Spatial Analysis of the Relationship of Grasshopper Outbreaks to Soil Classification

  • Daniel L. Johnson
Part of the Lecture Notes in Statistics book series (LNS, volume 55)


Two spatial modeling methods, based on analysis of either area or point data, were applied to test hypotheses regarding the relationship of grasshopper population density to soil type and texture in southern Alberta. Grasshopper abundance over the last 10 years was higher in certain soil zones than in others. This difference has been attributed in some previous studies to soil surface texture (“intrinsic hypothesis”). Alternatively, the effect may actually be caused by geographical covariables such as weather, vegetation and farming practices (“extrinsic hypothesis”). A geographic information system was utilized to test these hypotheses in two ways. First, area modeling was employed by overlaying maps of grasshopper population density, previous year’s population density, soil type, and soil texture. The resulting unique conditions were subjected to analysis of covariance, with the map intersection area as the weight. The second method relied on analysis of point data from the grasshopper survey database, using the same statistical model. The results of both analytical methods indicated that grasshopper abundance was related to soil type (P < 0.001), but not to soil texture (P > 0.1), and the intrinsic hypothesis was rejected.


Geographic Information System Soil Texture Soil Zone Chernozemic Soil Grasshopper Species 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Agriculture Canada Expert Committee on Soil Survey. 1987. The Canadian system of soil classification, 2nd ed. Agrie. Can. Pub. No. 1646.Google Scholar
  2. Bishop, Y. M. M., S. E. Fienberg & P. W. Holland. 1980. Discrete multivariate analysis: theory and practice. The MIT Press, Cambridge, Massachusetts.Google Scholar
  3. Danks, H. V. 1979. Canada and its insect fauna. Mem. Entomol Soc. Can.108: 1 – 573.Google Scholar
  4. Diggle, P. J. 1983. Statistical analysis of spatial point patterns. Academic Press.MATHGoogle Scholar
  5. Gilbert, N. E. 1972. Biometrica! interpretation. Clarendon Press, Oxford, U.K.Google Scholar
  6. Grace, B. D. & D. L. Johnson. 1985. The drought of 1984 in southern Alberta: its severity and effects. Can. Wat. Res. J.10: 28 – 38.CrossRefGoogle Scholar
  7. Hewitt, G. B. 1985. Review of the factors affecting fecundity, oviposition, and egg survival of grasshoppers in North America. U.S.D.A. ARS–36.Google Scholar
  8. Hunter, G. M. & K. Steiglitz. 1979. Operations on images using quad trees. I.E.E.E. Trans. Pattern Analysis and Machine Intelligence. PAMI–1: 145 – 153.Google Scholar
  9. Isely, F. B. 1937. Seasonal succession, soil relations, numbers, and regional distribution of north—eastern Texas acridians. Ecol Monog. 8: 553 – 604.Google Scholar
  10. Isely, F. B. 1938. The relations of Texas acrididae to plants and soils. Ecol Monog. 8: 553 – 604.CrossRefGoogle Scholar
  11. Johnson, D. L. & R. C. Andrews. 1986. 1986 Grasshopper Forecast. 35 x 62 cm color map sheet (1:2,000,000), Alberta Bureau of Surveying and Mapping, Edmonton, Alberta.Google Scholar
  12. Johnson, D. L., R. C. Andrews, M. G. Dolinski & J. W. Jones. 1986. High numbers but low reproduction of grasshoppers in 1985. Can. Agrie. Insect Pest Rev.63: 8 – 10.Google Scholar
  13. Johnson, D. L., R. C. Andrews, M. G. Dolinski & J. W. Jones. 1986. High numbers but low reproduction of grasshoppers in 1985. Can. Agrie. Insect Pest Rev.63: 8 – 10.Google Scholar
  14. Mark, D. M. & J. P. Lauzon. 1985. Approaches for quadtree-based geographic information systems at continental or global scales. In AUTO-CARTO 7 Proc., pp. 355–364. Washington, D.C.Google Scholar
  15. Moran, P. A. P. 1950. Notes on continuous stochastic phenomena. Biometrika37: 17 – 23.MathSciNetMATHGoogle Scholar
  16. Morton, G. 1966. A computer—oriented geodetic data base, anda new technique in file sequencing. Internal memorandum, March, 1966. IBM Canada, Ltd.Google Scholar
  17. Riegert, P. W. 1968. A history of grasshopper abundance surveys and forecasts of outbreaks in Saskatchewan. Mem. Entomol Soc. Can.52: 1 – 99.CrossRefGoogle Scholar
  18. Samet, H. 1984. The quadtree and related hierarchical data structures. Computing Surveys 16: 187 – 260.MathSciNetCrossRefGoogle Scholar
  19. Samet, H., C. A. Schaffer, R. C. Nelson, Y. Huang, K. Fujimara & A. Rosenfeld. 1986. Recent developments in quadtree-based geographic information systems. In Proceedings Second International Symposium on Spatial Data Handling. International Geographical Union, Williams ville, N.Y.Google Scholar
  20. SAS Institute. 1982. SAS user’s guide: statistics. SAS Institute, Cary, N.C.Google Scholar
  21. Sokal, R. R. & N. L. Oden. 1978. Spatial autocorrelation in biology. 1. Methodology. Biol J. Linn. Soc.10: 199 – 228.CrossRefGoogle Scholar
  22. Tobler, W. & Z. Chen. 1986. A quadtree for global information storage. Geog. Anal18: 360 – 371.CrossRefGoogle Scholar
  23. TYDAC Technologies. 1987. Spatial Analysis System. Reference guide. Ottawa, Ontario.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1989

Authors and Affiliations

  • Daniel L. Johnson
    • 1
  1. 1.Agriculture Canada Research StationLethbridgeCanada

Personalised recommendations