DOI: 10.5061/dryad.7m0cfxq39
title: Data from: Responses of C4 grasses to aridity reflect species-specific strategies in a semiarid savanna

description: The C4 Poaceae are a diverse group both in terms of evolutionary lineage
 and biochemistry. There is a distinct pattern in the distribution of C4
 grass groups with aridity, however, the mechanistic basis for this
 distribution is not well understood. Additionally, few studies have
 investigated the functional strategies of cooccurring C4 grass species for
 dealing with aridity in their natural environments. We explored the
 coordination of leaf-level gas exchange, water use, and morphology among
 five co-occurring semiarid C4 grasses belonging to divergent clades,
 biochemical subtypes, and size classes at three sites along a natural
 aridity gradient. More specifically, we measured pre-dawn and midday water
 potential, stomatal conductance, water use efficiency, and photosynthesis.
 Leaf tissue was also collected for analysis of stable isotopes of carbon
 and oxygen as well as for measurement of specific leaf area (SLA) and leaf
 width. Species differences in the responsiveness of stomata to changes in
 vapor pressure deficit were also assessed. It was expected that NAD-me
 species would maintain higher rates of photosynthesis, higher water use
 efficiency, and have more responsive stomata than other cooccurring
 species based on observed biogeographic patterns and past greenhouse
 studies. We found that Aristidoideae and Chloridoideae NAD-me-type grasses
 had greater stomatal sensitivity to VPD, consistent with a more isohydric
 strategy. However, midgrasses had both greater apparent water access and
 water use efficiency, regardless of subtype or lineage. PCK-type had less
 responsive stomata and maintained lower levels of photosynthesis with
 increasing aridity. There were strong interspecific differences
 in 13C, leaf width, and SLA, however these were not significantly
 correlated with water use efficiency. C4 grasses in our study did not fit
 discretely into functional groups as defined by lineage, biochemistry, or
 size class. Interspecific differences, evolutionary legacy, and
 biochemical pathways are likely to interact to determine water use and
 photosynthetic strategies of these plants. Control of water loss via
 highly responsive stomata may form the basis for the dominance of certain
 C4 grass groups in arid environments. These findings build on our
 understanding of contrasting strategies of C4 grasses for dealing with
 aridity in their natural environments.
description: <em>Species and Site Descriptions</em>
 To explore relationships between physiology, morphology, and
 grass subtype across a natural aridity gradient (across sites) and across
 species (within sites), we measured a suite of gas exchange parameters
 (photosynthesis, stomatal conductance, water use efficiency), water stress
 (leaf water potential), morphology (specific leaf area, leaf width), and
 bulk leaf stable isotopes of carbon and oxygen in a variety of
 C<sub>4</sub> grasses commonly found in North American
 semi-arid savannas. Five species representing different combinations of
 biochemical subtype, phylogenetic lineage, and physiognomy
 (<em>Aristida wrightii</em> Nash [NADP-ME, Aristidoideae,
 midgrass], <em>Bouteloua curtipendula</em> (Michx.) Torr.
 [NAD-ME/PCK, Chloridoideae, midgrass], <em>Erioneuron
 pilosum</em> (Buckley) Nash [NAD-ME, Chloridoideae, shortgrass],
 <em>Eriochloa sericea</em> (Scheele) Munro ex Vasey [PCK,
 Panicoideae, midgrass], and <em>Hilaria belangeri</em>
 (Steud.) Nash [PCK, Chloridoideae, shortgrass]) was studied across three
 sites representing a natural precipitation gradient on the Edwards
 Plateau, Texas (Appendix S1: Table S2). Measurements were made from May to
 September across three summer growing seasons (2019-2021) at three Texas
 A&M AgriLife Research Ranches (Figure 1c-f), Martin (30° 48'
 N, 99° 50' W; MAP= 630 mm), Sonora (30° 16' N, 100° 34' W;
 MAP= 570 mm) and Read (30° 32' N, 101°03' W; MAP= 480 mm). The
 sites are characterized as Low Stony Hill and Limestone Hill ecological
 sites which are dominated by midgrasses and shortgrasses, annual forbs,
 and woody-encroaching species (<em>Prosopis glandulosa</em>,
 <em>Quercus virginiana</em>, <em>Juniperus
 ashei</em>, and <em>Juniperus pinchotii</em>; Soil
 Survey Team, NRCS, 2022). At Martin, the woody vegetation is primarily
 dominated by <em>P. glandulosa</em>, while at Sonora and Read
 <em>Juniperus </em>spp. dominate. Many of the tallgrasses
 (<em>Sorghastrum nutans, Schizachyrium scoparium</em>) that
 were historically common at these sites have been extirpated due to a
 legacy of overgrazing until the 1960s. For the past few decades, the three
 sites have been either ungrazed or only lightly grazed with low stocking
 rates of goats. Deer are also present at all sites. Soils at Martin are
 primarily Tarrant-type soils with 1-8% slopes and very cobbly clay in the
 top horizon (Soil Survey Team, NRCS, 2022). At Sonora, soils in our plots
 are dominated by the Eckrant-Rock outcrop complex with 1-20% slopes and
 cobbly silty clay in the top horizon as well as the Tarrant-Valera complex
 with 0-3% slopes and very cobbly clay to 15 inches. Soils at Read are
 primarily Tarrant-rock outcrop complexes (1-15% slopes or dry, 8-30%
 slopes). Weather towers at each site were used to monitor temperature,
 humidity, and rainfall throughout the course of each growing season and
 measurement campaign using an EE181-L and TB4MM-L wired to a CR1000
 datalogger (Campbell Scientific, Logan, UT). Plots for physiological
 measurements were located within approximately 500 m of each tower.
