DOI: 10.5061/dryad.hhmgqnkcz
title: Effects of seed morphology and elaiosome chemical composition on attractiveness of five Trillium species to seed-dispersing ants

description: Morphological and chemical attributes of diaspores in myrmecochorous
 plants have been shown to affect seed dispersal by ants, but the relative
 importance of these attributes in determining seed attractiveness and
 dispersal success is poorly understood. We explored whether differences in
 diaspore morphology, elaiosome fatty acids, or elaiosome phytochemical
 profiles explain the differential attractiveness of five species in the
 genus Trillium to eastern North American forest ants. Species were ranked
 from least- to most-attractive based on empirically-derived seed dispersal
 probabilities in our study system, and we compared diaspore traits to test
 our hypotheses that more attractive species will have larger diaspores,
 greater concentrations of elaiosome fatty acids, and distinct elaiosome
 phytochemistry compared to the less attractive species. Diaspore length,
 width, mass, and elaiosome length were significantly greater in the more
 attractive species. Using gas-chromatography mass spectrometry, we found
 significantly higher concentrations of oleic, linoleic, hexadecenoic,
 stearic, palmitoleic, and total fatty acids in elaiosomes of the more
 attractive species. Multivariate assessments revealed that elaiosome
 phytochemical profiles, identified through liquid-chromatography mass
 spectrometry, were more homogeneous for the more attractive species.
 Random forest classification models (RFCM) identified several elaiosome
 phytochemicals that differed significantly among species. Random forest
 regression models revealed that some of the compounds identified by RFCM,
 including methylhistidine (α -amino acid) and d-glucarate (carbohydrate),
 were positively related to seed dispersal probabilities, while others,
 including salicylate (salicylic acid) and citrulline (L-α -amino acid),
 were negatively related. These results supported our hypotheses that the
 more attractive species of Trillium—which are geographically widespread
 compared to their less-attractive, endemic congeners—are characterized by
 larger diaspores, greater concentrations of fatty acids, and distinct
 elaiosome phytochemistry. Further advances in our understanding of seed
 dispersal effectiveness in myrmecochorous systems will benefit from a
 portrayal of dispersal-unit chemical and physical traits, and their
 combined responses to selection pressures.  
description: This data file is broken down into <strong>three
 datasets</strong>, each collected with a different laboratory
 method. All data were collected on a set of seeds from five southeastern
 US species of <em>Trillium</em> (<em>T. catesbaei, T.
 cuneaum, T. lancifolium, T. discolor, </em>and <em>T.
 decumbens</em>). Approximately 100 seeds were collected from mature
 fruits of each species just prior to natural dehiscence from a total of
 eight study sites across the southern Appalachian region in the summer of
 2018. Upon return to the laboratory, we stored seeds at -20°C for 1 month
 prior to diaspore morphological analyses and 4 months prior to chemical
 analyses. <strong>The first data set is
 <em>Trillium </em>diaspore (seed + elaiosome) morphology
 data</strong>. To address our first study objective, which was to
 quantify interspecific differences in diaspore morphology, we measured
 diaspore and elaiosome length, width, and mass for 26 diaspores of each
 species, representing multiple individuals from each study site (n = 130).
 We took the fresh mass of entire diaspores (g) and then measured the
 length and width of diaspores (mm) using digital calipers (Mitutoyo
 Digimatic Caliper, 0.01 mm resolution, Sakado, Japan). We then removed the
 elaiosome from each seed using a straight razor and repeated the above
 measurements for the elaiosome. Elaiosome-seed mass ratios were calculated
 by dividing the mass of the elaiosome by the mass of the entire
 diaspore. <strong>The second data set
 is <em>Trillium </em>elaiosome fatty acid
 data</strong>. To address our second study objective, which was to
 quantify interspecific differences in concentrations of elaiosome fatty
 acids among species of <i>Trillium</i>, we assessed
 triglyceride, diglyceride, and free fatty acid forms using
 gas-chromatography mass spectrometry (GC-MS). We removed elaiosomes from
 frozen seeds as described above and placed them in 15-mL glass
 scintillation vials. Twelve samples were generated, representing the five
 study species, each from at least two field sites. Each sample contained a
 minimum of 30 mg of elaiosomes from one individual plant. The number of
 elaiosomes used for each sample depended on the species but ranged from 14
 to 35. To each sample, we added 2.0 mL of the extraction solvent (2 : 1
 dichloromethane : methanol). Samples were centrifuged for 30 m (170 rpm),
 and supernatants were siphoned off and transferred to new glass
 scintillation vials. We repeated this treatment three times, until 6 mL of
 supernatants were generated for each sample. The vials containing all of
 the combined extraction supernatants were placed in a nitrogen drying
 apparatus until all the extraction solvent had been evaporated. The dry
 residue in each vial was suspended in 1 mL isooctane : ethyl acetate (10 :
 1) and was then applied to an alumina column (4-cm Pasteur pipette filled
 with flash alumina pre-equilibrated with isooctane : ethyl acetate 10 :
 1), as in Boulay et al. (2006). The column was eluted with 4 ml isooctane
 : ethyl acetate (10 : 1) to yield triglyceride fractions for each sample.
