DOI: 10.5061/dryad.k6djh9wd5
title: Data from: Herbivores disrupt clinal variation in plant responses to water limitation

description: Plasticity in plant traits, including secondary metabolites, is critical
 to plant survival and competitiveness under stressful conditions. The
 ability of a plant to respond effectively to combined stressors can be
 impacted by crosstalk in biochemical pathways, resource availability, and
 evolutionary history, but such responses remain underexplored. In
 particular, we know little about intraspecific variation in response to
 combined stressors or whether such variation is associated with the stress
 history of a given population. Here, we investigated the consequences of
 combined water and herbivory stress for plant traits, including relative
 growth rate, leaf morphology, and various measures of phytochemistry,
 using a common garden of Asclepias fascicularis milkweeds. To
 examine how plant trait means and plasticities depend on the history of
 environmental stress, seeds for the experiment were collected from across
 a gradient of aridity in the Great Basin, USA. We then conducted a
 factorial experiment crossing water limitation with herbivory. Plants
 responded to water limitation alone by increasing the evenness of
 UV-absorbent secondary metabolites, and to herbivory alone by increasing
 the richness of metabolites. However, plants that experienced combined
 water and herbivory stress exhibited similar phytochemical diversity to
 well-watered control plants. This lack of plasticity in phytochemical
 diversity in plants experiencing combined stressors was associated with a
 reduction in relative growth rates. Leaf chemistry means and plasticities
 exhibited clinal variation corresponding to seed-source water deficits.
 The total concentration of UV-absorbent metabolites decreased with
 increasing water availability among seed sources, driven by
 higher concentrations of flavonol glycosides, which are hypothesized to
 act as antioxidants, among plants from drier sites. Plants sourced from
 drier sites exhibited higher plasticity in flavonol glycoside
 concentrations in response to water limitation, which increased
 phytochemical evenness, but simultaneous herbivory dampened plant
 responses to water limitation irrespective of seed source. Synthesis:
 These results suggest that climatic history can affect intraspecific
 phytochemical plasticity, which may confer tolerance to water limitation,
 but that co-occurring herbivory disrupts such patterns. Global change is
 increasing the frequency and intensity of stress combinations, such that
 understanding intraspecific responses to combined stressors is critical
 for predicting the persistence of plant populations.
description: <em>Study
 system</em> <em>Asclepias fascicularis
 </em>(narrowleaf milkweed)
 is one of the most widely distributed milkweed species in the western
 United States and an important food-plant species for the monarch
 butterfly (<em>Danaus plexippus</em>), among other specialist
 herbivores (Woodson 1954, Dilts et al. 2019). Narrowleaf milkweed is found
 across a wide range of water availabilities, including in very dry
 locations (down to at least 100 mm of annual precipitation) (Woodson
 1954). Milkweeds are well known for their chemical defenses, which are
 sequestered by monarch butterflies. Unlike many milkweeds, <em>A.
 fascicularis </em>contains few cardiac glycosides, but the leaves
 contain high concentrations of flavonols and both leaves and roots contain
 diverse pregnane glycosides (Rasmann and Agrawal 2011, Mundim and Pringle
 2020, Diethelm et al. 2022). Flavonols are hypothesized to play a role in
 the mitigation of plant water stress by functioning as antioxidants, and
 the biological role of the pregnane glycosides remains unknown
 (Kaminska-Rozek and Pukacki 2004, Zehnder and Hunter 2007, Diethelm et al.
 2022).             To explore
 the role of climatic history in plant trait responses to combined
 stressors, we collected seeds from five sites spanning a west-to-east
 transect of 385 km of the Great Basin Desert, USA, in fall 2016 (Fig. S1).
 Annual precipitation at these five sites encompasses the
 5<sup>th</sup>–95<sup>th</sup> percentiles of
 annual precipitation among 453 occurrence records for <em>A.
 fascicularis </em>in the western United States (Dilts et al.
