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Estimating Biomass in Coastal Baccharis pilularis Dominated Plant Communities
Author(s) -
Will Russell,
Ryan E. Tompkins
Publication year - 2005
Publication title -
fire ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.809
H-Index - 29
ISSN - 1933-9747
DOI - 10.4996/fireecology.0101020
Subject(s) - basal area , biomass (ecology) , canopy , environmental science , crown (dentistry) , leaf area index , vegetation (pathology) , range (aeronautics) , linear regression , hydrology (agriculture) , forestry , ecology , geography , mathematics , biology , statistics , geology , medicine , materials science , geotechnical engineering , dentistry , pathology , composite material
Communities dominated by Baccharis pilularis (coyote brush) are expanding in coastal California, altering fuel load on a landscape scale, yet there is no standard method for estimating biomass in this vegetation type. In an attempt to develop a non -destructive field method for estimating biomass in Baccharis- dominated communities, we compared three indirect measures including crown canopy height, basal stem diameter, and leaf area index (LAI); estimated using hemispherical photography. Data were collected on 90 one square meter randomly selected plots in Point Reyes National Seashore and Golden Gate National Recreation Area. Linear regression analysis was used to determine the effectiveness of each predictor. The best single predictor of biomass was crown canopy height with an adjusted R2 of 0.46 for both sites combined. The linear regression equation developed for biomass versus height predicted 3930 grams per meter in height ± 430 grams for each square meter on these sites found. Basal stem diameter was determined to be a weak predictor in this vegetation type (R2 = 0.17). Estimated LAI had no predictive value. Multiple linear regressions with biomass versus height and basal area was also tested resulting in a slightly stronger model with an adjusted R2 of 0.48. Because of its predictive value and ease of implementation, we suggest the use of crown canopy height as the most efficient biomass predictor in this vegetation type.

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