 Meteorological data were gap-filled using TexMesoNet sites with 25 km of
 each field site when necessary (Texas Water Development Board,
 2023). Measurements at each of the three sites were
 made once in 2019 and 2020 and twice in 2021 within the same weeklong
 period to avoid temporal differences between sites, and were only made on
 full-sun days approximately 2-weeks post rainfall events to avoid
 intervals when grasses were senescing or curling. Repeated growing season
 measurements were not made in 2019 and 2020 due to repeated drought events
 at some sites. We did not attempt to characterize growing season
 “dry-down” as these are pulse-driven systems with variability in timing
 and intensity of summer rainfall events but instead sought to characterize
 the behavior of species across sites when most physiologically active. All
 measurements were made on the top-most fully expanded leaves of perennial
 grass tillers currently in flower in order to ensure consistency of
 phenological stage across species and sites for each measurement
 campaign. <em>Water
 Relations</em> A Scholander-type pressure chamber
 (Model 600, PMS Instrument Co., Albany, OR) fitted with a grass
 compression gland and base was used to make predawn
 (Ѱ<sub>pre</sub>)<sub> </sub>and midday
 (Ѱ<sub>mid</sub>) measurements of leaf water potential. Leaf
 blades were cut slightly above the ligule using a razorblade, wrapped in
 plastic, and pressurized in the chamber until sap was visibly exuded from
 veins, and equilibrium pressure was recorded. Four leaf blades were used
 for each of the measurement intervals at each site. Predawn measurements
 were made between 4:30 and 6:30 AM. Midday measurements were made between
 1:30 and 3:30 PM to assess the water status of grasses during the most
 stressful point in the day. <em>Gas
 Exchange</em>             At each of the three
 sites, two approximately 20 x 30 m plots were established with two grass
 patches per species in each plot used for gas exchange measurements (net
 photosynthesis, A<sub>net</sub>; stomatal conductance to water
 vapor, g<sub>s</sub>; transpiration, E; instantaneous water
 use efficiency, WUE<sub>i</sub>
 (A<sub>net</sub>/E) and intrinsic water use efficiency,
 WUE<sub>g</sub>
 (A<sub>net</sub>/g<sub>sw</sub>). A LI-6800
 Portable Photosynthesis System fitted with a 2x3 cm side-to-side clear-top
 chamber and 3x3 cm light source was used for gas exchange measurements.
 Chamber settings were adjusted to mimic ambient conditions: light source
 was set to 1,500 µmol m⁻² s⁻¹, source carbon dioxide to 420 ppm, fan speed
 to 10,000 rpm, flow rate of 700 µmol s⁻¹, and temperature and relative
 humidity adjusted to track ambient conditions measured using a Kestrel
 3000 Weather Meter throughout the day. Measurements were made beginning at
 approximately 9:30 AM each day after the dew had dissipated and warm-up
 and system tests had been performed. The infrared gas analyzers were
 matched in between each measurement and instantaneous measurements were
 logged three times per leaf blade once steady-state conditions were
 reached in the chamber (stable ΔCO<sub>2</sub> and
 ΔH<sub>2</sub>O, positive intercellular
 CO<sub>2</sub> [C<sub>i</sub>]). The middle
 section of one or two leaf blade(s) was placed in the chamber for each
 species per patch per plot (8 individuals per species per day). Leaf
 tissue within the chamber was collected and placed in plastic bags with
 wetted paper towels on ice until corrections could be made for the leaf
 area within the chamber at the end of each field day using an Epson
 Perfection V39 flatbed scanner. Species measurements were randomly rotated
 so that the same species was not measured in the same order or during the
 same time of day. Gas exchange measurements were discarded if accidentally
 logged before reaching a steady state or if very low negative values of
 C<sub>i</sub> were observed.
