Climate Change, Drought, and Wetland Vegetation

  • Brant W. Touchette
  • Sara E. Steudler
Conference paper


Many climate change models forecast global increases in drought severity and duration along mid-latitudes. Areas that would typically receive ample precipitation allowing for the maintenance of complex vegetative assemblages may undergo substantial declines in species numbers as available surface and subsurface waters diminish. Few studies have considered climate-associated changes on aquatic and wetland systems. This is of particular concern, as wetlands are among the most threatened ecosystems globally. Therefore the purpose of this study was to understand how low-water availability influences the growth and productivity of an ecologically-important wetland plant, water willow (Justicia americana L.), in southeastern United States. In this study we used experimental microcosms maintained under controlled greenhouse conditions. Plants were allowed to acclimate in the microcosms for six weeks prior to initiating experimental treatments which included completely emerged (control), simulated 1-in-5 year precipitation low (for 2-, 4-, and 6-wks), and 1-in-25 year drought (for 2-, 4-, 6-wks). The results indicate that J. americana is relatively resistant to short periods of water deficit, even as relative water content (θ) in tissues fell below 40 percent of full capacity. Extended periods of severe drought (greater than 2 wks for 1-in-25 yr precipitation), however, resulted in substantial reductions in plant growth, productivity, and survival. Thus, while J. americana can tolerate short durations of water sacristy, long-term water deprivation as predicted in some climate change models could adversely affect the overall productivity and survival of this ecologically-important wetland plant species.


Relative Water Content Climate Change Model Belowground Tissue Simulated Drought Vegetative Assemblage 
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.



We are grateful for the research assistance provided by A. Jennings, N. Painter, N. Wright, and K. Chuchra-Zbytniuk. This study was partially sponsored through a grant-in-aid from Elon University.


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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Brant W. Touchette
    • 1
  • Sara E. Steudler
    • 1
  1. 1.Elon UniversityElonUSA

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