Recurrent fires do not affect the abundance of soil fungi in a frequently burned pine savanna

Fungal Ecology 42 (2019) 100852

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Recurrent fires do not affect the abundance of soil fungi in a frequently burned pine savanna Paige M. Hansen a, *, Tatiana A. Semenova-Nelsen a, William J. Platt b, Benjamin A. Sikes a a b

Department of Ecology and Evolutionary Biology and Kansas Biological Survey, University of Kansas, Lawrence, KS, 66047, USA Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, 70803, USA

a r t i c l e i n f o

a b s t r a c t

Article history: Received 3 December 2018 Received in revised form 11 July 2019 Accepted 18 July 2019 Available online xxx

While the negative effects of infrequent, high-intensity fire on soil fungal abundance are wellunderstood, it remains unclear how the short-term history of frequent, low-intensity fire in firedependent ecosystems impacts abundance, and whether this history governs any abundance declines. We used prescribed fire to experimentally alter the short-term fire history of patches within a firefrequented old-growth pine savanna over a 3 y period. We then quantified fungal abundance before and after the final fire using phospholipid fatty acid (PLFA) assays and Droplet Digital™ PCR (ddPCR). Short-term fire history largely did not affect total fungal abundance nor pre-to post-fire abundance shifts. While producing similar conclusions, PLFA and ddPCR data were not correlated. In addition to piloting a new method to quantify soil fungal abundance, our findings indicate that, within fire-frequented pine savannas, recurrent fires do not consistently decrease total fungal abundance, and abundance changes are not contingent upon short-term fire history. This suggests that many fungi in fire-dependent ecosystems are fire-tolerant. © 2019 Elsevier Ltd and British Mycological Society. All rights reserved.

Corresponding Editor: Prof. L. Boddy

1. Introduction Fires are significant disturbances across many terrestrial ecosystems that can impact soil fungi through multiple direct and indirect mechanisms. Soil heating during wildfires, ranging from 100 to 700
C (Certini, 2005), can be lethal to fungi (Cairney and Bastias, 2007), with mortality occurring as temperatures surpass 60
C (Neary et al., 1999; Hart et al., 2005). High soil temperatures can also alter fungal reproductive capacity, hindering their ability to recover following a fire (Covington and DeBano, 1990; Hart et al., 2005). Additionally, fires can negatively affect fungi by altering soil physical and chemical properties. Following fire, the soil surface is often more water repellent (DeBano, 2000) and blackened (Ulery and Graham, 1993), negatively impacting fungi that need moist soils and that cannot survive substantial diurnal temperature swings. High surface temperatures during fire can also volatilize soil carbon and nitrogen, increasing nutrient limitation and removing substrates on which fungi rely (Hart et al., 2005; Dooley and Treseder, 2012). Similarly, the loss of aboveground plant biomass, including losses of more than 75% of total pre-fire biomass

* Corresponding author. E-mail address: [email protected] (P.M. Hansen). https://doi.org/10.1016/j.funeco.2019.07.006 1754-5048/© 2019 Elsevier Ltd and British Mycological Society. All rights reserved.

from severe fires (Campbell et al., 1977), can impact plantassociated fungi and serve as an important limitation on their post-fire recovery (Hart et al., 2005). Through these mechanisms, wildfires, especially those that are infrequent and high-intensity, across a wide variety of biomes can diminish fungal populations
rcenas-Moreno et al., by an average of 50% (Hamman et al., 2007; Ba 2011; Dooley and Treseder, 2012; Holden et al., 2016; Pressler et al., 2019), with fungal communities taking up to 15 years to recover to pre-fire abundances (Dooley and Treseder, 2012). The negative effects of fire on fungi known from studies on infrequent, high-intensity wildfires may not apply to firedependent systems, where frequent, low-intensity fires maintain diverse vegetation and ecosystem functions. In these systems, including most grasslands and savannas, low-intensity fires impose strong selective pressure on plants (Archibald et al., 2013; Pausas et al., 2016), producing biotic communities that depend on recurrent fires (DeBano et al., 1998). In addition to releasing litter-bound nutrients, low-intensity fires suppress fire-sensitive vegetation, favoring an often hyper-diverse set of fire-tolerant plant species (Certini, 2005, Lamont and Downes, 2011; Pausas et al. 2018). Recent molecular studies have characterized the unique fungal communities living in these ecosystems (Brown et al., 2013; Egidi et al., 2016), and reveal that like plants, soil fungal communities

