate resilience strategies in naturalized genotypes (R-65 and R-70) and commercial rootstocks (101-14Mgt and 110-R). Using root pressure probes, uniaxial tensile tests, pressure-volume analyses, and fluorescence microscopy, we evaluated the effects of salinity (0–250 mM NaCl) and severe drought on fine root functionality. The results reveal that the hyper-arid genotypes integrate superior hydraulic conductivity, elastic-plastic mechanical behavior, and reduced cortical damage to withstand high salinity and water stress. Although R-65 and R-70 maintained larger root diameters, higher water content, and stable osmolality under extreme salinity and drought conditions, commercial rootstocks showed increased stiffness, significant cortical lacunae formation, and reduced recovery capacity. These responses align with xerophytic adaptations that safeguard fine root functionality through enhanced energy dissipation, structural flexibility, and water retention, thereby minimizing permanent damage. Complementary hydraulic and biomechanical traits are critical for maintaining fine root integrity and stress resilience in hyperarid environments. This integrated analysis of hydraulic and mechanical traits highlights the potential of Atacama-adapted genotypes as genetic resources for breeding resilient crops. These findings contribute to the development of sustainable agricultural practices in saline- and drought-prone regions.
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 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 | 0.000 | 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) | 0.000 | 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".