DOI: 10.5061/dryad.gmsbcc2x1
title: Phosphorus controls symbiotic nitrogen fixation in fire-dependent longleaf pine savannas

description: Symbiotic nitrogen (N) fixation has the potential to replenish
 fire-induced N losses in frequently burned ecosystems. A strong
 relationship between fire and fixation may exist because fire volatilizes
 N and mineralizes phosphorus (P), creating N-poor, P-rich soils that favor
 plants capable of N-fixation. However, human activities have enriched
 ecosystems with N, which may complicate the interplay among fire,
 fixation, and soil P. We evaluated how N and P modulate the relationship
 between fire and symbiotic N fixation in longleaf pine savannas, where it
 was previously documented that N fixation fails to replenish N losses from
 fire. Across gradients of stand age and fire frequency, we investigated
 how N and P availability influence fixation, and we established a nutrient
 addition experiment to evaluate the effects of N and P on legume growth,
 fixation, and mycorrhizal investment. We uncovered a clear signal of P
 limitation of herbaceous legumes. Legume growth and fixation were linked
 to the availability of soil mineral P and were further stimulated by P
 additions. In contrast, neither soil N availability nor N additions
 affected legume growth or fixation. Synthesis: Our findings suggest that
 symbiotic N fixation in sandhill longleaf pine savannas is controlled by
 soil P availability, which varies according to soil age and parent
 material. Therefore, fixation may only counterbalance N losses from fire
 if there is enough P in the soil to support this process. Nonetheless,
 recent N enrichment in contemporary longleaf pine ecosystems may have
 reduced the importance of N fixation as a post-fire recovery mechanism.
description: <em>Study site          </em> We
 conducted this research at Fort Moore Military Installation in
 southwestern Georgia (previously known as Fort Benning, and hereafter
 referred to as Moore) and Eglin Air Force Base in northwestern Florida
 (hereafter, Eglin), which respectively represent the Fall-Line and Coastal
 Plain ranges of sandhill longleaf pine savanna ecosystems (Peet, 2006). We
 received permission to conduct fieldwork through contract RC-2328 with the
 Strategic Environmental Research and Development Program. The climate at
 both sites is humid subtropical, and precipitation occurs throughout the
 year with a wetter season from May to September (MAT = 18.0˚ and 18.7˚C
 and MAP = 1,260 and 1,802 mm at Moore and Eglin, respectively).
 Both military installations were acquired by the Department of
 Defense in the 1930s and have a mixed history of land use including
 agriculture, silviculture, grazing, and fire exclusion (Frost, 1993;
 Rodgers & Provencher, 1999), but now support large tracts of land
 that are managed with regular, low-intensity prescribed fire to promote
 longleaf pine restoration and conservation (mean ± SE fire return
 intervals of our research plots are 2.5 ± 0.1 and 6.2 ± 1.1 years, at
 Moore and Eglin respectively). These prescribed fires are intended to
 mimic the historical fire regime (Van Lear et al., 2005) and maintain an
 open-canopy longleaf pine overstory with pyrogenic grasses and forbs in
 the understory. Scrub oaks (especially <em>Quercus laevis</em>
 Walt.) are common in the midstory. All study plots were established on
 Lakeland sands (thermic, coated Typic Quartzipsamments), and have similar
 soil properties, including soil texture, structure, and carbon (C) and N
 content, which we determined in a prior study (Tierney et al., 2019; Table
 S1). In 2014, we established 54 1-ha plots of longleaf
 pine distributed across two gradients at Moore and Eglin: fire return
 interval (1.5 – 20 years, calculated from 20 years of prescribed and
 natural fire events) and stand age (2-227 years old). These included 18
 planted and 10 naturally regenerated (“mature”) stands at Moore and 18
 planted and 10 naturally regenerated stands at Eglin. Plots were randomly
 positioned within plantations and burn units. Many of the plantations were
 previously mixed-pine forests that were harvested and site prepped (drum
 chopping sometimes paired with herbicide application) prior to planting
 with longleaf seedlings. Stand ages of naturally regenerated stands were
 estimated with tree ring analysis of the oldest tree (Tierney et al.,
 2019). At random locations within each 1-ha plot, we
 established either two, three, or four 100-m<sup>2</sup>
 subplots in which we conducted soil sampling and legume censuses. These
 subplots were established to capture within-hectare variability, but due
 to logistical constraints, we could only establish the maximum number of
 subplots (4) in 16 of the 54 larger study plots.