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have fire-tolerant taxa that can survive and proliferate following fires. Even though numerous taxa that are suppressed or favored by fire have been identified (Oliver et al., 2015), it remains largely unexplored how low-intensity fires affect total fungal abundance in these fire-dependent ecosystems. Because fire-dependent ecosystems like grasslands and savannas burn repeatedly, high fire frequency as well as variations in the short-term history of fires may shape both the pre- and postfire abundance of soil fungi. For instance, consecutive annual fires over 2 y in savanna ecosystems reduced fungal abundance by ~25% compared to unburned areas (Ponder et al., 2009), but single prescribed fires during a 2 y period did not change fungal abundance, regardless of whether the fire occurred in the first or second year (Blankenship and Arthur, 1999). It is unclear if variations in fire history over longer periods may cause differences in total fungal abundance. Further, such differences may diverge from reductions due to increased fire frequency alone. For instance, sites burning two out of four years may be similar, regardless of whether they burned in the first two or final two years. Because fungi are critical to many ecosystem-level processes, changes in prescribed fire frequency and short-term history of low-intensity fires may have direct impacts on total fungal abundance, and, in turn, have broader consequences on the functioning of fire-frequented ecosystems. To explore the effects of differences in repeated, low-intensity fires on total fungal abundance, we manipulated short-term fire history in an old-growth, fire-dependent pine savanna using a series of prescribed fires. We established plots that burned in three consecutive years, burned in two consecutive or non-consecutive years, burned only in the last year, or never burned. Fungal abundance before and after the final year's fire was measured using phospholipid fatty acid (PLFA) biomarkers, and a newly-developed molecular method for quantifying fungi using Droplet Digital™ PCR (ddPCR). ddPCR provides highly precise and absolute quantities of target DNA by dividing reactions into 20,000 droplets, and then determining the fraction of droplets containing a positive, fluorescing PCR product in each reaction following all PCR cycles. We assessed whether PLFA data corresponded with abundance estimates generated by ddPCR. We then used both methods to explore whether total fungal abundance responded to differences in the short-term history of frequent, low-intensity fires, and if these histories shaped any pre-to post-fire changes in fungal abundance. 2. Methods 2.1. Field site and experimental design Field Site: We conducted our study in old-growth pine savanna on the Wade Tract (30
450 N; 84
000 W; Thomas County, Georgia, USA). The several-hundred hectare site is situated on moderatelydissected (elevation range of 10e20 m) terrain 25e50 m above sea level on the Arcadia Plantation in the Red Hills region of northern Florida-southern Georgia (see description in Robertson et al., 2019). The soils consist of Pliocene-aged surface sands underlain by Miocene-aged clay hardpans (Typic and Arenic Kandiudults; Carr et al., 2009). Substantial rainfall (~1350 mm average) and a 10e11 month growing season result in rapid post-fire regrowth of ground layer plants, generating sufficient fine fuels for fires every 1e2 y (Fill et al., 2015). Management actions over the past two centuries have included “open-woods burning” of the plantation lands every 1e2 y from the early 1800's until 1978 (Platt et al., 1988; Crawford and Brueckheimer, 2012). Beginning in 1979, late-spring ecologically-orientated prescribed fires were conducted every 1e2 y; fire return intervals in both of the two fire management units that each contain part of the ecological easement area used in this study (Fig. S1) averaged 1.5 y over the past 40 y

(Robertson et al., 2019). The site's long history of frequent fires have maintained an open savanna/woodland physiognomy characterized by an old-growth population of longleaf pine (Pinus palustris) and diverse, herbaceous-dominated ground layer vegetation (Fill et al., 2015; Peet et al. 2018). Field Plot Selection: We established unburned and burned plots in June 2014, following 2014 prescribed fires. We used GPS maps of burned areas on the Wade Tract (as described in Robertson et al., 2019) to locate patches of ground layer vegetation that were >5 m2 and unburned in 2014. Time since last fire for these patches ranged from 1 to 3 y, based on maps from prior fires. Each patch was inspected in the field to make sure it was at least 5 m in diameter to minimize possible fire-edge effects, and located in upland pine savanna habitat not containing human disturbances. We randomly selected 20 of these unburned patches, divided equally among the two fire management units. Similarly, we next selected 20 patches at least 5 m in diameter that had burned in the 2014 prescribed fires and, based on GPS post-fire maps, had also burned in the three fires prior to 2014. Each burned patch was selected randomly from burned area within 5e15 m of an unburned patch so as to pair burned and unburned patches across the larger Wade Tract landscape (Fig. S1). We then established plots to be sampled. Each 1 m2 plot was centrally located (to the extent possible) within a patch, such that they did not contain any fallen trees, large branches, or woody debris. We GPS mapped each plot and marked corners with flags and numbered aluminum tags for relocation in the field. This selection process resulted in 10 pairs of burned and unburned plots in close proximity to one another. Short-term fire regimes: We generated differences in short-term fire histories (2014e2016) as depicted in Fig. 1. Unburned and burned areas in 2014 reflected the behavior of the two prescribed fires conducted in both fire management units that year. In 2015, we controlled fire management units of the preserve to determine which plots burned; the east side of the preserve was burned with prescribed fire, while the west side was not (Fig. S1). We then burned all plots with two prescribed fires in 2016. Our experimental design, as depicted in Fig. 1, therefore includes replicated plots with patterns of 1, 2, and 3 fires between 2014 and 2016. Following the 2016 fires, we used fire maps to identify patches that did not burn in 2014, 2015, or 2016, and established 5 additional plots in randomly selected patches of upland pine savanna (Fig. S1). In this way, we generated a total of five different short-term fire histories that involved 0, 1, 2, and 3 fires over the 3 y period of study (Fig. 1; see Platt et al., 2015): 1) unburned all 3 y (000), 2) burned only in the final year (001), 3) burned in the final 2 y (011), 4)

Fig. 1. The experimental design used to create variations in short-term fire history, as well as when pre- and post-fire samples were taken. A “0” in the treatment code represents a year where a plot was left unburned, while a “1” indicates a year the plot was burned.

