The Interrelationship Between The Spatial Distribution Of Microorganisms And Vegetation In Forest Soils

  • Sherry J. Morris
  • William J. Dress

Recent advances in techniques for investigating soil organisms and evaluating spatial structure have improved our understanding of the spatial dynamics of the soil microbial community. Identifying the scale at which microbial community function and interact in forest soils is essential to designing sampling schemes that will allow us to adequately evaluate the complex relationships between the microbial community and vegetation. Geostatistical tools useful for evaluating these relationships include tools that allow researchers to identify the extent to which the data are spatially structured and allow for the creation of maps for linking organisms and ecosystem characteristics that might exist at different scales. Research on the microbial community in forest soils using these and other scaling techniques has demonstrated that microbial communities both are patterned by and influence the spatial dynamics of the vegetation in their environment at scales that range from centimeter to stand size. Microbes are key to nutrient cycling and microbial community dynamics respond to the vegetation in their immediate vicinity in ways that reflect both the specific identity of the microbe and plant and the spatially patterning of the processes. The mechanisms that underlie these tight relationships of pattern and function reflect the dependence of autotrophs on decomposers and mutualists for nutrient acquisition and the long evolutionary history of these organisms. Improved understanding of the complex spatial relationships between the microbial community and vegetation will improve our ability to provide management guidelines that will allow managers to protect our forest resources. Keywords: forest soils, bacteria, fungi, microorganism, ecosystem function, community structure


Microbial Community Microbial Biomass Arbuscular Mycorrhizal Fungus Arbuscular Mycorrhizal Forest Soil 
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.


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Copyright information

© Springer 2007

Authors and Affiliations

  • Sherry J. Morris
    • 1
  • William J. Dress
    • 2
  1. 1.Biology DepartmentBradley UniversityPeoriaUSA
  2. 2.Science DepartmentRobert Morris UniversityMoon TownshipUSA

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