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A COMPETITION SUBMODEL FOR PARASITES AND PREDATORS

Published online by Cambridge University Press:  31 May 2012

Abstract

A generalized competition model for predators or parasites was developed from data obtained from a specific parasite–host system. It was structured in three parts. The first simulates the effects of exploitation, where the number of attacks and their distribution among prey or hosts determine how many prey or hosts survive. Since the negative binomial distribution described these distributions consistently, the exploitation submodel was developed from it. The second portion of the competition model concerned interference between searching predators and parasites. Although interference is a universal phenomenon, we were able to show that its effects become important only at predator densities much higher than those that occur in nature. Thus the interference component can be essentially ignored. The third and final component concerned the outcome of competition between parasite progeny within their host. It was developed from Fujii’s competition model which allows for the simulation of both scramble and contest types of competition.These three submodels of competition were combined and coupled with a previously published model of the effects of prey density on attack. In this way the full consequences of different prey and predator densities could be simulated using a model whose constituent parts had been carefully tested for descriptive adequacy. The simulations showed the way individual predator attack, per cent predation, and progeny production were affected by different degrees of contagion in the distribution of attacks, by scramble vs. contest competition, and by the degree to which parasites could avoid hosts already attacked.

Type
Research Article
Copyright
Copyright © Entomological Society of Canada 1969

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References

Anscombe, F. J. 1948. On estimating the population of aphids in a potato field. Ann. appl. Biol. 35: 567571.10.1111/j.1744-7348.1948.tb07398.xCrossRefGoogle Scholar
Anscombe, F. J. 1949. The statistical analysis of insect counts based on the negative binomial distribution. Biometrics 5: 165173.10.2307/300191818151959CrossRefGoogle Scholar
Beverton, R. J. H., and Holt, S. J.. 1957. On the dynamics of exploited fish populations. Fishery Invest. Ser. II. Vol. 19. Ministry Agric. Fisheries and Food, London.Google Scholar
Blackman, G. E. 1942. Statistical and ecological studies in the distribution of species in plant communities I. Dispersion as a factor in the study of changes in plant populations. Ann. Bot. n.s. 6 :351370.CrossRefGoogle Scholar
Bliss, C. I. 1953. Fitting the negative binomial distribution to biological data. Biometrics 9: 176196.10.2307/3001850CrossRefGoogle Scholar
Burnett, T. 1953. Effects of temperature and parasite density on the rate of increase of an insect parasite. Ecology 34: 322329.10.2307/1930899CrossRefGoogle Scholar
Burnett, T. 1958. Effect of host distribution on the reproduction of Encarsia formosa Gahan. (Hymenoptera: Chalcidoidea). Can. Ent. 90: 179191.10.4039/Ent90179-3CrossRefGoogle Scholar
Clapham, A. R. 1936. Overdispersion in grassland communities and the use of statistical methods in plant ecology. J. Ecol. 24: 232251.10.2307/2256277CrossRefGoogle Scholar
Cole, L. C. 1946. A study of the cryptozoa of an Illinois woodland. Ecol. Monogr. 16: 4986.10.2307/1943574CrossRefGoogle Scholar
Doutt, R. L. 1959. The biology of parasitic Hymenoptera. Ann. Rev. Ent. 4: 161182.10.1146/annurev.en.04.010159.001113CrossRefGoogle Scholar
Fisher, R. A. 1953. Note on the efficient fitting of the negative binomial. Biometrics 9: 197200.Google Scholar
Force, D. C., and Messenger, P. S.. 1965. Laboratory studies on competition among three parasites of the spotted aphid Therioaphis maculata (Buckton). Ecology 46: 853859.10.2307/1934018CrossRefGoogle Scholar
Fujii, K. 1965. A statistical model of the competition curve. Res. on Pop. Ecol. 7: 118125.10.1007/BF02518795CrossRefGoogle Scholar
Griffiths, K. J. 1959. Observations on the European pine sawfly, Neodiprion sertifer (Geoff.), and its parasites in southern Ontario. Can. Ent. 91: 501512.10.4039/Ent91501-8CrossRefGoogle Scholar
Griffiths, K. J. 1961. The life history of Aptesis basizona (Grav.) on Neodiprion sertifer (Geoff.) in southern Ontario. Can. Ent. 93: 10051010.10.4039/Ent931005-11CrossRefGoogle Scholar
Griffiths, K. J. 1969. The importance of coincidence in the functional and numerical responses of two parasites of the European pine sawfly, Neodiprion sertifer . Can. Ent. 101: 673713.10.4039/Ent101673-7CrossRefGoogle Scholar
