Hostname: page-component-586b7cd67f-rdxmf Total loading time: 0 Render date: 2024-11-25T04:06:06.289Z Has data issue: false hasContentIssue false
  • English
  • Français

A model for predicting invasive weed and grass dynamics. II. Accuracy evaluation

Published online by Cambridge University Press:  20 January 2017

Matthew J. Rinella  and
Roger L. Sheley
Roger L. Sheley
Affiliation:
U.S. Department of Agriculture–Agricultural Research Service, 67826-A Highway 205, Burns, OR 97720
Get access Rights & Permissions [Opens in a new window]

Abstract

The impact of invasive weed management on plant community composition is highly dependent on location-specific factors. Therefore, treatment means from experiments conducted at a given set of locations will not reliably predict community response to weed management elsewhere. We developed a model that rescales treatment means to better match local conditions. The goal of this paper was to determine if this rescaling improves predictions. We used our model to predict leafy spurge stem length density and grass biomass data from field experiments. The experiments consisted of herbicide-treated plots, untreated controls, and, in some cases, grass seeding treatments. When herbicides suppressed leafy spurge, the model explained 21 to 48% more variation in grass response than did mean grass response to the same or similar herbicide treatments applied at other sites. When herbicides killed grass, the model explained 53% more variation in leafy spurge response than did mean leafy spurge response to the same herbicide treatment applied at other sites. We regressed model predictions against observed data and tested the null hypothesis that resulting slope terms were equal to 1.0. Because the null hypothesis was rejected in two of four tests, the model may systematically over- or underpredict in some situations. However, measurement error in the observed data, unintended herbicide injury, or an inaccurate allometric relationship may account for a major proportion of the systematic deviations, and these factors would not cause prediction error in some management applications. Because the model tends to be better than the means from experiments at predicting plant community composition, we conclude that the model could advance managers' ability to predict plant community responses to invasive weed management.

Keywords

Leafy spurge, Euphorbia esula L. EPHESCompetition modelmeta-analysispopulation dynamicsvalidation
Type
Weed Biology and Ecology
Information
Weed Science , Volume 53 , Issue 5 , October 2005 , pp. 605 - 614
Copyright
Copyright © Weed Science Society of America 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Literature Cited