 2019).<em> </em>To estimate the typical drought stress at each
 of these sites, we calculated the cumulative annual climatic water deficit
 (CWD) using monthly precipitation and temperature data from the PRISM Data
 Explorer tool (PRISM Climate Group 2019) for the years 2004-2016 (for more
 details, see Diethelm et al. 2022). The seed-source sites with high CWD
 (hereafter, dry) experience more water limitation on an annual basis than
 the sites with low CWD (hereafter, wet). From dry to wet, these sites
 were: Fallon, NV (FN; 989.0 ± 19.7 mm), Georgetown, CA (CA; 985.7 ± 39.4
 mm), Battle Mountain, NV (BM; 847.7 ± 29.5 mm), Reno, NV (RN; 587.2 ±
 20.0), and Verdi, NV (VE; 460.3 ± 16.7) (Fig. S1 and Table S1). In a
 previous study conducted in a glasshouse, plants from drier source
 populations had higher constitutive concentrations of leaf flavonols,
 whereas plants from wetter source populations exhibited higher induction
 of leaf flavonols under acute water stress (Diethelm et al.
 2022).  <em>Water limitation ´ herbivory experiment </em> To investigate how exposure to combined stressors affects plant traits, we conducted a factorial common-garden experiment manipulating water and herbivory. This experiment was conducted from May–October 2017 in a 30x10-m outdoor plot at the University of Nevada, Reno, Valley Road Agricultural Station. <em>Asclepias fascicularis </em>seeds from each of the five seed-source sites, representing four maternal families per site and 16 plants per family (<em>N</em> = 320), were germinated in May 2017 and transferred to 0.5-L pots of Full Circle® Soar potting soil to grow for two months. In July 2017, the 236 plants that had successfully germinated were transplanted into the plot in a randomized block design (Fig. S2). Some of these plants failed to establish in the plot, reducing the total number of plants to 147 (see Table 1 for <em>n </em>per treatment). All plants were fertilized once per month with 1:1:1 Triple Pro® NPK fertilizer pellets.             To evaluate plant responses to single versus combined stressors, plants were randomly assigned to one of four possible treatments: well-watered control (well watered + no herbivory); dry (dry + no herbivory); herbivory (well watered + herbivory); or dry + herbivory. Well watered and dry irrigation lines alternated from east to west through the plot. To enhance establishment, all plants were watered daily to soil saturation using drip-irrigation for the first 8 weeks. For 38 days beginning 6 September 2017, well watered plants received 84 mL of water to an ~157 cm<sup>2</sup> soil area per day while the dry lines received no irrigation. The well watered treatment was designed to simulate precipitation at the wettest seed source site (VE), whereas the drought treatment was designed to bring plants to their wilting point. After one month,<sub> </sub>plants in the dry treatment were beginning to show severe wilt, so we supplemented them with 84 mL over two days in early October.               Herbivory treatments were implemented over 3 weeks, starting in mid-September. Leaves were mechanically damaged to produce ~15% tissue loss, using a rolling leather punch to simulate herbivory (Baldwin 1990). To test if this mechanical damage effectively simulated damage by herbivores, a randomly selected subset of plants (<em>n </em>= 30) received ~5% leaf removal by monarch (<em>Danaus plexippus</em>)<em> </em>caterpillars in addition to ~10% mechanical damage. Caterpillars were obtained from a laboratory colony at the University of California, Davis, and were maintained on <em>A. fascicularis</em> plants in a glasshouse. In the common garden, larvae were restrained to ~5% of aboveground plant tissue using netted bags. We checked plants periodically and removed caterpillars once all of the leaves in the netted section had been consumed. Plant responses to the treatments were quantified as follows. To calculate changes in aboveground relative growth rate (RGR), we measured: (i) plant height (cm) and (ii) the number of  &gt;4-cm-long stems branching directly from the main plant axis at the beginning and end of treatments. We then multiplied (i) by (ii) to estimate plant size. Relative growth rate was