 Elution of the diglyceride fraction was realized with 5 mL of isooctane :
 ethyl acetate (3 : 1). The free fatty acids were captured after elution
 with 6 mL of isooctane : ethyl acetate : acetic acid (75 : 25 : 2).
 Fractions were collected in standard glass test tubes. Resulting fractions
 of triglycerides, diglycerides, and free fatty acids were dried under
 nitrogen for 3 h. Residue was dissolved in 1 mL of the extraction solvent,
 and then 400 μL of this volume was transferred to a 2.0 mL vial containing
 20 μg of octadecane in methanol as an internal standard. Solutions were
 dried again under nitrogen and subsequent steps taken to convert fatty
 acids to their methyl esters for GC-MS analysis. Transesterification for
 tri- and diglyceride fractions was accomplished by adding 100 µL of
 methanolic KOH (0.5 M) to the dry triglyceride and diglyceride fractions.
 After 30 m, the reaction was stopped with 100 µL of HCl (1 N). Then 20 µL
 of hexane was added to the mixture. Methylation of the free fatty acid
 fraction was carried out by treating the solution with 200 µL of toluene,
 1.5 mL of methanol, and 300 µL of an 8% (w/v) solution of HCL in
 methanol/water (85 : 15, w/v), sequentially, to the fraction, as in
 Ichihara and Fukubayashi (2009). The fraction was then heated to 100 C for
 1 h. All the obtained fractions were dried one final time and dissolved in
 100 μL of isooctane, of which 1 µL was injected into an Agilent 7820 gas
 chromatograph coupled with a 5977 mass spectrometer set to 70eV electron
 ionization and equipped with an HP5-MS column (30 m x 0.25 mm i.d.,
 0.25 μm film thickness;
 Agilent Technologies, Santa Clara, CA, USA). Ultra-pure helium was used as
 a carrier gas at a flow rate of 1 mL min -1, a split flow ratio of 100:1,
 and a front inlet temperature of 275 °C. The following oven conditions
 were employed: initial temperature 120 °C, hold time 2 min; ramp 1: 15°C
 min<sup>-1</sup> to 250 °C, hold time 0 min; ramp 2: 5 °C
 min<sup>-1</sup> to 300°C, hold time 10 min; total run time of
 30.67 min. Data were recorded as TIC chromatograms. We produced standard
 curves by running five concentrations (10, 1, 0.5, 0.1, 0.05, and 0.025
 mg/mL) of a fatty acid methyl ester standard mixture of methyl linoleate
 (20 wt %), methyl linolenate (20 wt %), methyl oleate (20 wt %), methyl
 palmitate (20 wt %), and methyl stearate (20 wt %) (CRM1891, Millipore
 Sigma, Merck KGaA, Darmstadt, Germany). Linearity across the relevant
 range of concentrations was evaluated visually and by calculating the
 slope of the line and R<sup>2</sup> values for each compound.
 GC-MS produced TIC chromatograms for each elaiosome fraction (free, di-,
 and triglycerides). Peaks were identified based on comparisons of
 retention times and spectra with authentic standards. Compounds for which
 we did not have standards were tentatively identified using the NIST 14
 library (Scientific Instrument Services, 1027 Old York Rd., Ringoes, New
 Jersey, U.S.A). Peak areas were determined using the Integrator Trapezoid
 feature in OpenChrom (Wenig & Odermatt, 2010) and concentrations
 were determined for known compounds based on response factors of standards
 relative to the internal standard. Concentrations for tentatively
 identified compounds were estimated as internal standard equivalents. All
 concentrations were converted to a percentage of elaiosome fresh weight
 for final reporting and statistical analyses.
 <strong>The third data set
 is <em>Trillium </em>elaiosome phytochemistry
 data. </strong>To address our third study objective, which was to
 assess interspecific differences in elaiosome phytochemistry, we
 characterized and compared profiles of elaiosome metabolites using
 liquid-chromatography mass spectrometry (LC-MS). Each of the five study
 species was represented by six sample replicates (N = 30). Samples were
 taken from 2 – 6 individuals from the representative study sites. Using
 sterile techniques, we removed elaiosomes from frozen diaspores of each
 species using a straight razor. We recorded the fresh mass of single
 elaiosomes (g) and placed each elaiosome in a 2.0 mL centrifuge tube.
