Skip to main content

Local Analysis of Tree Competition and Growth

Buy Article:

$29.50 plus tax (Refund Policy)


The relationships between the Local Indicator of Spatial Association (LISA) and traditional tree competition indices and individual tree growth were investigated. The results show that like most of the competition indices, LISA had moderate correlations with tree basal area growth. For predicting the tree basal area growth in a linear regression model, the local Gi performed better than many (73%) competition indices at a plot aggregation level and had higher explanatory power than most (91%) competition indices at an individual plot level. LISA also had linear and strong relationships with some traditional competition indices, such as the Lorimer index. The relationships were stronger (ρ > 0.90) at an individual plot level than for all plots combined (ρ > 0.75). More importantly, LISA could be statistically tested to identify local clusters of trees of similar or dissimilar sizes, even though there was no discernible pattern as summarized by a global statistic of spatial autocorrelation. These significant “hot spots” (clusters of trees of similar sizes) or “cold spots” (clusters of trees of dissimilar sizes) indicated subareas in a forest stand where the competition among trees may be more severe than the average. Therefore, LISA can replace the traditional competition indices for exploring the competitive status of neighboring trees, investigating the relationships between tree competition and growth, and estimating individual tree growth as a predictor variable in a forest growth simulator. The hot spots or cold spots identified by LISA provide useful information for the design of silvicultural and management treatments, such as selection thinning. Furthermore, LISA can be readily incorporated into visualization tools, such as a geographic information system (GIS), because it provides georeferenced information at a local level. FOR. SCI. 49(6):938–955.

Keywords: Spatial autocorrelation; environmental management; forest; forest growth-and-yield modeling; forest management; forest resources; forestry; forestry research; forestry science; geographic information system (GIS); natural resource management; natural resources; tree competition

Document Type: Miscellaneous

Affiliations: 1: Research Assistant Faculty of Forest and Natural Resources Management, College of Environmental Science and Forestry, State University of New York, One Forestry Drive, Syracuse, NY, 13210, Phone: 315-426-0290 2: Associate Professor of Forest Biometrics Faculty of Forest and Natural Resources Management, College of Environmental Science and Forestry, State University of New York, One Forestry Drive, Syracuse, NY, 13210, Phone: 315-470-6558, Fax: 315-470-65

Publication date: 2003-12-01

More about this publication?
  • 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.

    2015 Impact Factor: 1.702
    Ranking: 16 of 66 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