Intraspecific trait variation across multiple scales: the leaf economics spectrum in coffee
Bibliographic record
Abstract
Summary Understanding species differences in plant functional traits has been critical in developing a mechanistic understanding of terrestrial ecological processes. Greater attention is now being placed on understanding the extent, causes and consequences of intraspecific trait variation ( ITV ). ITV is especially important in governing ecological processes in cropping systems, where only a small number of species or genotypes exist in high abundances. However, it remains unclear if key principles of trait‐based ecology – namely the leaf economics spectrum ( LES ) – also describe intraspecific variation in crop functional biology. There also remains a need to understand whether ITV within crops is random, or structured across environmental, management‐related or biological levels of organization in agroecosystems. We employed a nested design field survey to evaluate ITV in leaf traits in coffee ( Coffea arabica ), one of the world's most widespread tropical crops. We evaluated ITV in eight physiological, morphological and chemical leaf traits, across five nested categorical levels (sites, management systems, spatial location, plant identity, branch identity). We compared patterns of LES trait covariation in coffee, to interspecific patterns observed across over 700 wild plant species. Patterns of bivariate and multivariate ITV in coffee were broadly consistent with, but considerably weaker than, interspecific patterns associated with the LES , indicating that crops may systematically diverge from global patterns of trait trade‐offs observed in wild plants. Physiological traits varied most widely (coefficient of variation (cv) 42–107%), followed by morphological traits (cv = 15–38%) and chemical traits (cv = 3–11%). Physiological ITV was best explained by the site in which a coffee plant was growing (17–55% explained), while ITV for chemical traits was best explained by management treatments within sites (25–36%); morphological ITV was higher even at the individual tree level or branch level and remained largely unexplained. Our results support the hypothesis that artificial selection and high‐resource agricultural environments lead crops to systematically deviate from patterns of leaf trait covariation observed across wild plants species. Coupled with an understanding of how different traits vary systematically across multiple levels of biological organization, these findings help integrate ITV into future analyses of agroecosystem structure and function. A lay summary is available for this article.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".