Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-28T01:57:56.846Z Has data issue: false hasContentIssue false

EDGE-EFFECT BIAS IN THE SAMPLING OF SUB-CORTICAL INSECTS1

Published online by Cambridge University Press:  31 May 2012

L. Safranyik
Affiliation:
Forest Research Laboratory, Department of Fisheries and Forestry, Edmonton, Alberta
K. Graham
Affiliation:
Faculty of Forestry, The University of British Columbia, Vancouver, British Columbia

Abstract

Two general models are presented to describe the relations between the average number of insects bisected by sampling unit boundaries, the per cent edge-effect bias of mean-brood-density estimates, the shape and size of the average individual, and the shape and size of the sampling unit. The two general models, when expanded specifically for sampling late-stage mountain pine beetle broods, gave excellent fit to experimental data. The expanded equations are approximations since individual insects were considered as being rectangular in shape and the angles of the long axes of their orientation relative to the sampling unit boundary were considered to have a uniform frequency distribution. Edge-effect bias was a function of the size and shape of the organism and those of the sampling unit. Edge-effect bias resulting from faulty sampling-unit-area delineation is also considered, and suggestions are made for its reduction in sample surveys of sub-cortical insects.

Type
Articles
Copyright
Copyright © Entomological Society of Canada 1971

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

Aberdeen, J. E. C. 1958. The effect of quadrant size, plant size, and plant distribution on frequency estimates in plant ecology. Aust. J. Botany 6: 4758.Google Scholar
Aldred, A. H. 1964. Evaluation of transect area-meter method of measuring map area. For. Chronicle 40: 175183.Google Scholar
Christidis, B. G. 1931. The importance of the shape of plots in field experimentation. J. agric. Sci. 21: 1437.Google Scholar
Christidis, B. G. 1939. Variability of plots of various shapes as affected by plot orientation. Empire J. exp. Agric. 28: 330342.Google Scholar
Cochran, W. C. 1953. Sampling techniques. Wiley, New York.Google Scholar
Ghent, A. W. 1963. Studies of regeneration in forest stands devastated by spruce budworm. III: Problems of sampling precision and seedling distribution. For. Sci. 9: 295310.Google Scholar
Greig-Smith, P. 1957. Quantitative plant ecology. Butterworth, London.Google Scholar
Jessen, R. J. 1942. Statistical investigation of a sample survey for obtaining farm facts. Iowa agric. Exp. Stn Res. Bull. 304.Google Scholar
Johannsen, A. 1918. Manual of petrographic methods. McGraw-Hill, New York and London.Google Scholar
Johnson, F. A. 1941. A statistical study of sampling methods for tree nursery inventories. Iowa agric. Exp. Stn Project 611.Google Scholar
Osborne, J. G. 1942. Sampling errors of systematic and random surveys of cover-type areas. J. Am. stat. Ass. 37: 256264.Google Scholar
Safranyik, L. 1968. Development of a technique for sampling mountain pine beetle populations in lodgepole pine. Ph.D. Thesis, Univ. of British Columbia, Vancouver.Google Scholar
Spurr, S. H. 1960. Photogrammetry and photo-interpretation. Ronald Press, New York. 2nd ed.CrossRefGoogle Scholar
Van Wagner, C. E. 1968. The line intersect method of forest fuel sampling. For. Sci. 14: 2026.Google Scholar
Warren, W. G., and Olsen, P. F.. 1964. A line intersect technique for assessing logging waste. For. Sci. 10: 267276.Google Scholar