calculated per day using the natural logarithms of the change in size, divided by the 38 days of the experiment. At harvest, six leaves from the second whorl of the main axis of each plant were separated to assess the following traits: leaf width, leaf mass per area (LMA), foliar water content, trichome density, and secondary metabolites. We measured leaf width (mm) at the widest point of three leaves and averaged those measurements per plant. To determine LMA (mg dry mass /mm<sup>2</sup>), we cut a rectangle of 1-cm length of the 4<sup>th</sup> leaf, measured the width, and dried it at 60° C for 48 h before reweighing. Foliar water content was calculated as the difference between the wet and dry masses divided by the area of the same rectangle. To determine trichome density, we counted the number of trichomes on a randomly selected 2-mm wide leaf edge. To assess plant water-use efficiency, we analyzed 13-carbon isotopes (Farquhar et al. 1989) from homogenously ground leaf and stem material at the UC Davis Stable Isotope Facility. These same samples were also analyzed for percent carbon (C) and percent nitrogen (N), which we used to calculate the C:N ratio.             To investigate how our treatments affected plant secondary metabolites, we performed an untargeted analysis of the UV-absorbent metabolites in the remaining two leaves. Leaves were stored in a –80 °C freezer until they were freeze-dried, ground, and extracted in methanol with a digitoxin internal standard. Extracts were then run on an UPLC-UV system; for detailed methods, see Appendix S1. UV absorbance spectra were recorded from 200 to 330 nm, and peak areas were quantified at 219 nm. Compounds were differentiated based on retention time and the UV spectrum (Appendix S2) and concentrations were estimated as digitoxin equivalents. We also quantified the richness (S), evenness (J), and the Shannon diversity index (H) of all of the UV-absorbent peaks for each sample with a minimum area of 8,000 absorbance units (AU) and a minimum height of 5,000 AU. The chemical identities of key peaks were verified by LC-Q-ToF-MS at the Max Planck Institute for Chemical Ecology (Appendix S1,S3). <em>Statistical analysis</em> To analyze plant responses to experimental treatments, we used generalized linear mixed models  (GLMMs) from the <em>glmmTMB</em> package (Brooks et al. 2017) in R version 3.6.1 (R Core Team 2022). To compare effect sizes, we z-transformed all continuous variables before analysis using the <em>BBmisc</em> package (Bischl et al. 2017), and we report beta coefficients (<em>β</em>) from the models with standard errors. We assessed the residuals of each fitted model, and we square-root or log-transformed response variables when these transformations provided a better fit to the gaussian distribution as needed. All plant-trait models used plant maternal family nested within the seed-source site as random intercept effects to account for non-independence within families or sites. To determine the predictors of plant traits, we used backward selection (Zuur et al. 2009) in the <em>MuMIn</em> package (Barton 2019); see Tables S2-S3 for model selection details. To determine whether interactive effects between stressors depended on seed source, each saturated model began with a three-way interaction among water treatment, herbivory treatment, and seed-source CWD. Neither the concentration nor the richness of UV-absorbent plant metabolites was changed by the addition of monarch herbivory to mechanical damage (respectively, Welch's <em>t</em> = -0.12, df = 56.1, <em>P</em> = 0.9; Welch's <em>t</em> = 0.53, df = 65.8, <em>P</em> = 0.6; Fig. S3), so all herbivory treatments were pooled in subsequent analyses. Models were selected based on the lowest sample-size corrected Akaike Information Criterion (AICc), and any marginally significant predictors were evaluated using log-likelihood ratio tests (LRT) between models in the <em>lmtest </em>package (Zeileis and Hothorn 2002). Marginal and conditional <em>R<sup>2</sup></em> values were calculated for selected models in the <em>MuMIn</em> package. Data are archived in Dryad (Diethelm et al. 2023).
description: This readme.txt file was generated on 2023-10-20 by Aramee Diethelm.