 Sample contents were suspended in 1.3 mL of extraction solvent (40 : 40 :
 20 HPLC grade methanol, acetonitrile, water with 0.1% formic acid), and
 were kept at 4°C. Extraction proceeded for 20 min at -20°C before samples
 were centrifuged for 5 min (16.1 relative centrifugal force [rcf]) at 4°C.
 Supernatants were transferred to new vials. The remaining elaiosome
 contents were resuspended in 200 μL of cold (4°C) extraction solvent. The
 extraction again proceeded for 20 min at -20°C before being centrifuged
 for 5 min (16.1 rcf) at 4°C. Once more, we transferred supernatants to the
 vials, and added another 200 μL of extraction solvent to the pelleted
 elaiosomes for a final wash by repeating the extraction once more. The
 vials containing all of the combined extraction supernatants were then
 placed in a nitrogen drying apparatus until all the extraction solvent had
 been evaporated. We resuspended the residue in 300 μL of sterile water to
 isolate the polar fraction of phytochemicals and transferred this to 300
 μL autosampler vials. Samples were immediately placed in a 4°C autosampler
 for LC-MS analysis. A 10 μL injection of each sample was separated through
 a Synergi 2.5 micron Hydro-RP 100 Å, 100 mm × 2.00 mm LC column
 (Phenomenex, Torrance, CA, USA) maintained at 25°C. The mass spectrometer
 and chromatographic separation were performed similar to the method
 outlined in Lu et al., 2010. The eluent was introduced into the mass
 spectrometer via an electrospray ionization source in negative mode before
 entering an Exactive Plus orbitrap mass spectrometer (Thermo Scientific,
 Waltham, MA, USA) through a 0.1-mm internal diameter fused silica
 capillary tube. Samples were run with a spray voltage of 3 kV, a nitrogen
 sheath gas flow rate of 10 units, a capillary temperature set at 320°C,
 and an AGC target set to 3e6. Samples were analyzed in full scan mode with
 a resolution of 140,000 and a scan window of 85 to 800 m/z for from 0 to 9
 min and 110 to 1000 m/z from 9 to 25 min. Solvent A consisted of 97:3 HPLC
 grade water : methanol, 10 mM tributylamine, and 15 mM acetic acid.
 Solvent B was HPLC grade methanol. The mobile phase gradient from 0 to 5
 min was 0% B, from 5 to 13 min was 20% B, from 13 to 15.5 min was 55% B,
 from 15.5 to 19 min is 95% B, and from 19 to 25 min was 0% B while
 maintaining a constant flow rate of of 200 μL/min. Raw files obtained from
 Xcalibur MS software (Thermo Electron Corp., Waltham, MA) were converted
 into the mzML format using ProteoWizard (Chambers et al., 2012). The
 converted files were imported into MAVEN (Metabolomic Analysis and
 Visualization Engine for LC–MS Data), a software package (Clasquin et al.
 2012). Peaks for the known metabolites were picked in MAVEN, which
 automatically performs non-linear retention time correction and calculates
 peak areas across samples, using a preliminary mass error of ± 20 ppm and
 retention time window of 5 min. The University of Tennessee, Knoxville
 Biological and Small Molecule Mass Spectrometry Core (BSMMSC), through
 which this analysis took place, has replicated and expanded the method of
 Rabinowitz and coworkers (Lu et al., 2010). Final metabolite annotations
 were made using a library of 263 retention time-accurate m/z pairs taken
 from MS1 spectra. The annotation parameters have been verified previously
 with pure standards in the course of establishing the method. For a
 metabolite to be annotated as a known compound, the eluted peak had to be
 found within 2 min of the expected retention time, and the metabolite mass
 had to be within ± 5 ppm of the expected value. Metabolite identities were
 confirmed using the MAVEN software package (Clasquin et al., 2012), and
 peak areas for each compound were integrated using the Quan Browser
 function of the Xcalibur MS Software. LC-MS produced relative
 concentrations of phytochemical compounds across samples, which were
 compared using z-transformed peak areas from extracted ion
 chromatograms.
description: <strong><em>Trillium</em> diaspore morphology
 data set: </strong>This dataset is contained in a single excel file
 entitled "Trillium_diaspore_morphology," and consists of a data
 matrix (11 columns, 131 rows). ID refers to the replicate for each species
 (1 - 26). Data are stacked. Units are included in parentheses after the
 column header. Replicates include information about the study sites
 (including the latitude/longitude coordinates for each site) from which
 they were collected.   
