Local Analysis of Tree Competition and Growth

By Haijing Shi, Lianjun Zhang. Forestry sciences, 2003

60 citations recorded by the source corpus.

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.

Subfields: Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/9e74e56f2a9bf11932092c31dd16d67447d7342c

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In Nature and Landscape Conservation

Citations in the corpus

Spatial residual analysis of six modeling techniques (2005, methodology): “LISA is designed to reveal such peculiarities (Boots, 2002; Dale and Fortin, 2002), and has been used to successfully identify clusters in biological datasets, and to identify "hot spots" (positive autocorrelation, or similarity) and "cold spots" (negative autocorrelation, or dissimilarity) of Wee g”
Spatial autocorrelation analysis in plant population: An overview (2015, methodology): “Shi and Zhang (2003) have utilized various LISA approaches to explore the relationship between LISA and traditional tree competition indices, to explore the possibility that LISA can be used as a predictor variable for predicting tree growth, and to demonstrate the usefulness of LISA for…”
Spatial Assessment of Model Errors from Four Regression Techniques (2005, methodology): “LISA has proven to be a useful tool to identify “hot spots” (positive autocorrelation, or similarity) and “cold spots” (negative autocorrelation, or dissimilarity) of values (Boots 2002, Shi and Zhang 2003).”
Scale-dependent competition at the stand level assessed from crown areas (2008, methodology): “Continuous marks such as diameter at breast height or tree height have been used successfully to quantify the range of spatial scales of competition for genuine point locations in forests, and involving methods such as the mark-correlation function, weighted Ripley’s K-function, or spatial autocorre”
A comparison of methods for the statistical analysis of spatial point patterns in plant ecology (2006, background): “For example, Shi and Zhang (2003) and Zhang and Shi (2004), explored links between spatial pattern and competition using localised forms of spatial autocorrelation measures (LISA) and GWR.”