Using forest inventory data along with spatial lag and spatial error regression to determine the impact of southern pine plantations on species diversity and richness in the central Gulf Coastal Plain

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  • Authors: Hartsell, Andrew J.
  • Publication Year: 2012
  • Publication Series: Paper (invited, offered, keynote)
  • Source: In: Morin, Randall S.; Liknes, Greg C., comps. Moving from status to trends: Forest Inventory and Analysis (FIA) symposium 2012; 2012 December 4-6; Baltimore, MD. Gen. Tech. Rep. NRS-P-105. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. [CD-ROM]: 150-156.

Abstract

This study investigates the impacts of southern yellow pine plantations on species evenness and richness in the gulf coastal plain. This process involves using spatial lag and spatial error regression techniques using GeoDa software and U.S. Forest Service's Forest Inventory and Analysis data. The results indicate that increasing plantation area is negatively correlated to species evenness and richness. Preliminary results indicate that for every 10 percent increment increase in southern yellow pine plantation area, Shannon's E decreases by 0.02 and species richness declines by 1.6 species. However, these models account for less than 50 percent of the data's variance, an indication that the models are incomplete and more research is needed.

  • Citation: Hartsell, Andrew J. 2012. Using forest inventory data along with spatial lag and spatial error regression to determine the impact of southern pine plantations on species diversity and richness in the central Gulf Coastal Plain. In: Morin, Randall S.; Liknes, Greg C., comps. Moving from status to trends: Forest Inventory and Analysis (FIA) symposium 2012; 2012 December 4-6; Baltimore, MD. Gen. Tech. Rep. NRS-P-105. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. [CD-ROM]: 150-156.
  • Keywords: statistics, estimation, sampling, modeling, remote sensing, forest health, data integrity, environmental monitoring, cover estimation, international forest monitoring
  • Posted Date: February 5, 2013
  • Modified Date: February 5, 2013
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