 GENERAL INFORMATION -Title of Dataset: "Herbivores disrupt clinal
 variation in plant responses to water limitation" -Author
 Information: A. Principal Investigator Contact Information Name: EG
 Pringle Institution: University of Nevada, Reno Address: 1664 N Virginia
 St, Reno, NV 89557 Email: epringle@unr.edu B. Associate or Co-investigator
 Contact Information Name: AC Diethelm Institution: University of Nevada,
 Reno Address: 1664 N Virginia St, Reno, NV 89557 Email:
 adiethelm@nevada.unr.edu -Year (MM/YYYY) and geographic location of data
 collection: 11/2016 - 11/2020 in Reno, NV -Funding sources that supported
 the collection of the data: NSF Graduate Research Fellowship Hitchcock
 Chemical Ecology Fellowship University of Nevada, Reno Alexander von
 Humboldt Fellowship NSF DEB 2145757 Bureau of Land Management L20AC00440
 SHARING/ACCESS INFORMATION -Licenses/restrictions placed on the data: None
 -Environmental variables originated from Diethelm et al., 2022. Climatic
 history, constraints, and the plasticity of phytochemical traits under
 water stress. Ecosphere 13; e4167. Original Sources: 1. PRISM Climate
 Group. http://prism.oregonstate.edu 2. USDA. (2018). Soil survey
 geographic (SSURGO) database (Soil Survey Staff, Natural Resource
 Conservation Service, U.S. Department of Agriculture). Available online at
 https://sdmdataaccess.sc.egov.usda.gov -Recommended citation for this
 dataset: Diethelm, A.C., M.R. Reichelt, and E.G. Pringle. 2023. Herbivores
 disrupt clinal variation in plant responses to water limitation. J Ecol
 DATA & FILE OVERVIEW -File List: 1. Diethelm.etal.2017AfCode.R for
 analyzing the data and generating figures 2. "cwd_AF_JEcol.csv"
 for assessing mean +/- SE for climatic water deficit values per seed
 provanence for manuscript figures (see environmental variables section) 3.
 "2017_Af_outdoorCG_physicaltraits_Diethelm.csv" 4.
 "2017_Af_outdoorCG_isotopes_Diethelm.csv" 5.
 "2017_Af_outdoorCG_chemistry_Diethelm.csv" 6.
 "2017_Af_outdoorCG_chem_diversity_matrix_Diethelm.csv" as matrix
 for determining diversity metrics for outdoor common garden plant
 secondary chemistry METHODOLOGICAL INFORMATION 1. Instrument- or
 software-specific information needed to interpret the data: - R: A
 Language and Environment for Statistical Computing, Version 4.2.0, © R
 Foundation for Statistical Computing - RStudio 2021.09.1 Build 372 ©
 2009-2021 RStudio, PBC - R Packages required: plyr, dplyr, ggplot2.
 ggpubr, ggforce, glmmTMB, DHARMa, lmtest, lattice, MuMIn, car, rcompanion,
 BBmisc, vegan and effects. 2. Standards and calibration information, if
 appropriate: - For an internal standard for chemistry, a known
 concentration of the compound Digitoxin (≥95.0%; HPLC-grade) was added to
 all samples. - To control for size of plant, the number of stems with
 aphids present was standardized by the number of stems per plant
 investigated. - Again, to control for size of plant, plant growth was
 standardized using a relative growth rate instead of a raw value: a rate
 of growth per unit time, as a proportion of its size at that moment in
 time. 3. Describe any quality-assurance procedures performed on the data:
 - Data was checked by EGP and ACD - Version control maintained through
 file hosting services of Box.com 4. Specialized formats or other
 abbreviations used: BM = Battle Mountain, NV, FN = Fallon, NV, CA =
 Georgetown, CA, LW = Reno (note difference from the paper, where it's
 abbreviated RN), NV, VE = Verdi, NV, MAX = maximum value, and MIN =
 minimum value - NA indicates "Not available" and is missing data
 DATA-SPECIFIC INFORMATION FOR: "cwd_AF_JEcol.csv" 1. Number of
