Interactive comment on “ Plant controls on post-fire nitrogen availability in a pine savanna ” by

The study presented in this paper investigates soil nitrogen availability in longleaf pine savanna after prescribed burning. This piece of work fits within the scope of Biogeosciences as it studies nitrogen (N) pool sizes and transformations in an open canopy savanna-like ecosystem. The paper presents a novel dataset of weekly data points spanning across a preand post-fire period of nine weeks. The description of the study site and scientific methods applied are clearly articulated.


Introduction
Temporal heterogeneity in resource supply is ubiquitous across ecosystems (Schimel and Bennett, 2004;James and Richards, 2006;Archer et al., 2014) and such resource pulses can be important if they contribute disproportionately to the overall resource budget of an ecosystem (McClain et al., 2003).
Because they vary in magnitude and frequency, nutrient pulses across ecosystems differ in their 5 potential to influence community and ecosystem dynamics.Despite compelling modelling-based evidence suggesting that nutrient pulses can influence ecological dynamics including species richness (Tilman and Pacala, 1993), physiological nutrient uptake constraints (Bonachela et al., 2011), and stoichiometric coupling (Appling and Heffernan, 2014), it can be difficult to predict when and where temporal nutrient heterogeneity will occur.This uncertainty makes it difficult to assess the conditions 10 under which temporal heterogeneity in nutrient supply might influence community-or ecosystem-level functioning.
Nutrient dynamics in pyrogenic systems may be especially variable in time because fire is a major disturbance that influences nitrogen (N) availability (Wan et al., 2001).There is a consensus that, across ecosystems, pulses of soil N, in particular ammonium (NH 4 + ), occur in response to fire (Huber et al., 15 2013;Wan et al., 2001).However the duration and magnitude of these pulses vary strongly by fuel composition, and consequently among forest and fire types (Wan et al., 2001).In northern conifer forests, stand-replacing fires can result in increased soil NH 4 + concentrations detectable more than one year following the burn (Smithwick et al., 2005;Turner et al., 2007).In contrast, in pine forests of the south eastern US (e.g.longleaf pine savannas), where prescribed fires only consume the understory 20 vegetation, NH 4 + concentrations following fires are more variable; some studies have documented no change in soil N pools following fire (Christensen, 1977;Richter et al., 1982), while others have documented immediate increases (2 days) that quickly dissipate (Lavoie et al., 1992).This suggests a need for localized studies with ecosystem-specific temporal data resolution to evaluate the mechanism behind changes in soil N availability following fire.25 Pine savannas in the southeastern US are often managed with prescribed fires in the absence of recurring natural fires to maintain habitat for endangered species (Sorrie et al., 2006) and nutrient losses or pool redistributions from these fires can be substantial (Boring et al., 2004;Wilson et al., Biogeosciences Discuss., doi:10.5194/bg-2016-303, 2016 Manuscript under review for journal Biogeosciences Published: 12 August 2016 c Author(s) 2016.CC-BY 3.0 License.
2002; Wilson et al., 1999).In addition to large quantities of carbon (C) released through fuel consumption (Boring et al., 2004), prescribed fires can release up to 50% of the phosphorus (P) and up to 75% of the N that was stored in the understory biomass (Carter and Foster, 2004;Wan et al., 2001).
These nutrients can be lost through volatization, or redistributed as ash in low intensity fires.Despite the ecosystem-level nutrient losses associated with fires, short-term pulses of increased N availability in 5 the soil are also observed following prescribed forest fires across vegetation types (Certini, 2005;Schafer and Mack, 2010;Smithwick et al., 2005;Wan et al., 2001) including in longleaf pine (Pinus palustris) savannas (Boring et al., 2004).In longleaf pine savannas, because these nutrient pulses occur during a period of rapid post-fire plant regrowth, they may influence successional patterns (Shenoy et al., 2013), plant diversity, and ecosystem productivity.10 The mechanisms driving these ephemeral increases in N availability following fire remain poorly resolved, and so it remains difficult to predict how a specific fire will influence local N availability and turnover.Fire can decrease N availability if N is volatized and lost from the system in high intensity fires (Lavoie et al., 2010;Certini, 2005).On the other hand, fire can increase N availability if it spurs microbial turnover of organic matter (Wilson et al., 2002;Certini, 2005), returns nutrient-rich ash to the 15 system (Boring et al., 2004), or decreases the vegetation demand for N. Short term increases in soil N availability may not translate to longer-term ecosystem retention if N is lost through leaching or as gaseous products during turnover.
In addition to difficulties associated with assessing the relative importance of each mechanism influencing post-fire N availability, logistical challenges remain to accurately measure N availability.20 First, changes to soil N availability are likely to occur rapidly following fire.Since microbial turnover occurs on a span of hours to days, and plants in fire-adapted systems begin re-sprouting within days to weeks, changes to N availability in pyrogenic systems are also likely to be ephemeral.Previous studies of post-fire N dynamics in longleaf pine savannas have relied on monthly or less-frequent soil samples (Wilson et al., 2002;Lavoie et al., 2010), but this sampling resolution may be too coarse if changes in N 25 dynamics occur rapidly following fire, or are transient.Secondly, net N cycling rates are often calculated as the difference in pool size between two time points.When measured in the field, repeated sampling of the same soil core would control for spatial heterogeneity in starting conditions, but would Biogeosciences Discuss., doi :10.5194/bg-2016-303, 2016 Manuscript under review for journal Biogeosciences Published: 12 August 2016 c Author(s) 2016.CC-BY 3.0 License.likely distort estimates of N dynamics because soil disturbance can increase rates of C mineralization and microbial respiration.Instead, to avoid disturbance associated with repeated sampling of the same core, nutrients are assumed to be distributed homogeneously in a small sampling area.Thus, it is assumed that cores collected in close proximity to each other are comparable, and can be considered replicates.However, nutrient pool sizes can vary by orders of magnitude within a meter (Jackson and 5 Caldwell, 1993), and so these assumptions, while practical, are problematic and often invalid.As such, field estimates of net cycling rates calculated from the difference between two nearby cores may be influenced by the idiosyncrasies of N spatial heterogeneity, and may not accurately represent local or larger-scale N dynamics.Without using expensive tracers, field-based sampling protocols to estimate net nutrient cycling remain imperfect and researchers must collect extensive soil replicates to overcome 10 the issues associated with environmental heterogeneity.
In this study, our broad aim was to evaluate alternative mechanisms driving post-fire changes in N availability while addressing the above mentioned methodological and analytical challenges to estimating net cycling rates.We measured soil N status every week for nine weeks during the 2014 growing season in five longleaf pine savannas sites in North Carolina.Our study is the first that we 15 know of to provide high-resolution temporal (i.e.weekly) data on the effects of prescribed fire on soil N dynamics in longleaf pine savannas.We then used a Bayesian hierarchical linear model to account for heterogeneous in situ N availability.The goals of our study were (1) to evaluate the short-term effects of fire on soil inorganic N availability and (2) evaluate whether changes in N pool sizes following fire could be attributed to changes in net microbial cycling rates or ash deposition.20

