DOI: 10.5061/dryad.d7wm37q28
title: Limited legacy effects of extreme multi-year drought on carbon and nitrogen cycling in a mesic grassland

description: The intensification of drought throughout the US Great Plains has the
 potential to have large impacts on grassland functioning, as has been
 shown with dramatic losses of plant productivity annually. Yet, we have a
 poor understanding of how grassland functioning responds after drought
 ends. This study examined how belowground nutrient cycling responds after
 drought and whether legacy effects persist post-drought. We assessed the
 two-year recovery of nutrient cycling processes following a four-year
 experimental drought in a mesic grassland by comparing two different
 growing season drought treatments - chronic (each rainfall event reduced
 by 66%) and intense (all rain eliminated until 45% of annual rainfall was
 achieved) – to the control (ambient precipitation) treatment. At the
 beginning of the first growing season post-drought, we found that in situ
 soil CO2 efflux and laboratory-based soil microbial respiration were
 reduced by 42% and 22% respectively in the intense drought treatment
 compared to the control, but both measures had recovered by mid-season
 (July) and remained similar to the control treatment in the second
 post-drought year. We also found that extractable soil ammonium and total
 inorganic N were elevated throughout the growing season in the first year
 after drought in the intense treatment. However, these differences in
 inorganic N pools did not persist during the growing season of the second
 year post-drought. The remaining measures of C and N cycling in both
 drought treatments showed no post-drought treatment effects. Thus,
 although we observed short-term legacy effects following the intense
 drought, C and N cycling returned to levels comparable to non-droughted
 grassland within a single growing season regardless of whether the drought
 was intense or chronic in nature. Overall, these results suggest that key
 aspects of C and N cycling in mesic tallgrass prairie do not exhibit
 persistent legacies from four years of experimentally-induced drought.
description: <em>Study Site and Climate Conditions</em>
 This study was conducted during the growing seasons (May –
 August) of 2018 and 2019 at the Konza Prairie Biological Station, a
 native, tallgrass prairie research site located in the Flint Hills of
 northeastern Kansas (39.09º N, 96.48º W). The climate consists of warm,
 wet summers and dry, cold winters. Mean annual precipitation is ~835 mm
 with ~75% of rainfall occurring during the growing season (April –
 September). Annual precipitation for the two years of the study was 811 mm
 in 2018 and 971 mm in 2019, with ~64% and 75% of the precipitation
 occurring during the growing season in each year, respectively (Figure
 S1). For this study, we utilized a large-scale, well-replicated drought
 experiment (the Extreme Drought in Grasslands Experiment, EDGE) that was
 established in 2013 in an annually burned and ungrazed native tallgrass
 prairie site. The site was located on a flat, level upland with relatively
 deep (~1 m or more), well-drained clay loam soils characterized as silty
 clay Mollisols. <em>Experimental
 Design</em> The EDGE experiment imposed drought
 in two ways from 2014-2017 using large rainfall exclusion shelters (n = 20
 total), each 6 x 6 m in size and hydrologically isolated to a depth of ~1
 m (see Griffin-Nolan et al. 2019 for more details). For the chronic
 drought treatment, 10 shelters were covered with strips of clear
 corrugated polycarbonate spaced so as to reduce each growing season
 rainfall event by 66% (April – September). For the intense drought
 treatment, the remaining 10 shelters were completely covered with panels
 of clear corrugated polycarbonate to exclude all rainfall events with no
 precipitation entering the intense treatment plots until a similar amount
 of total growing season rainfall was excluded as the chronic treatment
 (May – July), resulting in a shorter, but more intense reduction in
 rainfall.  Both drought treatments resulted in a ~45% reduction in annual
 rainfall. Shelter roofs were put in place in May each year for both
 drought treatments. Roofs were removed each year in early Sept for the
 chronic treatment, while they were removed after a similar amount of
 rainfall was reduced in the intense treatment; this was typically reached
 after ~ 2 months of the panels being installed (typically May – July). The
 control treatment plots were unsheltered (n = 10), but still
 hydrologically isolated and received ambient rainfall throughout the
