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Toward management guidelines for soybean aphid, Aphis glycines, in Quebec. II. Spatial distribution of aphid populations in commercial soybean fields

Published online by Cambridge University Press:  02 April 2012

Marc Rhainds ,
Jacques Brodeur ,
Daniel Borcard  and
Pierre Legendre
Marc Rhainds*
Affiliation:
Institut de Recherche en Biologie Végétale, Université de Montréal, 4101 rue Sherbrooke est, Montréal, Québec, Canada H1X 2B2
Jacques Brodeur
Affiliation:
Institut de Recherche en Biologie Végétale, Université de Montréal, 4101 rue Sherbrooke est, Montréal, Québec, Canada H1X 2B2
Daniel Borcard
Affiliation:
Département de Sciences Biologiques, Université de Montréal, C.P. 6128, Succursale Centre-Ville, Montréal, Québec, Canada H3C 3J7
Pierre Legendre
Affiliation:
Département de Sciences Biologiques, Université de Montréal, C.P. 6128, Succursale Centre-Ville, Montréal, Québec, Canada H3C 3J7
*
1Corresponding author (e-mail: mrhainds@purdue.edu).
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Abstract

The study was conducted to document the spatial distribution of the soybean aphid, Aphis glycines Matsumura (Hemiptera: Aphididae), in commercial fields of soybean, Glycine max (L.) Merr. The abundance of aphids was assessed weekly at 12 sites in 2005 and 2006 on more than 135 georeferenced plants per site. Variograms and principal coordinates of neighbour matrices (PCNM) were used to detect significant spatial structures. Variograms indicated a spatially random distribution of aphid populations in a majority (84%) of fields-weeks. For the variograms with a defined structure, the variance between pairs of observations generally increased rapidly with the distance between plants up to a distance where it stabilized, a pattern adequately fitted by spherical models. Structured spatial distributions were more prevalent in 2005 than in 2006, especially at the end of the season. In 2006, PCNM analysis was more sensitive in detecting spatial trends than were variograms. PCNM analysis revealed significant patterns across a broad range of scales, with dominant periods averaging 22.6 and 47.1 m for the short and long transects, respectively. Sampling plants along a 100 m long transect at about 7.5 m intervals in soybean fields would allow detection of the spatial structures identified in this study.

Résumé

Cette étude a été entreprise afin de documenter la distribution spatiale du puceron du soya, Aphis glycines Matsumura (Hemiptera: Aphididae), dans des champs commerciaux de soya, Glycine max (L.) Merr., de la province de Québec. Le nombre de pucerons a été évalué sur une base hebdomadaire dans 12 sites en 2005 et 2006, en échantillonnant pour chaque site plus de 135 plants géoréférencés. Des variogrammes ainsi que des analyses des coordonnées principales de matrices de voisinage (CPMV) ont été utilisés pour détecter l’existence de patrons spatiaux significatifs. Les variogrammes ont indiqué un patron de distribution spatialement aléatoire des populations de pucerons pour une majorité (84 %) des sites-semaines. En ce qui concerne les variogrammes présentant une structure spatiale définie, la variance entre les paires d’échantillons augmentait généralement rapidement avec la distance entre les échantillons jusqu’à une certaine distance après laquelle la variance se stabilisait; ce patron de distribution spatiale était adéquatement décrit par un modèle de type sphérique. Des distributions spatiales structurées étaient plus fréquemment observées en 2005 qu’en 2006, surtout en fin de saison. En 2006, les analyses basées sur les CPVM furent plus sensibles que les variogrammes pour détecter les tendances spatiales. Les CPVM ont révélé des patrons significatifs pour une vaste gamme d’échelle spatiale, avec des périodes dominantes de 22.6 et 47.1 m en moyenne pour les transects courts et longs. L’échantillonnage des plants de soya le long d’un transect de 100 m avec un pas d’environ 7.5 m permettrait de détecter les patrons de distribution spatiale identifiés par la présente étude.

Type
Articles
Information
The Canadian Entomologist , Volume 140 , Issue 2 , April 2008 , pp. 219 - 234
Copyright
Copyright © Entomological Society of Canada 2008

