DOI: 10.5061/dryad.70rxwdbxf
title: Structural and compositional dimensions of phytochemical diversity in the genus Piper reflect distinct ecological modes of action

description: Context: An increasing number of ecological studies have used chemical
 diversity as a functionally relevant, scalable measure of phytochemical
 mixtures, demanding more rigorous attention to how chemical diversity is
 estimated. Most studies have focused on the composition of phytochemical
 mixtures and have largely ignored structural concerns, which may have
 greater importance for ecological function. Here we explore the
 development of structural complexity and compositional diversity resulting
 from different biotic and abiotic interactions in Piper kelleyi Tepe
 (Piperaceae). We also describe how variation in structural complexity and
 compositional diversity differ between two congeners, P. kelleyi and Piper
 reticulatum. To better interpret these results, we have developed a
 hypothesis-driven framework for interpreting these dimensions of chemical
 diversity in phytochemical mixtures. Approach: We used the tropical shrub,
 P. kelleyi, as a model system to examine interactions between ecological
 factors and dimensions of phytochemical diversity. We also compared
 compositional diversity and metabolic complexity in P. kelleyi and P.
 reticulatum using liquid chromatography and 1H NMR respectively to examine
 tradeoffs between compositional diversity and structural complexity. A
 framework is provided to generate meaningful estimates of the structural
 complexity of phytochemical mixtures as measured by 1H NMR. Results and
 Conclusions: Piper is an abundant plant genus that supports diverse insect
 communities throughout the tropics. Subtle changes in understory forest
 light were associated with increases in herbivory that directly increased
 compositional diversity and indirectly decreased structural complexity in
 P. kelleyi. This was attributed to the production of oxidation products
 resulting from herbivory-driven decomposition of structurally complex
 defense compounds. This type of complex result would remain undetected
 using standard chemical ecology approaches and accounts for the detailed
 molecular changes that are likely to affect species interactions.
 Synthesis: Our quantitative framework provides a method for considering
 tradeoffs between structural complexity and compositional diversity and
 the interpretation of analytical approaches for each. This methodology
 will provide new theoretical insights and a more sophisticated model for
 examining the ecology and evolution of chemically mediated interactions.
description: <i>Piper reticulatum</i> (Piperaceae) foliar samples
 were collected from<i> </i>individual plants &gt; 10 m
 apart along both sides of the Sendero Surá (55-255 m), Sendero
 Arriera-Zampopa (110-804 m) and Sendero Tres Rios (155-2450 m) trails in
 La Selva Biological Station (LS, 10°25’ N 84°00' W), Costa Rica. In a
 prior experiment (Glassmire et al., 2019), we collected <i>P.
 kelleyi</i> from experimental plots of clones in Yanayacu Biological
 Station (YBS, 00°36′ S 77°53′ W) in Ecuador grown at high and low canopy
 height<i> </i>along an elevational gradient (2000-2400 m) of
 the Eastern Andes mountains. Canopy cover and direct light transmittance
 of <i>P. kelleyi</i> plants were measured using a Canon EOS
 Rebel-T4 camera with a hemispherical fisheye lens and processed using Gap
 Light Analyzer software (GLA software methods, Frazer et al., 1999).