 growing season. The three treatments were arranged in blocks, each
 containing a replicate of each treatment, for a total of 10 blocks (n = 30
 plots). To assess post-drought legacy effects on C and
 N cycling, we removed the shelters after the four years of drought
 treatments and allowed ambient rainfall to fall onto all of the treatments
 in 2018 and 2019 (the first two years following drought). This allowed us
 to measure whether legacy effects were present and whether recovery
 occurred. <em>Soil Sampling 
 </em> In 2018 and 2019, we collected soils
 monthly throughout the growing season (late May, early July, and
 mid-August) to measure soil C and N cycling. We homogenized four random
 soil core samples (15 cm depth x 5.7 cm diameter) collected from each
 “destructive plot” as detailed in Griffin-Nolan et al. (2019). The samples
 were immediately placed on ice and sieved to 2 mm within 24 hours. A
 subsample of these soils was kept fresh and unfrozen for laboratory-based
 microbial respiration measurements. The rest of the soil was transferred
 to a -20°C freezer until further analysis for all other non-in situ
 measurements. All analyses on frozen soils were performed within a year
 after collection. <em>Soil
 Moisture</em> We measured soil moisture in both
 the field and the lab to assess if soil moisture exhibited any legacies as
 a mechanism for the reponses we measured. We used a hand-held TDR to
 measure in-situ soil moisture to a depth of 15 cm at each time of soil
 sampling. We additionally dried field-collected soil (the same soil used
 to measure nutrients) for 48 hours at 60°C to calculate moisture and soil
 wet soil/dry conversion factors for subsequent nutrient and enzyme
 analyses. <em>Soil Nutrient Fluxes and Pools
 </em> To characterize legacy effects of drought
 on C and N cycling, we measured in situ belowground respiration, lab-based
 soil microbial respiration, extractable inorganic N (ammonium and
 nitrate), extractable total dissolved organic C and N, in situ net N
 mineralization, and total soil organic C and N concentrations to measure
 main components of C and N cycling. Belowground
 respiration was measured in situ using a Li-Cor 8100 infrared gas sampler
 (Lincoln, Nebraska). Two PVC collars were installed in each plot to a 6 cm
 depth and left in the field for the duration of the growing season. All
 biomass and living plants were removed from the collars at the beginning
 of the season and prior to every measurement. We then used a Li-Cor 8100
 infrared gas sampler to measure CO<sub>2</sub> flux from the
 soil over a 60 second interval. Measurements were taken midday and during
 sunny and non-windy conditions to ensure uniform conditions for each
 measurement. Measurements were taken monthly in 2018 and weekly in 2019.
 More detailed methods can be found in Slette et al. (2021).
 To measure soil microbial respiration in the lab, we placed 30
 grams of sieved, fresh soil (the fresh unfrozen subsample; extracted from
 the field &lt; 24 hours prior) from each plot in a sealable mason-jar
 (8 cm wide x 15 cm deep). We kept the soils at the same moisture from the
 field by sealing the soils in plastic bags and sealing the jars
 immediately after adding the soil. We measured microbial respiration once
 within 24 hours of extracting soil by opening the jars to allow
 re-equilibration with ambient CO<sub>2</sub> and then
 re-sealing the jars for 1-2 hours to measure accumulated headspace
 CO<sub>2</sub>. Respiration was then quantified as detailed in
 Zeglin and Myrold (2013). To measure extractable
 inorganic N, we extracted ammonium and nitrate from the previously frozen
 soil subset collected monthly. We shook 11 g of thawed field-moist soil
 with 1M KCl for 1 hour and then filtered the samples using Whatman filters
 (grade 42 – 2.5 mm filter). We then froze the extracts in a -20°C freezer
 until analysis. Extractable N was expressed on a per gram soil dry weight
 basis. To measure net N mineralization, a twelve-centimeter deep PVC tube
 (3.81 cm diameter) with the top two centimeters above ground was pounded
 into the ground next to the initial soil cores taken on the same date. The
 PVC tubes were capped, with holes in the aboveground portion of the tubes
 for gas exchange, and left in place for ~30 days. Cores were retrieved at
 the end of the incubation interval, then sieved within 24 hours, frozen in
 a -20°C freezer, and later extracted with 1 M KCl using the methods above.
 We used an Alpkem analyzer to measure extractable ammonium and nitrate on
 all KCl extracts (Saskatoon, SK). Net N mineralization was measured as the
 difference between extractable inorganic N in the initial and final cores.
