DOI: 10.5061/dryad.73n5tb330
title: Data from: Carbon isotope trends across a century of herbarium specimens suggest CO2 fertilization of C4 grasses

description: Increasing atmospheric CO2 is changing the dynamics of tropical savanna
 vegetation. C3 trees and grasses are known to experience CO2
 fertilization, whereas responses to CO2 by C4 grasses are more ambiguous.
 Here, we sample stable carbon isotope trends in herbarium collections of
 South African C4 and C3 grasses to reconstruct 13C discrimination. We
 found that C3 grasses showed no trends in 13C discrimination over the past
 century but that C4 grasses increased their 13C discrimination through
 time, especially since 1950. These changes were most strongly linked to
 changes in atmospheric CO2 rather than to trends in rainfall climatology
 or temperature. Combined with previously published evidence that grass
 biomass has increased in C4-dominated savannas, these trends suggest that
 increasing water use efficiency due to CO2 fertilization may be changing
 C4 plant-water relations. CO2 fertilization of C4 grasses may thus be a
 neglected pathway for anthropogenic global change in tropical savanna
 ecosystems.
description: <em>Specimen collection.
 </em>Samples for analysis were collected from herbaria at Skukuza
 Biological Reference Collection in Kruger National Park and the South
 African National Biodiversity Institute (SANBI). In total, we sampled 344
 grass specimens of four species: two C<sub>4</sub>
 (<em>Digitaria eriantha </em>[N = 121]<em>, Hyparrhenia
 hirta </em>[N = 56]) and two C<sub>3</sub> species
 (<em>Pentameris pallida </em>[N = 84]<em>, Koeleria
 capensis </em>[N = 73]). To minimize variation based on underlying
 physiology, both C<sub>4</sub> species were chosen to have the
 same C<sub>4</sub> photosynthetic subtype (using a
 nicotimamide-adenine dinucleotide phosphate-depending malic enzyme,
 commonly referred to as the NADP-ME subtype), with species selected
 depending on herbarium specimen availability. Sample collection dates
 spanned 110 years (1890s-2000s), with a mode in the early to mid 1900s
 (Table S2). Each herbarium specimen is associated with
 a quarter-degree square, commonly used to geolocate specimens. Most of our
 samples, especially pre-1950, only have locations accurate to the
 quarter-degree square. We prioritized quarter-degree squares with more
 than five records spanning at least 40 years. In total, our sampling
 covers 35 quarter-degree squares focused in the Cape Floristic Region,
 Drakensberg and Highveld grasslands, and Lowveld savannas of South Africa,
 and covering considerable variation in rainfall regimes (Figure
 1). <em>Isotope Analyses.
 </em>Specimens were sampled for approximately 10 mg of leaf tissue,
 avoiding tissue with obvious deterioration or surface contamination. We
 did not sample specimens that had been treated with chemical fumigants as
 part of their mounting process due to contamination risk. As an additional
 precaution against surface contamination, we swabbed sample material with
 ethanol immediately after sampling. In preparation for analysis, we ground
 samples to integrate carbon across leaf parts. After samples were ground,
 they were re-dried at 60<sup>o</sup>C for 3+ days before
 weighing for analysis. Chemical analyses of C and N
 concentrations, 𝛿<sup>13</sup>C, and
 𝛿<sup>15</sup>N were done at the Yale Analytical Stable
 Isotope Center using standard methods on a Thermo DeltaPlus Advantage mass
 spectrometer with a Costech ECS 4010 Elemental Analyzer with a Conflo III
 interface. Precision for our isotope values was estimated by the standard
 deviations of measurements of known working standards. Samples were run in
 randomized order, and all batch quality control standards were measured to
 within one standard deviation or ± 0.2‰ against standard sample
 𝛿<sup>13</sup>C and 𝛿<sup>15</sup>N.
