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Predicting the likelihood of yellow starthistle (Centaurea solstitialis) occurrence using landscape characteristics

Published online by Cambridge University Press:  20 January 2017

Bahman Shafii ,
William J. Price ,
Timothy S. Prather ,
Lawrence W. Lass  and
Donald C. Thill
William J. Price
Affiliation:
Statistical Programs, College of Agricultural and Life Sciences, University of Idaho, Moscow, ID 83844
Timothy S. Prather
Affiliation:
Department of Plant, Soil, and Entomological Sciences, College of Agricultural and Life Sciences, University of Idaho, Moscow, ID 83844
Lawrence W. Lass
Affiliation:
Department of Plant, Soil, and Entomological Sciences, College of Agricultural and Life Sciences, University of Idaho, Moscow, ID 83844
Donald C. Thill
Affiliation:
Department of Plant, Soil, and Entomological Sciences, College of Agricultural and Life Sciences, University of Idaho, Moscow, ID 83844
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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.

Keywords

Yellow starthistle, Centaurea solstitialis L. CENSOEstimationlandscape variablesnonlinear regressionvalidation
Type
Weed Biology
Information
Weed Science , Volume 51 , Issue 5 , October 2003 , pp. 748 - 751
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Bork, E. W., Hudson, R. J., and Bailey, A. W. 1997. Upland plant community classification in Elk Island National Park, Alberta Canada using disturbance history and physical site factors. Plant Ecol 130:171–190.Google Scholar
Brown, D. G. 1994. Predicting vegetation types at treeline using topography and biophysical disturbance variables. J. Veg. Sci 5:641–656.CrossRefGoogle Scholar
Carmel, Y. and Kadmon, R. 1999. Effects of grazing and topography on long-term vegetation changes in a Mediterranean ecosystem in Israel. Plant Ecol 145:243–254.Google Scholar
Dewey, S. A., Price, K. P., and Ramsey, D. 1991. Satellite remote sensing to predict potential distribution of dyers woad. Weed Technol 5:479–484.Google Scholar
Jensen, M. E., Dibenedetto, J. P., Barber, J. A., Montagne, C., and Bourgeron, P. S. 2001. Spatial modeling of rangeland potential vegetation environments. J. Range. Manag 54:528–536.CrossRefGoogle Scholar
Lass, L. W., McCaffrey, J. P., Thill, D. C., and Callihan, R. H. 1999. Yellow Starthistle Biology and Management in Pasture and Rangeland. University of Idaho Bulletin No. 805. Moscow, Idaho: University of Idaho. 18 p.Google Scholar
Myster, R. W., Thomlinson, J. R., and Larsen, M. C. 1997. Predicting landslide vegetation in patches on landscape gradients in Puerto Rico. Landsc. Ecol 12:299–307.CrossRefGoogle Scholar
Perelman, S. B., Leon, R. J. C., and Oesterheld, M. 2001. Cross-scale vegetation patterns of flooding Pampa grasslands. J. Ecol 89:562–577.CrossRefGoogle Scholar
Prather, T. S. and Shafii, B. 1994. Predicting common crupina habitat with geographic and remote sensing data. Pages 122–135 in Proceedings of Kansas State University Conference on Applied Statistics in Agriculture. Manhattan, KS: Kansas State University.Google Scholar
[SAS] Statistical Analysis Systems. 1999a. SAS/STAT User's Guide. Version 6, 4th ed, Volume 2. Cary, NC: SAS Institute.Google Scholar
[SAS] Statistical Analysis Systems. 1999b. SAS/GRAPH User's Guide. Version 6, 4th ed, Volume 2. Cary, NC: SAS Institute.Google Scholar
Sheley, R. L., Larson, L. L., and Jacobs, J. S. 1999. Yellow starthistle. Pages 408–416 in Sheley, R. L. and Petroff, J. K. eds. Biology and Management of Noxious Rangeland Weeds. Corvallis, OR: Oregon State University Press.Google Scholar
Snee, R. D. 1977. Validation of regression models: methods and examples. Technometrics 19:415–428.Google Scholar
Stage, A. R. 1976. An expression for the effect of aspect, slope and habitat type on tree growth. For. Sci 22/4:457–460.Google Scholar
Vivian-Smith, G. 1997. Microtopographic heterogeneity and floristic diversity in experimental wetland communities. J. Ecol 85:71–82.Google Scholar