, Volume 109, Issue 1, pp 63–83

Soil microbial responses to fire and interacting global change factors in a California annual grassland

  • Kathryn M. Docherty
  • Teri C. Balser
  • Brendan J. M. Bohannan
  • Jessica L. M. Gutknecht

DOI: 10.1007/s10533-011-9654-3

Cite this article as:
Docherty, K.M., Balser, T.C., Bohannan, B.J.M. et al. Biogeochemistry (2012) 109: 63. doi:10.1007/s10533-011-9654-3


Wildfire in California annual grasslands is an important ecological disturbance and ecosystem control. Regional and global climate changes that affect aboveground biomass will alter fire-related nutrient loading and promote increased frequency and severity of fire in these systems. This can have long-term impacts on soil microbial dynamics and nutrient cycling, particularly in N-limited systems such as annual grasslands. We examined the effects of a low-severity fire on microbial biomass and specific microbial lipid biomarkers over 3 years following a fire at the Jasper Ridge Global Change Experiment. We also examined the impact of fire on the abundance of ammonia-oxidizing bacteria (AOB), specifically Nitrosospira Cluster 3a ammonia-oxidizers, and nitrification rates 9 months post-fire. Finally, we examined the interactive effects of fire and three other global change factors (N-deposition, precipitation and CO2) on plant biomass and soil microbial communities for three growing seasons after fire. Our results indicate that a low-severity fire is associated with earlier season primary productivity and higher soil-NH4+ concentrations in the first growing season following fire. Belowground productivity and total microbial biomass were not influenced by fire. Diagnostic microbial lipid biomarkers, including those for Gram-positive bacteria and Gram-negative bacteria, were reduced by fire 9- and 21-months post-fire, respectively. All effects of fire were indiscernible by 33-months post-fire, suggesting that above and belowground responses to fire do not persist in the long-term and that these grassland communities are resilient to fire disturbance. While N-deposition increased soil NH4+, and thus available NH3, AOB abundance, nitrification rates and Cluster 3a AOB, similar increases in NH3 in the fire plots did not affect AOB or nitrification. We hypothesize that this difference in response to N-addition involves a mediation of P-limitation as a result of fire, possibly enhanced by increased plant competition and arbuscular mycorrhizal fungi–plant associations after fire.


Fire Grassland Soil microbiology PLFA Global change 

Supplementary material

10533_2011_9654_MOESM1_ESM.pdf (24 kb)
Supplementary Fig. 1The interactive effect of fire and nitrogen deposition on the relative abundance (mol%) of a Gram-positive bacterial (15:0 iso) and b Gram-negative bacterial (16:1 ω7c) lipid indicators. Least squared means from ANOVA tests are presented for burned (white bars) and unburned (black bars) plots, and under ambient (left) and elevated (right) N-deposition treatments. Error bars represent one standard error associated with the statistical least squared means model. The interaction between fire and N-deposition was significant in April 2004 for Gram positive bacteria (F1,42.1 = 7.32, p = 0.010) and in April 2005 for Gram negative bacteria (F1,41.3 = 4.23, p = 0.05). (PDF 25 kb)
10533_2011_9654_MOESM2_ESM.pdf (32 kb)
Supplementary Table 1Summary of F-values, degrees of freedom, and p values from split-plot ANOVAs to test the treatment effects of fire and global changes on the following: aboveground biomass = log g m−2; belowground biomass = log g m−2; litter = log g m−2; pH = pH in water; percent soil moisture (%), and NH3 (2004 only) = calculated ammonia from extractable N-NH4+ (log M). 2003 values were based on all 128 plots of the JRGCE, while 2004 and 2005 data were based on the burn subset of data (see “Methods”). (PDF 33 kb)
10533_2011_9654_MOESM3_ESM.pdf (48 kb)
Supplementary Table 2Summary of F-values, degrees of freedom, and p values from split plot ANOVAs to test the treatment effects of fire and global changes on microbial indicators. 2003 values were based on all 128 plots of the JRGCE, while 2004 and 2005 data were based on the burn subset of data (see “Methods”). Microbial indicators: total microbial biomass (nmol lipid g soil−1); fungal:bacterial lipids = ratio of fungal to bacterial lipids; general fungi = 18:2 ω6,9c mol%; Gram-positive bacteria = 15:0 iso mol%; Gram-negative bacteria = 16:1 ω7c mol%; arbuscular mycorrhizal fungi = 16:1 ω5c mol%; AOB abundance (2004 only) = abundance of ammonia-oxidizing bacteria (copies of Bacterial amoA g dry soil−1); AOB Cluster 3a (2004 only) = community structure of ammonia-oxidizing bacteria (proportion of T-RF 434: total peak height); nitrification potential (2004 only) (ng N h−1 g dry soil−1). The ‘Multivariate’ column contains p values from Permanova nonparametric testing to determine treatment effects multivariately. Permanova analysis was performed on lipid relative abundance (mol%) data. The statistical model was based on the split-plot full factorial design of the JRGCE, run with 1000 permutations. (PDF 48 kb)

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Kathryn M. Docherty
    • 1
    • 2
  • Teri C. Balser
    • 3
    • 4
  • Brendan J. M. Bohannan
    • 2
  • Jessica L. M. Gutknecht
    • 3
    • 5
  1. 1.Department of Biological SciencesWestern Michigan UniversityKalamazooUSA
  2. 2.Center for Ecology and Evolutionary Biology (CEEB)University of OregonEugeneUSA
  3. 3.Department of Soil ScienceUniversity of Wisconsin-MadisonMadisonUSA
  4. 4.Department of Soil and Water ScienceUniversity of FloridaGainesvilleUSA
  5. 5.Department of Soil EcologyHelmoltz-Centre for Environmental Research—UFZHalleGermany

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