Aguiar, M. R., Lauenroth, W. K., and Peters, D. P. 2001. Intensity of intra- and interspecific competition in coexisting shortgrass species. J. Ecol 89:40–47.Google Scholar
Chow, P. N. P. 1984. Control of leafy spurge in pastures using dicamba and 2,4-D. J. Range Manage 37:159–162.Google Scholar
Cousens, R. 1985. A simple model relating yield loss to weed density. Ann. Appl. Biol 107:239–252.CrossRefGoogle Scholar
Cousens, R. 1995. Can we determine the intrinsic dynamics of real plant populations? Funct. Ecol 9:15–20.Google Scholar
Cousens, R. and Mortimer, A. M. 1995. Dynamics of Weed Populations. Cambridge, Great: Britain: Cambridge University Press.Google Scholar
Efron, B. and Tibshirani, R. 1993. An Introduction to the Bootstrap. New York: Chapman & Hall.Google Scholar
Eriksson, O. 1989. Seedling dynamics and life histories in clonal plants. Oikos 55:231–238.Google Scholar
Ferrell, M. A., Whitson, T. D., and Alley, H. P. 1989. Control of leafy spurge (Euphorbia esula) with growth regulator–herbicide combinations. Weed Technol 3:479–484.Google Scholar
Firbank, L. G. and Watkinson, A. R. 1986. Modelling the population dynamics of an arable weed and its effects upon crop yield. J. Appl. Ecol 23:147–159.Google Scholar
Freckleton, R. P. and Watkinson, A. R. 1998. Predicting the determinants of weed abundance: a model for the population dynamics of Chenopodium album in sugar beet. J. Appl. Ecol 35:904–920.CrossRefGoogle Scholar
Gaudet, C. L. and Keddy, P. A. 1988. Predicting competitive ability from plant traits: a comparative approach. Nature 334:242–243.Google Scholar
Goldberg, D. E. 1987. Neighborhood competition in an old-field plant community. Ecology 68:1211–1223.Google Scholar
Grime, J. P. 2001. Plant Strategies, Vegetation Processes, and Ecosystem Properties. 2nd ed. West Sussex, Great Britain: J. Wiley.Google Scholar
Gylling, S. R. and Arnold, W. E. 1985. Efficacy and economics of leafy spurge (Euphorbia esula) control in pasture. Weed Sci 33:381–385.Google Scholar
Hein, D. G. 1988. Single and Repetitive Picloram Treatments on Leafy Spurge and Resulting Changes in Shoot Density, Canopy Cover, Forage Production and Utilization by Cattle. Ph.D. dissertation. University of Wyoming, Laramie, WY.Google Scholar
Hjorth, J. S. U. 1994. Computer Intensive Statistical Methods. London: Chapman & Hall.Google Scholar
Keddy, P. 2001. Competition. Boston, MA: Kluwer.Google Scholar
Kropff, M. J. 1988. Modelling the effects of weeds on crop production. Weed Res 28:465–471.Google Scholar
Lym, R. G. 2000. Leafy spurge (Euphorbia esula) control with glyphosate plus 2,4-D. J. Range Manage 53:68–72.CrossRefGoogle Scholar
Lym, R. G. and Messersmith, C. G. 1994. Leafy spurge (Euphorbia esula) control, forage production, and economic return with fall-applied herbicides. Weed Technol 8:824–829.CrossRefGoogle Scholar
Lym, R. G. and Messersmith, C. G. 1985. Leafy spurge control and improved forage production with herbicides. J. Range Manage 5:386–391.Google Scholar
Lym, R. G. and Tober, D. A. 1997. Competitive grasses for leafy spurge (Euphorbia esula) reduction. Weed Technol 11:782–792.Google Scholar
Markle, D. M. and Lym, R. G. 2001. Leafy spurge (Euphorbia esula) control and herbage production with Imazapic. Weed Technol 15:474–480.Google Scholar
Maxwell, B. D. 1984. Changes in an Infested Plant Community after an Application of Picloram, the Effect of Glyphosate on Bud Dormancy, the Effect of Pulling and the Fuel Potential of Leafy Spurge (Euphorbia esula L). . Montana State University, Bozeman, MT.Google Scholar
Mitchell, R. J., Zutter, B. R., Gjerstad, D. H., Glover, G. R., and Wood, C. W. 1999. Competition among secondary-successional pine communities. A field study of effects and responses. Ecology 80:857–872.Google Scholar
Newman, E. I. 1973. Competition and diversity in herbaceous vegetation. Nature 244:310–311.Google Scholar
Pacala, S. W. 1997. Dynamics of plant communities. Pages 532–555 in Crawley, M. J. ed. Plant Ecology. Cambridge, Great Britain: Cambridge University Press.Google Scholar
Parker, I. M. 2000. Invasion dynamics of Cytisus scoparius: a matrix model approach. Ecol. Appl 10:726–743.CrossRefGoogle Scholar
Peltzer, D. A. and Kochy, M. 2001. Competitive effects of grasses and woody plants in mixed-grass prairie. J. Ecol 89:519–527.Google Scholar
Ricker, W. 1954. Stock and recruitment. J. Fish. Res. Board Can 11:559–623.CrossRefGoogle Scholar
Rinella, M. J. and Sheley, R. L. 2005a. A model for predicting invasive weed and grass dynamics: I. Model development. Weed Sci 53:586–593.CrossRefGoogle Scholar
Rinella, M. J. and Sheley, R. L. 2005b. Influence of soil water availability on competition among leafy spurge (Euphorbia esula) and grasses. West. North Am. Nat 65:233–241.Google Scholar
Shea, K. and Kelly, D. 2004. Modeling for management of invasive species: musk thistle (Carduus nutans) in New Zealand. Weed Technol 18:1338–1341.Google Scholar
Sheley, R. L. and Petroff, J. K. 1999. Biology and Management of Noxious Rangeland Weeds. Corvallis, OR: Oregon State University Press.Google Scholar
Vore, R. E. 1984. Effect of Herbicide Treatments on Leafy Spurge Shoot and Root Control, Forage Production and Herbicide Residue. Ph.D. dissertation. University of Wyoming, Laramie, WY.Google Scholar
Watkinson, A. R. 1981. Interference in pure and mixed populations of Agrostemma githago L. J. Appl. Ecol 18:967–976.Google Scholar
Werner, P. A. and Caswell, H. 1977. Population growth rates and age versus stage-distribution models for teasel (Dipsacus sylvestris Huds). Ecology 58:1103–1111.Google Scholar
Wilson, S. D. and Tilman, D. 1991. Components of plant competition along an experimental gradient of nitrogen availability. Ecology 72:1050–1065.Google Scholar