Holling, C. S. 1959 a. The components of predation as revealed by a study of small mammal predation of the European pine sawfly. Can. Ent. 91: 293320.10.4039/Ent91293-5CrossRefGoogle Scholar
Holling, C. S. 1959 b. Some characteristics of simple types of predation and parasitism. Can. Ent. 91: 385398.10.4039/Ent91385-7CrossRefGoogle Scholar
Holling, C. S. 1965. The functional response of predators to prey density and its role in mimicry and population regulation. Mem. ent. Soc. Can., No. 45.CrossRefGoogle Scholar
Holling, C. S. 1966. The functional response of invertebrate predators to prey density. Mem. ent. Soc. Can., No. 48.CrossRefGoogle Scholar
Lloyd, D. C. 1938. A study of some factors governing the choice of hosts and distribution of progeny by the chalcid Ooencyrtus kuvanae Howard. Phil. Trans. R. Soc. Lond. B 229: 275322.10.1098/rstb.1938.0005Google Scholar
Lloyd, D. C. 1940. Host selection by hymenopterous parasites of the moth Plutella maculipennis Curtis. Proc. R. Soc. Lond. B 128: 451484.10.1098/rspb.1940.0021Google Scholar
Lyons, L. A. 1964. The European pine sawfly, Neodiprion sertifer (Geoff.) (Hymenoptera: Diprionidae). A review with emphasis on studies in Ontario. Proc. ent. Soc. Ont. 94: 537.Google Scholar
Messenger, P. S., and Force, D. C.. 1963. An experimental host–parasite system: Therioaphis maculata (Buckton), Praon palitans Muesebeck (Homoptera: Aphidae, Hymenoptera: Braconidae). Ecology 44: 532540.10.2307/1932532CrossRefGoogle Scholar
Morris, R. F. 1954. A sequential sampling technique for spruce budworm egg surveys. Can. J. Zool. 32: 302313.10.1139/z54-028CrossRefGoogle Scholar
Nicholson, A. J. 1955. An outline of the dynamics of animal populations. Aust. J. Zool. 2: 965.10.1071/ZO9540009CrossRefGoogle Scholar
Nicholson, A. J., and Bailey, V. A.. 1935. The balance of animal populations. Part 1. Proc. Zool. Soc. Lond. 1935: 551598.CrossRefGoogle Scholar
Pramanik, L. M., and Choudhury, M. K.. 1963. Effect of superparasitism on the development, sex ratio and progeny of Bracon greeni Ashmead. Entomophaga 8: 8386.10.1007/BF02381340CrossRefGoogle Scholar
Ricker, W. E. 1954. Stock and recruitment. J. Fish. Res. Bd Can. 11: 559623.10.1139/f54-039CrossRefGoogle Scholar
Salt, G. 1932. Superparasitism by Collyria calcitrator Grav. Bull. ent. Res. 23: 211216.10.1017/S0007485300004119CrossRefGoogle Scholar
Salt, G. 1934. Experimental studies in insect parasitism. II. Superparasitism. Proc. R. Soc. Lond. B 114: 455476.10.1098/rspb.1934.0019Google Scholar
Salt, G. 1936. Experimental studies in insect parasitism. IV. The effect of superparasitism on populations of Trichogramma evanescens . J. exp. Biol. 13: 363375.CrossRefGoogle Scholar
Snedecor, G. W. 1946. Statistical methods applied to experiments in agriculture and biology. The Iowa State College Press, Ames.Google Scholar
Thompson, W. R. 1924. La théorie mathématique de l'action des parasites entomophages et le facteur du hasard. Annls Fac. Sci. Marseille 2: 6989.Google Scholar
Tripp, H. A. 1962. The relationship of Spathimeigenia spinigera Townsend (Diptera: Tachinidae) to its host Neodiprion swainei Midd. (Hymenoptera: Diprionidae). Can. Ent. 94: 809818.10.4039/Ent94809-8CrossRefGoogle Scholar
Ullyett, G. C. 1936. The physical ecology of Microplectron fuscipennis Zett. Bull. ent. Res. 27: 195217.10.1017/S0007485300058417CrossRefGoogle Scholar
Ullyett, G. C. 1943. Some aspects of parasitism in field populations of Plutella maculipennis Curt. J. ent. Soc. S. Afr. 6: 6580.Google Scholar
Ullyett, G. C. 1949 a. Distribution of progeny by Chelonus texanus Cress. (Hymenoptera: Ichneumonidae). Can. Ent. 81: 2544.10.4039/Ent8125-2CrossRefGoogle Scholar
Ullyett, G. C. 1949 b. Distribution of progeny by Cryptus inornatus Pratt (Hymenoptera: Ichneumonidae). Can. Ent. 81: 285299.10.4039/Ent81285-12CrossRefGoogle Scholar
Waters, W. E., and Henson, W. R.. 1959. Some sampling attributes of the negative binomial distribution with special reference to forest insects. Forest Sci. 5: 397412.CrossRefGoogle Scholar
Watt, K. E. F. 1959. A mathematical model for the effect of densities of attacked and attacking species on the number attacked. Can. Ent. 91: 129144.10.4039/Ent91129-3CrossRefGoogle Scholar
Wylie, H. G. 1965. Some factors that reduce the reproductive rate of Nasonia vitripennis (Walk.) at high adult population densities. Can. Ent. 97: 970977.10.4039/Ent97970-9CrossRefGoogle Scholar
Wylie, H. G. 1966. Some mechanisms that affect the sex ratio of Nasonia vitripennis (Walk.) (Hymenoptera: Pteromalidae) reared from superparasitized housefly pupae. Can. Ent. 98: 645653.10.4039/Ent98645-6CrossRefGoogle Scholar