 Herbivory was estimated from actual and estimated (pre-damage) leaf area
 using Image J before arcsine square root transformation (Glassmire et al.,
 2019). All <i>Piper</i> samples were finely ground using a
 tissue lyser (TissueLyser II, Quiagen; Hilden, Germany) and 100 mg
 portions from each individual plant were weighed before adding 10 mL of
 methanol (Fisher, Optima). Methanolic suspensions were sonicated for 10
 min and extracted overnight with wrist-action shaking before filtering
 over a cotton plug and concentrating to dryness <i>in
 vacuo</i>.  Nuclear magnetic resonance
 spectroscopy Dried <i>Piper
 </i>extracts were re-dissolved in 1.00 mL
 <i>d<sub>1</sub></i>-methanol for deuterium
 exchange in order to minimize water signal (as HOD) in
 <sup>1</sup>H NMR spectra. An aliquot of this solution was
 diluted 1:10 using protonated methanol for LC-MS analysis before the
 remainder was dried <i>in vacuo</i>. A second deuterium
 exchange was performed before dissolving in
 <i>d<sub>4</sub></i>-methanol containing 0.05%
 tetramethylsilane (TMS) as an internal standard and acquiring
 <sup>1</sup>H NMR (32 scans) on a Varian MR-400 NMR. Raw FIDs
 were processed using MestreNova (Mestrelab Research, S.L. Santiago de
 Compostela, Spain), including auto-phasing, ablative baseline correction
 and global spectral deconvolution (GSD) peak picking. Spectra were
 exported to csv files and normalized to total peak area after removing
 solvent and TMS regions (0-0.5, 3.3-3.32, 4.79-4.92, and 7.25-7.28 ppm)
 before binning at 0.04 ppm. Bins with an area below
 10<sup>-4</sup> were set to zero before calculating diversity
 measures. Liquid chromatography-UV/VIS-mass
 spectrometry <i>Piper
 </i>extracts were characterized via liquid chromatography-mass
 spectrometry (LC-MS), using an Agilent (Santa Clara, CA) 1200 analytical
 HPLC equipped with a binary pump, autosampler, column compartment and
 diode array UV/Vis detector, coupled to an Agilent 6230 Time-of-Flight
 mass spectrometer via an electrospray ionization source (ESI-TOF; gas
 temperature: 325 °C, flow: 10 L/m; nebulizer pressure: 35 psig; VCap: 3500
 V; fragmentor: 165 V; skimmer: 65 V; octopole: 750 V). Extracts (0.20 μL)
 for <i>P. kelleyi</i> were co-injected with anofinic acid
 internal standard (1.00 uL, 0.2 M) and eluted at 0.500 mL/min through a
 Kinetex EVO C18 column (Phenomenex, 2.1 x 100 mm, 2.6 μ, 100 Å) at 40 °C.
 The linear binary gradient was comprised of buffers A (water containing
 0.1 % formic acid) and B (acetonitrile containing 0.1 % formic acid)
 changing over 20 minutes accordingly: 0-1 min 20% B, ramp to 50% B at 6
 min, ramp to 100% B at 12 min, 12-16 min hold at 100% B, 16-17 min ramp to
 20% B, 17-20 min hold at 20% B. Raw LC-MS data were converted to mzML
 format using ProteoWizard (Kessner et al., 2008) before processing using
 the XCMS package in the R statistical programming software (Team, 2014).
 Chromatographic features were retention time corrected and aligned using
 density grouping. LC-MS features were sum-aggregated based on CAMERA
 classification (Kuhl et al., 2012) as to not overstate compositional
 richness. LC-UV chromatograms were integrated (l = 254) using Agilent
 MassHunter. This wavelength was chosen because it is representative of the
 relative concentrations of the NMR-apparent molecules in <i>P.
 kelleyi</i>, which allows for valid inferences about the composition
 of molecules in crude <sup>1</sup>H NMR spectra.
 Compositional diversity vs.