 variables: 3 2. Number of cases/rows: 5 3. Variable List: "site"
 the location of seed collection site (seed provanence), where BM = Battle
 Mountain, NV, FN = Fallon, NV, CA = Georgetown, CA, LW = Reno, NV, and VE
 = Verdi, NV "CWD_mm" the annual cummulative climatic water
 deficit per site (seed provanenence) in millimeters (mm) calculated from
 2004-2016 "CWD_se" the standard error around the annual
 cummulative climatic water deficit value in "CWD_mm"
 DATA-SPECIFIC INFORMATION FOR:
 "2017_Af_outdoorCG_physicaltraits_Diethelm.csv" 1. Number of
 variables: 29 2. Number of cases/rows: 147 3. Variable List:
 "plant" 2017 outdoor common garden planting order starting at
 row 1 "plant_lab_code" lab specific code for plant from which
 the seeds were collected (not important for data anyalysis)
 "treatment" water availability treatment: water-limited
 (drought) or well watered (control) "rep" seed number as sourced
 from same mother plant "site" seed provanence or location of
 seed collection, where BM = Battle Mountain, NV, FN = Fallon, NV, CA =
 Georgetown, CA, LW = Reno, NV, VE = Verdi, NV "CWD" annual
 cummulative climatic water deficit per seed collection site (provanenence)
 in millimeters (mm) "herbivory" herbivory treatment: yes
 (damged) or no (undamaged) updated 2022_Mar22 "wet_mass_g"
 above-ground weight post harvest in grams "dry_mass_g"
 above-ground weight after freeze-drying plants in grams
 "aboveground_water_content" difference between wet and dry
 weight for aboveground plant tissue "foliar_water" the diference
 between wet and dry weight of LMA leaf section "LMA" final value
 for leaf mass per area in mm/mg "trichomes" the number of leaf
 edge trichomes counted in a 10 X view (2 mm)
 "aphids_standaridized" number of stems with aphids at the end of
 the experiment as divided by number of stems on plant; note that we did
 not note aphid presence on plant 68 so this value is reported as
 "NA"=Not Available to indicate missing data
 "height_cm_1" height of the plant in cm at start of experiment
 "height_cm_2" height of the plant in cm at end of experiment
 "stems_1" number of stems at start of experiment over 4 cm
 "stems_2" number of stems at end of experiment over 4 cm
 "plant_area1" plant height * # stems (at start of experiment)
 "plant_area2 plant height * # stems (at the end of experiment)
 plant_area_change" plant area2 (area at the end of the experiment)
 subtracted from plant area1 (area at the start) "RGR_size2" part
 of relative growth rate (RGR) calculation, this cell is formula for the
 natural log of the plant size at time 2 "RGR_size1" part of
 relative growth rate (RGR) calculation, this cell is formula for the
 natural log of the plant size at time 1 "RGR_numerator" part of
 relative growth rate (RGR) calculation, this cell is the calculation for
 the numerator (LN(size2)-LN(size1)) "RGR_final" this is the
 final cell for calculating the relative growth rate (RGR), or the rate of
 accumulation of new dry mass per unit of existing dry mass as calculated
 by (ln(mass2)-ln(mass1))/(t2-t1), in which the numerator is divided by the
 experimental time period "growth_stems" difference between start
 and end number of stems "avg_leaf_length_mm" average length of 3
 leaves from same plant in mm "avg_leaf_width_mm" average width
 of 3 leaves from same plant in mm "field_code" code as written
 on plant tags in the outdoor common garden, including seed collection
 location (##), mother plant number (###), and replicate (seeds from same
 pod) which lab specific (not important for data anyalysis) DATA-SPECIFIC
 INFORMATION FOR: "2017_Af_outdoorCG_isotopes_Diethelm.csv" 1.
