Skip to main content

Inherent Correlations Between Stand Biomass Variables Calculated from Tree Measurements

Buy Article:

$21.50 plus tax (Refund Policy)

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):279–284.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

Keywords: Monte Carlo simulation; environmental management; forest; forest management; forest resources; forestry; forestry research; forestry science; natural resource management; natural resources; prediction equations; production ecology; simple correlation; spurious correlation

Document Type: Miscellaneous

Affiliations: 1: School of Renewable Natural Resources, Louisiana Agricultural Experiment Station, Louisiana State University Agricultural Center, Baton Rouge, LA, 70803, Phone: (225) 578-4216; Fax: (225) 578-4227 [email protected] 2: School of Renewable Natural Resources, Louisiana Agricultural Experiment Station, Louisiana State University Agricultural Center, Baton Rouge, LA, 70803,

Publication date: 2003-04-01

More about this publication?
  • Important Notice: SAF's journals are now published through partnership with the Oxford University Press. Access to archived material will be available here on the Ingenta website until March 31, 2018. For new material, please access the journals via OUP's website. Note that access via Ingenta will be permanently discontinued after March 31, 2018. Members requiring support to access SAF's journals via OUP's site should contact SAF's membership department for assistance.

    Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
    Forest Science is published bimonthly in February, April, June, August, October, and December.

    2016 Impact Factor: 1.782 (Rank 17/64 in forestry)

    Average time from submission to first decision: 62.5 days*
    June 1, 2016 to Feb. 28, 2017

    Also published by SAF:
    Journal of Forestry
    Other SAF Publications
  • Submit a Paper
  • Membership Information
  • Author Guidelines
  • Podcasts
  • Ingenta Connect is not responsible for the content or availability of external websites
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more