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Controlled experiments to predict horseweed (Conyza canadensis) dispersal distances

Published online by Cambridge University Press:  20 January 2017

David A. Mortensen
Affiliation:
Department of Crop and Soil Science, Pennsylvania State University, University Park, PA 16802
Robert Humston
Affiliation:
Department of Biology, Virginia Military Institute, Lexington, VA 24450

Abstract

Controlled-environment experiments were conducted to predict the dispersal distance of horseweed seed. Seed were released from a fixed height and collected at three distances from the introduction point along a 6-m wind tunnel. Dispersal potential was assessed at wind speeds of 8 and 16 km hr−1 and release heights of 50.8 and 76.2 cm. In separate experiments, settlement velocity was determined to be 0.323 m sec−1 (SD = 0.0687). These data were used to parameterize a mechanistic model and compared to a quantile extrapolation (QE) of wind-tunnel results. The QE method predicted a greater mean dispersal distance than the mechanistic model, with large disparities between maximum dispersal distances. Quantile extrapolation predicted dispersal distances over 100 m, whereas the mechanistic model predicted a maximum distance of approximately 30 m. Air turbulence within the wind tunnel and complex dynamics of seed flight may have contributed to the discrepancy between models. Predicting the mean and numerical distribution of seed dispersal distance is crucial when estimating the spread of wind-dispersed seed and for the design of a field-sampling protocol. Although controlled-environment experiments lack the wind variability present in natural systems, predictions from wind-tunnel studies provide a better first approximation of dispersal distance than the mechanistic model. Field experiments designed on the basis of these outcomes are more likely to capture the true dispersal distribution. This should provide more accurate data to inform management decisions for wind-dispersed species.