 This was then divided by the days the cores were left in the field to
 calculate a daily rate. To measure total dissolved
 organic C (DOC) and N (DON), we extracted 20 g field-moist subsamples of
 the previously frozen soil with 100 mL of 0.5M
 K<sub>2</sub>SO<sub>4</sub>. We shook the soils
 for four hours and filtered the samples using Whatman 42 filters,  then
 froze the extracts in a -20°C freezer. We used a Schimadzu TOC-L analyzer
 (Kyoto, Japan) to measure DOC and DON. To measure total
 C and N, we oven dried the soils at 60°C for several days until the soil
 was deplete of any moisture. The soils were then ground and analyzed for
 total C and N in a LECO TruSpec CN combustion analyzer (St. Joseph, MI) at
 the KSU Soil Testing Lab. <em>Extracellular
 Enzyme Activity</em> We measured the potential
 extracellular enzyme activities of several microbially-produced enzymes as
 an index of nutrient limitation. We measured C-cleaving enzymes:
 a-Glucosidase (AG), b-Glucosidase (BG), b-D-cellulosidase (CB), and
 b-Xylosidase (XYL); N-cleaving enzymes: N-acetyl glucosaminidase (NAG) and
 leucyl aminopeptidase (LAP); and phosphorus-cleaving enzymes: phosphatase
 (PHOS). Substrates for each enzyme were attached to a highly fluorescent
 cleavage product. The substrates for AG, BG, CB, XYL, NAG, and PHOS were
 attached to 4-methylumbelliferyl (MUB), and the substrate for LAP was
 attached to 7-amino-4-methylcoumarin (MUC). We added a Tris buffer
 adjusted to a pH of 8 to our soils to create a soil slurry and shook our
 samples for 40 minutes. We then added our samples to a 96 well-plate and
 added substrates to our soil slurries with two replicates per sample.
 Additionally, we created MUB and MUC standard curves for each individual
 soil. To simulate standard soil conditions, the plates were incubated for
 3 hours in the dark at 25°C. Fluorescence was measured using a multiplate
 reader (Tecan Infinite M200 plate reader, Switzerland) with a 365-nm
 excitation and 460-nm emission filters. A quench control was used. More
 detailed methods can be found in Bell et al. (2013) and Trivedi et al.
 (2016). We summed the C enzymes for total C enzyme activity and the N
 enzymes for total N enzyme activity (Bell et al., 2013; Dove et al.,
 2020). <em>Statistical
 Analyses</em> To compare treatments across each
 year’s growing season, we calculated confidence intervals and standard
 error using mixed models that accounted for repeated measures over the
 growing season (monthly sampling). We conducted separate statistical
 analyses for 2018 and 2019 due to the different climatic conditions of the
 two years. Further discussion of why the two years were split can be found
 in results 3.1. Our mixed model contained both fixed and random effects.
 Time and treatment were both fixed variables with an interaction term to
 account for the repeated measures aspect of this experiment (lme4
 package). As mentioned previously, our experiment had a blocked design.
 Blocks were treated as a random variable except for some models where we
 had to treat block as a fixed variable. In our 2018 enzyme analysis, we
 ran into a problem of overfitting due to block variance being estimated as
 zero in the model. To correct this overfitting, we treated block as a
 fixed effect and used this model to draw conclusions. Additionally, we
 applied a natural log conversion to all enzyme activity data due to
 unequal variances detected from the residual vs. fitted plot of the
 original non-transformed models. For the belowground respiration models,
 we included soil moisture as a covariate, since soil moisture has strong
 effects on belowground respiration. Further, we calculated correlation
 coefficients for soil moisture and belowground respiration in both years.
 For all statistical analyses, we utilized R statistical software (R Core
 Team, 2013) and used several packages including lme4, lmerTest, pbkrtest,
 emmeans, and GGally. We also used R to create the graphics for this paper
 using ggplot2 and Hmisc to create 95% confidence intervals for each
 graphic.
description: INT = intense drought, CHR = chronic drought, CON = control
 treatment There are a few missing values due to lost
 data. I recommend the enzyme data be ln transformed and outliers
 removed. TOCN = dissolved organic carbon and dissolved
 nitrogen