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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 defences may be constrained by abiotic stressors. 

{"Abstract":["Plants interact with their environment through diverse specialized\n metabolites that protect them from abiotic stressors like drought or\n radiation and biotic stressors like herbivores or pathogens. However, few\n studies have considered the chemical properties of metabolites as a\n potential axis of functional trait variation along environmental\n gradients.  Here, we examined how the chemical properties of\n foliar metabolomes, such as mean aromaticity, hydrophobicity, and\n polarity, as well as commonly used morphological traits, vary with climate\n and elevation among 16 forest plots in the tropical Andes of Bolivia. We\n found that chemical properties were weakly related to morphological traits\n among tree species, yet both varied significantly with climate and\n elevation. In particular, abundance-weighted mean hydrophobicity\n decreased, and polar surface area increased with elevation and in colder\n and drier climates. Additionally, co-occurring species showed increasing\n chemical similarity with elevation for the most-aromatic and most-polar\n metabolites. These results suggest that abiotic stress associated with\n colder, drier climates and solar radiation acts as a filter for metabolome\n chemical properties. This contrasts with chemical dissimilarity observed\n at lower elevations, which is likely driven by pressure from\n host-specialized enemies in warmer, wetter climates. Our results introduce\n the possibility that chemical defenses may be constrained by abiotic\n stressors. Morphological traits and foliar metabolome chemical properties\n for each species-by-plot are reported in Dataset S1. Community-weighted\n mean values are reported in Dataset S2. The structural similarities among\n 20,571 metabolites are reported as a Qemistree dendrogram in .tre\n phylogeny format as Dataset S3. Masses, molecular formulae, predicted\n structures, classifications, and chemical properties and sample-level\n abundances for 20,571 unique metabolites are provided in Dataset S4."],"Methods":["Forest plot data: The Madidi Project\n             Floristic data were collected as part of the Madidi\n Project (www.mobot.org/madidi), a collaboration of more than two decades between the Herbario Nacional de Bolivia and the Missouri Botanical Garden to document the flora of the Madidi region in the Andes of Bolivia (35). The region features wide variation in plant communities over an extreme elevational gradient, from lowland rainforests located at 200 m above sea level (a.s.l.) to alpine environments above the tree line at 6,000 m a.s.l. (48). The Madidi Project includes 50 1-ha permanent forest plots ranging in elevation from 212 m to 3334 m a.s.l. We selected 16 1-hectare (ha) permanent plots in which leaves were sampled for chemical analysis and which represent broad variation in elevation (662-3324 m a.s.l.), climate, and tree species richness (17-137 species per 1-ha plot). The 16 plots include three seasonally dry, low-elevation forest plots and 13 moist, montane forest plots (28). Abundant genera include: Miconia (Melastomataceae), Sloanea (Elaeocarpaceae), and Ocotea (Lauraceae) in the low-elevation moist plots; Weinmannia (Cunoniaceae), Hedyosmum (Chloranthaceae), and Clethra (Clethraceae) in the high-elevation (>2500 m) plots; and Weinmannia, Hedyosmum, and Clethra in the seasonally dry plots. Tree species richness declines with elevation among the 13 moist forest plots, whereas the three seasonally dry lowland plots display low species richness (28). In each 1-ha plot, all free-standing woody plants with a diameter at breast height of ³ 10 cm were mapped, measured, and identified to a botanically valid species or morphospecies. Morphological Functional Traits            Protocols for morphological functional trait data collection are described in detail in the Madidi Project manual (www.mobot.org/madidi). We selected five morphological leaf and stem traits that reflect a species position on a tradeoff axis from conservative traits associated with defense and survival to acquisitive traits associated with fast growth (Box 1). Leaf area and specific leaf area (SLA; area per unit mass) are associated with a resource-acquisitive life history strategy; leaf thickness, bark thickness, and twig specific density are associated with a resource-conservative life history strategy (49). Morphological traits for each species-by-plot are reported in Dataset S1. Community-weighted mean trait values are reported in Dataset S2. Chemical Analysis             We collected leaf samples from 473 tree species representing 906 unique species-by-plot. Within each forest plot, we collected leaf samples from 62-90% of the species in the plot (mean = 80% of the species per plot; (28)). Leaves of up to five individual trees per species per plot were collected between 2010 and 2019 and dried with silica gel upon collection in the field. Leaf samples were extracted for untargeted metabolomics analysis following Sedio et al. (31). Briefly, 50 mg of dried leaf tissue was