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1 September 2003 Predicting the likelihood of yellow starthistle (Centaurea solstitialis) occurrence using landscape characteristics
Bahman Shafii, William J. Price, Timothy S. Prather, Lawrence W. Lass, Donald C. Thill
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Abstract

Yellow starthistle is an invasive plant species common in the semiarid climate of central Idaho and other western states. Early detection of yellow starthistle and estimation of its infestation potential in semiarid grasslands have important scientific and managerial implications. Weed detection and delineation of infestations are often carried out by using ground survey techniques. However, such methods can be inefficient and expensive in detecting sparse infestations. The distribution of yellow starthistle over a large region may be affected by various landscape variables such as elevation, slope, and aspect. These exogenous variables may be used to develop prediction models to estimate the potential for yellow starthistle invasion into new areas. A nonlinear prediction model has been developed using a polar coordinate transformation of landscape characteristics to predict the likelihood of yellow starthistle occurrence in north-central Idaho. The study region included the lower Snake River and parts of the Salmon and Clearwater basins encompassing various land-use (range, pasture, and forest) categories. The model provided accurate estimates of yellow starthistle incidence within each specified land-use category and performed well in subsequent statistical validations. This prediction model can assist land managers in focusing their efforts by identifying specific areas for survey.

Nomenclature: Yellow starthistle, Centaurea solstitialis L. CENSO.

Bahman Shafii, William J. Price, Timothy S. Prather, Lawrence W. Lass, and Donald C. Thill "Predicting the likelihood of yellow starthistle (Centaurea solstitialis) occurrence using landscape characteristics," Weed Science 51(5), 748-751, (1 September 2003). https://doi.org/10.1614/P2002-161
Received: 1 November 2002; Accepted: 1 April 2003; Published: 1 September 2003
KEYWORDS
estimation
landscape variables
nonlinear regression
validation
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