 Number of variables: 31 2. Number of cases/rows: 145 3. Variable List:
 "plant" unique numerical value that represents the order of
 plants transplanted into common garden "plant_lab_code" code as
 written on plant tags in the outdoor common garden, including seed
 collection location (##), mother plant number (###), and replicate (seeds
 from same pod) which lab specific (not important for data anyalysis)
 "treatment" water availability treatment: water-limited
 (drought) or well watered (control) "rep" seed number as sourced
 from same mother plant - non-repeating replicate value "site"
 seed provanence or location of seed collection "CWD" annual
 cummulative climatic water deficit "herbivory" herbivory
 treatment: yes (damged) or no (undamaged) "d13C" value of delta
 13 carbon isotope in dry tissue per sample as determined by UC Davis
 Stable Isotope Facility "C_ug" ug of carbon in dry tissue per
 sample as determined by UC Davis Stable Isotope Facility
 "C_percent" percent of carbon in dry tissue per sample as
 determined by UC Davis Stable Isotope Facility "d15N" value of
 delta 15 nitrogen isotope in dry tissue per sample as determined by UC
 Davis Stable Isotope Facility "N_ug" ug of nitrogen in dry
 tissue per sample as determined by UC Davis Stable Isotope Facility
 "N_percent" percent of nitrogen in dry tissue per sample as
 determined by UC Davis Stable Isotope Facility "C_N_ratio" ratio
 of carbon to nitrogen in dry tissue per sample as determined by UC Davis
 Stable Isotope Facility "dry_mass_g" above-ground weight after
 freeze-drying plants in grams "aboveground_water_content"
 difference between wet and dry weight for aboveground plant tissue
 "foliar_water_content" the diference between wet and dry weight
 of leaf "LMA" final value for leaf mass per area in mm/mg
 "trichomes" the number of leaf edge trichomes in a 10X field of
 view "aphids_standardized" number of stems with aphids divided
 by number of stems on plant "height_1" height of the plant in cm
 at start of experiment "height_2" height of the plant in cm at
 end of experiment "stems_1" number of stems at start of
 experiment "stems_2" number of stems at end of experiment
 "plant_area1" plant height * number of stems at the start of
 experiment "plant_area2" plant height * number of stems at the
 end of experiment "plant_area_change" plant area2 (area at the
 end of the experiment) subtracted from plant area1 (area at the start)
 "RGR_final" Relative growth rate (RGR), or the rate of
 accumulation of new dry mass per unit of existing dry mass as calculated
 by (ln(mass2)-ln(mass1))/(t2-t1) in
 "2017_Af_outdoorCG_physicaltraits_Diethelm.xslx"
 "avg_leaf_length" average length from 3 leaves from one plant in
 millimeters "avg_leaf_w_mm" average width of 3 leaves from one
 plant in millimeters "field_code" code as written on plant tags
 in the outdoor common garden, including seed collection location (##),
 mother plant number (###), and replicate (seeds from same pod) which lab
 specific (not important for data anyalysis) 4. Specialized formats or
 other abbreviations used: BM = Battle Mountain, NV, FN = Fallon, NV, CA =
 Georgetown, CA, LW = Reno, NV, VE = Verdi, NV DATA-SPECIFIC INFORMATION
 FOR: "2017_Af_outdoorCG_chemistry_Diethelm.csv" 1. Number of
 variables: 79 2. Number of cases/rows: 143 3. Variable List:
 "sample_code" two letters for location of seed collection (where
 BM = Battle Mountain, NV, FN = Fallon, NV, CA = Georgetown, CA, LW = Reno,
 NV, VE = Verdi, NV), 3-digit code for subsite within location, one letter
 for water treament where C is for control and D is for drought, and
 finally one number to represent the unique value for the mother plant (lab
 specific and not important for data anyalysis) "plant_lab_code"
 lab specific code for plant from which the seeds were collected (not
 important for data anyalysis) "rep" seed number as sourced from
 same mother plant (non-repeating replicate value) "treatment"
 water availability treatment: water-limited (drought) or well watered
 (control) "herbivory" herbivory treatment: yes (damged) or no
 (undamaged) "Herb_Type" the type of herbivory that was
 implemented on the plant, where “Both” = plant received both rolling
 puncture tool (i.e., mechanical damage) and caterpillar herbivory, “Man” =