ground to a fine powder and 10 mg weighed for extraction in 1800 mL 90:10 methanol:water pH 5 overnight at 4 °C. Extracts of up to five individuals per species per plot were pooled to create 906 pools representing unique species-by-plot. All individual extracts and species pools were filtered and analyzed using ultra-high performance liquid chromatography-heated electrospray ionization-tandem mass spectrometry (UHPLC-HESI-MS/MS) using a Thermo Fisher Scientific (Waltham, MA, USA) Vanquish UHPLC with a C18 column and a Thermo QExactive quadrupole-orbitrap MS. Separation of metabolites by UHPLC was followed by HESI ionization in positive mode using full scan MS1 and data-dependent acquisition of MS2. Detailed instrumental methods are described by Sedio et al. (31). Spectra were deposited as a public MassIVE dataset on the Global Natural Products Social (GNPS) Molecular Networking server (doi:10.25345/C52R3P21H). Raw spectra were centroided and processed for peak detection, peak alignment, and filtering using MZmine 2 (50). Aligned chromatograms were used to create a feature-based molecular network (FBMN; (51)) using GNPS (52). The structural similarities of all metabolites as represented in the resulting network were used to create a dendrogram using the software Qemistree (53), which is reported in Dataset S3. Metabolites were annotated by predicting molecular formulae using Sirius (54), predicting molecular structures using CSI:FingerID (55) and classifying compounds using Canopus (56) according to the organic chemical taxonomy scheme of ClassyFire (57) and according to biosynthetic origins using NPClassifier (58). For a comparison of intra- and inter-specific variation for selected species-rich high- and low-elevation genera, see (28). To calculate chemical properties of metabolites, we used the highest-confidence molecular structure predicted by CSI:FingerID, represented as a SMILES text string, to query the Chemistry Development Kit (CDK; (59)) using the R package ‘rcdk’ (60). The CDK library includes 51 variables that describe chemical and physical properties, but Walker et al. (27) found that many of these are highly correlated and hence represent five major axes of variation. A correlation matrix of 21 chemical properties for metabolites in our data closely matched that of Walker et al. (27). Hence, like Walker et al. (27), we chose one of each of five major dimensions of variation (Box 1). Molecular formulae, predicted structures, classifications, and chemical properties and sample-level abundances for 20,571 unique metabolites are provided in Dataset S4. Foliar metabolome chemical properties for each species-by-plot are reported in Dataset S1. Community-weighted mean values are reported in Dataset S2. We calculated the chemical structural-compositional similarity (CSCS) of species, which accounts for the structural similarity of unique metabolites (30). We calculated CSCS with respect to metabolites in the upper and/or lower quartile of nAtomP, ALogP, TopoPSA, and Fsp3, respectively, for the species co-occurring in each of the 16 forest plots. Climate Data             We selected four climatic variables to represent variation among the 16 forest plots in temperature, precipitation, and seasonality. Annual mean temperature and annual range in temperature were derived from WorldClim Version 2.1 (61). Annual precipitation and precipitation seasonality, calculated as the ratio of the standard deviation to the mean precipitation of each month, were derived from the Tropical Rainfall Measuring Mission (TRMM), a regional database that provides greater accuracy in precipitation measurements relative to WorldClim in the Bolivian Andes (28). We scaled and centered the four variables and carried out a principal components analysis, of which the first principal component represented 71.2% of the variation and was clearly interpretable as a gradient from cold, dry environments (values < 0) to warm, wet environments (values > 0; (28)). Elevation and position on climate PC1 for each of the 16 forest plots are reported in Dataset S2. Discipline-Specific Metadata The DisciplineSpecificMetadata.json file contains parameter values for experimental and instrumental protocols used in liquid chromatography-mass spectrometry (LC-MS) data collection. These methods are also reported in Sedio et al. 2021 "Chemical similarity of co-occurring trees decreases with precipitation and temperature in North American forests". Front. Ecol. Evol. 9.679638. doi: 10.3389/fevo.2021.679638"],"TechnicalInfo":["# Chemical properties of foliar metabolomes represent a key axis of\n functional trait variation in forests of the tropical Andes Dataset DOI:\n [10.5061/dryad.2rbnzs83c](https://doi.org/10.5061/dryad.2rbnzs83c) ##\n Description of the data and file structure ### Files and variables ####\n File: Chadwick_ProcB_10.1098_rspb.2025.1721_DatasetS1.csv\n **Description:** Metadata file that provides nomenclature and trait data\n for every species-by-plot (i.e. a species that occurs in n plots is\n represented n times), including mean metabolome chemical properties,\n morphological trait values, and abudance-weighted chemical similarity to\n co-occurring