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Limited legacy effects of extreme multi-year drought on carbon and nitrogen cycling in a mesic grassland

Cite this dataset

Vilonen, Leena et al. (2022). Limited legacy effects of extreme multi-year drought on carbon and nitrogen cycling in a mesic grassland [Dataset]. Dryad.


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.


Study Site and Climate Conditions

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.

Experimental Design

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.

Soil Sampling 

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.

Soil Moisture

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.

Soil Nutrient Fluxes and Pools

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 CO2 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 < 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 CO2 and then re-sealing the jars for 1-2 hours to measure accumulated headspace CO2. 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 K2SO4. 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.

Extracellular Enzyme Activity

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).

Statistical Analyses

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.

Usage notes

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