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burned in the first and final years (101), and 5) burned all 3 y (111). We conducted prescribed fires similarly in 2014, 2015, and 2016. All were ignited under similar conditions and occurred under similar weather conditions during those times of the year that fires were likely to occur naturally, when ignited by lightning (see Platt et al., 2015). For example, in all 3 y, head and flanking fires were ignited in the two fire management units described above between mid-March and early May. Fires in both units were ignited under Keetch-Byram Drought Indices of 60e250, using drip torches along burn unit boundaries 1e7 days after rain in the mid-late morning, with winds of 10e20 km/h and relative humidities of 30e40%. Flaming fronts typically had flames 1e2 m in height. Fine fuel consumption in burned patches was estimated each year as 60e80%. Because fires were conducted under similar weather conditions and at similar times of the year, short-term fire histories in Fig. 1 were considered to differ mainly in the numbers of fires.

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represent an opportunity to more closely link fungal abundance with community composition data, and to better characterize shifts in fungal abundance (Fierer et al., 2005). ddPCR is the next evolution of DNA-based quantification methods, largely advancing the precision and reproducibility of earlier methods such as quantitative PCR (qPCR; Ruijter et al., 2013; Nathan et al., 2014; Taylor et al., 2017). ddPCR accomplishes this by detecting the amplification in each of 20,000 droplets following all PCR cycling, thereby providing an absolute measure of abundance and eliminating the need for qPCR's standard curve. Because of this, ddPCR allows users to more precisely quantify fungal biomarkers and their response to fire disturbances across all fungi lineages identifiable by molecular methods. Moreover, since ddPCR relies on the same extracted DNA used to assess compositional shifts, abundance data from ddPCR can be directly linked to community composition data. 2.5. Droplet Digital PCR methodology

2.2. Sample collection and DNA extraction We collected soil samples 1 week prior to and 3 months following the 2016 fires (i.e., pre-fire and post-fire replicates, Fig. 1). Unburned (000) patches were sampled only after the 2016 fire; we could not predict which patches would remain unburned following the prescribed fires. From each of five replicated 1 m2 plots within each treatment, we collected 3 subsamples of the upper 1.5 cm of soil (A horizon) within a 9  9 cm area directly below the loose litter layer (see Fig. S2); because the sites burn almost every year, there is not an organic horizon in which litter is worked into the soil. We focused on this surface soil layer because it is most strongly affected by heat (Gagnon et al., 2015; Platt et al., 2016). The subsamples were pooled together, resulting in ~200 g of soil per sample, immediately placed in a cooler, and frozen at 20
C until shipped overnight on ice to Kansas, where they were frozen at 80
C until further processing. Prior to DNA extraction, samples were thawed and thoroughly homogenized within sealed collection bags, without any particle size selection. A ~4 g and a ~0.5 g subsample of each sample was taken for PLFA and molecular analyses, respectively. Genomic DNA was extracted from each ~0.5 g subsample with the NucleoSpin Soil® kit (Macherey-Nagel Gmbh & Co., Düren, Germany) according to the manufacturer's protocol and following the kit's “SL2” cell lysis option. 2.3. Phospholipid fatty acid analyses PLFA analyses were carried out by the Core Facility for Ecological Analyses at Southern Illinois University. Four fungal PLFAs were used to estimate total fungal abundance: 16:1u5, a marker of arbuscular mycorrhizal fungi (AMF), and 18:1u9, 18:2u6,9, and 18:3u3,6,9, markers of other fungi. AMF PLFAs were extracted using the Olsson et al. protocol (1995), while the three remaining PLFAs were extracted using the McKinley et al. protocol (2005). The sum concentration in nmol/g soil of these biomarkers was used to estimate total fungal abundance of each sample. 2.4. Why use Droplet Digital PCR? Despite the success and widespread utility of PLFA assays (Frostegård et al., 1993, 2011; Quideau et al., 2016), the difference in target material between PLFA assays and the DNA metabarcoding now used to explore fungal compositional shifts creates a logistical and analytical disconnect between fungal abundance and community composition data. This discrepancy limits our ability to appropriately quantify fungal responses to fire and other disturbances in a way that is directly connected to composition data. Recent advances in molecular quantification techniques like ddPCR