species with respect to metabolites that represent the upper\n or lower quartile of metabolites for particular chemical properties. NA\n indicates data not available in the Madidi Project morphological traits\n dataset. ##### Variables * sampleCode: Name of the LC-MS data file *\n speciesCode: Six-character species code or morpho-species code * Plot:\n Madidi Project forest plot * Species_binomial: Species Latin binomial *\n Genus: Botanical genus * Family: Botanical family * Abundance: Number of\n individuals in the forest plot * nAtomP: The number of atoms in the\n largest aromatic, conjugated pi system, which is high in pigments and\n light-absorbing molecules that function to protect plants from ultraviolet\n radiation. * ALogP: Hydrophobicity measured as the log-ratio of solubility\n in octanol versus water; may be related to a plant-defense spectrum\n defined by unsaturated, aromatic, and nonpolar metabolites versus\n saturated and polar metabolites; negatively correlated with polarity and\n hence desiccation resistance. * TopoPSA: Topological polar surface area,\n the sum of the surface area of polar atoms in Ångstroms (Å), which may\n contribute to desiccation resistance, but is negatively correlated with\n passive transport through cell membranes. * Fsp3: The fraction of carbon\n atoms with sp3 electron orbits (i.e. only single bonds) to the total\n number of carbon atoms in the molecule, which is positively correlated\n with melting point, solubility, and the likelihood of a compound to\n exhibit bioactivity in pharmaceutical assays. * MW: Molecular weight in\n Daltons (Da); greatest in peptides and lignans, may be related to leaf\n longevity because peptides and lignans function as long-lived physical and\n storage structures. * SLA: Specific leaf area; a leaf economics-resource\n capture trait associated with high photosynthetic rates, high relative\n growth rates, low carbon investment in lignin or tannins, and\n resource-rich environments. NA indicates data not available in the Madidi\n Project morphological traits dataset. * LeafArea: Leaf area; associated\n with a resource-acquisitive life history strategy. NA indicates data not\n available in the Madidi Project morphological traits dataset. *\n LeafThickness: Leaf thickness; associated with tolerance to disturbance\n and nutrient stress and a resource-conservative life history strategy. NA\n indicates data not available in the Madidi Project morphological traits\n dataset. * TwigBarkThickness_Relative: Twig bark thickness relative to\n stem diameter; associated with protection from attack by pathogens and\n herbivores. NA indicates data not available in the Madidi Project\n morphological traits dataset. * TwigSpecDens: Twig specific\n density; associated with low relative growth rates, high survival, and\n resistance to pathogens, herbivores or physical damage. NA indicates data\n not available in the Madidi Project morphological traits dataset. *\n nAtomP_75: Abundance-weighted CSCS chemical similarity to co-occurring\n tree species with respect to the upper quartile of metabolites with\n respect to nAtomP * ALogP_25: Abundance-weighted CSCS chemical similarity\n to co-occurring tree species with respect to the lower quartile of\n metabolites with respect to ALogP * TopoPSA_75: Abundance-weighted CSCS\n chemical similarity to co-occurring tree species with respect to the upper\n quartile of metabolites with respect to TopoPSA *\n TopoPSA_25: Abundance-weighted CSCS chemical similarity to co-occurring\n tree species with respect to the lower quartile of metabolites with\n respect to TopoPSA * Fsp3_75: Abundance-weighted CSCS chemical similarity\n to co-occurring tree species with respect to the upper quartile of\n metabolites with respect to Fsp3 * Elevation: Elevation of the forest plot\n in which the species occurs in meters (m) * Plot_simp: Simplified name of\n the forest plot #### File:\n Chadwick_ProcB_10.1098_rspb.2025.1721_DatasetS2.csv **Description:** Data\n table of variables defined for each of the 16 Madidi forest plots used in\n the study, including elevation, climate, phylogenetic similarity,\n abundance-weighted mean chemical properties, and abundance-weighted mean\n chemical similarity of co-occurring species with respect to suites of\n compounds that represent upper or lower quartiles of metabolites with\n respect to particular chemical properties. ##### Variables * Plot: Madidi\n Project forest plot * Elevation: Elevation of the forest plot in meters\n (m). * Climate_PC1: Position of the forest plot on the first principal\n component in a principal component analysis (PCA); greater values reflect\n greater temperature and precipitation. * Climate_PC2: Position of the\n forest plot on the second principal component in a principal component\n analysis (PCA); greater values reflect precipitation and temperature\n seasonality. * invSimpson: Species diversity represented as the inverse\n Simpson index. * nAtomP: Community-weighted mean nAtomP, the number of\n atoms in the largest aromatic, conjugated pi system, which is high in\n pigments and light-absorbing molecules that function to protect plants\n from ultraviolet radiation. * ALogP: