Stage-Structured Populations

1. Front Matter

   Pages i-xii

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2. Stage-structured populations

   • Bryan F. J. Manly

   Pages 1-6

3. Sampling for population estimation

   • Bryan F. J. Manly

   Pages 7-32

4. Maximum likelihood estimation of models

   • Bryan F. J. Manly

   Pages 33-41

5. Analysis of multi-cohort stage-frequency data

   • Bryan F. J. Manly

   Pages 42-85

6. Analysis of single cohort stage-frequency data

   • Bryan F. J. Manly

   Pages 86-100

7. Matrix and other models for reproducing populations

   • Bryan F. J. Manly

   Pages 101-117

8. Key factor analysis

   • Bryan F. J. Manly

   Pages 118-151

9. Case studies

   • Bryan F. J. Manly

   Pages 152-167

10. Back Matter

    Pages 168-187

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This book provides a review of methods for obtaining and analysing data from stage-structured biological populations. The topics covered are sam­ pling designs (Chapter 2), the estimation of parameters by maximum likelihood (Chapter 3), the analysis of sample counts of the numbers cif individuals in different stages at different times (Chapters 4 and 5), the analysis of data using Leslie matrix types of model (Chapter 6) and key factor analysis (Chapter 7). There is also some discussion of the approaches to modelling and estimation that have been used in five studies of particular populations (Chapter 8). There is a large literature on the modelling of biological populations, and a multitude of different approaches have been used in this area. The various approaches can be classified in different ways (Southwood, 1978, ch. 12), but for the purposes of this book it is convenient to think of the three categories mathematical, statistical and predictive modelling. Mathematical modelling is concerned largely with developing models that capture the most important qualitative features of population dynamics. In this case, the models that are developed do not have to be compared with data from natural populations. As representations of idealized systems, they can be quite informative in showing the effects of changing parameters, indicating what factors are most important in promoting stability, and so on.

• biological
• cluster
• cotton
• development
• dynamics
• population
• population dynamics
• simulation
• stability
• structure
• temperature
• temperature effects

• Department of Mathematics and Statistics, University of Otago, Dunedin, New Zealand

  Bryan F. J. Manly

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