Data from: Testing for latitudinal gradients in defense at the macroevolutionary scale
Bibliographic record
Abstract
Plant defences against herbivores are predicted to evolve to be greater in warmer climates, such as lower latitudes where herbivore pressure is also thought to be higher. Instead, recent findings are often inconsistent with this expectation, suggesting alternative hypotheses are needed. We tested for latitudinal gradients in plant defence evolution at the macroevolutionary scale by characterizing plant chemical defences across 80 species of the evening primroses, spanning both North and South America. We quantified phenolics in leaves, flowers and fruits, using advanced analytical chemistry techniques. Dominant individual ellagitannin compounds, total concentrations of ellagitannins, flavonoids, total phenolics, and compound diversity were quantified. Variation in these compounds were predicted with latitude, temperature, precipitation, and continent using phylogenetic generalized least squares multiple regression models. Latitude did not strongly explain patterns for the chemical defences. Instead, fruit total ellagitannins, oenothein A and total phenolics were greater in species inhabiting regions with colder climates. Using analytical chemistry and 80 species in two continents, we show that contrary to classic predictions, concentrations of secondary metabolites are not greater at lower latitudes or in warmer regions. We propose higher herbivore pressure in colder climates and gradients in resource availability as potential drivers of the observed patterns.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.073 | 0.050 |
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".