Community-weighted mean ALogP,\n hydrophobicity measured as the log-ratio of solubility in octanol versus\n water; may be related to a plant-defense spectrum defined by unsaturated,\n aromatic, and nonpolar metabolites versus saturated and polar metabolites;\n negatively correlated with polarity and hence desiccation resistance. *\n TopoPSA: Community-weighted mean TopoPSA, topological polar surface area,\n the sum of the surface area of polar atoms in Ångstroms (Å), which may\n contribute to desiccation resistance, but is negatively correlated with\n passive transport through cell membranes. * Fsp3: Community-weighted mean\n Fsp3, the fraction of carbon atoms with sp3 electron orbits (i.e. only\n single bonds) to the total number of carbon atoms in the molecule, which\n is positively correlated with melting point, solubility, and the\n likelihood of a compound to exhibit bioactivity in pharmaceutical assays.\n * MW: Community-weighted mean molecular weight in Daltons (Da); greatest\n in peptides and lignans, may be related to leaf longevity because peptides\n and lignans function as long-lived physical and storage structures. * SLA:\n Community-weighted mean specific leaf area; a leaf economics-resource\n capture trait associated with high photosynthetic rates, high relative\n growth rates, low carbon investment in lignin or tannins, and\n resource-rich environments. * LeafArea: Community-weighted mean leaf\n area; associated with a resource-acquisitive life history strategy. *\n LeafThickness: Community-weighted mean leaf thickness; associated with\n tolerance to disturbance and nutrient stress and a resource-conservative\n life history strategy. * TwigBarkThickness_Relative: Community-weighted\n mean twig bark thickness relative to stem diameter; associated with\n protection from attack by pathogens and herbivores. * TwigSpecDens:\n Community-weighted mean twig specific density; associated with low\n relative growth rates, high survival, and resistance to pathogens,\n herbivores or physical damage. * PhyloPCoA1: Position of the forest plot\n on the first axis of a principal coordinates analysis that reflects the\n phylogenetic similarity among the 16 forest plots * PhyloPCoA2: Position\n of the forest plot on the second axis of a principal coordinates analysis\n that reflects the phylogenetic similarity among the 16 forest plots *\n nAtomP_75: Community (abundance)-weighted mean CSCS chemical similarity of\n species to other co-occurring tree species with respect to metabolites in\n the upper quartile of nAtomP * ALogP_25: Community (abundance)-weighted\n mean CSCS chemical similarity of species to other co-occurring tree\n species with respect to metabolites in the lower quartile of ALogP *\n TopoPSA_75: Community (abundance)-weighted mean CSCS chemical similarity\n of species to other co-occurring tree species with respect to metabolites\n in the upper quartile of TopoPSA * TopoPSA_25: Community\n (abundance)-weighted mean CSCS chemical similarity of species to other\n co-occurring tree species with respect to metabolites in the lower\n quartile of TopoPSA * Fsp3_75: Community (abundance)-weighted mean CSCS\n chemical similarity of species to other co-occurring tree species with\n respect to metabolites in the upper quartile of Fsp3 #### File:\n Chadwick_ProcB_10.1098_rspb.2025.1721_DatasetS3.tre\n **Description:** Qemistree dendrogram that reflects the structural\n similarity among metabolites. Tip labels correspond to LC-MS feature IDs\n in Dataset S4. #### File:\n Chadwick_ProcB_10.1098_rspb.2025.1721_DatasetS4.csv\n **Description:** Metabolome mastertable that contains metabolite\n annotations, including predicted structures, classifications, chemical\n properties, and abundance in each sample. NAs indicate missing annotations\n due to insufficient data. ##### Variables * id: Metabolite feature ID\n number generated by Qemistree; matches tip labels in the Qemistree\n dendrogram. * X.featureID: Metabolite feature ID number generated by\n MZmine. * csi_smiles: Predicted structure generated by Sirius. *\n table_number: Dataset input number for Qemistree * smiles: Predicted\n structure generated by Sirius. * structure_source: Sirius module used to\n predict metabolite chemical structure. * kingdom: Classyfire\n chemotaxonomical classification at the kingdom level. *\n superclass: Classyfire chemotaxonomical classification at the superclass\n level. * class: Classyfire chemotaxonomical classification at the class\n level. * subclass: Classyfire chemotaxonomical classification at the\n subclass level. * direct_parent: Classyfire chemotaxonomical\n classification at the most-specific, direct-parent level. *\n class_results: NPClassifier chemotaxonomical classification at the class\n level. * superclass_results: NPClassifier chemotaxonomical classification\n at the superclass level. * pathway_results: NPClassifier chemotaxonomical\n classification at the biosynthetic pathway level. *\n isglycoside: NPClassifier indicator, whether the metabolite is a glycoside\n (contains a sugar moiety). * custom: Classification used by the authors. *\n MW: Molecular weight in Daltons (Da); greatest in peptides and lignans,\n