Trait integration varies with resource acquisition strategies in a common perennial crop
Bibliographic record
Abstract
Abstract Resource‐acquisitive plant species are expected to show stronger trait integration versus resource‐conservative species, due to simultaneous selection for multiple resource requirements including light, water and nutrients. While this hypothesis has been invoked to predict interspecific differences in trait variation and integration, it has not been tested to explain intraspecific trait variation (ITV) and trait integration among varieties of crop species. We quantified nine leaf physiological, water‐use, chemical and morphological traits related to the acquisition and use of light, CO 2 , water and nutrients, across six varieties of wine grapes ( Vitis vinifera L.), in order to quantify the extent of ITV and trait integration among one of the world's most common and economically important perennial crops. This dataset was also used to test the hypothesis that, within a crop species, resource‐acquisitive varieties express stronger trait integration than resource‐conservative varieties. All leaf traits varied significantly across wine grape varieties and formed an intraspecific resource‐acquisitive–resource‐conservative axis of variation within wine grapes. Consistent with hypotheses on trait variation and integration, wine grape varieties expressing resource‐acquisitive trait syndromes were associated with stronger trait integration versus those expressing resource‐conservative trait syndromes. Specifically, varieties expressing greater values of light‐saturated photosynthesis ( A sat ), stomatal conductance ( g s ), maximum carboxylation ( V cmax ) and electron transport ( J max ) rates, leaf nitrogen concentrations and leaf area expressed an ~45%–65% increase in the number of significant bivariate trait correlations compared to resource‐conservative varieties. However, within all varieties, we detected strong and consistent integration among leaf physiological traits, indicating a mechanistic physiological basis that governs an intraspecific leaf economics spectrum in wine grapes. Strong trait integration in resource‐acquisitive wine grape varieties supports the hypothesis that ‘fast trait’ plants have simultaneously been selected to optimize the multiple rates of resource uptake, through multiple suites of traits. Our work clarifies the mechanisms by which resource‐acquisitive species, particularly crops, are able to capture multiple limiting resources to enhance their growth performance. This study also addresses a gap in our knowledge regarding the magnitude of intraspecific variation in trait integration. Read the free Plain Language Summary for this article on the Journal blog.
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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) | 0.004 | 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".