Study Site and Fire Characteristics
Our study was carried out in a longleaf pine savanna on Fort Bragg Military Reservation (35.1391°N 78.9991°W) near Fayetteville, NC, USA.This area is characterized by deep, sandy and sandy loam soils from the Candor and Blaney series, which lack an O horizon.Mean monthly temperature ranges from 25 6.9 -26.0 °C, and mean annual precipitation is 127.5 cm.This area includes numerous Since the 1980s, prescribed burns have been used as a management tool to maintain the longleaf pine savannas on the reservation; since the mid-1990s, these burns have occurred on 3-year rotations to promote longleaf pine regeneration and maintain habitat for rare and endangered species (Sorrie et al., 2006).Fort Bragg is composed of burn parcels (hereafter "sites") with independent burn histories.10 Permanent vegetation sampling transects spanning the topographic gradient have been maintained in 32 sites since 2011; the burn regime in the majority of these sites has been experimentally altered, with a subset of sites being maintained in 3-year burn intervals (Ames et al., 2015).From these sites, we selected three sites scheduled to burn in 2014, and three not schedule to burn in 2014 for use in a before-after-control-impact experiment.To avoid any artefacts associated with different historical burn 15 characteristics (e.g.historical burns occurring in wetter or dryer years; historical burn intensity and frequency), we limited in the number of burned sites to those with similar burn histories (i.e.all on 3year burn rotations).However, one site not scheduled to burn until 2016 experienced a wildfire in July 2014, and another site scheduled to burn did not.The site that burned prematurely due to a wildfire was grouped with other burned sites, despite its shortened fire return interval (one year) relative to the other 20 burned sites (three years).The site that failed to burn was considerably different from the remaining sites (% soil moisture and % soil organic matter were both more than double that of the other sites), and as such was dropped from further analyses.Thus, we were left with 5 study sites: B1, B2, B3 experienced burns in 2014; C1 and C2 were control sites that remained unburned.Our study sites, renamed here for clarity, correspond to sites 3, 11 (wildfire site), and 15b (B1-3), and 9 and 32 (C1 and 25 C2) described in Ames et al. (2015).At each site, we established a sampling area approximately 5 m upslope of the ecotone.This topographic location was chosen to minimize the effects of extremely welldrained, hydrologically disconnected (as found in the uplands) or saturated, anoxic (as found in the Burns occurred in treatment sites B1, B2 (wildfire), B3 on 4 July, 9 July, and 7 July 2014, respectively (Julian days 185,190,and 188).Using metal tags marked with Tempilaq temperature-sensitive paint (Air Liquide America 296 Corporation, South Plainfield, NJ, USA), we collected data on aboveground 5 fire temperature at B1 (6 tags) and B3 (8 tags).We did not collect fire temperature data at B2 because it was not initially scheduled to burn.

