Chemical Similarity of Co-occurring Trees Decreases With Precipitation and Temperature in North American Forests
Bibliographic record
Abstract
Plant diversity varies immensely over large-scale gradients in temperature, precipitation, and seasonality at global and regional scales. This relationship may be driven in part by climatic variation in the relative importance of abiotic and biotic interactions to the diversity and composition of plant communities. In particular, biotic interactions may become stronger and more host specific with increasing precipitation and temperature, resulting in greater plant species richness in wetter and warmer environments. This hypothesis predicts that the many defensive compounds found in plants’ metabolomes should increase in richness and decrease in interspecific similarity with precipitation, temperature, and plant diversity. To test this prediction, we compared patterns of chemical and morphological trait diversity of 140 woody plant species among seven temperate forests in North America representing 16.2°C variation in mean annual temperature (MAT), 2,115 mm variation in mean annual precipitation (MAP), and from 10 to 68 co-occurring species. We used untargeted metabolomics methods based on data generated with liquid chromatography-tandem mass spectrometry to identify, classify, and compare 13,480 unique foliar metabolites and to quantify the metabolomic similarity of species in each community with respect to the whole metabolome and each of five broad classes of metabolites. In addition, we compiled morphological trait data from existing databases and field surveys for three commonly measured traits (specific leaf area [SLA], wood density, and seed mass) for comparison with foliar metabolomes. We found that chemical defense strategies and growth and allocation strategies reflected by these traits largely represented orthogonal axes of variation. In addition, functional dispersion of SLA increased with MAP, whereas functional richness of wood density and seed mass increased with MAT. In contrast, chemical similarity of co-occurring species decreased with both MAT and MAP, and metabolite richness increased with MAT. Variation in metabolite richness among communities was positively correlated with species richness, but variation in mean chemical similarity was not. Our results are consistent with the hypothesis that plant metabolomes play a more important role in community assembly in wetter and warmer climates, even at temperate latitudes, and suggest that metabolomic traits can provide unique insight to studies of trait-based community assembly.
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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".