In this study, we developed a ddPCR assay for quantifying total fungal abundance using comprehensive primers designed for qPCR (FungiQuant method; Liu et al., 2012). These primers target a wellconserved, ~351bp region within the fungal 18S rRNA gene (Liu et al., 2012). ddPCR was carried out using a QX200™ ddPCR™ system (Bio-Rad Laboratories, Hercucles, CA, USA) according to the manufacturer's standard EvaGreen® protocol. Optimized ddPCR reagent concentrations included 20 ml reaction volumes containing 10 ng of DNA template, 100 nM of each forward (5’ - GGRAAACTCACCAGGTCCAG - 30 ) and reverse (50 -GSWCTATCCCCAKCACGA-30 ) FungiQuant primer (Liu et al., 2012), 1X ddPCR EvaGreen Supermix, and molecular-grade water to 20 ml. Reactions were then loaded into the sample wells of a DG8 droplet generation cartridge (BioRad). 70 ml of Droplet Generation Oil for EvaGreen (Bio-Rad) was loaded into the oil wells, and the cartridge was placed in the QX200 Droplet Generator (Bio-Rad). The resulting droplets were transferred to a 96-well PCR plate (Bio-Rad). DNA amplification was carried out on the C1000 Touch™ Thermal Cycler (Bio-Rad) using the following PCR conditions: 5 min at 95
C; then 40 cycles: 30 s at 95
C, 1 min at 55
C, and 30 s at 72
C; followed by 5 min at 4
C and 5 min at 90
C for signal stabilization. Each step had a 2
C/s ramp rate. After amplification, plates were loaded into the QX200 Droplet Reader (Bio-Rad) for detection of PCR-positive and negative droplets. DNA concentrations in fungal 18S rDNA copies/mL were determined using QuantaSoft® AP software (Bio-Rad), and were then converted to fungal 18S rDNA copies/g soil for easier comparison to PLFA measurements. 2.6. Statistical analyses We used one-way ANOVAs to assess if total fungal abundance responded to differences in short-term fire history. These ANOVAs were applied to only the post-fire abundances of all five fire history treatments. Any significant differences in post-fire abundances were further investigated with post-hoc Tukey multiple comparisons. We then utilized linear mixed effect models to explore whether fire history governed any pre-to post-fire shifts in total fungal abundance in only the plots that were burned during the 2016 fire (i.e., excluding the 000 treatment that was not sampled pre-fire). These models assumed an interactive fixed effect between fire treatment and pre- and post-fire sampling dates. Plots were considered nested random effects within each fire treatment and sampling date. These models were built using the lmer function in the R package lme4 (Bates et al., 2015). Any significant interactions were further investigated with post hoc pairwise comparisons using the lsmeans function in the R package lsmeans (Lenth, 2016). These ANOVAs and linear mixed effects models were applied to data

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obtained using both PLFA assays and ddPCR. Pearson's productmoment correlation and linear regression were used to test for correlations between PLFA and ddPCR abundance measurements. All statistical analyses were performed in R (R Core Team, 2018). 3. Results 3.1. Fire history effects on post-fire fungal abundance On average, 20e30 nmol total fungal PLFA/g soil and 50,000e200,000 fungal 18S rDNA copies/g soil were detected in our fire treatments, depending on fire history. Fire history did not contribute to differences in post-fire total fungal abundance measured with PLFA assays. We observed no differences in total post-fire fungal PLFAs (ANOVA; F4,20 ¼ 0.844, P ¼ 0.514, Fig. 2A) or individual PLFA biomarkers (Figs. S3e6, Table S1) following 3 y of altered fire history. However, fungal quantification with ddPCR revealed a significant effect of fire history on post-fire abundance (ANOVA; F4,20 ¼ 4.97, P ¼ 0.006, Fig. 2B). This effect was due to lower post-fire abundances in patches located in the 001 (P ¼ 0.019), 101 (P ¼ 0.006), and 111 (P ¼ 0.031) fire treatments compared to the patches that never burned (000). 3.2. Fire history effects on pre-to post-fire abundance shifts Fire history also rarely had an impact on pre-to post-fire shifts in fungal abundance. While quantification using PLFA revealed a significant pre to post-fire effect of fire on total fungal abundance (linear mixed effects model; F3,16 ¼ 5.77, P ¼ 0.007; Fig. 3A), this effect resulted from a decline in abundance in only one fire history treatment, 001 (t16 ¼ 4.05, P ¼ 0.0009; Fig. 3A). The decrease within this treatment was specifically due to a decline in the AMF PLFA biomarker 16:1u5 as well as fungal PLFA biomarkers 18:2u6,9 and 18:3u3,6,9, but not 18:1u9 (Figs. S3e6, Table S1). For the other fire histories, there were no significant shifts in total fungal abundance from before to after fire (P > 0.05 for all other pairwise comparisons) nor for the abundance of individual PLFA biomarkers (Table S1). Based on ddPCR, there were no pre-to post-fire changes in fungal abundance regardless of fire history (linear mixed effects

model; F3,16 ¼ 0.611, P ¼ 0.613; Fig. 3B). 3.3. Correlation between ddPCR and PLFA measurements Although both PLFA and ddPCR failed to find strong differences in fungal abundance based on fire history, the two metrics were poorly related. Total fungal abundances determined using ddPCR did not correlate with those determined using PLFA biomarkers (Pearson's R ¼ 0.135, adjusted R2 ¼  0.0045, P ¼ 0.376; Fig. 4). Nevertheless, both our ddPCR and PLFA data suggest that alterations in recent fire history largely did not affect the total abundance of soil fungi. 4. Discussion Regardless of quantification method, changes in short-term fire history had little effect on fungal abundance, likely due to relatively large fine-scale variation in fungal abundance compared to the effects of fire history. Our results showed no differences in total post-fire PLFAs among patches with different fire histories. Post-fire 18S copy number differed only between patches that were and were never burned; there were no copy number differences between patches that were burned at least once, regardless of number or timing of fire. Similarly, pre-to post-fire declines in total fungal abundance only occurred for a single fire history when abundance was measured using PLFA. If this difference, along with the difference in post-fire 18S copy number, is biologically important, these divergences may reflect the lack of fire in these plots for a relatively long period of time. As fires are nearly ubiquitous at the Wade Tract, sites unburned for relatively long periods of time may reflect anomalies in total fungal abundance from those across most of this fire-frequented ecosystem. Nonetheless, our data suggest that changes in fungal abundance due to low-intensity fire are largely not contingent on recent fire history, or on the effects of the most recent fire. Rather, frequent fire in our fire-dependent pine savanna may filter for fungi that are tolerant of low-intensity fire, regardless of the recent fire return interval. While these results diverge from studies conducted on the
rcenas-Moreno et al., effects on infrequent, high-intensity fire (Ba