Soil Analyses
From May 30 through July 25, 2014, soil cores were collected weekly (nine weeks) from each site for pool size measurements (Figure 1).We collected pool size cores for a minimum of three weeks after a 10 prescribed burn.As such, our site-specific sampling allowed us to collect data before and after burns and detect any immediate changes in N concentration in response to the burn.Each week, three cores (each 5 cm diameter x 12 cm deep and adjacent to each other) were randomly collected from each site, for a total of 27 cores collected for pool size measurements at each site over the nine-week sampling period.(In burned sites, the number of cores collected prior to versus following prescribed fires differed 15 between sites depending on when the burns occurred.In B1, N=15 cores were collected prior to burns and N=12 cores were collected following prescribed burns.In B2 and B3, N=18 cores were collected prior to burns and N=9 cores were collected following prescribed burns.)These cores were used to compare the pool sizes of nitrate (NO 3 -) and NH 4 + throughout the growing season (Figure 1).After collection, all cores were stored on ice, immediately transported back to the laboratory, and kept at 4 °C 20 until they were analysed for inorganic N, soil moisture, soil organic matter, pH, and δ 15 N.All cores were homogenized by passing through a 2 mm sieve.Frequent fires in this ecosystem consume aboveground vegetation and litter, preventing the development of an O horizon in these soils (Boring et al., 2004).As such, sieving removed coarse root fragments, rather than partially decomposed organic matter.Subsamples (~10 g) from each core were extracted within 48 hours of collection with 2 M KCl 25 for inorganic N concentrations.The samples were shaken for 30 minutes, centrifuged, and the extract was then filtered out and stored frozen until analysis on a Lachat QuikChem 8500.Additional soil Biogeosciences Discuss., doi :10.5194/bg-2016-303, 2016 Manuscript under review for journal Biogeosciences Published: 12 August 2016 c Author(s) 2016.CC-BY 3.0 License.subsamples were oven dried for gravimetric soil moisture analysis, and combusted at 450 °C to measure organic matter content.Finally, we measured soil pH in 2:1 dH 2 O:soil ratios with a bench top pH probe.
To measure net cycling rates, we also installed three PVC collars (5 cm internal diameter x 12 cm deep) in each site every week (N=27 cycling rate cores collected at each site over nine-week sampling period; the number of cores collected prior to versus following the prescribed burns differed between sites 5 depending on when burns occurred, as above).These collars were installed adjacent to the soil cores collected for pool size measures (Figure 1), and were incubated in situ for one week, after which time they were collected and analysed as above for inorganic N pool sizes, soil moisture, soil organic matter, and pH.After allowing for error in the initial pool size of the incubated soil core (see below for model details), net nitrogen cycling rates were calculated based on the difference in extractable NO 3 -and NH 4 + 10 in the incubated cores and the un-incubated cores.That is, while traditional methods assume that the N pool sizes in the initial and incubated soil cores are equivalent, our analyses calculated net cycling rates allowing for differences in initial conditions between the two cores.

δ 15 N Analysis and Mixing Models
To assess plant-derived ash inputs to soil N pools after fire, we analysed soils from a subsample of time 15 points for δ 15 N. Plants generally discriminate against 15 N in favour of 14 N uptake (Craine et al., 2015;Hogberg, 1997), and, as such, vegetation tends to be depleted in 15 N relative to soil.If appreciable plant-derived N was deposited on burned sites as ash, we expected to see a decrease in δ 15 N while observing an increase in N pool size.Although ash is deposited on surficial soils, heavy rains occur frequently during June-August in this region of North Carolina (11.5, 14.8, and 11.5 cm, respectively;20 May 25, 2016), and the well-drained, sandy soils could leach nutrients through the soil profile.
Moreover, because plants begin resprouting within days of a fire (personal observation), we did not want to discount the potential for nutrients to be redistributed by roots.As a consequence of our uncertainty regarding the vertical distribution of deposited 15 N, we subsampled the full soil cores (0-12 cm) for 15 N analyses.The enrichment of the sample in 15 N is reported on a per mille basis (‰) and was 25 calculated as follows: where R sample is the ratio of 15 N: 14 N in the sample, and R standard = 0.0036, the ratio in atmospheric N 2 .
We subsampled cores collected at each site for pool size estimates from the two sampling weeks preburn and the two sampling weeks post-burn, for a total of four consecutive sampling weeks surrounding each burn (for B1-3, N=6 unburned and N=6 burned samples; for C1 and C2, N=12 samples).For unburned sites, we subsampled δ 15 N for four weeks corresponding to the same consecutive weeks 5 surrounding the burn dates in burned sites (hereafter burn season).For example, site B1 burned on July 3, 2014 corresponding to our sixth sampling week.For this site, and for the unburned site C1, we therefore measured δ 15 N from soil subsamples in the 4 th -7 th sampling weeks.Because our sites were not truly paired, we chose time points for δ 15 N analyses in the unburned sites based on the burn dates of the closest burn site.In this way, we allowed ourselves to detect any ash drifting between sites.Soils were 10 oven dried at 40°C until a constant weight, then ground finely, encapsulated in tin capsules, and combusted on a Carlo-Erba Elemental Analyzer coupled to a mass spectrometer at the Duke Environment Isotope Laboratory.
We used two end-member mixing models to estimate the mass of ash-N deposited onto the system.We used δ 15 N and N concentrations from pre-burn soil, and published δ 15 N signatures of ash (-0.81 δ15N;15 Huber et al., 2013) as the end-members, and post-burn soil δ 15 N and N concentrations as the resulting mixture from the two end-members.We solved for the mass of ash-N needed to be deposited in order to account for the observed shift in soil δ 15 N signature.
To assess whether ash inputs could be detected in post-burn soil cores using the natural abundance of 15 N, we also calculated the mass N needed to be deposited to observe a shift in soil isotopic signature of 20 the minimum external precision (0.1‰ δ 15 N at 1 standard deviation).

Pool Sizes
To assess the spatial distribution of nutrient (NO 3 -and NH 4 + ) and SOM availability, we calculated the coefficient of variation (CV) for each site prior to prescribed burns.To understand how fire and soil 25 variables affect N pool sizes, we fitted a Bayesian hierarchical linear model; this is akin to a multiple regression that also allows for variability in the relationship between true soil N pool sizes (µ) and measured pool sizes (y) which might occur, for example, though analytical error.Any effect of soil environmental conditions on N pool sizes would occur on µ , not y .Each core was modelled independently, and we accounted for site blocking effects by including random intercepts for each site.
For core i = 1….n at site j = 1…5, observed N pool size (NH 4 + or NO 3 -) was modelled as a function of random site effects, percent soil moisture (SM), percent soil organic matter (SOM), soil pH, and the 5 number of days since the previous burn, days since fire (DSF) as follows For NO 3 -, we added an additional predictive parameter, β #< NH 4 + < to allow for NO 3 -concentrations to additionally vary with nitrification substrate (NH 4 + ) availability.We expected the effects of a burn to 10 diminish with time, so we transformed DSF to DSF -1 , so that as DSF increased, DSF -1 → 0. Full models included all main effects and no interactions.