 <em>Observational study</em>
 <em>Soil P and N availability</em>
 We sampled mineral soils to determine phosphate availability with
 both the Mehlich-III method and the anion-resin method, which measure two
 different pools of mineral phosphate with different levels of biological
 availabilities. While anion resins are well-suited to measure the more
 labile pool of phosphate that closely mimics P availability in the
 rhizosphere (Robertson et al. 1999), Mehlich-III additionally extracts a
 potentially less-available pool of phosphate that is adsorbed to soil
 colloids and may not be immediately available for plant uptake (Mehlich
 1984). In each subplot, we collected 6 soil cores (2.5 cm diameter and 20
 cm depth) and homogenized them by depth increments (0-10 cm and 10-20 cm).
 We recorded the time passed between a fire event and soil sample
 collection, which ranged between 51 days and 10.8 years. Samples were
 removed of rocks, roots and organic matter and transported in coolers back
 to our laboratory at the University of Georgia for immediate analysis.
 Further details of the laboratory methods to determine Mehlich-III and
 resin-extractable P are provided in the Supplemental Methods.
 To quantify N availability, we assessed N mineralization per
 subplot at a 20 cm depth with four sequential extractions (December 2015
 to April 2016) using buried bag technique with KCl extractions (Robertson
 et al., 1999; Tierney et al., 2019). Details of the field and laboratory
 methods for this analysis are also provided in the Supplemental
 Methods. <em>Soil extracellular
 enzymes</em> We measured the activity of two
 extracellular enzymes important to the liberation of P and N from organic
 matter in soils – acid phosphatase (AP) and leucyl aminopeptidase (LAP).
 AP activity was determined using methylumbelliferyl-linked substrates and
 LAP activity with methylcouramin-linked substrates as previously described
 (Bell et al., 2013), and enzyme activities were expressed per g of dry
 soil. Soils were collected as described above and were kept at 4˚C until
 analysis, which we performed within two weeks of sample collection. All
 soil sampling for the determination of phosphate availability, N
 mineralization, and enzyme activity was completed before applying
 nutrient-addition treatments.                          
 <em>Nutrient-addition experiment</em>
 From our 54 plots of longleaf pine, we selected 20 plots to
 establish a nutrient-addition experiment (9 plots at Moore and 11 at
 Eglin). Each selected plot contained at least three
 100-m<sup>2</sup> subplots in which we had conducted the
 observational study that would also serve as “treatment” subplots
 (hereafter treatment plots). Of these 20 plots, four were plantations
 between 3 and 4 years of stand age, four between 11 and 15 years, four
 between 19 and 23 years, and 8 stands were naturally regenerated (67 to at
 least 227 years old; Fig S1a). These 20 plots also varied in their fire
 return interval and time since fire (Fig. S1). In each plot, each of the
 three treatment plots were randomly assigned one of three treatments
 (hereafter treatment plots): N addition (50 kg N
 ha<sup>-1</sup> year<sup>-1</sup>, ammonium
 nitrate fertilizer), P addition (25 kg P ha<sup>-1</sup>
 year<sup>-1</sup>, triple super phosphate fertilizer) or
 control (no fertilizer addition). <em>Legume
 biomass, N fixation, mycorrhizal colonization, and tissue
 chemistry</em> We estimated legume biomass in
 each treatment plot by pairing census data with species-specific
 allometric equations (Table S2). Two pre-treatment censuses (2015-2016)
 and one post-treatment census (2017) were conducted in each subplot at the
 end of the growing season (July-August) in which we identified legumes to
 species and counted the number of individuals, stems, and leaves of each
 species present. Then, we paired abundance and size measurements with
 site- and species-specific allometric equations to determine legume
 aboveground biomass in each subplot (Tierney et al. 2019). We calculated
 the change in biomass as the difference between the average pre-treatment
 legume biomass (2015-2016) and post-treatment biomass (2017) in each
 subplot. In July 2017, after 4 months of fertilization,
 we excavated the root systems of herbaceous legumes in each subplot.
 Overall, we excavated 524 individuals total from 23 species of herbaceous
 legumes to determine the prevalence of root nodules, which are the
 structures that house symbiotic N-fixing bacteria. Specifically, we
 targeted 14 to 35 individuals from nine of the most abundant species at
 each plot, for which we had previously determined species-specific N
 fixation rates. These species represented 87.9 and 88.3% of the total
 legume biomass at Moore and Eglin, respectively (Tierney et al. 2019).
 From 106 individuals growing within treatment plots, we collected live
 nodules and determined nodule mass-specific N fixation rates using
 acetylene reduction assays according to the methods of Tierney et al.