Fig. 2. (A) Total concentration of fungal phospholipid fatty acids (PLFA) among post-fire samples that differ in short-term fire history. Mean values in nmol/g of soil are indicated by bars ± standard errors. Short-term fire histories are indicated across the x-axis. (B) Total concentration of fungal 18S rDNA among post-fire samples that differ in short-term fire history. Mean values in fungal 18S rDNA copies/g soil are indicated by bars ± standard errors. As in (A), fire histories are indicated across the x-axis. The “a” indicates a statistically significant (P < 0.05) difference in pairwise comparisons between post-fire samples marked with a “b.” The “ab” indicates no significant difference in pairwise comparisons between both samples marked with “a” and “b.”

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Fig. 3. (A) Pre-to post-fire shifts in total concentration of fungal phospholipid fatty acids (PLFA) among samples that differ in short-term fire history. Mean values in nmol/g of soil are indicated by bars ± standard errors. Short-term fire histories are indicated across the x-axis. Light bars represent values prior to fire in the final year (pre-fire) and dark bars represent values following the final fire (post-fire). The star indicates a statistically significant (P < 0.05) difference in pairwise comparisons between pre- and post-fire samples. (B) Pre-to post-fire shifts in total concentration of fungal 18S rDNA among samples that differ in short-term fire history. Mean values in fungal 18S rDNA copies/g soil are indicated by bars ± standard errors. As in (A), short-term fire histories are indicated across the x-axis, and light and dark bars represent pre- and post-fire values, respectively.

Fig. 4. Pearson correlation between ddPCR and PLFA abundance measurements. Total measured PLFA concentrations (nmol/g soil) dfor all collected samples are indicated along the x-axis, and ddPCR fungal 18S rDNA concentrations (fungal 18S copies/g soil) for the same samples are represented on the y-axis. A nonsignificant regression line denotes slope ± standard error.

2011; Holden et al., 2016), they align with a recent meta-analysis suggesting that overall, low-intensity or prescribed fires do not significantly diminish fungal abundance (Dooley and Treseder,

2012). The idea that low-intensity fires in fire-dependent systems do not elicit the same negative effects on fungal abundance as infrequent, high-intensity fire is supported by multiple studies

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(Palese et al., 2004; Hamman et al., 2007; Dooley and Treseder, 2012). Our data corroborate these findings, and in highlighting a lack of historical contingency of fire on changes in fungal abundance, further demonstrate that the negative effects of fire on total soil fungal abundance known from wildfire studies do not necessarily extend to fire-dependent systems. Repeated fires may have selected for fungi with fire-adapted traits, and the suppression of fire in these fire-dependent systems may actually cause more significant changes to fungal communities than frequent fires. We speculate that the same pressures that created unique, fire-adapted plant communities found in firefrequented ecosystems (DeBano et al., 1998; Certini, 2005) have created fungal communities with fire-adapted traits. These traits may include abundant fruiting shortly following fire, the ability to resist high temperatures and changes in edaphic properties brought about by fire, and production of spores that are heatresistant or that germinate in response to heat. Pyrophilic taxa such as Trichophaea abundans and other Pezizales are common in fire-dependent systems (Fujimura et al., 2005; Brown et al., 2013), and the near lack of change in total fungal abundance in response to fire in our study may suggest that similar fire-resilient taxa are present at our study site. If fungi at our site are not resilient to fire, we venture that compensatory dynamics may explain the observed null abundance change. Although our methods cannot clearly differentiate specific fungal taxa, we speculate that if fire-sensitive taxa are eliminated by fire, they appear to be replaced by fireresistant or even fire-stimulated fungi. Excepting the slightly higher total abundance in unburned sites when fungi were quantified using ddPCR, this may have resulted in no clear changes in total fungal abundance. Fungal compositional changes due to repeated, prescribed fire are often a product of taxon-reordering (Oliver et al., 2015) and changes in the frequencies of less common taxa (Brown et al., 2013), which may create little net change in total fungal abundance. Both of these potential scenarios, however, may apply only to the saprotrophic and other fungi found at shallow soil depths. As the uppermost soil horizons are often dominated by saprotrophic over mycorrhizal fungi (Lindahl et al., 2007; McGuire et al., 2013), our inclusion of only the top 1.5 cm of soil in our analyses may be representative of mostly saprotrophic species. It is possible that fungal functional groups found at different soil depths, such as mycorrhizal fungi, react differently to fire, as their response may depend largely on the response of their plant hosts (Hart et al., 2005). Therefore, the resilience and compensatory dynamics that we speculate explain the null change in fungal abundance may not be representative of soil fungi overall, but instead is specific to the fungi found in the uppermost soil layer. It is important to note that analyses of fungal compositional shifts are still needed to confirm each of these speculations, and data from the next-generation sequencing methods necessary to do so can now be directly connected to our ddPCR data. Our results indicate ddPCR is a viable technique to quantify total fungal abundance in the environment, but PLFA and ddPCR measures cannot be directly substituted for one another. Total PLFA abundance measurements were not correlated with ddPCR measurements. This may be explained by differences in measured units between PLFA assays and ddPCR. The discrepancy in target material, DNA and fatty acids, makes it difficult to directly compare data generated by these methods, and may contribute to the lack of correlation between them. Additionally, our PLFA and ddPCR assays may be capturing different portions of the fungal community. Although PLFA assays are representative of mostly living cells, they are not inclusive of or exclusive to all fungal groups (Frostegård et al., 2011). This may skew our PLFA abundance measurements towards those fungi that are detectable by PLFA assays. On the other

