Sources of variation in foliar secondary chemistry in a tropical forest tree community

Abstract

Abstract. Specialist herbivores and pathogens could induce negative conspecific density dependence among their hosts and thereby contribute to the diversity of plant communities. A small number of hyperdiverse genera comprise a large portion of tree diversity in tropical forests. These closely related congeners are likely to share natural enemies. Diverse defenses could still allow congeners to partition niche space defined by natural enemies, but interspecific differences in defenses would have to exceed intraspecific variation in defenses. We ask whether interspecific variation in secondary chemistry exceeds intraspecific variation for species from four hyperdiverse tropical tree genera. We used novel methods to quantify chemical structural similarity for all compounds present in methanol extracts of leaf tissue. We sought to maximize intraspecific variation by selecting conspecific leaves from different ontogenetic stages (expanding immature vs. fully hardened mature), different light environments (deep understory shade vs. large forest gaps), and different seasons (dry vs. wet). Chemical structural similarity differed with ontogeny, light environment, and season, but interspecific differences including those among congeneric species were much larger. Our results suggest that species differences in secondary chemistry are large relative to within-species variation, perhaps sufficiently large to permit niche segregation among congeneric tree species based on chemical defenses.

Recent breakthroughs in metabolomics promise to reveal the cryptic chemical traits that mediate plant community composition, character evolution and lineage diversification

“…() and Sedio et al . (in press) describe CDI and CSCS metrics, respectively, for quantifying the structural diversity or similarity of complex mixtures. The capacity to derive metabolomics data from complex mixtures without isolating or identifying compounds makes crude‐extract NMR and especially MS/MS molecular networking massively scalable.…”

“…Sedio et al . (in press) developed a chemical structural‐compositional similarity (CSCS) metric that weights the structural similarity of every pair of compounds in a network by their relative ion intensity in two plant species. Conventional methods of calculating similarity in ecology, such as Bray–Curtis similarity, consider the relative abundance of shared compounds, but ignore structural relationships between molecules.…”

“…Secondary chemistry exhibits phylogenetic signal at broad phylogenetic scales (Wink, ). However, secondary chemistry is often not conserved within species‐rich genera, and closely related species can differ dramatically chemically in Bursera (Becerra, ), Inga (Kursar et al ., ), Protium (Fine et al ., ), Solanum (Haak et al ., ), Piper (Richards et al ., ; Salazar et al ., ), Eugenia , Ocotea and Psychotria (Sedio et al ., in press). Furthermore, co‐occurring species of Bursera (Becerra, ), Inga (Kursar et al ., ) and Piper (Salazar et al ., ) are less similar chemically than by chance, suggesting that niche partitioning based on defense compounds stabilizes coexistence among species in these genera.…”

The study of plant specialized metabolism: Challenges and prospects in the genomics era

“…However, this is rapidly changing, and approaches such as molecular networking are enabling researchers to gain useful insights, e.g., into communitylevel metabolite differences in ecological contexts (Sedio et al, 2017). Further innovation in MS data analysis can boost novel metabolite discovery, identify patterns of co-and differentially regulated metabolites, and help identify emergent properties of plant metabolic networks through comparative analyses.…”

Untargeted metabolic profiling reveals geography as the strongest predictor of metabolic phenotypes of a cosmopolitan weed

“…When investigating the complex and often interrelated effects that environmental factors have on plant metabolic phenotypes, it is becoming increasingly popular to use untargeted metabolomic approaches, as fewer a priori assumptions are made, allowing for the detection of metabolic responses that were overlooked using targeted approaches (Pankoke et al., 2013; Schweiger et al., 2014; Sedio, Rojas Echeverri, Boya, & Wright, 2017; Sutter & Muller, 2011). Despite the extensive chemical ecology literature that exists for Plantago , still little is understood about metabolome‐wide responses (but see Sutter & Muller, 2011; Pankoke et al., 2013, 2015; and Schweiger et al., 2014).…”