This book covers a wide range of methods for estimating wildlife populations. A general statistical approachmaximum likelihood estimationis used throughout the book as the common underlying statistical framework for all the methods presented. The book deals with closed populations that remain effectively unchanged during the investigation period.
The book is divided into four parts, the first part consisting of 2 chapters. Chapter one presents three basic approaches for estimating the number of animals in a closed population. The first approach uses a feature of the survey (i.e. number of observers) or a feature of the animals (i.e. size) to estimate the proportion detected. Line transect and point transect methods are examples of approach 1. The second approach uses the changes in the number of detections while the population is being reduced by the removal of objects. These methods are commonly known as removal methods. The basic idea of the third approach is that information about the population can be obtained by marking animals; this approach is known as the capture-recapture or mark-recapture method. In the case of just two samples of sizes n 1 and n 2 , and m 2 recaptures, a simple but famous estimator of the population size N, the Petersen estimator, is available. It is obtained by equating the population proportion of marked (n 1 /N) in the population with the proportion of marked in the second sample (m 2 /n 2 ) to get N N ΒΌ n 1 n 2 =m 2 . In Chapter 2 the basics of maximum likelihood estimation and constructing confidence intervals is presented.
The second part of the book consists of six chapters and presents the "simple" abundance estimation methods. In Chapter 3 two fundamental components of the likelihood function, the state and the observation model, are introduced. The state model describes the distribution of animals in space and relevant characteristics (i.e. sex, size, age). The observation model describes how animals are detected, given the searched region, their locations and characteristics. These concepts are used for the methods discussed in chapters 4 to 8. Plot sampling methods (Chapter 4) are based on the assumption that all animals in the covered region are detected. In Chapter 5 (removal methods) and Chapter 6 (markrecapture) estimating both detection/capture probability and abundance are considered. Differences in detectability, due to animal-level variables, were neglected so far, which can result in extremely negatively biased abundance estimates. An attractive feature of distance sampling methods (Chapter 7) is that estimates are very insensitive to unmodelled heterogeneity. The last chapter of Part 1 discusses nearest neighbour and point-to-nearest object methods, an extension of plot sampling in which plot size is a function of object density.
The third part of the book deals with advanced methods for estimating animal abundance, especially spatial and temporal modelling and the issue of heterogeneity. Chapter 9 extends the conceptual framework introduced in Chapter 3. State models were largely absent in the models considered in Part 2 but play an important role in the advanced methods. Models presented in Chapter 10 allow estimation of spatial and/or temporal distribution when detection of animals is certain within those parts of the survey region that are searched. To accommodate for heterogeneity, different ways of adapting the methods of Part 2 are discussed in Chapter 11. Chapter 12 displays three methods that integrate apparently different methods, for instance distance sampling methods with mark-recapture methods. The last two chapters include material beyond that published in the literature. The final chapter of this part introduces methods for open populations.
Part 4 contains only one chapter which gives guidelines for choosing between the wide range of techniques and emphasizing strengths and weaknesses of each method.