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Comparison of root distributions of species in North American grasslands using GIS
Author(s) -
Sun Guowei,
Coffin Debra P.,
Lauenroth William K.
Publication year - 1997
Publication title -
journal of vegetation science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.1
H-Index - 115
eISSN - 1654-1103
pISSN - 1100-9233
DOI - 10.2307/3237211
Subject(s) - forb , root (linguistics) , spatial distribution , abundance (ecology) , grassland , biology , environmental science , botany , ecology , geography , remote sensing , philosophy , linguistics
. We quantified the spatial distribution of roots of individual plants using detailed drawings from the literature of species of grasses, forbs, and shrubs in the Central Great Plains grasslands of North America. We scanned each two‐dimensional drawing electronically and used ARC/INFO, a Geographic Information System, to calculate root length (cm) and density (cm root length/cm soil) with depth in the soil profile. We then selected one of three mathematical models that best fit the data, and classified each species as either shallow‐, medium‐ or deep‐rooted. 66 root drawings from 55 species were evaluated. Most plants were shallow‐rooted with largest root densities occurring at depths < 20 cm; most maximum rooting depths were > 1m. Grasses had the shallowest maximum depth and shrubs the deepest. Deep‐rooted forbs had the smallest root densities by depth. Most plants had two sections to their distribution of root density: an initial increase from the soil surface followed by a decrease in density with increasing depth. Our results suggest that the abundance and importance of different species and growth forms in North American grasslands is related to similarities and differences in the spatial distributions of their root systems. Our approach provides quantitative information on root distributions to be used for comparisons among species, and in simulation modeling analyses that could not be done with conventional methods that are qualitative in nature.

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