hand, molecular methods of quantification capture on average 30% relic DNA, and cannot differentiate between living and dead cells (Lennon et al., 2018). The potential presence of relic DNA in our samples along with copy number variation in fungal ribosomal genes both within and among species (Herrera et al., 2009; Simon and Weib, 2008) may impact the accuracy of our DNA-based abundance measurements. Together, the advantages and drawbacks of each method indicate that the two techniques may capture different portions of fungal communities. In combination with a discrepancy in quantification units, this inconsistency in fungal communities may explain the lack of correlation between ddPCR and PLFA data. Despite a lack of correlation, both methods failed to find an effect of fire history on fungal abundance in nearly every case. Our ability to quantify two independent metrics, lipid concentrations and DNA copy number, and make similar conclusions on data generated from both metrics not only strengthens our conclusion about the effects of short-term fire history on fungi, but also demonstrates that the application of ddPCR is just as effective as PLFA assays in this study system. Furthermore, because ddPCR is a DNA-based quantification method, it poses the added advantage of greater parsimony for those who use next-generation sequencing techniques to assess fungal compositional shifts. Together, these factors demonstrate that ddPCR is a viable tool to quantify total fungal abundance in complex environments like soil. In summary, our study elucidated how total abundance of fungi in fire-dependent systems responds to repeated fires. Data collected using two independent metrics, fungal 18S rDNA copy number and total measured PLFA concentration, demonstrated that total fungal abundance was largely not affected by variation in recent fire history. Likewise, pre-to post-fire abundance changes were rarely contingent on short-term fire history. Although additional research is required to verify the presence of fire-tolerant fungi at our study site, these findings also demonstrate that strong selective pressures imposed by fire may filter for fungal communities with traits that allow them to tolerate frequent, lowintensity fires. Active management to reduce fire suppression alongside the projected (and ongoing) global increase in wildfires due to climate change (Jolly et al., 2015) mean our study can give important insights as to how fungi in a variety of ecosystems may respond to increasingly frequent fires in the future. Acknowledgements The Wade Foundation provided access to the Wade Tract. Staff at Tall Timbers Research Station and Arcadia Plantation conducted prescribed fires. We thank Kevin Robertson and Jim Cox for providing GPS maps of burned and unburned patches, and Jean Huffman and Neil Jones for collecting pre- and post-fire soil samples. We thank Cynthia Ripoll, marketing manager at MachereyNagel, for sending free DNA extraction kits, and Tim James at the University of Michigan for his recommendation to adapt the FungiQuant qPCR methodology to ddPCR. We also thank the University of Kansas Genomic Sequencing Core for permission to use their facilities and ddPCR machine, and Jenn Klaus for her assistance in optimizing our ddPCR protocol. We thank Sara Baer and Mandy Rothert at the Southern Illinois University Core Facility for Ecological Analyses for running PLFA analyses. Portions of this study were financially supported through a collaborative National Science Foundation award (DEB 1557000, BAS, PI and 1556837, WJP, PI), an Institutional Development Award (IDeA) from the National Institute of General Medical Sciences of the National Institutes of Health (Kansas P20 GM103418), and an Undergraduate Research Award from the University of Kansas Center for Undergraduate Research. This content is solely the responsibility of the authors and