Cycling Rates
We built a hierarchical state-space model within a Bayesian framework to understand how fire and soil variables affected N cycling rates.As in the models of N pool sizes above, our cycling rate models 15 allowed for variation in the relationship between true (µ) and measured (z) cycling rates, modeled below as τ.In addition, we also allowed for error associated with the assumption that the N concentrations in initial cores (y 0 ; as from equation 2a) were equal to the initial concentrations of the incubating cores (y 1 ).By including these errors into our model, we essentially relaxed the assumption that paired cores (un-incubated and incubated cores) were true replicates and had equal initial N 20 concentration and edaphic conditions (SOM, pH, SM).We removed four core pairs (of 135) that exhibited NH 4 + or NO 3 -concentrations below the detection limit in the initial concentration.For core i = 1…n at each site j = 1…5, cycling rate was modelled as Prior to all analyses, we removed two cores from B1 (burned) and two from C2 that had pool size values below the analytical detection limit (four of 135 cores).All models were built with the rjags 5 package (version 3.15) in R version 3.2.1 (R Development Core Team, 2011).All predictors were modelled with normally distributed, uninformative priors.All values are reported with 95% credible intervals (CI).

Site Conditions and Fire Characteristics 10
Although plant community composition varied, Gaylusacia frondosa, Clethra alnifolia, and Arudinaria tecta were dominant in all of our study sites.Study sites were dry, low in organic matter, and acidic (Figure 2).Prescribed burns in all three sites thoroughly consumed all or most of the aboveground biomass.Aboveground understory vegetation in B1 and B3 was completely consumed.In B2 some scorched leaves remained on the woody vegetation, but the herbaceous understory species were 15 completely consumed.Fire temperatures were similar between B1 and B3: average maximum fire temperature at B1 was 612 °C ±18 and at B3 was 635 °C ±18.Fire temperature was not measured in B2, but is generally affected by fuel load and moisture (Ellair and Platt, 2012).

Pool Sizes
To assess the fine-scale spatial variability in NH 4 + and NO 3 -concentrations, we compared the CV of In the first week of sampling, initial pool sizes of inorganic N were similar between sites.Over the preburn season, sites had greater NO 3 -than NH 4 + availability (3.06 ±0.16 µg NO 3 -per gram of dry soil [gds -1 ] and 0.86 ±0.07 µg NH 4 + gds -1 ).However, the ratio of NH 4 + : NO 3 -increased following prescribed burns and sites B2 and B3 both experienced a shift in the dominant inorganic N form to NH 4 + immediately after a burn.5 There were observable increases in NH 4 + pool sizes immediately after a burn relative to the same time points in unburned control sites and time points in burned sites immediately prior to the burn (Figure 6).6) and pH (β pH = 6.16, 95% CI = 1.79-10.46;Figure 6).Because we fit our pool size models to the inverse of DSF (i.e.DSF -1 ; see Methods), the positive correlation between NH 4 + and DSF indicates decreasing pool sizes as time since fire lengthens.Pool sizes of NH 4 + were larger for less acidic soils and in recently burned soils.Pool sizes of NH 4 + were slightly greater in soils with more organic matter (β SOM = 0.2, 95% CI = 0.01-0.30),but did not vary with soil moisture 20 (β SM = -0.11,95% CI = -0.30-0.07; Figure 6).
Net nitrification rates were temporally heterogeneous throughout the full growing season, but were not appreciably more variable immediately following burns.Net nitrification rates were very low across the 20 growing season in burned (-0.08 ±0.05 µg gds -1 day -1 ) and unburned sites (-0.04 ±0.04 µg gds -1 day -1 ; Figure 7).Measured edaphic parameters were poorly correlated with observed net nitrification rates, although there was a slight positive relationship between net nitrification rates and soil moisture (Figure 8; β SM = 0.07, 95% CI = 0.03-0.11).

