A comparison of five methods to assess embolism resistance in trees
Bibliographic record
Abstract
Vulnerability to drought-induced embolism is a key trait that shapes drought resistance and that could be increasingly used to design climate-smart forest management guidelines and to anticipate the outcome of climate change on populations dynamics and ecosystems functioning. A panel of methods is currently available to measure embolism resistance. This makes crucial a proper identification of which methods are the most accurate for determining this trait. However, the measurement of embolism resistance is sensitive to numerous artifacts that may lead to large errors for a given species. In addition, not all methods are easily accessible because of the cost of some large equipment and/or certain lab facilities. The emergence of the easy and low cost Pneumatic method allows to perform vulnerability curves at high throughput. However, only few studies have evaluated the reliability of this method compared to others. In this study, we proposed a comparison of five methods that allowed to assess embolism resistance in eleven tree species with contrasting xylem anatomy and vessels length (six short vessel angiosperms, two tracheid bearing conifers and three long-vessel angiosperms), covering a large part of the range of embolism resistance observed in trees. Consistent results were obtained among all the methods for short-vessel angiosperm species. In tracheid-bearing conifers, the Pneumatic method overestimated vulnerability to embolism. In long-vessel species, the Pneumatic method led to inconsistent results with accurate vulnerability to cavitation curves (VCs) for one species but led to r-shaped VCs with a underestimation of incipient embolism for the two other ones. The comparison of VC parameters with turgor loss point is proposed as an indicator of the validity of the VCs. The conditions of validity, the advantages and pitfalls of the five methods are discussed. Our results warned against the widespread usages of some methods before rigorous validation tests have been performed.
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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 | 0.000 |
| 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 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".