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does not necessarily represent the official views of the National Institute of General Medical Sciences or the National Institutes of Health. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.funeco.2019.07.006. References Covington, DeBano, 1990. Effects of fire on pinyon-juniper soils. Gen. Tech. Rep. US Dep. Agric. For. Serv. 78e86. RM-191. Archibald, S., Lehmann, C.E.R., Gomez-Dans, J.L., Bradstock, R.A., 2013. Defining pyromes and global syndromes of fire regimes. Proc. Natl. Acad. Sci. 110, 6442e6447.
Barcenas-Moreno, G., García-Orenes, F., Mataix-Solera, J., Mataix-Beneyto, J., Bååth, E., 2011. Soil microbial recolonisation after a fire in a Mediterranean forest. Biol. Fertil. Soils 47, 261e272. Bates, D., M€ achler, M., Bolker, B., Walker, S., 2015. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67. Blankenship, B.A., Arthur, M.A., 1999. Soil nutrient and microbial response to prescribed fire in an oak-pine ecosystem in eastern Kentucky. In: Stringer, J.W., Loftis, D.L. (Eds.), Proceedings of the 12th Central Hardwood Forest Conference. Gen. Tech. Rep. SRS-24. U.S. Department of Agriculture, Forest Service, Southern Research Station, Asheville, NC, pp. 39e50. Brown, S.P., Callaham, M.A., Oliver, A.K., Jumpponen, A., 2013. Deep Ion Torrent sequencing identifies soil fungal community shifts after frequent prescribed fires in a southeastern US forest ecosystem. FEMS Microbiol. Ecol. 86, 557e566. Cairney, J.W.G., Bastias, B.A., 2007. Influences of fire on forest soil fungal communities this article is one of a selection of papers published in the special forum on towards sustainable forestry d the living soil: soil biodiversity and ecosystem function. Can. J. For. Res. 37, 207e215. Campbell, R.E., Baker, M.B., Ffolliott, P.F., Larson, F.R., Avery, C.C., 1977. n.d. Wildfire Effects on a Ponderosa Pine Ecosystem: an Arizona Case Study 17. Carr, S.C., Robertson, K.M., Platt, W.J., Peet, R.K., 2009. A model of geographical, environmental and regional variation in vegetation composition of pyrogenic grasslands of Florida. J. Biogeogr. 36, 1600e1612. Certini, G., 2005. Effects of fire on properties of forest soils: a review. Oceologia 143, 1e10. Crawford, R.L., Brueckheimer, W.R., 2012. The Legacy of a Red Hills Hunting Plantation: Tall Timbers Research Station & Land Conservancy. University Press of Florida. DeBano, L., 2000. The role of fire and soil heating on water repellency in wildland environments: a review. J. Hydrol. 231e232, 195e206. DeBano, L.F., Neary, D.G., Ffolliott, P.F., 1998. Fire Effects on Ecosystems. John Wiley & Sons. Dooley, S.R., Treseder, K.K., 2012. The effect of fire on microbial biomass: a metaanalysis of field studies. Biogeochemistry 109, 49e61. Egidi, E., McMullan-Fisher, S., Morgan, J.W., May, T., Zeeman, B., Franks, A.E., 2016. Fire regime, not time-since-fire, affects soil fungal community diversity and composition in temperate grasslands. FEMS Microbiol. Lett. 363, fnw196. Fierer, N., Jackson, J.A., Vilgalys, R., Jackson, R.B., 2005. Assessment of soil microbial community structure by use of taxon-specific quantitative PCR assays. Appl. Environ. Microbiol. 71, 4117e4120. Fill, J.M., Platt, W.J., Welch, S.M., Waldron, J.L., Mousseau, T.A., 2015. Updating models for restoration and management of fiery ecosystems. For. Ecol. Manag. 356, 54e63. Frostegård, Å., Bååth, E., Tunlio, A., 1993. Shifts in the structure of soil microbial communities in limed forests as revealed by phospholipid fatty acid analysis. Soil Biol. Biochem. 25, 723e730. Frostegård, Å., Tunlid, A., Bååth, E., 2011. Use and misuse of PLFA measurements in soils. Soil Biol. Biochem. 43, 1621e1625. Fujimura, K.E., Smith, J.E., Horton, T.R., Weber, N.S., Spatafora, J.W., 2005. Pezizalean mycorrhizas and sporocarps in ponderosa pine (Pinus ponderosa) after prescribed fires in eastern Oregon, USA. Mycorrhiza 15, 79e86. Gagnon, P., Passmore, H., Slocum, M., Myers, J., Harms, K., Platt, W., Paine, C.E., 2015. Fuels and fires influence vegetation via above- and belowground pathways in a high-diversity plant community. J. Ecol. 103, 4. Hamman, S.T., Burke, I.C., Stromberger, M.E., 2007. Relationships between microbial community structure and soil environmental conditions in a recently burned system. Soil Biol. Biochem. 39, 1703e1711. Hart, S.C., DeLuca, T.H., Newman, G.S., MacKenzie, M.D., Boyle, S.I., 2005. Post-fire vegetative dynamics as drivers of microbial community structure and function in forest soils. For. Ecol. Manag. 220, 166e184. Herrera, M.L., Vallor, A.C., Gelfond, J.A., Patterson, T.F., Wickes, B.L., 2009. Straindependent variation in 18S ribosomal DNA copy numbers in Aspergillus

