Hostname: page-component-848d4c4894-8kt4b Total loading time: 0 Render date: 2024-06-27T06:36:59.776Z Has data issue: false hasContentIssue false
  • English
  • Français

Spatial patterns and sequential sampling plans for melolonthine larvae (Coleoptera: Scarabaeidae) in southern Queensland sugarcane

Published online by Cambridge University Press:  10 July 2009

P. G. Allsopp  and
R. M. Bull
P. G. Allsopp
Affiliation:
Bureau of Sugar Experiment Stations, P.O. Box 651, Bundaberg, Queensland 4670, Australia
R. M. Bull
Affiliation:
Bureau of Sugar Experiment Stations, P.O. Box 651, Bundaberg, Queensland 4670, Australia
Get access Rights & Permissions [Opens in a new window]

Abstract

The within-row dispersion characteristics of larvae of Antitrogus mussoni (Blackburn), A. parvulus Britton, Lepidiota crinita Brenske, L. negatoria Blackburn, L. noxia Britton and L. picticollis Lea in sugarcane were determined in studies in southern Queensland, Australia. The Poisson distribution, negative binomial distribution, Iwao's regression model and Taylor's power law analysis were used to determine the relationship between mean and variance of larval counts. All methods examined showed that the larvae were slightly aggregated. Taylor's power law generally gave equivalent or better fits to the population dispersion compared with the other models. The power law relationship for L. picticollis differed from those of the other five species, and a common relationship for those five species was determined. Relationships to determine sample sizes for fixed levels of precision and fixed-precision-level stop lines for sequential sampling were developed for both L. picticollis and the other five species. There were functional relationships between the variance and mean of untransformed population counts for all species, and the suitability of transformation functions is discussed.

Type
Original Articles
Information
Bulletin of Entomological Research , Volume 79 , Issue 2 , June 1989 , pp. 251 - 258
Copyright
Copyright © Cambridge University Press 1989

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Arbous, A. G. & Kerrich, J. E. (1951). Accidental statistics and the concept of accident-proneness.—Biometrics 7, 340–342.CrossRefGoogle Scholar
Bliss, C. I. (1967). Statistics in biology. Volume 1.—558 pp. New York, McGraw-Hill.Google Scholar
Bliss, C. I. & Owen, A. R. G. (1958). Negative binomial distributions with a common k.—Biometrika. 45, 37–58.CrossRefGoogle Scholar
Britton, E. B. (1985). Lepidiota noxia sp. n. (Coleoptera: Scarabaeidae: Melolonthinae), a pest of sugarcane in Queensland.—J. Aust. entomol. Soc. 24, 117–119.CrossRefGoogle Scholar
Bull, R. M. (1986). Lepidiota picticollis Lea, an increasing cane grub pest problem in the Bundaberg district.—Proc. Aust. Soc. Sug. Cane Technol. 8, 141–147.Google Scholar
Green, R. H. (1970). On fixed precision level sequential sampling.—Researches Popul. Ecol. 12, 249–251.CrossRefGoogle Scholar
Healy, M. J. R. & Taylor, L. R. (1962). Tables for power-law transformations.—Biometrika 49, 557–559.CrossRefGoogle Scholar
Illingworth, J. F. & Dodd, A. P. (1921). Australian sugar-cane beetles and their allies.—Bull. Bur. Sug. Exp. Stns Qd Div. Ent. no. 16, 1–104.Google Scholar
Itô, Y. & Kitching, R. L. (1986). The importance of non-linearity: a comment on the views of Taylor.—Researches Popul. Ecol. 28, 39–42.CrossRefGoogle Scholar
Iwao, S. (1968). A new regression method for analyzing the aggregation pattern of animal populations.—Researches Popul. Ecol. 10, 1–20.CrossRefGoogle Scholar
Iwao, S. & Kuno, E. (1968). Use of the regression of mean crowding on mean density for estimating sample size and the transformation of data for the analysis of variance.—Researches Popul. Ecol. 10, 210–214.CrossRefGoogle Scholar
Iwao, S. & Kuno, E. (1971). An approach to the analysis of aggregation pattern in biological populations.—pp. 461–513 in Patil, G. P., Pielou, E. C. & Waters, W. E. (Eds). Statistical ecology. Vol. 1.—582 pp. University Park, Pennsylvania State Univ. Press.Google Scholar
Lloyd, M. (1967). Mean crowding.—J. Anim. Ecol. 36, 1–30.CrossRefGoogle Scholar
Mungomery, R. W. (1965). Pests.—pp. 289–351 in King, N. J., Mungomery, R. W. & Hughes, C. G. (Eds.). Manual of cane-growing.—2nd edn, 375 pp. Sydney, Angus & Robertson.Google Scholar
Ruesink, W. G. (1980). Sequential sampling plans for soybean arthropods.—pp. 61–78in Kogan, M. & Herzog, D. C. (Eds). Sampling methods in soybean entomology.—587 pp. New York, Springer-Verlag.CrossRefGoogle Scholar
Southwood, T. R. E. (1978). Ecological methods with particular reference to the study of insect populations.—2nd edn, 524 pp. London, Chapman & Hall.Google Scholar
Taylor, L. R. (1961). Aggregation, variance and the mean.—Nature, Lond. 189, 732–735.CrossRefGoogle Scholar
Taylor, L. R. (1984). Assessing and interpreting the spatial distribution of insect populations.—A. Rev. Ent. 29, 321–357.CrossRefGoogle Scholar