 <strong><em>Trillium </em>elaiosome fatty acid
 data set: </strong>This dataset is contained in an excel file
 entitled "GC_MS_Trillium," containing 4 sheets. The data were
 generated using GC-MS as specified in the Methods above. The sheets
 include: IRF calcs, Sample Areas, Sample Quant mg_ml, and Sample Quant %
 fw.  IRF calcs: This sheet contains
 four data tables. In the first table ("Concentrations"), the
 concentration of the internal standard, octadecane, and the concentrations
 of the five methyl esters in the standard mixture are recorded (mg/mL). In
 the second table ("Raw Data Standard Areas"), the standard areas
 from the 9 GC runs for the internal standard and the five methyl esters in
 the standard mixture are recorded (these are area under the curve values
 without a unit). The third table ("RRFs") records the relative
 response factors for each of the five methyl esters in the standard
 mixture (relative to the internal standard) for each of the 9 GC runs. 
 The fourth table ("Avg") simply records the averaged RRFs for
 each of the five methyl esters in the standard mixture. 
 Sample Areas: This sheet consists of a data
 matrix with 37 columns and 19 rows. The data contained in the matrix are
 the raw sample areas (areas under the curve, no units) for each of 13
 compounds identified in the elaiosome samples. Column names specify the
 fatty acid form (di = diglyceride, tri = triglyceride, fa = free fatty
 acids) followed by an underscore and a two letter abbreviation for the
 species specific epithet ("cu" = cuneatum, "ca" =
 catesbaei, "de" = decumbens, "di" = discolor, and
 "la" = lancifolium) and a two letter abbreviation for the study
 site ("ca" = cave, "jg" = jocassee gorges,
 "wf" = whitewater falls, "bh" = blue hole,
 "om" = old mine, "pb" = pocket branch, "tb"
 = tilton bridge). The third row of the data matrix specifies the exact
 concentration of the internal standard (mg/mL), and the fourth row of the
 matrix specifies the fresh weight of each elaiosome sample (mg). The
 numbers contained in the parentheses after the names of each compound are
 the average retention times.  Sample Quant
 mg_ml: This sheet is similar to Sample Areas; it is a data
 matrix with 37 columns and 15 rows. The data contained in the matrix are
 the concentration of the compounds per sample (mg/mL); these are generated
 from a formula that multiples the internal standard concentration for each
 sample by the raw area under the curve for each sample; that product is
 then divided by the raw area under the curve of the internal standard for
 each sample.  Sample Quant % fw: The
 final sheet is again similar to both Sample Areas and Sample Quant mg_ml;
 it contains a data matrix with 37 columns and 15 rows, corresponding to
 the 36 elaiosome samples and the 13 compounds (plus some column headers).
 The data contained in this matrix are the sample concentrations as
 percentage fresh weight; these are generated from a formula that
 multiplies the sample concentration (mg/mL) by the total solvent volume in
 which the extract was suspended for analysis (0.1 mL), divides that
 product by the fraction of the total extract that was in the aliquot (0.4
 mL), and then divides that value again by the sample fresh weight (mg).
 Finally, the value is multiplied by 100 to obtain a percentage. The last
 row in this matrix is a summation of all 13 compounds, to generate a total
 percentage of fresh weight that is represented by fatty acids. A second
 table in this sheet lists the fresh weight of each sample (mg) and the
 number of elaiosomes used to generate the sample, and then calculates the
 average free oleic and free linoleic acid per elaiosome per
 species.   
 <strong><em>Trillium </em>elaiosome
 phytochemistry data set: </strong>This data set consists of two .csv
 files. The first is entitled "LC_MS_Trillium_Knowns.csv", and it
 contains a data matrix consisting of 124 columns and 31 rows. The first
 four columns are metadata columns, and contain information about the
 geographic distribution of each species (endemic v. widespread), the
 species specific epithet, the average probability of seed removal in the
 field, and two-letter abbreviation for the study site from which the
 sample was collected ("TB" = Tilton Bridge, "PB" =
 Pocket Branch, "OM" = Old Mine, "CA" = Cave,
 "WF" = WhiteWater Falls, "BR" = Boat Ramp, and
 "JG" = Jocassee Gorges). The remaining columns are the names of
 chemical compounds identified using Liquid-chromatography mass
 spectrometry. Each cell contains the raw area under the curve for each
 compound in each sample.  The second file is entitled
 "LC_MS_Trillium_Unknowns.csv", and it is identical to the Knowns
 data matrix, except that it contains &gt; 7,500 columns, each
 corresponding to an unknown chemical compound detected by LC-MS. These are
 simply denoted by numbers.