 (2019), and these data were included in allometric equations estimating
 biomass-based nitrogen fixation rates for these species (Tables S2 and
 S3). A conversion factor of 4.8 µmol ethylene: µmol
 N<sub>2</sub>, which was determined by a greenhouse experiment
 of the same species (Ament et al., 2018), was used to convert rates of
 acetylene reduction to rates of N fixation. To
 determine mycorrhizal colonization of legume roots, we refrigerated the
 fine roots of the legumes in 60% ethanol until they could be cleared with
 10% KOH (65°C for 6 h) and stained with 0.05% trypan blue (70°C for 15
 min) following the methods of Wurzburger and Wright (2015). We quantified
 mycorrhizal colonization as the percentage of root length containing
 mycorrhizal structures using the random intercept method detailed by
 McGonigle et al. (1990). We only counted arbuscules in this measure, as
 they are the mycorrhizal structures purportedly involved in the trading of
 C and nutrients (Hawkins et al., 2023). We collected
 legume foliage in each treatment plot, including 68 individuals from 12
 species, to determine how nutrient addition treatments may alter foliar
 nutrition and stoichiometry, which can indicate nutrient limitation in
 legumes (Batterman et al., 2013; Ren et al., 2017). We collected leaves
 only from the species that were present in all three treatment subplots
 within each of the 20 study plots to minimize the potentially confounding
 effects of plot-by-species interactions (Table S4). To quantify foliar
 chemistry, we dried the foliage at 70° C for 48 h, ground it to a fine
 powder, and determined tissue N content by combustion analysis and P
 content by acid digestion (D’Angelo et al., 2001; Greweling,
 1976). References: Ament, M. R.,
 Tierney, J. A., Hedin, L. O., Hobbie, E. A., & Wurzburger, N.
 (2018). Phosphorus and species regulate N2 fixation by herbaceous legumes
 in longleaf pine savannas. <em>Oecologia</em>,
 <em>187</em>(1), 291–290.
 https://doi.org/10.1007/s00442-018-4129-z Batterman, S.
 A., Wurzburger, N., & Hedin, L. O. (2013). Nitrogen and phosphorus
 interact to control tropical symbiotic N2 fixation: A test in Inga
 punctata. <em>Journal of Ecology</em>,
 <em>101</em>(6), 1400–1408.
 https://doi.org/10.1111/1365-2745.12138 Bell, C. W.,
 Fricks, B. E., Rocca, J. D., Steinweg, J. M., McMahon, S. K., &
 Wallenstein, M. D. (2013). High-throughput fluorometric measurement of
 potential soil extracellular enzyme activities. <em>Journal of
 Visualized Experiments</em><em> </em><em>:
 JoVE</em>, <em>81</em>, e50961.
 https://doi.org/10.3791/50961 D’Angelo, E.,
 Crutchfield, J., & Vandiviere, M. (2001). Rapid, sensitive,
 microscale determination of phosphate in water and soil. <em>Journal
 of Environmental Quality</em>, <em>30</em>(6),
 2206–2209. https://doi.org/10.2134/jeq2001.2206 Frost,
 C. C. (1993). Four centuries of changing landscape patterns in the
 longleaf pine ecosystem. <em>Proceedings of the Tall Timbers Fire
 Ecology Conference</em>, <em>18</em>, 17–43.
 Greweling, T. (1976). Chemical analysis of plant tissue.
 <em>Agronomie</em>, <em>6</em>, 1–35.
 Hawkins, H.-J., Cargill, R. I. M., Van Nuland, M. E., Hagen, S.
 C., Field, K. J., Sheldrake, M., Soudzilovskaia, N. A., & Kiers,
 E. T. (2023). Mycorrhizal mycelium as a global carbon pool.
 <em>Current Biology</em>, <em>33</em>(11),
 R560–R573. https://doi.org/10.1016/j.cub.2023.02.027
 McGonigle, T. P., Miller, M. H., Evans, D. G., Fairchild, G. L.,
 & Swan, J. A. (1990). A new method which gives an objective
 measure of colonization of roots by vesicular—Arbuscular mycorrhizal
 fungi. <em>New Phytologist</em>, <em>115</em>(3),
 495–501. https://doi.org/10.1111/j.1469-8137.1990.tb00476.x
 Peet, R. K. (2006). Ecological classification of longleaf pine
 woodlands. In <em>The Longleaf Pine Ecosystem</em> (pp.
