In this book, the authors cover the basic methods and advances within distance sampling that are most valuable to practitioners and in ecology more broadly. This is the fourth book dedicated to distance sampling. In the decade since the last book published, there have been a number of new developments. The intervening years have also shown which advances are of most use. This self-contained book covers topics from the previous publications, while also including recent developments in method, software and application. Distance sampling refers to a suite of methods, including line and point transect sampling, in which animal density or abundance is estimated from a sample of distances to detected individuals. The book illustrates these methods through case studies; data sets and computer code are supplied to readers through the book’s accompanying website. Some of the case studies use the software Distance, while others use R code. The book is in three parts. The first part addresses basic methods, the design of surveys, distance sampling experiments, field methods and data issues. The second part develops a range of modelling approaches for distance sampling data. The third part describes variations in the basic method; discusses special issues that arise when sampling different taxa (songbirds, seabirds, cetaceans, primates, ungulates, butterflies, and plants); considers advances to deal with failures of the key assumptions; and provides a check-list for those conducting surveys.

This advanced text focuses on the uses of distance sampling to estimate the density and abundance of biological populations. It addresses new methodologies, new technologies and recent developments in statistical theory and is the follow up companion to Introduction to Distance Sampling (OUP, 2001). In this text, a general theoretical basis is established for methods of estimating animal abundance from sightings surveys, and a wide range of approaches to analysis of sightings data is explored. These approaches include: modelling animal detectability as a function of covariates, where the effects of habitat, observer, weather, etc. on detectability can be assessed; estimating animal density as a function of location, allowing for example animal density to be related to habitat and other locational covariates; estimating change over time in populations, a necessary aspect of any monitoring programme; estimation when detection of animals on the line or at the point is uncertain, as often occurs for marine populations, or when the survey region has dense cover; survey design and automated design algorithms, allowing rapid generation of sound survey designs using geographic information systems; adaptive distance sampling methods, which concentrate survey effort in areas of high animal density; passive distance sampling methods, which extend the application of distance sampling to species that cannot be readily detected in sightings surveys, but can be trapped; and testing of methods by simulation, so that performance of the approach in varying circumstances can be assessed.

Our environment and natural food resources are continually coming under threat so that the monitoring of population trends is essential today. Whaling is a good example. Here politics and conservation often clash, and over the years more and more restrictions have been applied through the efforts of the International Whaling Commission in an endeavour to save some of our whale species from extinction. Localized fisheries also need to be monitored and quotas set each year. In some countries, sports fishing and hunting are popular so that information is needed about the populations being exploited in order to determine such things as the duration of hunting season and bag limits. Methods of estimating animal abundance have been developing steadily since the 1940s but over the last 20 years activity in this area has intensified and of this growth were two the subject has begun to blossom. At the centre of the authors of this book, David Anderson and Kenneth Burnham, who have widely published in this field. The need for computers in this area was soon recognized and David and Ken were joined by Jeffrey Laake who, with his computing expertise, helped to develop suitable software packages for implementing some of the new techniques. In the 1980s Stephen Buckland entered the arena and began to make his presence felt. Among other contributions, he firmly established the role of Monte Carlo and bootstrapping techniques in population estimation where the unique role of the computer could be fully exploited.

This advanced text focuses on the uses of distance sampling to estimate the density and abundance of biological populations. It addresses new methodologies, new technologies and recent developments in statistical theory and is the follow up companion to Introduction to Distance Sampling (OUP,2001).In this text, a general theoretical basis is established for methods of estimating animal abundance from sightings surveys, and a wide range of approaches to analysis of sightings data is explored. These approaches include: modelling animal detectability as a function of covariates, where theeffects of habitat, observer, weather, etc. on detectability can be assessed; estimating animal density as a function of location, allowing for example animal density to be related to habitat and other locational covariates; estimating change over time in populations, a necessary aspect of anymonitoring programme; estimation when detection of animals on the line or at the point is uncertain, as often occurs for marine populations, or when the survey region has dense cover; survey design and automated design algorithms, allowing rapid generation of sound survey designs using geographicinformation systems; adaptive distance sampling methods, which concentrate survey effort in areas of high animal density; passive distance sampling methods, which extend the application of distance sampling to species that cannot be readily detected in sightings surveys, but can be trapped; andtesting of methods by simulation, so that performance of the approach in varying circumstances can be assessed.

Introductory concepts. Introduction. Range of applications. Types of data. Known constants and parameters. Assumptions. Fundamental concept. Detection. History of methods. Program DISTANCE. Assumptions and modelling philosophy. Assumptions. Fundamental models. Philosophy and strategy. Robust models. Some analysis guidelines. Statistical theory. General formula. Hazard-rate modelling of the detection process. The key function formulation for distance data. Maximum likelihood methods. Choice of model. Estimation for clustered populations. Density, variance and interval estimation. Stratification and covariates. Line transects. Introduction. Example data. Truncation. Extensions and related work. Other models. Modelling variation in encounter rate and cluster size. Estimation of the probability of detection on the line or pint. On the concept of detection search effort. Fixed versus random sample size. Efficient simulation of distance data. Thoughts about a full likelihood approach. Distance sampling in three dimensions. ...

This broad text provides a complete overview of most standard statistical methods, including multiple regression, analysis of variance, experimental design, and sampling techniques. Assuming a background of only two years of high school algebra, this book teaches intelligent data analysis and covers the principles of good data collection. * Provides a complete discussion of analysis of data including estimation, diagnostics, and remedial actions * Examples contain graphical illustration for ease of interpretation * Intended for use with almost any statistical software * Examples are worked to a logical conclusion, including interpretation of results * A complete Instructor's Manual is available to adopters

This new handbook contains the most comprehensive account of sample surveys theory and practice to date. It is a second volume on sample surveys, with the goal of updating and extending the sampling volume published as volume 6 of the Handbook of Statistics in 1988. The present handbook is divided into two volumes (29A and 29B), with a total of 41 chapters, covering current developments in almost every aspect of sample surveys, with references to important contributions and available software. It can serve as a self contained guide to researchers and practitioners, with appropriate balance between theory and real life applications. Each of the two volumes is divided into three parts, with each part preceded by an introduction, summarizing the main developments in the areas covered in that part. Volume 29A deals with methods of sample selection and data processing, with the later including editing and imputation, handling of outliers and measurement errors, and methods of disclosure control. The volume contains also a large variety of applications in specialized areas such as household and business surveys, marketing research, opinion polls and censuses. Volume 29B is concerned with inference, distinguishing between design-based and model-based methods and focusing on specific problems such as small area estimation, analysis of longitudinal data, categorical data analysis and inference on distribution functions. The volume contains also chapters dealing with case-control studies, asymptotic properties of estimators and decision theoretic aspects. Comprehensive account of recent developments in sample survey theory and practice Discusses a wide variety of diverse applications Comprehensive bibliography