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SPATIAL AUTOCORRELATION, SPATIAL MODELING, AND IMPROVEMENTS IN GRASSHOPPER SURVEY METHODOLOGY1

Published online by Cambridge University Press:  31 May 2012

D.L. Johnson
Affiliation:
Agriculture Canada Research Station, Lethbridge, Alberta, Canada T1J 4B1

Abstract

Analysis of 10 years of grasshopper survey data (1978–1987) indicated that grasshopper populations in fields can be reliably predicted from roadside survey counts. Direct estimation of grasshopper densities in crop fields is no longer required for summaries of infestation levels or forecasts. Spatial autocorrelation was significant and positive for both roadside and field counts. The coefficient of variation of the field counts was greater than that of the roadside counts in each of the last 10 years. Population density was summarized by crop type and sampling method for the last 10 years. Linear regressions fitted to the 1978–1984 grasshopper survey data were used to estimate field population density from crop type and roadside counts in 1985–1987. Maps of population density were generated from the predicted and observed field counts with SPANS, a microcomputer-based geographic information system. Large coefficients of association (73–79%) between the predicted and observed maps attested to the sufficiency of road-side counts as the basis for production of population density maps.

Résumé

L’analyse de 10 ans de données d’enquête sur les criquets (1978–1987) a révélé qu’il est possible de prédire fidèlement les populations de criquets dans les champs cultivés à partir de comptages effectués sur le bord des routes. L’estimation directe de la densité de population de criquets dans les champs cultivés n’est donc plus nécessaire pour établir les sommaires ou les prévisions d’infestation. En effet, l’autocorrélation spatiale est significative et positive pour les comptages effectués au champ et sur le bord des routes. Le coefficient de variation des comptages au champ est plus élevé que celui des comptages sur le bord des routes, et ce, dans chacune des 10 dernières années. L’auteur résume la densité de population par type de culture et méthode de d’échantillonnage pour les 10 dernières années. Des régressions linéaires ajustées aux données d’enquête de 1978–1984 ont servi à estimer la densité de population au champ à partir du type de culture et des comptages sur le bord des routes de 1985–1987. SPANS, système d’information géographique géré par micro-ordinateur, a permis de dresser des cartes de densité de population à partir des comptages au champ observés et prédits. Les coefficients d’association élevés (73–79%) entre les cartes prédites et observées témoignent de l’acceptabilité des comptages effectués sur le bord des routes pour dresser des cartes de densité de population.

Type
Articles
Copyright
Copyright © Entomological Society of Canada 1989

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References

Bishop, Y.M.M., Fienberg, S.E., and Holland, P.W.. 1980. Discrete Multivariate Analysis: Theory and Practice. The MIT Press, Cambridge, MA.Google Scholar
Diggle, P.J. 1983. Statistical Analysis of Spatial Point Patterns. Academic Press. 148 pp.Google Scholar
Gage, S.H., and Mukerji, M.K.. 1977. A perspective of grasshopper population distribution in Saskatchewan and interrelationship with weather. Environ. Ent. 6: 469479.CrossRefGoogle Scholar
Grace, B., and Johnson, D.L.. 1985. The drought of 1984 in southern Alberta: its severity and effects. Can. Water Res. J. 10: 2838.CrossRefGoogle Scholar
Hunter, G.M., and Steiglitz, K.. 1979. Operations on images using quad trees. I.E.E.E. Trans. Pattern Analysis and Machine Intelligence. PAMI-1: 145153.Google Scholar
Johnson, D.L., and Andrews, R.C.. 1986. 1986 Grasshopper Forecast. 35 × 62 cm color map sheet (1:2,000,000). Alberta Bureau of Surveying and Mapping.Google Scholar
Johnson, D.L., Andrews, R.C., Dolinski, M.G., and Jones, J.W.. 1986. High numbers but low reproduction of grasshoppers in 1985. Can. Agric. Insect Pest Rev. (Ottawa) 63: 810.Google Scholar
Johnson, D.L., and Worobec, A.. 1988. Spatial and temporal computer analysis of insects and weather: grasshoppers and rainfall in Alberta. pp. 33–48 in Sahota, T.S., and Holling, C.S., Paths from a Viewpoint: The Wellington Festschrift on Insect Ecology. Mem. ent. Soc. Can. 146.Google Scholar
Jumars, P.A., Thistle, D., and Jones, M.L.. 1977. Detecting two-dimensional spatial structure in biological data. Oecologia 28: 109123.Google Scholar
Riegert, P.W. 1968. A history of grasshopper abundance surveys and forecasts of outbreaks in Saskatchewan. Mem. ent. Soc. Can. 52. 99 pp.Google Scholar
Samet, H. 1984. The quadtree and related hierarchical data structures. Computing Surveys 16: 187260.CrossRefGoogle Scholar
Samet, H., Schaffer, C.A., Nelson, R.C., Huang, Y., Fujimura, K., and Rosenfeld, A.. 1986. Recent developments in quadtree-based geographic information systems. Proc. Second Int. Symp. on spatial data handling. International Geographical Union, Williamsville, N.Y.Google Scholar
SAS Institute. 1982. SAS Users's Guide: Statistics. SAS Institute, Cary, NC.Google Scholar
Smith, D.S., and Holmes, N.D.. 1977. The distribution and abundance of adult grasshoppers (Acrididae) in crops in Alberta, 1918–1975. Can. Ent. 109: 575592.CrossRefGoogle Scholar
Sokal, R.R., and Oden, N.L.. 1978. Spatial autocorrelation in biology. 1. Methodology. Biol. J. Linn. Soc. 10: 199228.Google Scholar
TYDAC Technologies. 1987. Spatial Analysis System. Reference Guide. Ottawa, Ontario.Google Scholar