 51–93). Ren, F., Song, W., Chen, L., Mi, Z., Zhang, Z.,
 Zhu, W., Zhou, H., Cao, G., & He, J.-S. (2017). Phosphorus does
 not alleviate the negative effect of nitrogen enrichment on legume
 performance in an alpine grassland. <em>Journal of Plant
 Ecology</em>, <em>10</em>(5), 822–830.
 https://doi.org/10.1093/jpe/rtw089 Robertson, G. P.,
 Coleman, D. C., & Bledsoe, C. S. (1999). <em>Standard Soil
 Methods for Long-Term Ecological Research</em>. Oxford University
 Press.
 http://ebookcentral.proquest.com/lib/princeton/detail.action?docID=241674 Rodgers, H. L., & Provencher, L. (1999). Analysis of longleaf pine sandhill vegetation in Northwest Florida. <em>Castanea</em>, <em>64</em>(2), 138–162. Tierney, J. A., Hedin, L. O., & Wurzburger, N. (2019). Nitrogen fixation does not balance fire-induced nitrogen losses in longleaf pine savannas. <em>Ecology</em>, <em>100</em>(7), e02735. https://doi.org/10.1002/ecy.2735 Van Lear, D. H., Carroll, W. D., Kapeluck, P. R., & Johnson, R. (2005). History and restoration of the longleaf pine-grassland ecosystem: Implications for species at risk. <em>Forest Ecology and Management</em>, <em>211</em>(1), 150–165. https://doi.org/10.1016/j.foreco.2005.02.014 Wurzburger, N., & Wright, S. J. (2015). Fine‐root responses to fertilization reveal multiple nutrient limitation in a lowland tropical forest. <em>Ecology</em>, <em>96</em>(8), 2137–2146. https://doi.org/10.1890/14-1362.1
description: # Phosphorus controls symbiotic nitrogen fixation in fire-dependent
 longleaf pine savannas
 [https://doi.org/10.5061/dryad.gmsbcc2x1](https://doi.org/10.5061/dryad.gmsbcc2x1) ## Description of the data and file structure **plot_data.csv** A CSV including estimates of symbiotic N fixation by herbaceous legumes (n_fix; kg N ha^-1), phosphorus availability as determined by anion resins (resin_p; ppm), net N mineralization (net_nmin; kg N ha-1 year-1), soil leucyl aminopeptidase activity (lap; nmol gsoil^-1 hour^-1), and soil acid phosphatase activity (ap; nmol gsoil^-1 hour^-1) at each plot (plot_id; n = 54) at Eglin and Benning (site). The variables of time since fire (days_since_burn at the time of sampling for soil phosphorus availability), fire return interval (fri; years^-1), and stand age (stand_age; years) are also included.  NA values for fri indicate that there had been no fire events since the establishment of the longleaf pine plantation. **legume_treatment_responses.csv** A CSV including the responses of the legume community to fertilizer treatments. The variable legume_biomass_difference provides the difference in aboveground legume biomass in the growing seasons before and after fertilizer treatments (C = control; N = +nitrogen; P = +phosphorus) were administered (2016 and 2017), and the variable n_fix provides estimates of N fixation by herbaceous legumes (kg N ha-1 year-1) in each treatment subplot following fertilizer additions. **legume_census.csv** A CSV including legume nodulation data across all treatment subplots at Eglin and Moore. Each individual legume censused was identified to species, and their stems (n_stems) and leaves (n_leaves) were counted. Leaf counts for the species "DAPI" are not available (NA) because the leaves of this species were too numerous to count and are not included in allometric equations. We excavated their belowground root systems to record the presence or absence of live root nodules (nodulation = 1 indicates nodule presence; nodulation = 0 indicates nodule absence).  Species codes are as follows: "TECH" = Tephrosia chrysophyllum "GARE" = Galactia regularis "TEVI" = Tephrosia virginiana "RHCY" = Rhynchosia cytosoides "LUDI" = Lupinus diffusus "BACA" = Baptisia calycosa "CHNI" = Chamechrista nictans "DAPI" = Dalea pinnata "BALA" = Baptisia lanceolata "DEPA" = Desmodium paniculatum "MIQU" = Mimosa quadrivalvis "RHRE" = Rhynchosia reniformis "CEVI" = Centrosema virginianum "LEVI" = Lespedeza virginica "CHFA" = Chamechrista fasciculata "LEHI" = Lespedeza hirta "STBI" = Stylosanthes biflora "GAMO" = Galactia mollis "DEST" = Desmodium strictum "TEFL" = Tephrosia florida "DELA" = Desmodium laevigatum "DECI" = Desmodium cillare "DEFL" = Desmodium floridanum **legFolCNP.csv** A CSV including the percent carbon (percC), percent nitrogen (percN), and percent phosphorus (percP) in foliage collected from herbaceous legumes following fertilizer additions (treatment; C = control; N = +nitrogen; P = +phosphorus). Some samples did not contain enough dry matter for analysis of foliar phosphorus, and these missing data are indicated by NA values. **mycorrhizal_colonization.csv** A CSV including the colonization of legume fine roots by arbuscular mycorrhizae (percent_arbuscule_colonization; %) expressed as the percentage of root length containing arbuscules using the random intercept method (McGonigle et al. 1990).