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The Dynamics of Arthopod Predator-Prey Systems. (MPB-13), Volume 13 /
In this study of arthropod predador-prey systems Michael Hassell shows how many of the components of predation may be simply modeled in order to reveal their effects on the overall dynamics of the interacting populations. Arthropods, particularly insects, make ideal subjects for such a study because...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Princeton, NJ :
Princeton University Press,
[2020]
|
| Colección: | Monographs in Population Biology ;
111 |
| Temas: | |
| Acceso en línea: | Texto completo Texto completo |
MARC
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| 100 | 1 | |a Hassell, Michael Patrick, |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 4 | |a The Dynamics of Arthopod Predator-Prey Systems. (MPB-13), Volume 13 / |c Michael Patrick Hassell. |
| 264 | 1 | |a Princeton, NJ : |b Princeton University Press, |c [2020] | |
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| 520 | |a In this study of arthropod predador-prey systems Michael Hassell shows how many of the components of predation may be simply modeled in order to reveal their effects on the overall dynamics of the interacting populations. Arthropods, particularly insects, make ideal subjects for such a study because their generation times are characteristically short and many have relatively discrete generations, inviting the use of difference equation models to describe population changes. Using analytical models framed in difference equations, Dr. Hassell is able to show how the detailed biological processes of insect predator-prey (including host-parasitoid) interactions may be understood. Emphasizing the development and subsequent stability analysis of general models, the author considers in detail several crucial components of predator-prey models: the prey's rate of increase as a function of density, non-random search, mutual interference, and the predator's rate of increase as a function of predator survival and fecundity. Drawing on the correspondence between the models and field and laboratory data, Dr. Hassell then discusses the practical implications for biological pest control and suggests how such models may help to formulate a theoretical basis for biological control practices. | ||
| 538 | |a Mode of access: Internet via World Wide Web. | ||
| 546 | |a In English. | ||
| 588 | 0 | |a Description based on online resource; title from PDF title page (publisher's Web site, viewed 24. Mai 2022) | |
| 650 | 0 | |a Insect populations |x Mathematical models. | |
| 650 | 0 | |a Insects |x Predators of. | |
| 650 | 0 | |a Parasitoids. | |
| 650 | 0 | |a Predation (Biology) |x Mathematical models. | |
| 650 | 7 | |a SCIENCE / Life Sciences / Ecology. |2 bisacsh | |
| 653 | |a Nicholson-Bailey model. | ||
| 653 | |a age structure. | ||
| 653 | |a biological control. | ||
| 653 | |a density dependence. | ||
| 653 | |a disc equation. | ||
| 653 | |a equilibria. | ||
| 653 | |a extinction. | ||
| 653 | |a functional responses. | ||
| 653 | |a generalists. | ||
| 653 | |a hyperparasitoids. | ||
| 653 | |a interference. | ||
| 653 | |a life tables. | ||
| 653 | |a limit cycles. | ||
| 653 | |a multiparasitism. | ||
| 653 | |a negative binomial distribution. | ||
| 653 | |a non-random search. | ||
| 653 | |a optimal foraging. | ||
| 653 | |a oscillations. | ||
| 653 | |a parasitoids: contrasted with predators. | ||
| 653 | |a predator: aggregation. | ||
| 653 | |a preference. | ||
| 653 | |a random parasitoid equation. | ||
| 653 | |a searching behavior. | ||
| 653 | |a spatial heterogeneity. | ||
| 653 | |a survival. | ||
| 653 | |a switching. | ||
| 653 | |a time delays. | ||
| 653 | |a zero growth isoclines. | ||
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