@article {Dean:2003:0015-749X:279,
author = "Dean, T.J. and Cao, Q.V.",
title = "Inherent Correlations Between Stand Biomass Variables Calculated from Tree Measurements",
journal = "Forest Science",
volume = "49",
number = "2",
year = "2003",
publication date ="2003-04-01T00:00:00",
abstract = "
Correlating stand-level variables is an important component of forest production ecology; however, correlations among variables calculated with equations having common independent variables are potentially spurious. Monte Carlo simulation techniques were used to determine the inherent or null correlation coefficients among stand-level biomass variables calculated with published, individual-tree equations using loblolly pine (Pinus taeda L.) data. Null correlations of foliage mass/ha with branch mass/ha, stem mass/ha, and periodic annual increments of biomass were high with similar equation forms and exponents in the equations. Most, but not all, correlation coefficients of foliage mass/ha with other biomass components and periodic annual increments of biomass were significantly different from the corresponding, null correlation coefficients. Stating the probability of a greater difference between the observed and the null correlation coefficients proved crucial in distinguishing between potentially meaningful and spurious correlations because in many cases, the observed correlation coefficients were close to the null values. Interpretation of the correlations among stand variables varied with the equations used to predict the variables. Consequently, in addition to comparing correlation coefficients to appropriate null values, conclusions drawn from the correlation among stand-level variables depend on the accuracy and precision of the equations used to calculate them. FOR. SCI. 49(2):279284.",
pages = "279-284",
itemtype = "ARTICLE",
parent_itemid = "infobike://saf/fs",
issn = "0015-749X",
publishercode ="saf",
url = "http://www.ingentaconnect.com/content/saf/fs/2003/00000049/00000002/art00011",
keyword = "environmental management, forestry, spurious correlation, Monte Carlo simulation, prediction equations, forestry science, forest management, forestry research, production ecology, natural resources, simple correlation, forest resources, natural resource management, forest"
}