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References

Bacca, T., Lima, E.R., Picanco, M.C., Guedes, R.N.C., and Viana, J.H.M. 2006. Optimum spacing of pheromone traps for monitoring the coffee leaf miner Leucoptera coffeella. Entomologia Experimentalis et Applicata, 119: 39–45.Google Scholar
Borcard, D., and Legendre, P. 2002. All-scale spatial analysis of ecological data by means of principal coordinates of neighbour matrices. Ecological Modelling, 153: 51–68.CrossRefGoogle Scholar
Borcard, D., and Legendre, P. 2004. SpaceMaker 2; user's guide [online]. Département de Sciences Biologiques, Université de Montréal, Montréal, Que. Available from http://www.bio.umontreal.ca/legendre/.Google Scholar
Borcard, D., Legendre, P., Avois-Jacquet, C., and Tuomisto, H. 2004. Dissecting the spatial structures of ecological data at all scales. Ecology, 85: 1826–1832.CrossRefGoogle Scholar
Costamagna, A.C., and Landis, D.A. 2007. Quantifying predation on soybean aphid through direct field observations. Biological Control, 42: 16–24.CrossRefGoogle Scholar
Desneux, N., O'Neil, R., and Yoo, H.J.S. 2006. Suppression of population growth of the soybean aphid, Aphis glycines Matsumura, by predators: the identification of a key predator and the effects of prey dispersion, predator abundance, and temperature. Environmental Entomology, 35: 1342–1349.Google Scholar
Dungan, J.L., Perry, J.N., Dale, M.R.T., Legendre, P., Citron-Pousty, S., Fortin, M.J., Jakomulska, A., Miriti, M., and Rosenberg, M.S. 2002. A balanced view of scale in spatial analysis. Ecography, 25: 626–640.CrossRefGoogle Scholar
Ellsbury, M.M., Woodson, W.D., Clay, S.A., Malo, D., Schumacher, J., Clay, D.E., and Carlson, C.G. 1998. Geostatistical characterization of the spatial distribution of adult corn rootworm (Coleoptera: Chrysomelidae) emergence. Environmental Entomology, 27: 910–917.Google Scholar
Hodgson, E.W., Burkness, E.C., Hutchison, W.D., and Ragsdale, D.W. 2004. Enumerative and binomial sequential sampling plants for soybean aphid (Homoptera: Aphididae) in soybean. Journal of Economic Entomology, 97: 2127–2136.CrossRefGoogle ScholarPubMed
Hodgson, E.W., Koch, R.L., and Ragsdale, D.W. 2005. Pan trapping for soybean aphid (Homoptera: Aphididae) in Minnesota soybean fields. Journal of Entomological Science, 40: 409–419.CrossRefGoogle Scholar
Huang, F., Ding, X., Wang, X., and Huang, Z. 1992. Studies on the spatial distribution pattern of soybean aphid and sampling techniques. Journal of Shenyang Agricultural University, 23: 81–87.Google Scholar
Hurlbert, S.H. 1990. Spatial distribution of the montane unicorn. Oikos, 58: 257–271.CrossRefGoogle Scholar
Irmak, A., Batchelor, W.D., Jones, J.W., Irmak, S., Paz, J.O., Beck, H.W., and Egeh, M. 2002. Relationship between plant available soil water and yield for explaining soybean yield variability. Applied Engineering in Agriculture, 18: 471–482.CrossRefGoogle Scholar
Iwao, S. 1968. A new regression model for analysing the aggregation pattern of animal populations. Researches in Population Ecology, 10: 1–20.CrossRefGoogle Scholar
Jones, R.A.C. 2005. Pattern of spread of two nonpersistent aphid-borne viruses in lupin stands under four different infection scenarios. Annals of Applied Biology, 146: 337–350.CrossRefGoogle Scholar
Jumars, P.A., Thistle, D., and Jones, M.L. 1977. Detecting two-dimensional spatial structure in biological data. Oecologia, 28: 109–123.Google Scholar
Kabaluk, J.T., Vernon, R.S., and Henderson, D. 2006. Population development of the green peach aphid and beneficial insects in potato fields in British Columbia. The Canadian Entomologist, 138: 647–660.CrossRefGoogle Scholar
Legendre, L., and Legendre, P. 1998. Numerical ecology. Elsevier, Amsterdam.Google Scholar
Legendre, P., Dale, M.R., Fortin, M.J., Gurevitch, J., Hohn, M., and Myers, D. 2002. The consequences of spatial structure for the design and analysis of ecological field surveys. Ecography, 25: 601–615.Google Scholar
Liebhold, A.M., Rossi, R.E., and Kemp, W.P. 1993. Geostatistics and geographic information systems in applied insect ecology. Annual Review of Entomology, 38: 303–327.CrossRefGoogle Scholar
Macedo, T.B., Bastos, C.S., Higley, L.G., Ostlie, K.R., and Madhavan, S. 2003. Photosynthetic responses of soybean to soybean aphid (Homoptera: Aphididae) injury. Journal of Economic Entomology, 96: 188–193.CrossRefGoogle ScholarPubMed
Matheron, G. 1965. Les variables régionalisées et leur estimation. Masson, Paris.Google Scholar
