Hostname: page-component-8448b6f56d-42gr6 Total loading time: 0 Render date: 2024-04-24T04:27:06.627Z Has data issue: false hasContentIssue false
  • English
  • Français

ENERGY ACQUISITION AND ALLOCATION IN PLANTS AND INSECTS: A HYPOTHESIS FOR THE POSSIBLE ROLE OF HORMONES IN INSECT FEEDING PATTERNS

Published online by Cambridge University Press:  31 May 2012

A.P. Gutierrez ,
F. Schulthess ,
L.T. Wilson ,
A.M. Villacorta ,
C.K. Ellis  and
J.U. Baumgaertner
A.P. Gutierrez
Affiliation:
Division of Biological Control, University of California, Berkeley, California, USA
F. Schulthess
Affiliation:
International Institute of Tropical Agriculture, Ibadan, Nigeria
L.T. Wilson
Affiliation:
Department of Entomology, University of California, Davis, California, USA
A.M. Villacorta
Affiliation:
Fundãço Instituto Agronômico do Parana, Brazil
C.K. Ellis
Affiliation:
Division of Biological Control, University of California, Berkeley, California, USA
J.U. Baumgaertner
Affiliation:
Institut fur Phytomedizin, ETH, Zurich, Switzerland
Get access Rights & Permissions [Opens in a new window]

Abstract

A distributed delay age structure model is presented for plants and insects that describes the dynamics of per capita energy (dry matter) acquisition and allocation patterns, and the within-organism subunit (e.g. leaves, fruit, ova) number dynamics that occur during growth, reproduction, and development. Four species of plants (common bean, cassava, cotton, and tomato) and two species of insects (pea aphid and a ladybird beetle) are modeled. A common acquisition (i.e. functional response) submodel is used to estimate the daily photosynthetic rates in plants and consumption rates in pea aphid and the ladybird beetle. The focus of this work is to capture the essence of the common attributes between trophic levels across this wide range of taxa. The models are compared with field or laboratory data. A hypothesis is proposed for the observed patterns of reproduction in pea aphid and in a ladybird beetle.

Résumé

On a construit un modèle démographique avec distribution de délai applicable à des plantes et des insectes. Le modèle décrit la dynamique de l’appropriation et de la répartition de l’énergie (matière sèche) per capita, et la dynamique des nombres des sous-unités intra-organisme (ex. feuilles, fruits, oeufs). On a ainsi modélisé quatre sortes de plantes (fève, cassava, colon et tomate) et deux espèces d’insectes (puceron du pois et coccinelle). On utilise un sous-modèle commun d’acquisition (résponse fonctionnelle) pour estimer les vitesses journalières de photosynthèse des plantes et d’alimentation du puceron et de la coccinelle. Le but de ce travail est d’extraire les caractéristiques essentielles communes aux niveaux trophiques occupés par ces divers taxons. Les modèles sont comparés avec des données de terrain et de laboratoire. On propose une hypothèse pour expliquer les profils observés de reproduction du puceron du pois et de la coccinelle.

Type
Articles
Information
The Canadian Entomologist , Volume 119 , Issue 2 , February 1987 , pp. 109 - 129
Copyright
Copyright © Entomological Society of Canada 1987

