Experimental design
Rainfall manipulation treatments commenced in multiple years and
spanned a range of experimental durations at the time of field
sampling, from 4 years to 14 years. Plot size depended on the
experiment, either 2.5 x 2.5 m or 2.5 x 5.0 m. Most plots
contained patches of B. eriopoda, except drought treatments in the
older experiments where herbaceous patches exhibited mortality.
Most plots also contained a central Prosopis shrub except one
experiment ("LTREB2"), which excluded Prosopis from the
original design.
Treatment levels depended on the experiment; all experimental
plots had control plots that received ambient rainfall with no
rainout shelter or irrigation system. Water treatments were
achieved using rainout shelters that decreased incoming
precipitation by 50% or 80% and automated irrigation systems that
simultaneously applied 50% or 80% of incoming precipitation.
During precipitation events, shelters intercepted and redirected
incoming rainfall to a PVC irrigation system connected to
sprinklers surrounding +50% or +80% treatment plots by means of a
solar-powered pump. Manipulation intensities were based on
extremes of historical precipitation data for the region.
Monthly precipitation sums were obtained from Jornada Basin LTER
meteorological stations, available on the Environmental Data
Initiative, nearest to the experimental plots (Thatcher and
Bestelmeyer 2021), summed over the growing season
(June-September), and then adjusted according to the rainfall
manipulation treatment. The range of growing season precipitation
amount achieved experimentally was 11-206 mm over the experimental
years. We present precipitation amount as a continuous variable
for our results due to the multi-year, multi-treatment nature of
our study design. Some years were considerably drier than others
were, and the rainfall amount received is more biologically
meaningful to consider as a variable than categorical treatments.
Refer to the included "precip_treatments.csv" table for
a information about the effective precipitation amounts in all
experiments and treatments included in this study.
Field sampling and stable isotope analyses
Field collection of leaves and soils for isotopic analyses took
place during peak biomass months following the summer monsoon:
August 2011, September 2012, September 2018, and September 2020.
Leaves were collected from three Bouteloua patches (when possible;
some drought plots had zero Bouteloua cover) and the central
Prosopis shrub (if present). Specifically, the leaves were
collected from the four cardinal directions and the center (54).
Surface soil samples (0-10 cm) were collected using 2.54 cm
diameter soil corer and composited from five sub-plot samples that
were representative of the general ground cover of the plot,
ranging from bare ground to underneath dominant plant patches.
Soil samples were then passed through a 2 mm sieve. Foliar and
soil samples were subsequently dried at 70°C for 48 h and ground
into a fine powder using a Desktop High Energy Vibratory Ball Mill
(VQ-N ball mill Thomas Scientific) for foliar samples and a mortar
and pestle for soil samples. The stainless grinding tools were
carefully cleaned with ethanol between each sample.
Foliar samples were encapsulated in 4 x 6 mm tins while soil
samples were encapsulated in 5 x 9 mm tins. All 2011 and 2012
samples were run on a GVI IsoPrime and an Elementar Cube elemental
analyzer at the Boston University Stable Isotope Laboratory. QA/QC
procedures for the BU Stable Isotope Lab may be found here:
https://www.bu.edu/sil/quality/. One analytical replicate was run
per 10 samples, and any anomalous results were rerun. In-house
standards of peptone and glycine calibrated to USGS 40 and 41 were
alternately analyzed after every 15 unknown samples. All 2018 and
2020 samples were run in analytical triplicates and flash
combusted with a coupled continuous-flow elemental
analyzer-isotope ratio mass spectrometer system consisting of a
Costech EA interfaced to a Delta Advantage peripheral at the METAL
Core Laboratory of Arizona State University. Calibration curves
for 2018 and 2020 plant samples were built using tomato leaves
(NIST 1573a) and for 2018 and 2020 soils using low-nitrogen
Montana soil (NIST 2711). In-house glycine standards calibrated to
USGS 40 and 41 were analyzed after every third unknown sample and
at the beginning and end of each run.
Standards and unknowns were corrected for linearity, and unknowns
were normalized to isotopic values of standard reference materials
using a two-point calibration curve of in-house standards
calibrated to USGS 40 and 41 standard reference materials. Stable
isotope nitrogen ratios are standardized to atmospheric air and
expressed in permil (‰) as:
$$ \delta = \frac{R_{sa}}{R_{std} - 1} $$
Where Rsa is the molar 15N/14N isotopic ratio
of the sample and Rstd is the molar isotopic
ratio of atmospheric air (0.0036765). Please note: This
dataset does not include a thorough analysis of any carbon data,
and all soil carbon data should be interpreted with caution as
carbonates were NOT removed before analysis.
All standards and unknown samples presented in this manuscript
underwent quality assessment and quality control. Acceptable
accuracy of tomato leaf standards and in-house glycine or peptone
standards was defined as having a residual error of ≤ 0.2‰.
Acceptable accuracy of the low-nitrogen Montana soil standard was
defined as having a residual error of ≤ 0.3‰. Acceptable precision
for all standards was defined as having a standard deviation of ≤
0.2‰. A blank (empty tin cup) was included at the beginning of
each analytical run for all 2011, 2012, and 2018 plants and soils,
and after every 5-8 unknown samples for 2020 soils. Unknown
samples adhered to QA/QC when samples fell within the standard
calibration range and exhibited a standard deviation among
analytical replicates of ≤ 0.2‰. If unknowns had a standard
deviation > 0.2‰, an attempt to meet QA/QC requirements was
first conducted by removing one outlier replicate, reducing the
number of analytical replicates to 2. If this did not resolve the
precision measurement, the sample was flagged, re-ground, and
re-run on the IRMS. Any data that remained flagged after
re-running were discarded from the final data set.
This method step describes provenance-based metadata as specified in the LTER EML Best Practices.
This provenance metadata does not contain entity specific information.