 <em>Stable Isotopes (δ<sup>13</sup>C,
 δ<sup>18</sup>O)</em>            
 Approximately 15-20 leaf blades were collected at each site per plot per
 species for stable isotope analysis. Leaf tissue was dried at 65ºC for
 three days and ground using a Retesch Oscillating Mixer Ball Mill MM400 to
 homogenize whole tissue samples (approx. 10-20 leaf blades). Bulk leaf
 δ<sup>13</sup>C was determined using an Elemental Analyzer
 (Costech Analytical Technologies, Inc., Valencia, CA, USA) coupled to an
 Isotope Ratio Mass Spectrometer (Thermo Fisher Scientific, Waltham, MA,
 USA). Oxygen stable isotopes (δ<sup>18</sup>O) were also
 determined using an Elemental Analyzer coupled to an Isotope Ratio Mass
 Spectrometer (Thermo Fisher Scientific, Waltham, MA, USA). Isotopic ratios
 of bulk leaf tissue were expressed as δ<sup>13</sup>C or
 δ<sup>18</sup>O and calculated as follows:
                                                 δ =
 [(R<sub>sample</sub>/R<sub>standard</sub>) –
 1]/1000 where R<sub>sample</sub> and
 R<sub>standard</sub> are the ratios of heavy to light isotope
 of sample leaf tissue
 (<sup>13</sup>C/<sup>12</sup>C or
 <sup>18</sup>O/<sup>16</sup>O) and international
 standards respectively (here Vienna Pee Dee Belemnite, VPDB and Vienna
 Standard Mean Oceanic Water, VSMOW). All analyses were corrected to
 international standards with in-house reference materials and conducted at
 the Stable Isotopes for Biosphere Science Laboratory
 (http://sibs.tamu.edu). <em>Morphology
 </em>             We also measured specific leaf
 area (SLA) and leaf width (W<sub>leaf</sub>) in order to
 understand intraspecific and interspecific patterns in C<sub>4
 </sub>leaf characteristics along the aridity gradient. Eight leaf
 blades per species (4 per plot) were collected at each site, placed in a
 plastic bag with a wet paper towel, and scanned using an Epson Perfection
 V39 flatbed scanner at the end of each field day. ImageJ was used to
 calculate the surface area of each leaf. Leaves were then dried for 3 days
 in a 65ºC oven and weighed. Specific leaf area was expressed as the leaf
 dry mass (g) per one-sided leaf area (cm<sup>2</sup>) of each
 individual leaf blade. Leaf widths were obtained from the leaf area
 corrections made for gas exchange measurements, with width calculated from
 the average of the widths at two ends and midpoint of each 3 cm leaf
 clipping. <em>Statistical
 Analyses</em>             In 2020 and 2021, our
 westernmost site (Read) experienced abnormally dry conditions throughout
 the growing season and in the months preceding (U.S. Drought Monitor,
 2023), which severely limited leaf-out of C<sub>4</sub>
 grasses and prevented measurements at this site. For this reason, we ran a
 series of two-way analyses of variance (ANOVA, type III sums of squares to
 account for unbalanced data) with site and species as the main effects and
 A<sub>net</sub>, g<sub>sw</sub>, E,
 WUE<sub>i</sub>, WUE<sub>g</sub>,
 Ψ<sub>pre</sub>, Ψ<sub>mid</sub>, SLA, width,
 δ<sup> 13</sup>C, δ<sup>18</sup>O as response
 variables for the 2019 measurement campaign only in order to evaluate
 water relations and physiology with increasing aridity across the
 gradient, species-specific responses, and potential differences in species
 responses across sites. Gas exchange data were transformed using Box-Cox
 transformations to meet the assumptions of ANOVA
 (A<sub>net</sub>, g<sub>sw</sub>,
 WUE<sub>i</sub>, WUE<sub>g</sub> square root
 transformed, and E log-transformed). Post-hoc Tukey tests were performed
 to separate statistically different means in cases in which main effects
 were found to be significant, with <em>α</em> = 0.05 for all
 analyses. Since the full gradient could not be assessed in subsequent
 years, data for all measurement campaigns (July 2019 [Read, Sonora,
 Martin], June 2020 [Sonora, Martin], May 2021 [Sonora, Martin], September
 2021 [Sonora, Martin]) were pooled and relationships between gas exchange,
 water relations, isotopes, and morphological variables assessed with
 Pearson correlations to understand interactions between physiological
 variables and water status of different grass species. To assess the
 sensitivity of stomata to changes in aridity, we also examined the
 logarithmic relationship between stomatal conductance and mean daytime
 VPD, as well as the degree of isohydry based on relationships between
 midday and predawn water potential for each species. We also examined the
 relationship between leaf width or SLA with WUE to assess whether features
 of morphology were associated with higher water use efficiency among our
 study species. Variables for regression relationships were averaged for
 each species by location, measurement date, plot, and patch. All
 statistical analyses were performed in R version 3.1.1 (R
 Development Core Team 2014).