 plant received only mechanical damage with a rolling puncture tool, and
 “None” where the plant was not intentionally damaged during the experiment
 "site" two letter code for location of seed collection, where BM
 = Battle Mountain, NV, FN = Fallon, NV, CA = Georgetown, CA, LW = Reno,
 NV, VE = Verdi, NV "CWD" annual cumulative climatic water
 deficit in millimeters, please see "Environmental variables"
 section for description of how this value was calculated "Peak1"
 concentration of unique secondary metabolite (as determined by unique
 UV-signature and polarity using Ultra-High Performance Liquid
 Chromatography), with morphopeaks being assigned an arbitrary but unique
 numerical values "IGR_final" concentration of Peak2, identified
 as Isorhamnetin-glucoside-rhamnoside, with a final concentration as
 determined by EG Pringle Peaks listed as
 "3,4,8,9,11,12,14,15,17-40,42-63" also represent unique
 secondary metabolites (note: initally assigned a value by retention time
 in a C18 reversed phase column using untargeted analysis of the
 UV-absorbent metabolites with a Ultra-High Performance Liquid
 Chromatography and missing peak values reflect an inital oversplitting of
 morphopeak types that were later collapsed) "QGR_final"
 concentration of Peak64, identified as Quercetin-glucoside-rhamnoside,
 with a final concentration as determined by EG Pringle "Peak65"
 concentration of unique secondary metabolite "Total_Conc" sum of
 all concentrations per plant sample in mg per gram of dry plant tissue (as
 standardized to digitoxin equivalants) "PG_Conc" sum of
 concentrations from compounds identified as being in the chemical class
 "pregnane glycoside" per plant sample (mg per gram of dry plant
 tissue as standardized to digitoxin equivalants) "Flav_Conc" sum
 of concentrations from compounds identified as being in the chemical class
 "flavonoid glycloside" (mg per gram of dry plant tissue as
 standardized to digitoxin equivalants) "Num_Molec" richness (S)
 of UV-absorbent molecules as detected with Ultra-High Performance Liquid
 Chromatography excluding internal standards (per plant sample)
 "Num_Flavs" richness(S) of all flavonol glycoside molecules
 identified per plant sample "Num_PG" richness (S) of all
 pregnane glycoside molecules identified per plant sample "PG_PA"
 presence or absence of any pregnane glycoside molecules identified per
 plant sample, where 1 reflects plant has at least 1 pregnane glycoside
 molecule and 0 if plant has no pregnane glycoside molecules present
 "Peak1_PA" presence or absence of peak1, with 1 if plant has
 peak 1 present and 0 if not "H_Div" Shannon Diversity Index (H)
 value for unique morphopeaks (excluding internal standards) per plant
 sample "D_Div" the inverse Simpson Diversity Index (D) value for
 unique morphopeaks (excluding internal standards) per plant sample
 "J_Eveness" Pielou's measure of species evenness as
 calculated by H/LN(S) per plant sample. Note that species evenness ranges
 from zero to one, with zero signifying no evenness and one at complete
 evenness. "field_code" code as written on plant tags in the
 outdoor common garden, including seed collection location (##), mother
 plant number (###), and replicate (seeds from same pod) which lab specific
 (not important for data anyalysis) "plant" unique numerical
 value that represents the order of plants transplanted into common garden
 DATA-SPECIFIC INFORMATION FOR:
 "2017_Af_outdoorCG_chem_diversity_matrix_Diethelm.csv" 1. Number
 of variables: 57 2. Number of cases/rows: 143 3. Variable List:
 "SampleName" two letters for location of seed collection (where
 BM = Battle Mountain, NV, FN = Fallon, NV, CA = Georgetown, CA, LW = Reno,
 NV, VE = Verdi, NV), 3-digit code for subsite within location, one letter
 for water treament where C is for control and D is for drought, and
 finally one number to represent the unique value for the mother plant (lab
 specific and not important for data anyalysis) "Peak##" values
 information of for peaks 1-4,8-9,11-12,14-15,17-22,24-29,31-40,42-65
 (assignment is somewhat arbitrary but each value is unique numerically)
 that represent the concentration of each unique secondary metabolite (as
 determined by unique UV-signature and polarity using Ultra-High
 Performance Liquid Chromatography)