 <em>Atmospheric
 </em>𝛿<em><sup>13</sup></em><em>C and [CO<sub>2</sub>].</em><em> </em>Carbon isotope discrimination (∆<sup>13</sup>C) can be calculated with known plant tissue and atmospheric 𝛿<sup>13</sup>C via Equation 2 using estimated annual atmospheric 𝛿<sup>13</sup>C for 𝛿<sub>a</sub>. Estimates were derived<strong> </strong>by merging the Law Dome Ice Core record (Rubino <em>et al.</em>, 2013), which extends from the pre-Industrial to the early 2000s, and monthly carbon concentrations measured by CSIRO (Krummel <em>et al.</em>,<em> </em>2018) at the global atmospheric watch station in the South Pole which extends from 1991-2019. The 15-year overlap between records was used to standardize across datasets, and annual estimates were interpolated with a cubic-spline fitted from 1700 to 2018 on the merged datasets. Annual estimates of atmospheric CO<sub>2</sub> concentrations were derived in a similar fashion using a splined dataset from the Law Dome ice-core (pre-industrial-1996)<strong><em><sup> </sup></em></strong>(Rubino <em>et al.</em>, 2013) merged with data from NOAA’s South Pole Observatory (1975-2019) (NOAA ESRL GML CCGG Group 2019).  <em>Rainfall and Temperature.</em> Rainfall variables were calculated from station data, via the Global Historical Climatology Network (Menne <em>et al.</em>, 2012) and from Kruger National Park weather stations. We calculated annual wet-season rainfall (mm year<sup>-1</sup>), the length of the wet season (days), and the magnitude of the storm depth (amount of rainfall in mm per rainfall event during the wet season) via methods described by (Liebmann <em>et al.</em>, 2012). Station data were available within each quarter degree square sampled, so mean rainfall metrics across years always represent local estimates. However, annual rainfall metrics were not always available in the year and cell that a sample was collected; in these cases, we took the closest available estimate within 50 km from the center of the cell when one was available and otherwise excluded the sample. We included statistical models for both data subsets: all 334 samples with mean-annual rainfall metrics and the 251 samples for which annual rainfall metrics were available. Sample subsets and sizes used for each analysis are specified alongside results in Tables S2-S5. Temperature estimates were extracted from NOAA’s Merged Land-Ocean Surface Temperature (Martínez-Yrízar, 1995), a global raster dataset of monthly temperature anomalies estimated at a resolution of 5°. We were interested in growing-season temperature and so calculated wet-season temperature anomalies, selecting relevant rasters via annualized or averaged wet-season duration estimates as described above. Anomalies were calculated as the difference from the long-term mean growing-season temperature.
description: # Stable isotope analysis for South African herbarium grass specimens ---
 Carbon and Nitrogen isotope analysis values and accompanying environmental
 data values for 334 herbarium grass samples across a 115 year timescale.
 ## Description of the data and file structure This dataset contains the
 carbon isotope data and accompanying environmental variables of 334
 herbarium specimens sampled from the herbarium grass collections of the
 South African National Biodiversity Institute (SANBI) and the Kruger
 National Park (KNP) Skukuza Biological Reference Collection. The data set
 includes data for two C3 photosynthetic grass species (*Pentameris
 pallida*, *Koeleria capensis*) and two C4 photosynthetic grass species
 (*Hyparrhenia hirta*, *Digitaria eriantha*). Each specimen has its
 corresponding herbarium collection identificators listed, its species and
 photosynthetic pathway identified, its isotope analysis values, and a set
 of annual environmental variables such as atmospheric CO2 concentrations,
 rainfall climatology, and temperature anomaly. Data structure is as
 follows: qds – Quarter degree square where specimen was originally
 collected. year – Year specimen was collected. collector – Name of
