Open AccessSubsurface microbiological heterogeneity: current knowledge, descriptive approaches and applications
Author(s) -
Brockman Fred J,
Murray Christopher J
Publication year - 1997
Publication title -
fems microbiology reviews
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.91
H-Index - 212
eISSN - 1574-6976
pISSN - 0168-6445
DOI - 10.1111/j.1574-6976.1997.tb00311.x
Subject(s) - subsurface flow , environmental science , spatial variability , sampling (signal processing) , spatial ecology , spatial distribution , temporal scales , groundwater , spatial heterogeneity , soil science , groundwater recharge , spatial analysis , hydrology (agriculture) , ecology , geology , aquifer , remote sensing , computer science , statistics , biology , mathematics , computer vision , geotechnical engineering , filter (signal processing)
Improved understanding of the spatial and temporal distribution of microbiological properties and processes is critical due to the relative difficulty and high cost of obtaining large numbers of subsurface samples. Quantification of spatial patterns in subsurface environments is important because it is well known that geologic, hydrologic and geochemical properties are not constant in space; rather, they are spatially autocorrelated, or related over certain length scales. Preliminary research indicates that subsurface microbiological properties have similar length scales, and the microbiological properties appear to be spatially correlated to geologic, hydrologic and/or geochemical properties. Temporal variability can also be important in subsurface systems that receive seasonal recharge. In order to better understand heterogeneous subsurface systems, it is critical to sample such that the spatial and temporal patterns are adequately captured, and understand what is causing the variability and spatial patterns. Improved understanding in these two areas will yield more efficient sampling schemes, assist in defining factors that control the distribution of microbiological properties at the field scale, and increase the ability to predict and ultimately model the distribution of microbiological properties and the responses of microbial communities to environmental perturbations such as subsurface contaminant transport and bioremediation.