 metabolic complexity Compositional
 diversity (<i>D<sub>C</sub></i>) or metabolic
 complexity (<i>D<sub>M</sub></i>) can be
 calculated as Richness, Shannon or Simpson diversity, or beyond into
 higher q values using analogous methods for calculating Hill numbers for
 community data (Marion et al., 2015). Metabolic complexity arises in
 chemical mixtures, such as a crude plant extracts, from the aggregation of
 molecules, each with their own structural complexity, in proportion to
 their composition. Figure 1 outlines how the <sup>1</sup>H NMR
 spectra of individual molecules combine to yield crude mixture spectra
 within our framework for understanding the properties of chemical mixtures
 and their constituent molecules that yield metabolic complexity. If one
 were to analyze a hypothetical crude extract, separation methods such as
 liquid chromatography (LC) and gas chromatography (GC) provide a
 compositional profile of this mixture wherein each peak represents a
 different molecule (Fig. 1, compositional diversity). If each of these
 compounds is isolated to obtain a <sup>1</sup>H NMR spectrum,
 each spectrum reflects the complexity of that molecule (Fig. 1, structural
 complexity). The structural complexity index of a molecule is calculated
 in the same fashion as one would for compositional diversity, but instead
 of each peak representing a whole molecule, it represents a
 <i>structural feature </i>of that molecule; all peaks in a
 spectrum represent the spectral fingerprint of an entire
 molecule. Recombining these individual
 <sup>1</sup>H NMR spectra according to their relative
 abundance yields a <sup>1</sup>H NMR spectrum that reflects
 the crude mixture before separation. Similar structural features of
 molecules will lead to overlapping <sup>1</sup>H NMR signals
 that increase abundance for those signals, lowering signal evenness.
 Dissimilar structural features will have non-overlapping signals, leading
 to higher peak richness (Fig. 1, structural dissimilarity). One can
 therefore view the diversity of crude <sup>1</sup>H NMR as a
 gross measure of metabolic complexity, incorporating the composition,
 complexity and dissimilarity of phytochemical mixtures.
 Effective
 structural complexity (D<sub>Seff</sub>) Once we have obtained compositional diversity (<i>D<sub>C</sub></i>, from LC) and metabolic complexity (<i>D<sub>M,</sub></i><i><sub> </sub></i>from<sup> 1</sup>H NMR) as either Richness, Shannon or Inverse Simpson diversity, we can calculate effective structural complexity (<i>D<sub>Seff</sub></i>) as follows:   (Eqn. 1) Having removed the compositional contribution to metabolic complexity, effective structural complexity represents the remaining structural contribution. In terms of richness, this could be thought of as the average number of <sup>1</sup>H NMR peaks per compound or as an abundance-weighted average for higher order Hill numbers. While seemingly simple, effective structural complexity represents both the structural complexity and dissimilarity of molecules in a mixture. Metabolic complexity (<i>D<sub>M</sub></i>) of a mixture can be decomposed into beta dissimilarity (<i>β<sub>D</sub></i>) from mean structural complexity (<i>D</i><sub><sub><i>S</i></sub></sub>) of all constituent molecules in a mixture, weighted to their concentration, as described for mean alpha diversity using community data by (Jost, 2007):   (Eqn. 2) Highly dissimilar mixtures will lead to high metabolic complexity, even when the constituents of those mixtures have low structural complexity. Structural dissimilarity (<i>D<sub>D</sub></i>) can be further decomposed from <i>β</i><sub><sub><i><sub>D</sub></i></sub></sub> and compositional diversity:   (Eqn. 3) which yields a number between 0 and 1 that can be interpreted as the fraction of signal overlap between constituents of a phytochemical mixture per compound. Mixtures with high structural dissimilarity (near one) have very little signal overlap, while mixtures having low structural dissimilarity have high signal overlap per compound. While this decomposition provides theoretical insight into partitioning chemical diversity, it is impractical for experimental data where structures and spectra of individual phytochemicals within a mixture are unknown. However, it is possible to estimate compositional diversity (<i>D<sub>C</sub></i>) of phytochemical mixtures using hyphenated analytical methods (GC-, LC-), which can then be used to decompose structural complexity from metabolic complexity as in equation 1. If we combine equations 1-3, they simplify to give effective structural complexity as:   (Eqn. 4) Effective structural complexity results from both structural parameters of chemical mixtures: structural dissimilarity and mean structural complexity. High <i>D<sub>Seff</sub></i> can result from highly dissimilar mixtures or highly complex individual compounds.
description: These data include the measures of phytochemical diversity
 generated in this study and the ecological data with which they are
 associated. ReadMe files will be uploaded with keys describing column
 names in tables.