 specimen collector collector_number – number attributed to specimen within
 collection. accession_code – Specimen herbarium accession code family –
 specimen plant family species – specimen species sp_code – species code
 lat – latitude coordinates of collected specimen, if available, or
 centroid latitude coordinates of corresponding quarter degree square long
 – longitude coordinates of collected specimen if available, or centroid
 longitude coordinates of corresponding quarter degree square pathway –
 specimen photosynthetic pathway weight – sample weight after preparation
 for isotope analysis d.15NAIR – specimen concentration of 15N d.13CVPDB –
 specimen concentration 15C d13_atmos – Atmospheric concentrations of 13C
 compiled from Dlugokencky et al 2020, Conway et al 1994, and Rubino et al
 2019. delta_disc – Specimen 13C discrimination factor calculated on
 concentrations of 13C and annual atmospheric concentrations of 13C
 wetseason_start – Numeric day of the year of the start of the wet season
 for the corresponding quarter degree square, calculated according to
 methods described in Liebman et al 2012 with data derived Menne et al 2012
 wetseason_end – Numeric day of the year of the end of the wet season for
 the corresponding quarter degree square, calculated according to methods
 described in Liebman et al 2012 with data from Menne et al 2012
 wetseason_length – Length of the wetseason derived from the calculated
 start and end of the wet season wetseason_rainfall – rainfall (mm)
 experienced during the wetseason for that quarter degree square derived
 from Menne et al 2012 daily_rainfall – Average rainfall (mm) experienced
 per day of the length of the wetseason for the specified quarter degree
 square. storm_depth – Rainfall (mm) experienced per storm event during the
 wet season of that quarter degree square. avg_temp_anomally – Temperature
 anomaly experienced during the wetseason in that quarter degree square,
 derived from NOAA Merged Land Ocean Global Surface Temperature Analysis
 (Zhang et al 2020) co2_ppm – Annual atmospheric CO2 concentrations,
 derived from NOAA ESRL GML CCGG Group 2016 and Rubino et al 2019. Missing
 data code: NA ## Sharing/Access information Rainfall, temperature anomaly,
 and atmospheric CO2 and atmospheric 13C data was derived from the
 following sources: *Conway, T.J., P.P. Tans, L.S. Waterman, K.W. Thoning,
 D.R. Kitzis, K.A. Masarie, and N. Zhang, Evidence for interannual
 variability of the carbon cycle from the NOAA/GMD global air sampling
 network, J. Geophys. Res.,99,22831 22855, 1994. *Dlugokencky, E.J., J.W.
 Mund, A.M. Crotwell, M.J. Crotwell, and K.W. Thoning (2020), Atmospheric
 Carbon Dioxide Dry Air Mole Fractions from the NOAA GML Carbon Cycle
 Cooperative Global Air Sampling Network, 1968-2019, Version: 2020-07,
 [https://doi.org/10.15138/wkgj-f215](https://doi.org/10.15138/wkgj-f215)
 *Liebmann, B., Bladé, I., Kiladis, G. N., Carvalho, L. M. V., B Senay, G.,
 Allured, D., Leroux, S., and Funk, C. Seasonality of African precipitation
 from 1996 to 2009. Journal of Climate 25 (2012): 4304–4322. *Menne, M. J.,
 Durre, I., Vose, R. S., Gleason, B. E., and Houston, T. G. An overview of
 the Global Historical Climatology Network Daily Database. Journal of
 Atmospheric and Oceanic Technology 29 (2012): 897–910. *NOAA ESRL GML CCGG
 Group. Earth System Research Laboratory Carbon Cycle and Greenhouse Gases
 Group Flask-Air Sample Measurements of CO2 at Global and Regional
 Background Sites, 1967-Present. (2019). *Rubino, Mauro; Etheridge, David;
 Thornton, David; Allison, Colin; Francey, Roger; Langenfelds, Ray; Steele,
 Paul; Trudinger, Cathy; Spencer, Darren; Curran, Mark; Van Ommen, Tas;
 Smith, Andrew (2019): Law Dome Ice Core 2000-Year CO2, CH4, N2O and
 d13C-CO2. v1. CSIRO. Data Collection.
 [https://doi.org/10.25919/5bfe29ff807fb](https://doi.org/10.25919/5bfe29ff807fb) *Zhang, H. M., Huang, B., Lawrimore, J., Menne, M., and Smith, T. M. NOAA Global Surface Temperature Dataset, Version 5. (2019). For questions about the data, please contact [carla.staver@yale.edu](mailto:carla.staver@yale.edu)