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Microbial Ecology

, Volume 65, Issue 3, pp 671–678 | Cite as

Fungal Symbionts Alter Plant Drought Response

  • Elise R. Worchel
  • Hannah E. Giauque
  • Stephanie N. Kivlin
Plant Microbe Interactions

Abstract

Grassland productivity is often primarily limited by water availability, and therefore, grasslands may be especially sensitive to climate change. Fungal symbionts can mediate plant drought response by enhancing drought tolerance and avoidance, but these effects have not been quantified across grass species. We performed a factorial meta-analysis of previously published studies to determine how arbuscular mycorrhizal (AM) fungi and endophytic fungal symbionts affect growth of grasses under drought. We then examined how the effect of fungal symbionts on plant growth was influenced by biotic (plant photosynthetic pathway) and abiotic (level of drought) factors. We also measured the phylogenetic signal of fungal symbionts on grass growth under control and drought conditions. Under drought conditions, grasses colonized by AM fungi grew larger than those without mycorrhizal symbionts. The increased growth of grasses conferred from fungal symbionts was greatest at the lowest soil moisture levels. Furthermore, under both drought and control conditions, C3 grasses colonized by AM fungi grew larger than C3 grasses without symbionts, but the biomass of C4 grasses was not affected by AM fungi. Endophytes did not increase plant biomass overall under any treatment. However, there was a phylogenetically conserved increase in plant biomass in grasses colonized by endophytes. Grasses and their fungal symbionts seem to interact within a context-dependent symbiosis, varying with biotic and abiotic conditions. Because plant–fungal symbioses significantly alter plant drought response, including these responses could improve our ability to predict grassland functioning under global change.

Keywords

Arbuscular Mycorrhizal Fungus Arbuscular Mycorrhizal Plant Biomass Drought Condition Phylogenetic Signal 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This manuscript was greatly improved by comments from C.V. Hawkes, N.L. Fowler, J.A. Rudgers, and two anonymous reviewers.

Supplementary material

248_2012_151_MOESM1_ESM.docx (124 kb)
ESM 1 (DOCX 123 kb)

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Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  • Elise R. Worchel
    • 1
  • Hannah E. Giauque
    • 1
  • Stephanie N. Kivlin
    • 1
  1. 1.Section of Integrative BiologyUniversity of Texas at AustinAustinUSA

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