Total Soil δ 15 N and Ash Deposition
Soil N concentration was relatively stable throughout the burn season, and was similar between burned (0.35%N ±0.09) and unburned sites (0.38%N ±0.07;Table 1).Mean total soil N varied between sites (Table 1; Figure 9).On average, B2 had the lowest total soil N content (0.18%N ±0.02), and total soil N ranged from 0.08% at C1, to 0.98% at C2. Across all burned and unburned time points, confidence 5 intervals overlapped between burned and unburned conditions at each site, indicating no persistent change in total N over the full growing season (Table 1).
Across the full growing season, soil δ 15 N in burned sites was 2.76‰ (±0.36) and in unburned sites was 2.00‰ (±0.36).The response of δ 15 N to burning varied between sites.Soil δ 15 N in unburned Sites C1 and C2 was on average 1.22‰ (±0.52) and 1.79‰ (±0.36;Table 1), respectively.In burned sites, there 10 were shifts in δ 15 N, although the direction of shift varied between sites.Soils in B1 were depleted in 15 N after a prescribed burn relative to before the burn; δ 15 N shifted from 3.98‰ (±0.64) to 3.22‰ (±0.50).
In Site B2 the soil δ 15 N decreased from 2.67‰ (±0.57) before the prescribed burn to 2.45‰ (±0.24) after the burn.Finally, there was a slight enrichment in soil 15 N in Site B3 following fire; δ 15 N shifted from 1.37‰ (±0.36;Table 1) to 2.59‰ (±0.93;Table 1).The 95% CIs surrounding the mean δ 15 N (and 15 %N) overlapped for all burned sites, indicating the soil δ 15 N at each site was statistically indistinguishable pre-and post-burn.
We used pre-burn soil δ 15 N isotopic signature in mixing models to calculate the mass of ash-N needed to be deposited on our sites to achieve both the minimum and the empirically measured shift in soil δ 15 N. To achieve a shift in soil δ 15 N of the minimum external precision, sites B1, B2, and B3 would 20 need 11, 5, and 20 g N m -2 ash-N, respectively, deposited following fire.To achieve the measured shift in soil δ 15 N, sites B1 and B2 would need 100 and 11 g N m -2 added through ash deposition; B3 would need 175 g N m -2 to be removed from fire (Table 1).We also calculated the same values using fresh leaf δ 15 N from leaves collected from our sample site (-2.9‰±0.1;J. Wright, unpublished data), rather than published ash δ 15 N values (Supplementary Table S1).25

Discussion
In this study, we collected weekly measurements of soil inorganic N availability to document shortlived changes in N dynamics following fire and throughout the growing season of a pyrogenic forest in the southeastern US.As far as we know, this is the first study to pair estimates of N pool sizes and cycling rates at high temporal resolution in a longleaf pine savanna.Prior to prescribed burns, there was 5 high variability in N availability, particularly for NH 4 + pool sizes.This heterogeneity reinforces the need for a methodological approach that considers initial edaphic conditions when carrying out in situ experiments on paired soil cores.To address this, we relaxed the assumption that initial and incubating cores were true edaphic replicates; we used a Bayesian statistical framework to allow for variability in the relationship between true versus measured inorganic N concentrations in our soils.10 Immediately following prescribed burns, we found sharp increases in NH 4 + pool sizes in all of our study sites.However, the magnitude and duration of this increase varied between sites.Unlike studies in southeastern US pine savannas with monthly or less-frequent temporal sampling protocols, our weekly sampling allowed us to capture highly ephemeral changes in soil inorganic N pools.Furthermore, we found no changes in cycling rates and no evidence that ash deposition could account for the large 15 increases in N availability following fire.Instead, we propose that an ephemeral dampening of plant uptake could contribute to the observed increases in inorganic N following fire.

Changes in N dynamics across the growing season
Throughout the growing season, inorganic N availability and net cycling rates were low, as is common in longleaf pine savannas (Binkley et al., 1992).In unburned conditions over the growing season, there 20 was greater NO 3 -availability than NH 4 + .This pattern is consistent with previous work, which documented relatively high NH 4 + availability in the winter, followed by decreasing NH 4 + availability throughout the growing season (Christensen, 1977).Net nitrification rates were low across the growing season, may have been inhibited by the low soil pH.Net mineralization in our study was higher than measured over the summer months in previous studies (Wilson et al., 1999), so rather than low 25 mineralization rates, our low soil NH 4 + : NO 3 -ratios may be a result of preferential plant or microbial uptake of NH 4 + over NO 3 -.
We observed sharp increases in soil inorganic NH 4 + , but not NO 3 -, immediately following fire across three longleaf pine savanna sites in North Carolina (Figure 5).Although a global meta-analysis found that post-fire soil NO 3 -concentrations peak ten months after NH 4 + concentrations (Wan et al., 2001), studies in southeastern US ecosystems found no change in soil NO 3 -up to 30 days (pine savanna; Boring et al., 2004), 320 days (pine forest; Lavoie et al., 2010) and 500 days (shrubland; Schafer and 5 Mack, 2010) following fire.Across the full growing season, we measured NH 4 + pool sizes of burned sites that were nearly 5x that of unburned sites.The direction of the effect of fire was consistent across our study sites, however the magnitude of increase was highly site-specific.Within a site, increases in NH 4 + availability immediately following fire ranged from 5x to more than 25x the pre-burn levels.This NH 4 + pulse was short-lived, and only in B3 was the increased NH 4 + pool size sustained for longer than 10 one week.As a consequence, a decreased sampling frequency would not have detected the ephemeral changes in soil NH 4 + pool size in B1 and B2.
It remains unclear why, however, sites experience such variability in the magnitude of NH 4 + response following fire.Although we cannot rule out the possibility that our high-intensity sampling influenced nitrogen cycling and pool sizes, we saw no evidence of increasing inorganic N availability, or 15 increasing variability in N availability, in our control sites, which experienced the same levels of sampling disturbance without fire.However, differences in post-fire vegetation regrowth in sites B1, B2, and B3 (C.Ficken, unpublished data), may suggest an important role of plant uptake in regulating soil N concentration.While B1 and B2 exhibited rapid vegetation resprouting following fire, regrowth in B3 was patchy.Moreover, in 2012, the last year all sites were sampled prior to the 2014 burns, B3 20 had the smallest standing biomass stocks of all three burned sites (J.Wright unpublished data).If plant N uptake remained low following fire in B3, this might explain the persistent increase in N availability in this site.However, biomass stocks after three months of regrowth in 2014 were also substantially different between B1 and B2, despite these sites exhibiting similar patterns of NH 4 + availability over time.In unburned years, Mitchell et al. (1999) found that annual net primary productivity (ANPP) in a 25 longleaf pine savanna was positively correlated with local moisture availability and biomass estimates in this heterogeneous system are highly dependent on local woody versus herbaceous cover, as well as annual variability in environmental conditions.Factors controlling unburned ANPP may differ from those controlling biomass regeneration, and given the spatiotemporal heterogeneity of this system, teasing apart these drivers may require a large-scale manipulative experiment.