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

Bellows, T.S. Jr., 1982. Simulation models for laboratory populations of Callosobruchus chinensis and C. maculatus. J. Anim. Ecol. 51: 597–623.CrossRefGoogle Scholar
Bookman, S.S. 1983. Costs and benefits of flower abscission and fruit abortion in Asclepias speciosa. Ecology 64: 264–273.CrossRefGoogle Scholar
Chapman, R.F. 1982. The insects—structure and function. Harvard University Press, Cambridge, MA. 919 pp.Google Scholar
Cuff, W.R., and Hardman, J.M.. 1980. A development of the Leslie Matrix formulation for restructuring and extending an ecosystem model: the infestation of stored wheat by Sitophilus oryzae. Ecol. Modelling 9: 281–305.CrossRefGoogle Scholar
Curry, G.L., Feldman, R.M., and Smith, K.C.. 1978. A stochastic model of a temperature-dependent population. J. Theor. Biol. 13: 197–204.Google ScholarPubMed
De Angelis, D.L., Goldstein, R.A., and O'Neill, R.V.. 1975. A model for trophic interaction. Ecology 56: 881–892.CrossRefGoogle Scholar
Evans, L.T. 1975. Crop physiology — some case histories. Cambridge University Press, Cambridge, MA. 374 pp.Google Scholar
Fisher, R.A. 1930. The genetical theory of natural selection. Clarendon, Oxford.CrossRefGoogle Scholar
Frazer, B.D., and Gilbert, N.. 1976. Coccinellids and aphids: a quantitative study of the impact of adult lady birds (Coleoptera: Coccinellidae) preying on field populations of pea aphids (Homoptera: Aphididae). J. ent. Soc. B.C. 73: 33–56.Google Scholar
Gilbert, N., Gutierrez, A.P., Frazer, B.D., and Jones, R.E.. 1976. Ecological relationships. Freeman and Co., New York.Google Scholar
Gutierrez, A.P., and Baumgaertner, J.U.. 1984 a. Multitrophic level models of predator–prey-energetics: I. Age specific energetics models—pea aphid Acyrthosiphon pisum (Harris) (Homoptera: Aphidiae) as an example. Can. Ent. 116: 924–932.CrossRefGoogle Scholar
Gutierrez, A.P., and Baumgaertner, J.U.. 1984 b. Multitrophic level models of predator–prey energetics: II. A realistic model of plant–herbivore–parasitoid–predator interactions. Can. Ent. 116: 933–949.CrossRefGoogle Scholar
Gutierrez, A.P., Baumgaertner, J.U., and Hagen, K.S.. 1981. A conceptual model for growth, development and reproduction in the ladybird beetle, Hippodamia convergens (Coleoptera: Coccinellidae). Can. Ent. 113: 21–33.CrossRefGoogle Scholar
Gutierrez, A.P., Baumgaertner, J.U., and Summers, C.G.. 1984. Multitrophic level models of predator–prey energetics: III. A case study in an alfalfa ecosystem. Can. Ent. 116: 950–963.CrossRefGoogle Scholar
Gutierrez, A.P., Butler, G.D. Jr., Wang, Y., and Westphal, D.. 1977. The interaction of pink bollworm (Lepidoptera: Gelichiidae), cotton and weather: a detailed model. Can. Ent. 109: 1457–1468.CrossRefGoogle Scholar
Gutierrez, A.P., Christensen, J.B., Merritt, C.M., Loew, W.B., Summers, C.G., and Cothran, W.R.. 1976. Alfalfa and the Egyptian alfalfa weevil (Coleoptera: Curculionidae). Can. Ent. 108: 635–648.CrossRefGoogle Scholar
Gutierrez, A.P., DeVay, J.E., Pullman, G.S., and Friebertshauser, G.E.. 1983. A model of verticillium wilt in relation to cotton growth and development. Phytopath. 75: 89–95.CrossRefGoogle Scholar
Gutierrez, A.P., Falcon, L.A., Loew, W., Leipzig, P.A., and van den Bosch, R.. 1975. An analysis of cotton production in California: a model for Acala cotton and the effects of defoliators on its yield. Environ. Ent. 4: 125–136.CrossRefGoogle Scholar
Gutierrez, A.P., Leigh, T.F., Wang, Y., and Cave, R.. 1977. An analysis of cotton production in California: Lygus hesperus (Heteroptera: Miridae) injury — an evaluation. Can. Ent. 109: 1375–1386.CrossRefGoogle Scholar
Gutierrez, A.P., Pizzamiglio, M.A., Santos, W.J. Dos, Tennyson, R., and Villacorta, A.M.. 1984. A general distributed delay time varying life table plant population model: cotton (Gossypium hirsutum L.) growth and development as an example. Ecol. Modelling 26: 231–249.CrossRefGoogle Scholar