description: # Data from: Responses of C4 grasses to aridity reflect species-specific
 strategies in a semiarid savanna
 [https://doi.org/10.5061/dryad.7m0cfxq39](https://doi.org/10.5061/dryad.7m0cfxq39) Contains separate datasets for water potential, stable oxygen isotopes, stable carbon isotopes, photosynthetic gas exchange, specific leaf area, and leaf width. A file with plot averages used for regression analyses is also included. ## Description of the data and file structure All files included are .csv type files structured for use in R statistical software. We include here raw datasets for water potential measurements made in 2019 (**WP_2019_only.csv**), stable isotopes of oxygen for 2019 (**GrassIsotopes_2019_O.csv**), stable isotopes of carbon for 2019 (**C_data_2019.csv**), cleaned gas exchange measurements for 2019 from LI-6800 (**Photo_2019.csv**), specific leaf area data for 2019 (**SLA_2019.csv**), leaf width data for 2019 (**Leaf_Widths_2019.csv**), and data for all variables averaged by species by patch for all years for correlation analyses (**all_data_by_patch.csv**). **WP_2019_only.csv** * Species: 4-letter species code abbreviation * ARWR = *Aristida wrightii* * BOCU = *Bouteloua curtipendula* * ERPI = *Erioneuron pilosum* * ERSE = *Eriochloa sericea* * HIBE = *Hilaria belangeri* * Patch: patch number within plot (1-4) * Plot: letter and 2-digit code describing plot identity (ex: M12 = Martin, plots containing patch 1 and 2) * Loc: location (Martin, Sonora, or Read site) * TOD: time of day, either predawn or midday measurement * Month: month during which the measurement was made (all July in this case) * Year: year during which the measurement was made (all 2019 in this case) * Time: logged time for measurement using a 24-hour clock * Potential: recorded leaf water potential from pressure bomb (MPa) **GrassIsotopes_2019_O.csv** * Year: year material was collected (2019 in this case) * Material: an in-house identifier for the material being combusted (leaf tissue in this case) * Species_Code: 4-letter species code abbreviation * ARWR = *Aristida wrightii* * BOCU = *Bouteloua curtipendula* * ERPI = *Erioneuron pilosum* * ERSE = *Eriochloa sericea* * HIBE = *Hilaria belangeri* * Grass Species: the scientific name for species analyzed (*Aristida wrightii, Bouteloua curtipendula, Erioneuron pilosum, Eriochloa sericea,* or *Hilaria belangeri*) * Patch ID: patch from which tissue was collected, patch number within the plot (1-4) + letter code corresponding to site (S= Sonora, M= Martin, R=Read) * Date Field Collected: early (July collection) or late (August collection) * Sample ID: in-house identifier for dried, milled grass sample (NAH = Nicole A. Havrilchak, followed by order of samples processed) * d18O: corrected ratio of two the two stable oxygen isotopes: 18O:16O in the dried leaf tissue derived from the mass spectrometer **C_data_2019.csv** * Sample: an in-house identifier for dried, milled grass sample (NAH = Nicole A. Havrilchak, followed by order of samples processed) * Species: 4-letter species code abbreviation * ARWR = *Aristida wrightii* * BOCU = *Bouteloua curtipendula* * ERPI = *Erioneuron pilosum* * ERSE = *Eriochloa sericea* * HIBE = *Hilaria belangeri* * Location: site where tissue was collected (Read, Sonora, or Martin) * Year: year tissue was collected (2019 in this case) * d13C: corrected ratio of two the two stable carbon isotopes: 13C:12C in the dried leaf tissue derived from the mass spectrometer **Photo_2019.csv** * date: date of measurement following year/month/day format * year: year of measurement (2019 in this case) * month: month of measurement (July in this case) * ID#: the