Estimating Animal Abundance: Closed Populations / Edition 1by D.L. Borchers, S.T. Buckland, W. Zucchini
This is the first book to provide an accessible, comprehensive introduction to wildlife population assessment methods. It uses a new approach that makes the full range of methods accessible in a way that has not previously been possible. Traditionally, newcomers to the field have had to face the daunting prospect of grasping new concepts for almost every one of the… See more details below
This is the first book to provide an accessible, comprehensive introduction to wildlife population assessment methods. It uses a new approach that makes the full range of methods accessible in a way that has not previously been possible. Traditionally, newcomers to the field have had to face the daunting prospect of grasping new concepts for almost every one of the many methods. In contrast, this book uses a single conceptual (and statistical) framework for all the methods. This makes understanding the apparently different methods easier because each can be seen to be a special case of the general framework. The approach provides a natural bridge between simple methods and recently developed methods. It also links closed population methods quite naturally with open population methods.The book is accompanied by free software on the web, in the form of an R library, allowing readers to get some "hands-on" experience with the methods and how they perform in different contexts - without the considerable effort and expense required to do this in the real world. It also provides a tool for teaching the methods, including a means for teachers to generate examples and exercises customised to the needs of their students.As the first truly up-to-date and introductory text in the field, this book should become a standard reference for students and professionals in the fields of statistics, biology and ecology.
Table of Contents
1 Introduction.- 2 Using likelihood for estimation.- 3 Building blocks.- 4 Plot sampling.- 5 Removal, catch-effort and change-in-ratio.- 6 Simple mark-recapture.- 7 Distance sampling.- 8 Nearest neighbour and point-to-nearest-object.- 9 Further building blocks.- 10 Spatial/temporal models with certain detection.- 11 Dealing with heterogeneity.- 12 Integrated models.- 13 Dynamic and open population models.- 14 Which method?.- A Notation and Glossary.- A.1 Notation.- A.2 Glossary.- B Statistical formulation for observation models.- B.1 Detection function.- B.2 Multiple surveys.- C The asymptotic variance of MLEs.- C.1 Estimating the variance of an MLE.- C.2 Estimating the variance of a function of an MLE.- C.3 A one-parameter example.- C.3.1 Fisher information version 1.- C.3.2 Fisher information version 2.- C.3.3 Observed information.- D State models for mark-recapture and removal methods.- D.1 Static population.- D.2 Independent dynamics.- D.3 Markov dynamics.- References.
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