D.L. Borchers – författare

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. Authored by a leading team this text is aimed at professionals in government and environment agencies,statisticians, biologists, wildlife managers, conservation biologists and ecologists, as well as graduate students, studying the density and abundance of biological populations.

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.

We hope this book will make the bewildering variety of methods for estimat­ ing the abundance of animal populations more accessible to the uninitiated and more coherent to the cogniscenti. We have tried to emphasize the fun­ damental similarity of many methods and to draw out the common threads that underlie them. With the exception of Chapter 13, we restrict ourselves to closed populations (those that do not change in composition over the period(s) being considered). Open population methods are in many ways simply extensions of closed population methods, and we have tried to pro­ vide the reader with a foundation on which understanding of both closed and open population methods can develop. We would like to thank Miguel Bernal for providing the St Andrews example dataset used frequently in the book; Miguel Bernal and Jeff Laake for commenting on drafts of the book; Jeff Laake for providing Figure 10.1; NRC Research Press for allowing us to use Figures 10.2, 10.3, 10.4, 10.5, 10.6 and10.7; the International Whaling Commission for allowing us to use Figure 12.1; Sharon Hedley for providing Figures 12.1 and 12.2. D.L.B. is eternally indebted to Carol, Alice and Aidan for their support through writing the book, and for the many evenings and weekends that it has taken from them.

We hope this book will make the bewildering variety of methods for estimat­ ing the abundance of animal populations more accessible to the uninitiated and more coherent to the cogniscenti. We have tried to emphasize the fun­ damental similarity of many methods and to draw out the common threads that underlie them. With the exception of Chapter 13, we restrict ourselves to closed populations (those that do not change in composition over the period(s) being considered). Open population methods are in many ways simply extensions of closed population methods, and we have tried to pro­ vide the reader with a foundation on which understanding of both closed and open population methods can develop. We would like to thank Miguel Bernal for providing the St Andrews example dataset used frequently in the book; Miguel Bernal and Jeff Laake for commenting on drafts of the book; Jeff Laake for providing Figure 10.1; NRC Research Press for allowing us to use Figures 10.2, 10.3, 10.4, 10.5, 10.6 and10.7; the International Whaling Commission for allowing us to use Figure 12.1; Sharon Hedley for providing Figures 12.1 and 12.2. D.L.B. is eternally indebted to Carol, Alice and Aidan for their support through writing the book, and for the many evenings and weekends that it has taken from them.