Overview

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.

Full Product Details

Author:   Bryan Manly
Publisher:   Springer
Imprint:   Springer
Edition:   Softcover reprint of the original 1st ed. 1990
Dimensions:   Width: 15.50cm , Height: 1.10cm , Length: 23.50cm
Weight:   0.320kg
ISBN:  

9789401068666

ISBN 10:   9401068666
Pages:   187
Publication Date:   13 November 2013
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
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Table of Contents

1 Stage-structured populations.- 1.1 Introduction.- 1.2 Stage-frequency data.- 1.3 Key factor analysis.- 1.4 Case studies.- 2 Sampling for population estimation.- 2.1 Introduction.- 2.2 Populations and samples.- 2.3 Simple random sampling.- 2.4 Determining sample sizes with simple random sampling.- 2.5 Stratified random sampling.- 2.6 Ratio estimation.- 2.7 Regression estimation.- 2.8 Cluster sampling.- 2.9 Systematic sampling.- 2.10 Multi-stage sampling.- 2.11 Sampling for stage-frequency data.- 2.12 Sampling species assemblages.- 2.13 Special sampling methods.- Exercises.- 3 Maximum likelihood estimation of models.- 3.1 The method of maximum likelihood.- 3.2 Models for count data.- 3.3 Computer programs.- 3.4 Measuring goodness-of-fit.- 3.5 Comparing models.- 3.6 The heterogeneity factor.- 4 Analysis of multi-cohort stage-frequency data.- 4.1 Multi-cohort stage-frequency data.- 4.2 Temperature effects.- 4.3 Effect of mortality on stage durations.- 4.4 Methods for analysing multi-cohort stage-frequency data.- 4.5 Assessing estimates by simulation.- 4.6 The Kiritani—Nakasuji—Manly (KNM) method of analysis.- 4.7 The KNM method with iterative calculations.- 4.8 The Kempton method of estimation.- 4.9 Variations of the Kempton type of model.- 4.10 The Bellows and Birley model.- 4.11 Comparison of models.- Exercises.- 5 Analysis of single cohort stage-frequency data.- 5.1 Types of single cohort data.- 5.2 Analysis using multi-cohort methods.- 5.3 Data without mortality.- 5.4 Non-parametric estimation.- 5.5 Parametric models for estimation.- 5.6 Estimating the durations of stages.- Exercises.- 6 Matrix and other models for reproducing populations.- 6.1 Continually reproducing populations.- 6.2 The Bernardelli—Leslie—Lewis model.- 6.3 Lefkovitch’s model forpopulations grouped by life stages.- 6.4 Usher’s model.- 6.5 Further generalizations and extensions.- 6.6 Sampling variation and other sources of errors.- 6.7 Discussion.- 6.8 Other models for populations with continuous recruitment.- Exercise.- 7 Key factor analysis.- 7.1 Populations observed over a series of generations.- 7.2 Density-dependent survival.- 7.3 The Varley and Gradwell (1960) graphical method of key factor analysis.- 7.4 Extensions to the Varley and Gradwell approach.- 7.5 Detecting density-dependent k values.- 7.6 The Manly (1977) model for key factor analysis.- 7.7 The relative merits of different methods of key factor analysis.- 7.8 Using simulation with key factor analysis.- 7.9 Testing for delayed density-dependent mortality.- 7.10 Recent developments.- 7.11 Computer program.- Exercise.- 8 Case studies.- 8.1 Introduction.- 8.2 The sheep blowfly Lucilia cuprina.- 8.3 The nematode Paratrichodorus minor.- 8.4 The pink cotton bollworm moth Pectinophora gossypiella.- 8.5 The southern pine beetle Dendroctonus frontalis.- 8.6 The grey pup seal Halichoerus grypus.- References.- Author index.

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