Type
Weed Biology and Ecology
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Andersen, M. C. 1993. Diaspore morphology and seed dispersal in several wind-dispersed Asteraceae. Am. J. Bot 80:487492.Google Scholar
Augsburger, C. K. 2003. Morphology and dispersal potential of wind-dispersed diaspores of neotropical trees. Am. J. Bot 73:353363.Google Scholar
Aylor, D. E. 2002. Settling speed of corn (Zea mays) pollen. J. Aerosol Sci 33:16011607.Google Scholar
Aylor, D. E., Schultes, N. P., and Shields, E. J. 2003. An aerobiological framework for assessing cross-pollination in maize. Agric. For. Meteorol 119:111129.Google Scholar
Baker, P. S., Chan, A. S. T., and Zavala, M. A. J. 1986. Dispersal and orientation of sterile Ceratitis capitata and Anastrepha ludens (Tephritidae) in Chiapas, Mexico. J. Appl. Ecol 23:2738.Google Scholar
Bhowmik, P. C. and Bekech, M. M. 1993. Horseweed (Conyza canadensis) seed production, emergence, and distribution in no-tillage and conventional-tillage corn (Zea mays). Agronomy (Trends Agric. Sci.) 1:6771.Google Scholar
Bohm, H. 1995. Dynamic properties of orientation to turbulent air current by walking carrion beetles. J. Exp. Biol 198:19952005.Google Scholar
Buhler, D. D. 1992. Population dynamics and control of annual weeds in corn (Zea mays) as influenced by tillage systems. Weed Sci 40:241248.Google Scholar
Bullock, J. M. and Clarke, R. T. 2000. Long distance seed dispersal by wind: measuring and modeling the tail of the curve. Oecologia 124:506521.Google Scholar
Byers, J. A. 1988. Upwind flight orientation to pheromone in Western pine-beetle Dendroctonus brevicomis Lec (Coleoptera, Scolytidae) tested with rotating windvane Traps. J. Chem. Ecol 14:189198.CrossRefGoogle Scholar
Cain, M. L., Milligan, B. G., and Strand, A. E. 2000. Long-distance seed dispersal in plant populations. Am. J. Bot 87:12171227.Google Scholar
Capone, D. E. and Lauchle, G. C. 2000. Modeling the unsteady lift and drag on a finite-length circular cylinder in cross-flow. J. Fluids Struct 14:799817.CrossRefGoogle Scholar
Clark, J. S. 1998. Why trees migrate so fast: confronting theory with dispersal biology and the paleorecord. Am. Nat 152:204224.CrossRefGoogle ScholarPubMed
Dauer, J. T., Mortensen, D. A., Jones, B. P., and Van Gessel, M. 2004. Long-distance aerial transport of horseweed (Conyza canadensis) seed. Weed Sci. Soc. Am. Abstr 44:267.Google Scholar
Giddings, G. D., Hamilton, N. R. S., and Hayward, M. D. 1997. The release of genetically modified grasses 1. Pollen dispersal to traps in Lolium perenne . Theor. Appl. Genet 94:10001006.Google Scholar
Heap, I. 2005. International weed resistance monitoring site. http://www.weedscience.com.Google Scholar
Horn, H. S., Nathan, R., and Kaplan, S. R. 2001. Long-distance dispersal of tree seeds by wind. Ecol. Res 16:877885.CrossRefGoogle Scholar
Jongejans, E. and Schippers, P. 1999. Modeling seed dispersal by wind in herbaceous species. Oikos 87:362372.Google Scholar
Jongejans, E. and Telenius, A. 2001. Field experiments on seed dispersal by wind in ten umbelliferous species (Apiaceae). Plant Ecol 152:6778.Google Scholar
Kennedy, J. S. 1983. Zigzagging and casting as a programmed response to wind-borne odour: a review. Phys. Entomol 8:109120.CrossRefGoogle Scholar
Kot, M., Lewis, M. A., and Van den Driessche, P. 1996. Dispersal data and the spread of invading organisms. Ecology 77:20272042.Google Scholar
Leon, J. C., Babin, B., and Choi, C. Y. 1998. Design, construction, and evaluation of a compact recirculating wind tunnel for agricultural experiments. Trans. ASAE 41:213218.Google Scholar
Matlack, G. R. 1987. Diaspore size, shape, and fall behavior in wind-dispersed plant species. Am. J. Bot 74:11501160.Google Scholar
McCallum, K. 1989. Seed biology of nodding thistle and its interaction with biological control. University of Canterbury, Christchurch, New Zealand.Google Scholar
McEvoy, P. B. and Cox, C. S. 1987. Wind dispersal distances in dimorphic achenes of Ragwort, Senecio jacobaea . Ecology 68:20062015.Google Scholar
Medjibe, V. and Hall, J. S. 2002. Seed dispersal and its implications for silviculture of African mahogany (Entandrophragma spp.) in undisturbed forest in the Central African Republic. For. Ecol. Manage 170:249257.Google Scholar
Mulligan, G. A. and Findlay, J. N. 1970. Reproductive systems and colonization in Canadian weeds. Can. J. Bot 48:859860.Google Scholar
Nathan, R., Safriel, U. N., and Noy-Meir, I. 2001. Field validation and sensitivity analysis of a mechanistic model for tree seed dispersal by wind. Ecology 82:374388.Google Scholar
Portnoy, S. and Willson, M. F. 1993. Seed dispersal curves—behavior of the tail of the distribution. Evol. Ecol 7:2544.CrossRefGoogle Scholar
R Development Core Team. 2005. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-00-3. http://www.R-project.org.Google Scholar
Regehr, D. L. and Bazzaz, F. A. 1979. The population dynamics of Erigeron canadensis, a successional winter annual. J. Ecol 67:923933.Google Scholar
Rieger, M. A., Lamond, M., Preston, C., Powles, S. B., and Roush, R. T. 2002. Pollen-mediated movement of herbicide resistance between commercial canola fields. Science 296:23862388.Google Scholar
[SAS] Statistical Analysis Systems. 1999. SAS/STAT User's Guide. Version 8. Cary, NC: Statistical Analysis Systems Institute.Google Scholar
Scheffler, J. A., Parkinson, R., and Dale, P. J. 1993. Frequency and distance of pollen dispersal from transgenic oilseed rape (Brassica napus). Transgenic Res 2:356364.CrossRefGoogle Scholar
Skarpaas, O., Shea, K., and Bullock, J. M. 2005. Optimizing dispersal study design by Monte Carlo simulation. J. Appl. Ecol 42/4:731739.Google Scholar
Smisek, A. 1995. The evolution of resistance to paraquat in populations of Erigeron canadensis L. . University of Western Ontario, London, Canada.Google Scholar
Tackenberg, O., Poschlod, P., and Bonn, S. 2003a. Assessment of wind dispersal potential in plant species. Ecol. Monogr 73:191205.Google Scholar
Tackenberg, O., Poschlod, P., and Kahmen, S. 2003b. Dandelion seed dispersal: the horizontal wind speed does not matter for long-distance dispersal—it is updraft Plant Biol 5:451454.CrossRefGoogle Scholar
Thompson, S. K. 2002. Sampling. 2nd ed. New York: Wiley. 367 p.Google Scholar
vanDorp, D., vandenHoek, W. P. M., and Daleboudt, C. 1996. Seed dispersal capacity of six perennial grassland species measured in a wind tunnel at varying wind speed and height. Can. J. Bot 74:19561963.Google Scholar
VanGessel, M. J. 2001. Glyphosate-resistant horseweed from Delaware. Weed Sci 49:703705.Google Scholar
Watrud, L. S., Lee, E. H., Fairbrother, A., Burdick, C., Reichman, J. R., Bollman, M., Storm, M., King, G., and Van de Water, P. K. 2004. Evidence for landscape-level, pollen-mediated gene flow from genetically modified creeping bentgrass with CP4 EPSPS as a marker. Proc. Nat. Acad. Sci. USA 101:1453314538.Google Scholar
Weaver, S. E. 2001. The biology of Canadian weeds. 115. Conyza canadensis . Can. J. Plant. Sci 81:867875.Google Scholar
Willson, M. F. 1993. Dispersal mode, seed shadows, and colonization patterns. Vegetation 108:261280.Google Scholar
Zanen, P. O. and Cardè, R. T. 1999. Directional control by male gypsy moths of upwind flight along a pheromone plume in three wind speeds. J. Comp. Physiol. A 184:2135.Google Scholar