Stochastic Abundance Models

With Emphasis on Biological Communities and Species Diversity

  • S. Engen

Part of the Monographs on Applied Probability and Statistics book series (MSAP)

Table of contents

  1. Front Matter
    Pages i-x
  2. Theoretical treatment

    1. Front Matter
      Pages 1-1
    2. S. Engen
      Pages 3-15
    3. S. Engen
      Pages 34-66
    4. S. Engen
      Pages 67-73
  3. Ecological applications

    1. Front Matter
      Pages 83-83
    2. S. Engen
      Pages 85-93
  4. Back Matter
    Pages 111-126

About this book

Introduction

This monograph deals with the analysis of populations of elements. Each element is a member of one and only one class, and we shall mainly be concerned with populations with a large number of classes. No doubt the present theory has its outspring in ecology, where the elements symbolize the individual animals or plants, while the classes are the various species of the ecological community under consideration. Some basic ideas point back to a classical contribution by R.A. Fisher (1943, in collaboration with A.S. Corbet and c.B. Williams) representing a breakthrough for the theoretical analysis of diverse populations. Though most of the work in this field has been carried out by ecologists, statisticians and biometri­ cians have, over the past 15 years, shown an ever increasing interest in the topic. Besides being directed towards biometricians and statisticians, this monograph may hopefully be of interest for any research worker dealing with the classification of units into a large number of classes, in particular ecologists, sociologists and linguists. However, some background in statistics and probability theory is required. It would be unless to read the present book without some knowledge of the continuous and discrete probability distributions summarized in section 1.1, and the use of generating functions. In particular, a clear intuitive and formal understanding of the concept of condi­ tional probability and conditional distributions is required in order to interpret the various models correctly.

Keywords

Parameter probability probability distribution probability theory statistics

Editors and affiliations

  • S. Engen
    • 1
  1. 1.Department of MathematicsUniversity of TrondheimNorway

Bibliographic information

  • DOI https://doi.org/10.1007/978-94-009-5784-8
  • Copyright Information Springer Science+Business Media B.V. 1978
  • Publisher Name Springer, Dordrecht
  • eBook Packages Springer Book Archive
  • Print ISBN 978-94-009-5786-2
  • Online ISBN 978-94-009-5784-8
  • About this book