Assessing Mechanisms of N Pulses Following Fire
A nitrogen pulse may occur following fire through (1) an increase in microbial mineralization, (2) ash inputs, or (3) a decrease in plant uptake.Fire may stimulate microbial turnover of organic matter if 5 additions of C or N from ash deposition or root exudation (southern shrubland; Schafer and Mack, 2010) enhance microbial activity.Wilson et al (2002) found significant increases in microbial biomass following fire in a longleaf pine savanna.Although we did not directly measure microbial biomass, we found no changes in net microbial mineralization associated with the observed increase in pool size, although cycling rates were increasingly variable following fire in burned sites (but not in unburned 10 sites).Indeed, the increase in mass of NH 4 + following fire was much greater than the mass of NH 4 + produced on a daily basis by net microbial mineralization, reinforcing the conclusion that changes in microbial cycling rates could not account for the observed increase in pool sizes.
We also found no indication that the newly available NH 4 + substrate led to a delayed increase in net nitrification rate.This, along with high soil C:N (unburned sites-47:1; burned sites-53:1) relative to 15 other longleaf pine soils (Lucash et al., 2007), might suggest that autotrophic nitrifying microbes are competitively inferior to heterotrophic microbes under post-fire conditions in our study sites.
Alternatively, an unmeasured increase in gross nitrification might have allowed for a commensurate increase in microbial immobilization of NO 3 -following fire.In general, however, the lack of change in microbial N cycling rates suggests that changes in microbial activity fail to account for the observed 20 increases in NH 4 + availability following fire.
Direct additions of N into the soil from ash may provide an alternative mechanism for the observed increase in mineral N availability.To test this, we examined changes in both total N and δ 15 N immediately before and after burns.N from ash additions is primarily organic (Christensen, 1977;Huber et al., 2013;Raison, 1979), and is thought to increase N pools by stimulating microbial activity.We 25 found no change in total nitrogen (i.e.%N) or soil organic matter before and after prescribed burns.These results support the findings of a global meta-analysis of fires in forested systems, which found no effect of fire on total N (Wan et al., 2001).
We used the natural abundance of 15 N as an isotopic tracer of ash additions.However, fractionation during volatization preferentially releases 14 N, resulting in ash material that is enriched in 15 N relative to fresh plant matter and an increase in δ 15 N signature in ash relative to fresh plant material (Saito et al., 5 2007;Stephan et al., 2015).A study in a subalpine grassland reported foliar δ 15 N values (-2.9‰) and N concentrations in ash (11.63 ±0.80 mg N g-1 ash; Huber et al., 2013) comparable to foliar δ 15 N values (-2.9‰; ±0.1;J. Wright, unpublished data) and ash-N concentrations of our system (8.75 ±0.90 mg N g-1 ash; Christensen, 1977).Huber et al. (2013) also reported ash δ 15 N values in ash of -0.81‰, which we used in mixing models.Using this isotopic signature of ash, we found that 5 to 20 g ash-N m -2 10 would need to be deposited in our burned sites in order to observe a detectable shift in soil isotopic signature.These values are greater than the mass of ash-N deposition reported in a longleaf pine system (1.15 g m-2; Christensen, 1977), suggesting that this method may not be ideal for detecting ash inputs in systems with low aboveground vegetation stocks.Nevertheless, we estimated that 100, 11, and 175 g ash-N m -2 would need to be deposited on sites B1-3 to account for our measured shifts in soil δ 15 N. 15 These deposition levels are highly unlikely to have occurred at our sites, since they would require substantial aboveground vegetation accumulation and our system is burned every three years.However, it is unclear how quickly surface inputs can be expected to distribute throughout the soil profile.In the sandy soils of our system, frequent heavy summer rains or active root growth may quickly redistribute surface inputs.While one study of longleaf pine savannas found that changes (losses) in total soil N 20 following fire were concentrated at the soil surface (Binkley et al., 1992), another study of subalpine woodlands and grasslands detected no changes in total N in surface soils following burning (Huber et al., 2013).Our work is also in agreement with that of (Christensen, 1977), who found significant differences in δ 15 N with depth, but no change following fire.Given the uncertainties surrounding the redistribution of surface inputs down the soil profile, we cannot conclusively rule out the potential to 25 conclude that ash inputs are unlikely to fully account for the increase in measured soil inorganic N availability.
Finally, changes in plant and microbial immobilization could cause an increase in soil inorganic pool sizes.Prescribed fires in longleaf pine savannas are low intensity, and sharp declines in soil temperature with depth, particularly in dry soils, are unlikely to substantially damage the soil microbial community 5 below 5 cm (Hartford and Frandsen, 1992).In fact, previous work in longleaf pine savannas has documented increases in microbial biomass following fire (Wilson et al 2002).In contrast to the microbial community, prescribed burns in our study system generally topkill a majority of the aboveground herbaceous and woody biomass with stem diameters less than 10 cm (Just et al., 2015).If fire damage temporarily halted or slowed the plant uptake of inorganic N, we would expect to see an 10 accumulation of soil N if microbial immobilization did not increase sufficiently to deplete the pool.
However, N accumulating in excess of demand can only partly explain observed increases in inorganic N availability, since the pulse of N we detected following fire was many times greater than what was produced by net mineralization and net nitrification.Nevertheless, a change in plant sink strength may have contributed to post-fire NH 4 + pulse.15 Previous work found no evidence that plant species in an African savanna re-translocated nutrients from root biomass to resprouting shoot biomass following a fire (Vijver et al., 1999), indicating that soil pools can be an important source of N for regenerating biomass.Indeed, the biomass of resprouting vegetation following fire has been shown to be highly enriched in 15 N relative to pre-burn biomass (Huber et al., 2013;Schafer and Mack, 2014), an indication that the source of N in resprouting biomass 20 is also enriched (Evans, 2001).Root biomass is an important component of short-term N retention in grassland ecosystems (De Vries and Bardgett, 2016).In fire-prone systems, fire-tolerant plants could play an important role in preventing N leaching losses if they are able to resume N uptake quickly following fire.
We propose that plant-demand for inorganic N may have a strong influence on soil N pool sizes in this 25 savannas (Christensen, 1977).Furthermore, an increase in soil NH 4 + pool size after fire without a stable increase in net microbial mineralization rates could occur if there is a decrease in plant uptake.
Similarly, a muted effect of fire on NO 3 -pool size may occur if plants have diminished uptake of this inorganic N form, and plant uptake exerts a relatively weaker control on soil NO 3 -pools.Plant control on ecosystem N status has been well documented in northeastern US hardwood forests, where a 5 defoliation event resulted in substantial N losses from the ecosystem (Aber et al., 2002;Likens et al., 1969).
If post-fire patterns in N availability were related to plant uptake, we would expect differences in the magnitude and duration of soil N change following fire to be related to plant N-demand and regrowth following fire.In stand-replacing fires in temperate forests, where vegetation is killed, relatively 10 persistent increases in N pools should occur following fire.We similarly would expect smaller and more ephemeral changes in N pools in systems in which plants are only top-killed.Indeed, standreplacing fires have been shown to result in changes to soil N pools that persist more than one year following the fires (Smithwick et al., 2005;Turner et al., 2007).In contrast, elevated inorganic N immediately following fires in grassland decreases throughout the growing season (Augustine et al., 15 2014).Low-intensity fires in grass-dominated glades adjacent to oak-hickory forest sites in a Kentucky study resulted in increases in post-burn soil NO 3 -pool sizes, but no increase in lysimeter-detected NO 3 leaching losses below 10 cm (Trammell et al., 2004).Although the study did not examine microbial biomass, they found no effect of fire on net N mineralization, suggesting role of plant uptake in patterns of N loss and retention post-fire.Such instances of plant control of N availability provide an important 20 setting in which to examine the role of nutrient availability-and nutrient pulses in particular-on plant community composition and ecosystem productivity.Differences in the ability of species to capture this ephemeral resource may help explain differences in post-fire resprouting patterns and biomass regeneration following fire.