Gutierrez, A.P., and Regev, U.. 1983. The economics of fitness and adaptedness: the interaction of sylvan cotton (Gossypium hirsutum L.) and the boll weevil (Anthonomus grandis Boh.) — an example. Ecol. Gener. 4: 271–287.Google Scholar
Gutierrez, A.P., and Wang, Y.H.. 1976. Applied population ecology: models for crop production and pest management. pp. 255–280in Norton, G.A., and Holling, C.S. (Eds.), Pest Management, International Institute for Applied Systems Analysis Proc. Ser.Google Scholar
Hagen, K.S., and Sluss, R.R.. 1966. Quantity of aphids required for reproduction by Hippodamia spp. in the laboratory. pp. 47–59 in Hodek, I. (Ed.) Ecology of Aphidophagous Insects. Czechoslovak Acad. Sci., Prague.Google Scholar
Hardie, J., and Lees, A.D.. 1985. The induction of normal and teratoid viviparae by a juvenile hormone and kinoprene in two species of aphids. Physiol. Ent. 10: 65–74.CrossRefGoogle Scholar
Harper, J.L. 1979. Population biology of plants. Academic Press Inc., London. 891 pp.Google Scholar
Harper, J.L., and White, J.. 1974. Demography of plants. Annu. Rev. Syst. 5: 419–463.CrossRefGoogle Scholar
Holling, C.S. 1966. The functional response of invertebrate predators to prey density. Mem. ent. Soc. Can. 48. 86 pp.Google Scholar
Jones, J.W., Thompson, A.C., and Hesketh, D.J.. 1974. Analysis of SIMCOT: Nitrogen and growth. pp. 111–116in Beltwide Cotton Prod. Res. Conf., Memphis.Google Scholar
Kvalseth, T.O. 1985. Cautionary note about R2. Am. Stat. Assoc. 39: 279–285.Google Scholar
Law, J. 1983. A model for the dynamics of a plant population containing individuals classified by age and size. Ecology 64: 224–230.CrossRefGoogle Scholar
Leslie, P.H. 1945. On the use of matrices in certain population mathematics. Biometrika 33: 183–212.CrossRefGoogle ScholarPubMed
Loomis, R.S., and Williams, W.A.. 1963. Maximum crop productivity: an estimate. Crop Sci. 3: 67–72.CrossRefGoogle Scholar
Mack, T.P., Bajusz, B.A., Nolan, E.S., and Smilowitz, Z.. 1981. Development of a temperature-mediated functional response equation. Environ. Ent. 10: 573–579.CrossRefGoogle Scholar
Mahon, J.D., Lowe, S.B., Hunt, L.A., and Thiagarajah, M.. 1977. Environmental effects on photosynthesis and transpiration in attached leaves of cassava (Manihot esculenta Crantz). Photosynthetica 11: 121–130.Google Scholar
Manetsch, T.J. 1976. Time varying distributed delays and their use in aggregate models of large systems. IEEE Trans. Syst., Man and Cybern. 6: 547–553.CrossRefGoogle Scholar
May, R.M. 1982. Theoretical ecology. Sinauer Press, Sunderland, MA.Google Scholar
Noggle, G.R., and Fritz, G.J.. 1976. Introductory plant physiology. Prentice-Hall Inc., Englewood Cliffs, NJ. 675 pp.Google Scholar
Randolph, P.A., Randolph, J.C., and Barlow, C.A.. 1975. Age-specific energetics of the pea aphid. Acyrthosiphon pisum. Ecology 56: 357–369.Google Scholar
Sinko, J.W., and Streifer, W.. 1967. A new model for age-structure of a population. Ecology 48: 910–918.CrossRefGoogle Scholar
Vansickle, J. 1977. Attrition in distributed delay models. IEEE Trans. Syst., Man Cybern. 7: 635–638.CrossRefGoogle Scholar
von Foerster, H. 1959. Some remarks on changing populations. pp. 382–407in Stahlman, F. Jr. (Ed.), The Kinetics of Cellular Proliferation. Grune and Stratton, New York.Google Scholar
Wang, Y.H., and Gutierrez, A.P.. 1980. An assessment of the use of stability analyses in population ecology. J. Anim. Ecol. 49: 435–452.CrossRefGoogle Scholar
Wang, Y.H., Gutierrez, A.P., Oster, G., and Daxl, R.. 1977. A population model for cotton growth and development: coupling cotton-herbivore interactions. Can. Ent. 109: 1359–1374.CrossRefGoogle Scholar
Ward, S.A., and Dixon, A.F.G.. 1982. Selective resorption of aphid embryos and habitat changes relative to life span. J. Anim. Ecol. 51: 859–864.CrossRefGoogle Scholar