Estimating Ecosystem Nitrogen Addition by a Leguminous Tree: A Mass Balance Approach Using a Woody Encroachment Chronosequence
- 260 Downloads
Difficulty in quantifying rates of biological N fixation (BNF), especially over long time scales, remains a major impediment to defining N budgets in many ecosystems. To estimate N additions from BNF, we applied a tree-scale N mass balance approach to a well-characterized chronosequence of woody legume (Prosopis glandulosa) encroachment into subtropical grasslands. We defined spatially discrete single Prosopis clusters (aged 28–99 years), and for each calculated BNF as the residual of: soil N (0–30 cm), above- and below-ground biomass N, wet and dry atmospheric N deposition, N trace gas and N2 loss, leaching loss, and baseline grassland soil N at time of establishment. Contemporary BNF for upland savanna woodland was estimated at 10.9 ± 1.8 kg N ha−1 y−1, equal to a total of 249 ± 60 kg N ha−1 over about 130 years of encroachment at the site. Though these BNF values are lower than previous estimates for P. glandulosa, this likely reflects lower plant density as well as low water availability at this site. Uncertainty in soil and biomass parameters affected BNF estimates by 6–11%, with additional sensitivity of up to 18% to uncertainty in other scaling parameters. Differential N deposition (higher rates of dry N deposition to Prosopis canopies versus open grasslands) did not explain N accrual beneath trees; iterations that represented this scenario reduced estimated BNF estimates by a maximum of 1.5 kg N ha−1 y−1. We conclude that in this relatively well-constrained system, small-scale mass balance provides a reasonable method of estimating BNF and could provide an opportunity to cross-calibrate alternative estimation approaches.
KeywordsBNF δ15N deposition Prosopis soil
We thank Shauntle Barley and Chase Brett for assistance with sample collection, Kimberlee Sparks for technical support, and David and Stacy McKown for field logistics. This work was supported by the Cornell University Betty Miller Francis’47 Fund for Field Research and the Cornell University Program in Cross-Scale Biogeochemistry and Climate (supported by NSF-IGERT and the Atkinson Center for a Sustainable Future).
- Archer S, Boutton T, Hibbard K. 2001. Trees in grasslands: biogeochemical consequences of woody plant expansion. In: Schulze E-D, Harrison M, Heimann M, Holland E, Lloyd J, Prentice IC, Schimel D, Eds. Global Biogeochemical Cycles in the Climate System. San Diego: Academic Press. p 115–38.CrossRefGoogle Scholar
- Asner G, Martin R. 2004. Biogeochemistry of desertification and woody encroachment in grazing systems. Geoph Monog Series 153:99–116.Google Scholar
- Kantola IB. 2012 Biogeochemistry of woody plant invasion: phosphorus cycling and microbial community composition. PhD thesis. Texas A&M University, College Station, TX.Google Scholar
- Katz RW. 2002. Techniques for estimating uncertainty in climate change scenarios and impact studies. Cim Res 20:167–85.Google Scholar
- Li H, Wu J. 2006. Uncertainty analysis in ecological studies. In: Wu J, Jones KB, Li H, Loucks OL, Eds. Scaling and uncertainty analysis in ecology: Methods and applications. Netherlands: Springer. p 45–52.Google Scholar
- Likens GE. 2013. Nutrient Cycles and Mass Balances. In: Biogeochemistry of a Forested Ecosystem. New York: Springer. p 139–61.Google Scholar
- R Core Team. 2014. R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing. Vienna, Austria.Google Scholar
- Reichmann L, Sala O, Peters D. 2013. Water controls on nitrogen transformations and stocks in an arid ecosystem. Ecosphere 4(11):1–17.Google Scholar
- Stoker R. 1997. An Object oriented, spatially explicit simulation model of vegetation dynamics in a south Texas savanna. PhD Dissertation:1–263.Google Scholar
- Watts S. 1993. Rooting patterns of co-occurring woody plants on contrasting soils in a subtropical savanna. PhD thesis, Texas A&M University.Google Scholar
- Zahran HH. 1999. Rhizobium-legume symbiosis and nitrogen fixation under severe conditions and in an arid climate. Microbiol Mol Biol Rev. 63(968):989.Google Scholar