Ecological Implications of Fire-Associated N Pulse 25
To put the fire-associated pulse of inorganic N that we observed into context, we compared its mass to N inputs in the longleaf pine savannas ecosystem.Although the pulse of soil N following fire is most likely a redistribution of N from other ecosystem pools, it is conceptually helpful to understand the magnitude of this pulse relative to other components of the N cycle in longleaf pine savannas.The increase in soil inorganic N following fire (0.98 g N m -2 10 cm -1 ) was approximately 10x the daily total net inorganic N production (i.e.net mineralization + net nitrification; 0.11 g N m -2 10 cm -1 day -1 ).
These ephemeral increases in soil inorganic N availability occur during an important ontological stage 5 of plant development as longleaf pine understory species begin resprouting within a few days following fire (CD Ficken, personal observation).The mass of mineral N released fire was more than twice the mass of N longleaf pine understory species assimilate into their biomass over a full growing season (0.45 g N m -2 ; unpublished data).However, the extent to which plants or microbes have access to this ephemeral nutrient pulse remains unclear.Previous studies have documented rapid plant uptake 10 of N tracers by intact plants (Aber et al., 2002;Likens et al., 1969), suggesting that plants may have access to this N pulse during regrowth.

Conclusions
We have documented large pulses of mineral N following prescribed burns in a longleaf pine savanna in North Carolina.Our weekly sampling revealed that while one site experienced a persistent increase in 15 NH 4 + pool size, other sites experienced only very short-lived pulses that would not have been detected with monthly sampling.The marked differences in the duration of the NH 4 + pulse that we observed may explain why previous studies with less-frequent soil sampling showed no change in mineral N following fire.However, the factors that influence the magnitude of the system's response to fire are still unknown.We propose here a role for plant uptake in regulating post-fire N availability, and encourage 20 future work to explore the relationships between N availability and plant biomass dynamics immediately following fire.Due to the rapid changes in N availability following fire, as well as the fast resprouting of understory species, we recommend that responses of the the local plant community be considered when determining an appropriate sampling regime for biogeochemical responses to disturbance.In systems in which the plant community responds rapidly, soil samples should be 25 collectedly quickly and frequently to capture post-disturbance plant-nutrient and biogeochemical dynamics.
Biogeosciences Discuss., doi:10.5194/bg-2016-303,2016 Manuscript under review for journal Biogeosciences Published: 12 August 2016 c Author(s) 2016.CC-BY 3.0 License.microtopographical gradients represented by numerous low riparian wetlands in an upland matrix.The uplands are well-drained and savanna-like, with an open canopy of longleaf pine (Pinus palustris) and an understory dominated by wiregrass (Aristida stricta; Sorrie et al., 2006).Several hardwood species and Pinus serotina replace P. palustris in the wetlands lining streambeds; in these areas, the soil is often saturated and the ground covered with Sphagnum sp.Separating the uplands from the wetlands, the 5 ecotones have dense, shrubby vegetation dominated by Ericaceous species.
Biogeosciences Discuss., doi:10.5194/bg-2016-303,2016 Manuscript under review for journal Biogeosciences Published: 12 August 2016 c Author(s) 2016.CC-BY 3.0 License.wetlands) soils on microbial processing.Aboveground vegetation cover was recorded for each species in each site during the growing season of 2014.
surface additions to contribute to the observed NH 4 + pulse.Nevertheless, considering the unrealistic mass of ash-N needed to be deposited onto surface soils to account for our measured shifts in δ 15 N, we Biogeosciences Discuss., doi:10.5194/bg-2016-303,2016 Manuscript under review for journal Biogeosciences Published: 12 August 2016 c Author(s) 2016.CC-BY 3.0 License.
