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Modelling biological populations in space and time / Eric Renshaw.

By: Material type: TextTextSeries: Cambridge studies in mathematical biology ; 11.Publisher: Cambridge : Cambridge University Press, 1990Description: 1 online resource (xvii, 403 pages) : digital, PDF file(s)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9780511624094 (ebook)
Other title:
  • Modelling Biological Populations in Space & Time
Subject(s): Additional physical formats: Print version: : No titleDDC classification:
  • 574.5/248/072 20
LOC classification:
  • QH352 .R46 1990
Online resources: Summary: This volume develops a unifying approach to population studies, emphasising the interplay between modelling and experimentation. Throughout, mathematicians and biologists are provided with a framework within which population dynamics can be fully explored and understood. Aspects of population dynamics covered include birth-death and logistic processes, competition and predator-prey relationships, chaos, reaction time-delays, fluctuating environments, spatial systems, velocities of spread, epidemics, and spatial branching structures. Both deterministic and stochastic models are considered. Whilst the more theoretically orientated sections will appeal to mathematical biologists, the material is presented so that readers with little mathematical expertise can bypass these without losing the main flow of the text.
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Title from publisher's bibliographic system (viewed on 05 Oct 2015).

This volume develops a unifying approach to population studies, emphasising the interplay between modelling and experimentation. Throughout, mathematicians and biologists are provided with a framework within which population dynamics can be fully explored and understood. Aspects of population dynamics covered include birth-death and logistic processes, competition and predator-prey relationships, chaos, reaction time-delays, fluctuating environments, spatial systems, velocities of spread, epidemics, and spatial branching structures. Both deterministic and stochastic models are considered. Whilst the more theoretically orientated sections will appeal to mathematical biologists, the material is presented so that readers with little mathematical expertise can bypass these without losing the main flow of the text.

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