7

fumigatus. J. Clin. Microbiol. 47, 1325e1332. Holden, S.R., Rogers, B.M., Treseder, K.K., Randerson, J.T., 2016. Fire severity influences the response of soil microbes to a boreal forest fire. Environ. Res. Lett. 11, 035004. Jolly, W.M., Cochrane, M.A., Freeborn, P.H., Holden, Z.A., Brown, T.J., Williamson, G.J., Bowman, D.M.J.S., 2015. Climate-induced variations in global wildfire danger from 1979 to 2013. Nat. Commun. 6. Lamont, B.B., Downes, K.S., 2011. Plant Ecol 212, 2111. Lennon, J.T., Muscarella, M.E., Placella, S.A., Lehmkuhl, B.K., 2018. How, when, and where relic DNA affects microbial diversity. mBio 9. Lenth, R.V., 2016. Least-squares means: the R package lsmeans. J. Stat. Softw. 69. € gberg, P., Stenlid, J., Lindahl, B.D., Ihrmark, K., Boberg, J., Trumbore, S.E., Ho Finlay, R.D., 2007. Spatial separation of litter decomposition and mycorrhizal nitrogen uptake in a boreal forest. New Phytol. 173, 611e620. Liu, C.M., Kachur, S., Dwan, M.G., Abraham, A.G., Aziz, M., Hsueh, P.-R., Huang, Y.-T., Busch, J.D., Lamit, L.J., Gehring, C.A., Keim, P., Price, L.B., 2012. FungiQuant: a broad-coverage fungal quantitative real-time PCR assay. BMC Microbiol. 12, 255. McGuire, K.L., Allison, S.D., Fierer, N., Treseder, K.K., 2013. Ectomycorrhizal-dominated boreal and tropical forests have distinct fungal communities, but analogous spatial patterns across soil horizons. PLoS One 8, e68278. McKinley, V.L., Peacock, A.D., White, D.C., 2005. Microbial community PLFA and PHB responses to ecosystem restoration in tallgrass prairie soils. Soil Biol. Biochem. 37, 1946e1958. Nathan, L.M., Simmons, M., Wegleitner, B.J., Jerde, C.L., Mahon, A.R., 2014. Quantifying environmental DNA signals for aquatic invasive species across multiple detection platforms. Environ. Sci. Technol. 48, 12800e12806. Neary, D.G., Klopatek, C.C., DeBano, L.F., Ffolliott, P.F., 1999. Fire effects on belowground sustainability: a review and synthesis. For. Ecol. Manag. 122, 51e71. Oliver, A.K., Callaham, M.A., Jumpponen, A., 2015. Soil fungal communities respond compositionally to recurring frequent prescribed burning in a managed southeastern US forest ecosystem. For. Ecol. Manag. 345, 1e9. €derstro €m, B., 1995. The use of phospholipid and Olsson, P.A., Bååth, E., Jakobsen, I., So neutral lipid fatty acids to estimate biomass of arbuscular mycorrhizal fungi in soil. Mycol. Res. 99, 623e629. Palese, A.M., Giovannini, G., Lucchesi, S., Dumontet, S., Perucci, P., 2004. Effect of fire on soil C, N and microbial biomass. Agronomie 24, 47e53.
ria, B., Fidelis, A., 2018. Pausas, J.G., Lamont, B.B., Paula, S., Appezzato-da-Glo Unearthing belowground bud banks in fire-prone ecosystems. New Phytol 217, 1435e1448. Pausas, J.G., Pratt, R.B., Keeley, J.E., Jacobsen, A.L., Ramirez, A.R., Vilagrosa, A., Paula, S., Kaneakua-Pia, I.N., Davis, S.D., 2016. Towards understanding resprouting at the global scale. New Phytol. 209, 945e954. Peet, R.K., Platt, W.J., Costanza, J.K., 2018. Fire-Maintained Pine Savannas and Woodlands of the Southeastern United States Coastal Plain. Pp 39e59. In: Ecology and Recovery of Eastern Old-Growth Forests. Island Press, Washington, DC. Platt, W.J., Evans, G.W., Rathbun, S.L., 1988. The population dynamics of a long-lived conifer (pinus palustris). Am. Nat. 131, 491e525. Platt, W.J., Orzell, S.L., Slocum, M.G., 2015. Seasonality of fire weather strongly influences fire regimes in south Florida savanna-grassland landscapes. PLoS One 10, e0116952. Platt, W.J., Ellair, D.P., Huffman, J.M., Potts, S.E., Beckage, B., 2016. Pyrogenic fuels produced by savanna trees can engineer humid savannas. Ecol. Monogr. 86, 352e372. Ponder, F., Tadros, M., Loewenstein, E.F., 2009. Microbial properties and litter and soil nutrients after two prescribed fires in developing savannas in an upland Missouri Ozark Forest. For. Ecol. Manag. 257, 755e763. Pressler, Y., Moore, J.C., Cotrufo, M.F., 2019. Belowground community responses to fire: meta-analysis reveals contrasting responses of soil microorganisms and mesofauna. Oikos 128, 309e327. Quideau, S.A., McIntosh, A.C.S., Norris, C.E., Lloret, E., Swallow, M.J.B., Hannam, K., 2016. Extraction and analysis of microbial phospholipid fatty acids in soils. J. Vis. Exp. R Core Team, 2018. R: A language and environment for statistical computing. In: R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project. org/. Robertson, K.M., Platt, W.J., Faires, C., 2019. Frequent fires promote regeneration of longleaf pine (Pinus palustris Mill.) in pine savannas. Forests 10, 367. Ruijter, J.M., Pfaffl, M.W., Zhao, S., Spiess, A.N., Boggy, G., Blom, J., Rutledge, R.G., Sisti, D., Lievens, A., De Preter, K., Derveaux, S., Hellemans, J., Vandesompele, J., 2013. Evaluation of qPCR curve analysis methods for reliable biomarker discovery: bias, resolution, precision, and implications. Methods Transcr. Biomark. 59, 32e46. Simon, U.K., Weiß, M., 2008. Intragenomic variation of fungal ribosomal genes is higher than previously thought. Mol. Biol. Evol. 25, 2251e2254. Taylor, S.C., Laperriere, G., Germain, H., 2017. Droplet Digital PCR versus qPCR for gene expression analysis with low abundant targets: from variable nonsense to publication quality data. Sci. Rep. 7. Ulery, A.L., Graham, R.C., 1993. Forest fire effects on soil color and texture. Soil Sci. Soc. Am. J. 57, 135.