first or second measurement for a species made in each patch (1 or 2) * Grass Species: 4-letter species code abbreviation * ARWR = *Aristida wrightii* * BOCU = *Bouteloua curtipendula* * ERPI = *Erioneuron pilosum* * ERSE = *Eriochloa sericea* * HIBE = *Hilaria belangeri* * Patch ID: patch number (1-4) paired with letter abbreviation for the site (S= Sonora, M= Martin, R=Read) * loc: location (Martin, Read, or Sonora) * E: Transpiration rate (mol H2O m-2 s-1) * A: Assimilation rate (µmol CO2 m-2 s-1) * Ca: Ambient (to leaf) CO2 (µmol CO2 mol-1) * Ci: Intercellular CO2 (µmol CO2 mol-1) * Pci: Intercellular CO2 (Pa) * Pca: Ambient (to leaf) CO2 (Pa) * gsw: Stomatal conductance to water vapor (mol m-2 s-1) * gbw: Boundary layer conductance to water vapor (mol m-2 s-1) * gtw: Total conductance to water vapor (mol m-2 s-1) * gtc: Total conductance to CO2 (mol m-2 s-1) * WUEg: A/gsw (μmol CO2 mol-1 H2O) * WUEi: A/E (μmol CO2 mmol-1 H2O) * Tleaf: leaf temperature measured by the thermocouple (deg C) * VPDleaf: Vapor pressure deficit at leaf temp (kPa) * cica: Ratio of intercellular (Ci) to ambient (Ca) CO2 **SLA_2019.csv** * Species: 4-letter species code abbreviation * ARWR = *Aristida wrightii* * BOCU = *Bouteloua curtipendula* * ERPI = *Erioneuron pilosum* * ERSE = *Eriochloa sericea* * HIBE = *Hilaria belangeri* * Location: site collected (Read, Sonora, Martin) * Patch: which plot was collected in at the site (12 or 34) * Area: one-sided leaf area as scanned and calculated in ImageJ (cm2) * Mass: mass of the leaf in grams * SLA: ratio of area to mass (cm2/g) * Month: month collected (July in this case)       * Year: year collected (2019 in this case) **Leaf_Widths_2019.csv** * ID: chronological sample number  * Year: year measured (2019 in this case) * Month: month collected (July in this case) * Species: 4-letter species code abbreviation * ARWR = *Aristida wrightii* * BOCU = *Bouteloua curtipendula* * ERPI = *Erioneuron pilosum* * ERSE = *Eriochloa sericea* * HIBE = *Hilaria belangeri* * Loc_ID: patch number (1-4) paired with letter abbreviation for the site (S= Sonora, M= Martin, R=Read) * Loc: the site where the tissue was collected (Read, Sonora, or Martin) * Rep: replicate for each patch/location (1 or 2) * Width: width of the leaf in cm **all_data_by_patch.csv** * Species: 4-letter species code abbreviation * ARWR = *Aristida wrightii* * BOCU = *Bouteloua curtipendula* * ERPI = *Erioneuron pilosum* * ERSE = *Eriochloa sericea* * HIBE = *Hilaria belangeri*            * Location: site of collection (Read, Martin, Sonora) * Plot: plot number where measurement was made or collected (1-4) * Year: year measurement was made (2019, 2020, 2021a = May or 2021b = September) * d13C: corrected ratio of two the two stable carbon isotopes: 13C:12C in the dried leaf tissue derived from the mass spectrometer * E: Transpiration rate (mol H2O m-2 s-1) * A: Assimilation rate (µmol CO2 m-2 s-1) * gsw: Stomatal conductance to water vapor (mol m-2 s-1) cica: Ratio of intercellular (Ci) to ambient (Ca) CO2 * WP_mid: midday water potential (MPa)            * WP_pre: predawn water potential (MPa)       * WP_delta: change in water potential from predawn to midday (MPa)       * VPD: vapor pressure deficit for that specific measurement day at a site (kPa) computed from met data Note that "NA"s indicate data was not collected for this row/column combination. ## Sharing/Access information Links to other publicly accessible locations of the data: * [https://figshare.com/s/c6f43d5ee59908ef8103](https://figshare.com/s/c6f43d5ee59908ef8103) ## Code/Software All statistical analyses were performed in R version 3.1.1 (R Development Core Team 2014).