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Measuring community shifts in a weed seedbank study with the use of distance-based redundancy analysis

Published online by Cambridge University Press:  20 January 2017

Chris Reberg-Horton ,
Eric R. Gallandt  and
Tom Molloy
Eric R. Gallandt
Affiliation:
Department of Plant, Soil, and Environmental Sciences, University of Maine, Orono, ME 04469-5722
Tom Molloy
Affiliation:
Department of Plant, Soil, and Environmental Sciences, University of Maine, Orono, ME 04469-5722
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Abstract

Distance-based redundancy analysis (db-RDA), a recently developed ordination technique useful for both multivariate hypothesis testing and data interpretation, was used to evaluate treatment effects on weed communities in a long-term study of alternative potato cropping systems. The experiment consisted of a factorial arrangement of three pest management systems, conventional (CON), reduced input (RI), and biointensive (BIO), two soil management systems (amended vs. unamended), and two crop-rotation entry points. Soil samples collected in the spring of 1998 were subjected to exhaustive germination as a means of characterizing the weed community. Using partial ordinations, each factor in the factorial treatment structure was tested separately, revealing a significant interaction between pest and soil management systems. An ordination diagram of the pest by soil management interaction was used to interpret the results. Weed species that were highly correlated with the first two ordination axes included: common lambsquarters, broadleaf plantain, oakleaf goosefoot, common hempnettle and a complex of the Brassicaceae that included wild mustard, birdsrape mustard, and wild radish. Univariate analyses confirmed the response of these species to the factors examined. The BIO pest management system showed a different response to soil amendments than the other systems. Soil amendments caused an increase in the total weed density in the CON and RI systems, but caused a decrease in the BIO system. Given the need for better multivariate hypothesis testing and data interpretation in many types of weed science research, the use of db-RDA is expected to grow.

Keywords

Birdsrape mustard, Brassica rapa L. BRARAbroadleaf plantain, Plantago major L. PLAMAcommon hempnettle, Galeopsis tetrahit L. GAETEcommon lambsquarters, Chenopodium album L. CHEALoakleaf goosefoot, Chenopodium glaucum L. CHEGLwild mustard, Brassica kaber (DC.) L. C. Wheeler BRAKAwild radish, Raphanus raphanistrum L. RAPRApotato, Solanum tuberosum L.Multivariate analysisordinationweed community
Type
Research Article
Information
Weed Science , Volume 54 , Issue 5 , October 2006 , pp. 861 - 866
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Albrecht, H. and Pilgram, M. 1997. The weed seed bank of soils in a landscape segment in southern Bavaria. Plant Ecol. 131:31–43.Google Scholar
Anderson, M. J. and ter Braak, C. J. F. 2003. Permutation tests for multi-factorial analysis of variance. J. Stat. Comput. Simul. 73:85–113.CrossRefGoogle Scholar
Angonin, C., Caussanel, J. P., and Meynard, J. M. 1996. Competition between winter wheat and Veronica hederifolia: Influence of weed density and the amount and timing of nitrogen application. Weed Res. 36:175–187.Google Scholar
Barberi, P. and Cascio, B. L. 2001. Long-term tillage and crop rotation effects on weed seedbank size and composition. Weed Res. 41:325–340.Google Scholar
Cardina, J., Herms, C. P., and Doohan, D. J. 2002. Crop rotation and tillage system effects on weed seedbanks. Weed Sci. 50:448–460.Google Scholar
Cardina, J., Regnier, E., and Harrison, K. 1991. Long-term tillage effects on seedbanks in three Ohio soils. Weed Sci. 39:186–194.Google Scholar
Davis, A. S., Renner, K. A., and Gross, K. L. 2005. Weed seedbank and community shifts in a long-term cropping systems experiment. Weed Sci. 53:296–306.Google Scholar
DiTomaso, J. M. 1995. Approaches for improving crop competitiveness through the manipulation of fertilization strategies. Weed Sci. 43:491–497.CrossRefGoogle Scholar
Feldman, S. R., Alzugaray, C., Torres, P. S., and Lewis, P. 1997. The effect of different tillage systems on the composition of the seedbank. Weed Res. 37:71–76.Google Scholar
Forcella, F. 1992. Prediction of weed seedling densities from buried seed reserves. Weed Res. 32:29–38.CrossRefGoogle Scholar
Gallandt, E. R., Mallory, E. B., Alford, A. R., Drummond, F. A., Groden, E., Liebman, M., Marra, M. C., McBurnie, J. C., and Porter, G. A. 1998a. Comparison of alternative pest and soil management strategies for Maine potato production systems. Am. J. Altern. Agric. 13:146–161.CrossRefGoogle Scholar
Gallandt, E. R., Liebman, M., Corson, S., Porter, G. A., and Ullrich, S. D. 1998b. Effects of pest and soil management systems on weed dynamics in potato. Weed Sci. 46:238–248.Google Scholar
Kenkel, N. C., Derksen, D. A., Thomas, A. G., and Watson, P. R. 2002. Multivariate analysis in weed science research. Weed Sci. 50:281–292.Google Scholar
Legendre, P. and Anderson, M. J. 1999. Distance-based redundancy analysis: testing multispecies responses in multifactorial ecological experiments. Ecol. Monogr. 69:1–24.Google Scholar
Leps, J. and Smilauer, P. 2003. Mulivariate analysis of ecological data using CANOCO. Cambridge, UK: Cambridge University Press. 269 p.Google Scholar
Menalled, F. D., Gross, K. L., and Hammond, M. 2001. Weed aboveground and seedbank community responses to agricultural management systems. Ecol. Appl. 11:1586–1601.Google Scholar
Milberg, P. and Hallgren, E. 2002. Highlighting differential control of weeds by management methods using an ordination technique. Weed Technol. 16:675–679.CrossRefGoogle Scholar
Millar, R. B., Anderson, M. J., and Zunun, G. 2005. Fitting nonlinear environmental gradients to community data: a general distance-based approach. Ecology. 86:2245–2251.CrossRefGoogle Scholar
Mohler, C. L., Frisch, J. C., and Mt. Pleasant, J. 1997. Evaluation of weed management programs for corn (Zea mays). Weed Technol. 11:123–131.CrossRefGoogle Scholar
Paolini, R., Principi, M., Froud-Williams, R. J., and Del Puglia, S. 1999. Competition between sugarbeet and Sinapis arvensis and Chenopodium album as affected by timing of nitrogen fertilization. Weed Res. 39:425–440.Google Scholar
Porter, G. A. and McBurnie, J. C. 1996. Crop and soil research. Pages 8–62 in Alford, A. R., Drummond, F. A., Gallandt, E. R., et al. The Ecology, Economics, and Management of Potato Cropping Systems: A Report of the First Four Years of the Maine Potato Ecosystem Project. Orono, ME: Maine Agricultural and Forest Experiment Station Bulletin 843.Google Scholar
ter Braak, C. J. F. and Smilauer, P. 2002. CANOCO Reference Manual and CanoDraw for Windows User's Guide: Software for Canonical Community Ordination (Version 4.5). Ithaca, New York: Micro Computer Power. 500 p.Google Scholar
Unger, P. W., Miller, S. D., and Jones, O. R. 1999. Weed seeds in long-term dryland tillage and cropping system plots. Weed Res. 39:213–223.Google Scholar
Williams, J. T. and Harper, J. L. 1965. Seed polymorphism and germination. I. The influence of nitrates and low temperatures on the germination of Chenopodium album . Weed Res. 5:141–150.Google Scholar