Skip to main content
Dryad

Chemical properties of foliar metabolomes represent a key axis of functional trait variation in forests of the tropical Andes

Data files

Nov 13, 2025 version files 50.72 MB

Click names to download individual files

Abstract

Plants interact with their environment through diverse specialized metabolites that protect them from abiotic stressors like drought or radiation and biotic stressors like herbivores or pathogens. However, few studies have considered the chemical properties of metabolites as a potential axis of functional trait variation along environmental gradients.  Here, we examined how the chemical properties of foliar metabolomes, such as mean aromaticity, hydrophobicity, and polarity, as well as commonly used morphological traits, vary with climate and elevation among 16 forest plots in the tropical Andes of Bolivia. We found that chemical properties were weakly related to morphological traits among tree species, yet both varied significantly with climate and elevation. In particular, abundance-weighted mean hydrophobicity decreased, and polar surface area increased with elevation and in colder and drier climates. Additionally, co-occurring species showed increasing chemical similarity with elevation for the most-aromatic and most-polar metabolites. These results suggest that abiotic stress associated with colder, drier climates and solar radiation acts as a filter for metabolome chemical properties. This contrasts with chemical dissimilarity observed at lower elevations, which is likely driven by pressure from host-specialized enemies in warmer, wetter climates. Our results introduce the possibility that chemical defenses may be constrained by abiotic stressors. Morphological traits and foliar metabolome chemical properties for each species-by-plot are reported in Dataset S1. Community-weighted mean values are reported in Dataset S2. The structural similarities among 20,571 metabolites are reported as a Qemistree dendrogram in .tre phylogeny format as Dataset S3. Masses, molecular formulae, predicted structures, classifications, and chemical properties and sample-level abundances for 20,571 unique metabolites are provided in Dataset S4.