system, and a temporary decrease in this demand may have contributed to the observed post-fire NH 4 + pulse.Plant preference for NH 4 + would explain the relatively large pool sizes of NO 3 -relative to NH 4 + during the growing season, and this pattern is consistent with previous seasonal trends in a longleaf pine Biogeosciences Discuss., doi:10.5194/bg-2016-303,2016 Manuscript under review for journal Biogeosciences Published: 12 August 2016 c Author(s) 2016.CC-BY 3.0 License.

5 estimated
masses of N needed to be deposited onto each site to result in the observed post-burn δ 15 N. Note that Christensen (1977) measured 1.15 ±0.49g N m -2 deposited in ash fall in fire in a longleaf pine savannas.Biogeosciences Discuss., doi:10.5194/bg-2016-303,2016 Manuscript under review for journal Biogeosciences Published: 12 August 2016 c Author(s) 2016.CC-BY 3.0 License.

Figure 1 .
Figure 1.Schematic illustrating the paired-core sampling design across nine weeks throughout the growing season.Each circle represents the three soil cores and three incubated PVC collars installed every week.

Figure
Figure 2. Mean soil moisture (SM; a), pH (b), soil organic matter (SOM; c) and total inorganic N (TIN, i.e.NH 4 + and NO 3 -; d) prior to and following burns at each site.TIN is reported per gram of dry soil (gds).Values are reported ±95% CI.

)Figure 3 .
Figure 3. Histogram of soil NH 4 + concentrations (reported per gram of dry soil, gds) prior to prescribed burns.Solid vertical lines indicate the median concentration across all sites; dashed vertical lines indicate the site-specific median concentration.

Figure 4 .
Figure 4. Histogram of soil NO 3 -concentrations (reported per gram of dry soil, gds) prior to prescribed burns.Solid vertical lines 5

Figure 5 .
Figure 5. Changes in pool sizes of (a) NH 4 + in burned sites, (b) NH 4 + in control sites, (c) NO 3 -in burned sites, and (d) NO 3 -in control sites.The x-axis for A and C depicts time (in days) centred on the date of burn; days immediately before the burn are negative x-values while days immediately following a burn are positive; the burn date is at 0 and is demarcated with a vertical dotted line.The x-axis for sites B and D depicts time in Julian Days.Prescribed burns in B1, B2, and B3 occurred on Julian Days 5 , doi:10.5194/bg-2016-303,2016 Manuscript under review for journal Biogeosciences Published: 12 August 2016 c Author(s) 2016.CC-BY 3.0 License.

Figure 6 .
Figure 6.Mean posterior estimates for parameters predicting pool sizes of NH 4 + and NO 3 -.Parameters are days since fire (DSF), soil pH, soil moisture (SM), soil organic matter (SOM), and substrate availability (i.e.NH 4 + reported per gram of dry soil, gds).Thin black lines show 95% CI and thick lines show 50% CI.CI may be obscured by the mean point.

Figure 9 .
Figure 9. Bar chart showing the pulse of inorganic N relative to the total N pool size.Panel (a) shows the mean percent of inorganic N (i.e.NH 4 + and NO 3 -) in soil under in unburned and burned periods.Panel (b) shows the mean total N soil content in burned and unburned periods, reported per gram of dry soil (gds).Error bars are ±95% CI.
Site B3 was qualitatively different than changes observed in Sites B1 and B2.In Site B3, there was an approximate exponential increase in NH 4 + pool size that plateaued, but did not diminish, by the end of our sampling, more than 3 weeks post-burn.Three days following a burn in Site B3, NH 4 + pool size had increased from 0.84 ±0.16 µg NH 4 + gds -1 in the pre-burn season to 21.9 ±6.31 µg NH 4 + gds -1 .

Table 1 . Mean soil inorganic N pool sizes reported per gram of dry soil (gds), δ 15 N values, and total N for each study site. Values are reported ±95% CI. Sample sizes for inorganic N pool sizes differ between sites depending on when the prescribed burns occurred; see Methods for details on sample sizes. Soil %N and δ 15 N values were collected on a subset of time points; sample sizes for these variables are N=6 for burned site means, and N=12 for control (unburned) site means. Ash-derived N values are the
BiogeosciencesDiscuss., doi:10.5194/bg-2016Discuss., doi:10.5194/bg--303,2016Manuscript under review for journal Biogeosciences Published: 12 August 2016 c Author(s) 2016.CC-BY 3.0 License.