may be related to leaf longevity because peptides and lignans function as\n long-lived physical and storage structures. * nAtom: Number of atoms. *\n naAromAtom: Number of aromatic atoms * nAtomLC: Number of atoms in the\n longest chain. * nAtomP: The number of atoms in the largest aromatic,\n conjugated pi system, which is high in pigments and light-absorbing\n molecules that function to protect plants from ultraviolet radiation. *\n nB: Number of bonds * nAromBond: Number of aromatic bonds * nRotB: Number\n of rotatable bonds. * XLogP: Octanol/water partition coefficient\n calculated using a modified atom-additive model summing atomic\n contributions and correcting for intramolecular interactions; positive\n indicates affinity to octanol (i.e. nonpolar, hydrophobic), negative to\n water (i.e. polar, hydrophilic); * ALogP: Octanol/water partition\n coefficient calculated using a modified atom-additive model summing atom\n type contributions based on focal atom and bond characteristics; positive\n indicates affinity to octanol (i.e. nonpolar, hydrophobic), negative to\n water (i.e. polar, hydrophilic); may be related to a plant-defense\n spectrum defined by unsaturated, aromatic, and nonpolar metabolites versus\n saturated and polar metabolites; negatively correlated with polarity and\n hence desiccation resistance. * MLogP: Octanol/water partition coefficient\n calculated using a simple equation dependent on the number of C atoms and\n number of hetero atoms; smaller indicates affinity to octanol (i.e.\n nonpolar, hydrophobic), larger to water (i.e. polar, hydrophilic); *\n TopoPSA: Topological polar surface area, the sum of the surface area of\n polar atoms in Ångstroms (Å), which may contribute to desiccation\n resistance, but is negatively correlated with passive transport through\n cell membranes. * tpsaEfficiency: Molecular weight-specific topological\n polar surface area, the sum of the surface area of polar atoms in\n Ångstroms divided by the molecular weight in Daltons (Å Da^-1^), which may\n contribute to desiccation resistance, but is negatively correlated with\n passive transport through cell membranes. * HybRatio: Fraction of sp3 to\n sp2 carbon atoms; proxy for bond saturation and three-dimensional\n topological complexity, which is positively correlated with melting point,\n solubility, and the likelihood of a compound to exhibit bioactivity in\n pharmaceutical assays. * Fsp3: The fraction of carbon atoms with sp3\n electron orbits (i.e. only single bonds) to the total number of carbon\n atoms in the molecule, which is positively correlated with melting point,\n solubility, and the likelihood of a compound to exhibit bioactivity in\n pharmaceutical assays. * FMF: Ratio between size of molecular framework\n (ring atoms plus linkers) and size of metabolite; complexity measure\n positively correlated with promiscuity (number of protein targets >50%\n inhibited) at values above 0.65. * ECCEN:  Eccentric connectivity index,\n the distance-cum-adjacency topological descriptor; higher for longer\n chains with less branching; correlated with size and physicochemical\n properties such as boiling point. * WPATH: Wiener path number, a\n topological descriptor of molecular branching that can differentiate\n structural isomers; correlated positively with size and boiling point. *\n WPOL: Wiener polarity number, a variant of the Wiener path number\n calculated using vertices (C atoms) at distance 3; correlated positively\n with size and boiling point. * nHBDon: Number of hydrogen-bond donors\n (e.g. OH, NH, formal charge ≥ 0). * nHBAcc: Number of hydrogen-bond\n acceptors (e.g. O/N, formal charge ≤ 0, non-ether O, non-adjacent ON) *\n PC1: Position on the first principal component of a principal component\n analysis on metabolite chemical properties. * PC2: Position on the second\n principal component of a principal component analysis on metabolite\n chemical properties. * PC3: Position on the third principal component of a\n principal component analysis on metabolite chemical properties. *\n PC4: Position on the fourth principal component of a principal component\n analysis on metabolite chemical properties. * PC5: Position on the fifth\n principal component of a principal component analysis on metabolite\n chemical properties. * MDP0209.mzXML.Peak.area: Area under the curve of\n the chromatographic peak representing the quantity of the metabolite in\n sample MDP0209.mzXML; all subsequent columns columns display\n quantification data for pools representing every unique tree\n species-by-forest plot combination (906 species-by-plot). *\n MDP0213.mzXML.Peak.area: * MDP0206.mzXML.Peak.area: *\n MDP0221.mzXML.Peak.area: * MDP0211.mzXML.Peak.area: *\n MDP0215.mzXML.Peak.area: * MDP0212.mzXML.Peak.area: *\n MDP0204.mzXML.Peak.area: * MDP0220.mzXML.Peak.area: *\n MDP0208.mzXML.Peak.area: * MDP0214.mzXML.Peak.area: *\n MDP0223.mzXML.Peak.area: * MDP0207.mzXML.Peak.area: *\n MDP0224.mzXML.Peak.area: * MDP0219.mzXML.Peak.area: *\n MDP0222.mzXML.Peak.area: * MDP0210.mzXML.Peak.area: *\n MDP0205.mzXML.Peak.area: * MDP0216.mzXML.Peak.area: *\n MDP0203.mzXML.Peak.area: * MDP0218.mzXML.Peak.area: *\n MDP0217.mzXML.Peak.area: * MDP0587.mzXML.Peak.area: *\n MDP0585.mzXML.Peak.area: * MDP0572.mzXML.Peak.area: *\n MDP0586.mzXML.Peak.area: * MDP0571.mzXML.Peak.area: *\n MDP0554.mzXML.Peak.area: * MDP0580.mzXML.Peak.area: *\n MDP0596.mzXML.Peak.area: * MDP0553.mzXML.Peak.area: *\n MDP0561.mzXML.Peak.area: * MDP0576.mzXML.Peak.area: *\n MDP0582.mzXML.Peak.area: * MDP0594.mzXML.Peak.area: *\n MDP0545.mzXML.Peak.area: * MDP0573.mzXML.Peak.area: *\n MDP0574.mzXML.Peak.area: * MDP0568.mzXML.Peak.area: *\n MDP0579.mzXML.Peak.area: * MDP0548.mzXML.Peak.area: *\n MDP0583.mzXML.Peak.area: * MDP0599.mzXML.Peak.area: *\n MDP0534.mzXML.Peak.area: * MDP0584.mzXML.Peak.area: *\n MDP0563.mzXML.Peak.area: * MDP0529.mzXML.Peak.area: *\n MDP0528.mzXML.Peak.area: * MDP0593.mzXML.Peak.area: *\n MDP0567.mzXML.Peak.area: * MDP0549.mzXML.Peak.area: *\n MDP0543.mzXML.Peak.area: * MDP0564.mzXML.Peak.area: *\n MDP0535.mzXML.Peak.area: * MDP0537.mzXML.Peak.area: *\n MDP0551.mzXML.Peak.area: * MDP0559.mzXML.Peak.area: *\n MDP0532.mzXML.Peak.area: * MDP0542.mzXML.Peak.area: *\n MDP0546.mzXML.Peak.area: * MDP0547.mzXML.Peak.area: *\n MDP0569.mzXML.Peak.area: * MDP0565.mzXML.Peak.area: *\n MDP0541.mzXML.Peak.area: * MDP0555.mzXML.Peak.area: *\n MDP0556.mzXML.Peak.area: * MDP0558.mzXML.Peak.area: *\n MDP0544.mzXML.Peak.area: * MDP0591.mzXML.Peak.area: *\n MDP0539.mzXML.Peak.area: * MDP0581.mzXML.Peak.area: #### File:\n DisciplineSpecificMetadata.json **Description:** The\n DisciplineSpecificMetadata.json file contains parameter values for\n experimental and instrumental protocols used in liquid chromatography-mass\n spectrometry (LC-MS) data collection. ## Code/software\n **File: Chadwick_ProcB_10.1098_rspb.2025.1721.R** R code used for\n Chadwick, Sierra E., David Henderson, Dale L. Forrister, Leslie Cayola,\n Alfredo F. Fuentes, Belén Alvestegui, Nathan Muchhala, J. Sebastián Tello,\n Martin Volf, Jonathan A. Myers, and Brian E. Sedio. Proceedings B\n ([https://doi.org/10.1098/rspb.2025.1721)](https://doi.org/10.1098/rspb.2025.1721) is provided in the file. This file includes R code used for data manipulation, statistical analyses, and data visualization/figure creation for the Chadwick et al. Proc B manuscript. R version 4.4.1 on Mac OS 15.5. The R code assumes that the four supplementary data files are located in a folder with the path: ~Documents/Madidi_Project R package versions used in the file: \\# attached base packages: \\# [1] stats     graphics  grDevices utils     datasets  methods   base      \n \n \\# other attached packages:  # [1] V.PhyloMaker2_0.1.0 plotly_4.10.4    \n   adiv_2.2.1          phylosignal_1.3.1   picante_1.8.2        # [6]\n nlme_3.1-167        phytools_2.4-4      maps_3.4.2.1        ape_5.8-1    \n       corrplot_0.95       \\# [11] ggrepel_0.9.6       ggplot2_3.5.1      \n vegan_2.6-10        lattice_0.22-6      permute_0.9-7       \n \n \\# loaded via a namespace (and not attached):   # [1] DBI_1.2.3          \n     mnormt_2.1.1            deldir_2.0-4            phangorn_2.12.1      \n     # [5] rlang_1.1.5             magrittr_2.0.3          ade4_1.7-22    \n         compiler_4.4.1            # [9] mgcv_1.9-1              png_0.1-8\n               vctrs_0.6.5             reshape2_1.4.4           # [13]\n combinat_0.0-8          quadprog_1.5-8          stringr_1.5.1          \n pkgconfig_2.0.3          # [17] crayon_1.5.3            fastmap_1.2.0    \n       promises_1.3.2          rmarkdown_2.29           # [21] purrr_1.0.2\n             xfun_0.52               seqinr_4.2-36          \n clusterGeneration_1.3.8  # [25] jsonlite_1.8.9          progress_1.2.3   \n       later_1.4.1             adegenet_2.1.10          # [29] uuid_1.2-1 \n             jpeg_0.1-10             parallel_4.4.1         \n prettyunits_1.2.0        # [33] cluster_2.1.8           R6_2.5.1         \n       stringi_1.8.4           RColorBrewer_1.1-3       # [37] boot_1.3-31\n             numDeriv_2016.8-1.1     Rcpp_1.0.14            \n iterators_1.0.14         # [41] knitr_1.49             \n optimParallel_1.0-2     base64enc_0.1-3         adephylo_1.1-16          #\n [45] httpuv_1.6.15           Matrix_1.7-2            adegraphics_1.0-21   \n   splines_4.4.1            # [49] igraph_2.1.4           \n tidyselect_1.2.1        yaml_2.3.10             phylobase_0.8.12         #\n [53] doParallel_1.0.17       codetools_0.2-20        tibble_3.2.1         \n   plyr_1.8.9               # [57] shiny_1.10.0            withr_3.0.2    \n         coda_0.19-4.1           evaluate_1.0.3           # [61]\n xml2_1.3.6              lpSolve_5.6.23          pillar_1.10.1          \n KernSmooth_2.23-26       # [65] foreach_1.5.2           generics_0.1.3   \n       sp_2.2-0                hms_1.1.3                # [69]\n munsell_0.5.1           scales_1.3.0            xtable_1.8-4           \n rncl_0.8.7               # [73] glue_1.8.0              lazyeval_0.2.2   \n       scatterplot3d_0.3-44    tools_4.4.1              # [77]\n interp_1.1-6            data.table_1.16.4       rgl_1.3.17             \n XML_3.99-0.18            # [81] fastmatch_1.1-6         grid_4.4.1       \n       tidyr_1.3.1             RNeXML_2.4.11            # [85]\n crosstalk_1.2.1         latticeExtra_0.6-30     