Midgarden, D.G., Youngman, R.R., and Fleischer, S.J. 1993. Spatial analysis of counts of western corn-rootworm (Coleoptera, Chrysomelidae) adults on yellow sticky traps in corn — geostatistics and dispersion indexes. Environmental Entomology, 22: 1124–1133.CrossRefGoogle Scholar
Myers, S.W., Hogg, D.B., and Wedberg, J.L. 2005. Determining the optimal timing of foliar insecticide applications for control of soybean aphid (Hemiptera: Aphididae) on soybean. Journal of Economic Entomology, 98: 2006–2012.CrossRefGoogle ScholarPubMed
Onstad, D.W., Fang, S., Voegtlin, D.J., and Just, M.G. 2005. Sampling Aphis glycines (Homoptera: Aphididae) in soybean fields in Illinois. Environmental Entomology, 34: 170–177.CrossRefGoogle Scholar
Park, Y.L., and Obrycki, J.J. 2004. Spatio-temporal distribution of corn leaf aphids (Homoptera: Aphididae) and lady beetles (Coleoptera: Coccinellidae) in Iowa cornfields. Biological Control, 31: 210–217.CrossRefGoogle Scholar
Park, Y.L., and Tollefson, J.J. 2005. Characterization of the spatial dispersion of corn root injury by corn rootworms (Coleoptera: Chrysomelidae). Journal of Economic Entomology, 98: 378–383.CrossRefGoogle ScholarPubMed
Pearce, S., and Zalucki, M.P. 2006. Do predators aggregate in response to pest density in agroeco-systems? Assessing within-field spatial patterns. Journal of Applied Ecology, 43: 128–140.CrossRefGoogle Scholar
Peres-Neto, P.R., Legendre, P., Dray, S., and Borcard, D. 2006. Variation partitioning of species data matrices: estimation and comparison of fractions. Ecology, 87: 2614–2625.CrossRefGoogle ScholarPubMed
R Development Core Team. 2007. R: a language and environment for statistical computing [computer program]. R Foundation for Statistical Computing, Vienna, Austria. Available from http://www.R-project.org.Google Scholar
Ragsdale, D.W., Voegtlin, D.J., and O'Neil, R.J. 2004. Soybean aphid biology in North America. Annals of the Entomological Society of America, 97: 204–208.Google Scholar
Ragsdale, D.W., McCornack, B.P., Venette, R.C., Potter, B.D., MacRae, I.V., Hodgson, E.W., O'Neal, M.E., Johnson, K.D., O'Neil, R.J., Difonzo, C.D., Hunt, T.E., Glogoza, P., and Cullen, E.M. 2007. Economic threshold for soybean aphid (Homoptera: Aphididae). Journal of Economic Entomology, 100: 1258–1267.CrossRefGoogle ScholarPubMed
Rhainds, M., Roy, M., Daigle, G., and Brodeur, J. 2007. Toward management guidelines for soybean aphid in Quebec. I. Feeding damage in relationship with seasonality of infestation and incidence of native predators. The Canadian Entomologist, 139: 728–741.Google Scholar
Rutledge, C.E., and O'Neil, R.J. 2006. Soybean plant stage and population growth of soybean aphid. Journal of Economic Entomology, 99: 60–66.CrossRefGoogle ScholarPubMed
Rutledge, C.E., O'Neil, R.J., Fox, T.B., and Landis, D.A. 2004. Soybean aphid predators and their use in integrated pest management. Annals of the Entomological Society of America, 97: 240–248.Google Scholar
SAS Institute Inc. 2002. SAS statistical package, version 9.1. SAS Institute Inc., Cary, North Carolina.Google Scholar
Schotzko, D.J., and Quisenberry, S.S. 1999. Pea leaf weevil (Coleoptera: Curculionidae) spatial distribution in peas. Environmental Entomology, 28: 477–484.CrossRefGoogle Scholar
Shi, S.S., Yu, B.R., Li, D.S., and Yu, Y.J. 1994. Space tendancy of natural population of Aphis glycines Matsumura. Journal of Jilin Agricultural University, 16: 75–79.Google Scholar
Taylor, L.R. 1961. Aggregation, variance and the mean. Nature (London), 189: 732–735.CrossRefGoogle Scholar
van Den Berg, H., Ankasah, D., Muhammad, A., Rusli, R., Widayanto, H.A., Wirasto, H.B., and Yully, I. 1997. Evaluating the role of predation in population fluctuations of the soybean aphid Aphis glycines in farmers' fields in Indonesia. Journal of Applied Ecology, 34: 971–984.Google Scholar
Venette, R.C., and Ragsdale, D.W. 2004. Assessing the invasion by soybean aphid (Homoptera: Aphididae): where will it end? Annals of the Entomological Society of America, 97: 219–226.Google Scholar
Wang, R.Y., Kritzman, A., Hershman, D.E., and Ghabrial, S.A. 2006. Aphis glycines as a vector of persistently transmitted viruses and potential risks for soybean and other crops. Plant Disease, 90: 920–926.CrossRefGoogle ScholarPubMed
Winder, L., Perry, J.N., and Holland, J.M. 1999. The spatial and temporal distribution of the grain aphid Sitobion avenae in winter wheat. Entomologia Experimentalis et Applicata, 93: 277–290.CrossRefGoogle Scholar
Wu, A., Schenk-Hamlin, D., Zhan, W., Ragsdale, D.W., and Heimpel, G.E. 2004. The soybean aphid in China: a historical review. Annals of the Entomological Society of America, 97: 209–218.CrossRefGoogle Scholar