colorspace_2.1-1       \n cli_3.6.3                # [89] DEoptim_2.2-8           expm_1.0-0       \n       viridisLite_0.4.2       dplyr_1.1.4              # [93]\n gtable_0.3.6            digest_0.6.37           farver_2.1.2           \n htmlwidgets_1.6.4        # [97] htmltools_0.5.8.1       lifecycle_1.0.4  \n       httr_1.4.7              mime_0.12               \\# [101] MASS_7.3-64\n   ## Access information Other publicly accessible locations of the data: *\n The raw data from the Madidi Project are stored and managed in Tropicos\n ([https://tropicos.org/home](https://tropicos.org/home)), the botanical\n database of the Missouri Botanical Garden. The data for each forest plot\n can be accessed via the Madidi Project Plot Search page\n ([http://tropicos.org/PlotSearch.aspx?projectid=20](http://tropicos.org/PlotSearch.aspx?projectid=20)). * The raw LC-MS spectra were deposited as a public MassIVE dataset on the Global Natural Products Social (GNPS) Molecular Networking server ([https://massive.ucsd.edu/ProteoSAFe/dataset_files.jsp?task=a4a891a0f3b3467fb790a9c335783205#%7B%22table_sort_history%22%3A%22main.collection_asc%22%7D](https://massive.ucsd.edu/ProteoSAFe/dataset_files.jsp?task=a4a891a0f3b3467fb790a9c335783205#%7B%22table_sort_history%22%3A%22main.collection_asc%22%7D)) with FTP download link (ftp://massive.ucsd.edu/MSV000090549) and doi ([https://doi.org/10.25345/C52R3P21H](https://doi.org/10.25345/C52R3P21H))."]} 

Bolivian Begonia are among the most poorly studied of American begonias. We provide here a checklist and key to the Bolivian Begonia flora, which currently includes ten sections, 43 species, three subspecies and three varieties. We also fully discuss the nomenclature and typification of all Bolivian Begonia species and designate 16 lectotypes. We describe and provide an illustration of one new species and provide emended descriptions for eight species. We have assessed six species for extinction risk under IUCN Red List criteria and provide four new synonyms. We compare our checklist with previous treatments of Bolivian Begonia and discuss all changes in detail.Las begonias de Bolivia se encuentran entre las begonias americanas menos estudiadas. Proporcionamos una lista de verificación y una clave para la flora de begonias de Bolivia, que actualmente incluye diez secciones, 43 especies, tres subespecies y tres variedades. También proporcionamos una discusión completa de la nomenclatura y tipificación de todas las especies bolivianas de Begonia y designamos 16 lectotipos.  Describimos e ilustramos una nueva especie y adicionamos descripciones corregidas para ocho especies. Evaluamos seis especies en riesgo de extinción según los criterios de la lista roja de la UICN y proponemos cuatro nuevos sinónimos. Comparamos nuestra lista de verificación con tratamientos anteriores sobre las begonias bolivianas y discutimos todos los cambios en detalle. 

The science of tropical dendrochronology is now emerging in regions where tree-ring dating had previously not been considered possible. Here, we combine wood anatomical microsectioning techniques and radiocarbon analysis to produce the first tree-ring chronology with verified annual periodicity for a new dendrochronological species, Neltuma alba (commonly known as “algarrobo blanco”) in the tropical Andes of Bolivia. First, we generated a preliminary chronology composed of six trees using traditional dendrochronological methods (i.e., cross-dating). We then measured the 14 C content on nine selected tree rings from two samples and compared them with the Southern Hemisphere (SH) atmospheric 14 C curves, covering the period of the bomb 14 C peak. We find consistent offsets of 5 and 12 years, respectively, in the calendar dates initially assigned, indicating that several tree rings were missing in the sequence. In order to identify the tree-ring boundaries of the unidentified rings we investigated further by analyzing stem wood microsections to examine anatomical characteristics. These anatomical microsections revealed the presence of very narrow terminal parenchyma defining several tree-ring boundaries within the sapwood, which was not visible in sanded samples under a stereomicroscope. Such newly identified tree rings were consistent with the offsets shown by the radiocarbon analysis and allowed us to correct the calendar dates of the initial chronology. Additional radiocarbon measurements over a new batch of rings of the corrected dated samples resulted in a perfect match between the dendrochronological calendar years and the 14 C dating, which is based on good agreement between the tree-ring 14 C content and the SH 14 C curves. Correlations with prior season precipitation and temperature reveal a strong legacy effect of climate conditions prior to the current Neltuma alba growing season. Overall, our study highlights much potential to complement traditional dendrochronology in tree species with challenging tree-ring boundaries with wood anatomical methods and 14 C analyses. Taken together, these approaches confirm that Neltuma alba can be accurately dated and thereby used in climatic and ecological studies in tropical and subtropical South America. 

Seminal hypotheses in ecology and evolution postulate that stronger and more specialized biotic interactions contribute to higher species diversity at lower elevations and latitudes. Plant‐chemical defenses mediate biotic interactions between plants and their natural enemies and provide a highly dimensional trait space in which chemically mediated niches may facilitate plant species coexistence. However, the role of chemically mediated biotic interactions in shaping plant communities remains largely untested across large‐scale ecological gradients. Here, we used ecological metabolomics to quantify the chemical dissimilarity of foliar metabolomes among 473 tree species in 16 tropical tree communities along an elevational gradient in the Bolivian Andes. We predicted that tree species diversity would be higher in communities and climates where co‐occurring tree species are more chemically dissimilar and exhibit faster evolution of secondary metabolites (lower chemical phylogenetic signal). Further, we predicted that these relationships should be especially pronounced for secondary metabolites known to include antiherbivore and antimicrobial defenses relative to primary metabolites. Using structural equation models, we quantified the direct effects of rarefied median chemical dissimilarity and chemical phylogenetic signal on tree species diversity, as well as the indirect effects of climate. We found that chemical dissimilarity among tree species with respect to all metabolites and secondary metabolites had positive direct effects on tree species diversity, and that climate (higher temperature and precipitation, and lower temperature seasonality) had positive indirect effects on species diversity by increasing chemical dissimilarity. In contrast, chemical dissimilarity of primary metabolites was unrelated to species diversity and climate. Chemical phylogenetic signal of all metabolite classes had negative direct effects on tree species diversity, indicating faster evolution of metabolites in more diverse communities. Climate had a direct effect on species diversity but did not indirectly affect diversity through chemical phylogenetic signal. Our results support the hypothesis that chemically mediated biotic interactions shape elevational diversity gradients by imposing stronger selection for chemical divergence in more diverse communities and maintaining higher chemical dissimilarity among species in warmer, wetter, and more stable climates. Our study also illustrates the promise of ecological metabolomics in the study of biogeography, community ecology, and complex species interactions in high‐diversity ecosystems. 

Weinmannia epicae is described and illustrated. It grows in Andean high-montane forests of the Yungas region in the department of La Paz, northwestern Bolivia, between 2900 and 3300 m. It differs from other morphologically similar species as W. cundinamarcensis, W. haenkeana and W. pubescens by having the maximum number of leaflets pairs usually between 10 to 14, medial leaflets of 1.3 to 2.3 cm, with more or less rough surface and undersurfaces with hirsute to hirsute-villous indument, and mature capsules villous-pubescent of 5-7 × 1.8-2.7 mm, conspicuously pedicellated. The differences with morphological similar species are pointed out, and information on their geographical distribution, ecology, phenology and the assessment of conservation status according to IUCN criteria are provided. 

Patterns of species diversity have been associated with changes in climate across latitude and elevation. However, the ecological and evolutionary mechanisms underlying these relationships are still actively debated. Here, we present a complementary view of the well-known tropical niche conservatism (TNC) hypothesis, termed the multiple zones of origin (MZO) hypothesis, to explore mechanisms underlying latitudinal and elevational gradients of phylogenetic diversity in tree communities. The TNC hypothesis posits that most lineages originate in warmer, wetter, and less seasonal environments in the tropics and rarely colonize colder, drier, and more seasonal environments outside of the tropical lowlands, leading to higher phylogenetic diversity at lower latitudes and elevations. In contrast, the MZO hypothesis posits that lineages also originate in temperate environments and readily colonize similar environments in the tropical highlands, leading to lower phylogenetic diversity at lower latitudes and elevations. We tested these phylogenetic predictions using a combination of computer simulations and empirical analyses of tree communities in 245 forest plots located in six countries across the tropical and subtropical Andes. We estimated the phylogenetic diversity for each plot and regressed it against elevation and latitude. Our simulated and empirical results provide strong support for the MZO hypothesis. Phylogenetic diversity among co-occurring tree species increased with both latitude and elevation, suggesting an important influence on the historical dispersal of lineages with temperate origins into the tropical highlands. The mixing of different floras was likely favored by the formation of climatically suitable corridors for plant migration due to the Andean uplift. Accounting for the evolutionary history of plant communities helps to advance our knowledge of the drivers of tree community assembly along complex climatic gradients, and thus their likely responses to modern anthropogenic climate change.