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.
Reviews / Votes
"This
new volume by Dr. Buckland and his colleagues at the University of St. Andrews
serves as both an introduction to distance sampling for people new to the field
as well as a summary of recent developments. . I recommend the book as an
excellent entry into this fascinating area of estimation and research. The R
code alone is worth the purchase price." (Mark Miller, BTO British Trust for
Ornithology, bto.org, January, 2016)
Series
Edition
Language
Place of publication
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Research
Illustrations
80 s/w Abbildungen
XV, 277 p. 80 illus.
Dimensions
Height: 241 mm
Width: 160 mm
Thickness: 21 mm
Weight
ISBN-13
978-3-319-19218-5 (9783319192185)
DOI
10.1007/978-3-319-19219-2
Schweitzer Classification
Ken Newman is a mathematical statistician for the United States Fish and Wildlife Service. His primary research interest is in the use of state-space models to model the population dynamics of fish and wildlife to answer scientific questions and to serve as management decision support tools.
Stephen Buckland is Professor of Statistics at the University of St Andrews, and is also Co-Director of the UK National Centre for Statistical Ecology. His interest in modelling population dynamics dates from a project to develop a decision support system for managers of red deer populations in Scotland in the 1990s.
Byron Morgan is Honorary Professorial Research Fellow in the University of Kent, and Co-Director of the National Centre for Statistical Ecology. He is interested in integrated population modelling, which accounts for data collected on different aspects of the demography of wild animals. A convenient component of this work is the use of state-space models for describing ecological time series.
Ruth King is a Reader in Statistics at the University of St Andrews. Her research interests include the development of population dynamics models and model fitting tools in both the classical and Bayesian frameworks. This particularly includes the application of hidden Markov (or state-space) models.
David Borchers is a Reader in Statistics at the University of St Andrews. His research involves developing general statistical models for estimating population density and distribution, integrating hidden state or latent variable models with various kinds of observation model.
Diana Cole is a Senior Lecturer in Statistics at the University of Kent. Her primary research is on parameter redundancy or identifiability of models used in statistical ecology.
Panagiotis Besbeas is a lecturer in the Athens University of Economics and Business, and also a part-time post-doctoral research associate within the National Centre for Statistical Ecology group in the University of Kent, Canterbury. His research includes integrated population modelling, recently including the importance of replication for error estimation, and new methods for measuring goodness of fit as well as for conducting model selection.
Olivier Gimenez is senior scientist in statistical ecology at the Centre National de la Recherche Scientifique (CNRS) in France. His main research interest is animal demography using hidden structure models with contributions to the coexistence of humans and animals.
Len Thomas is a Reader in Statistics at the University of St Andrews, and is Director of the Centre for Research into Ecological and Environmental Modelling, an inter-disciplinary research centre at the university. He has two main research interests: (1) use of computer-intensive methods, particularly particle filters, to fit and compare state-space models of wildlife population dynamics; (2) development of methods and software for estimating animal population size and density.
Introduction.- The basic methods.- Designing surveys.- Designing distance sampling experiments.- Field methods and data checking.- Modelling detection functions.- Design-based estimation of animal density and abundance.- Model-based distance sampling: two-stage models.- Model-based distance sampling: full likelihood methods.- Variations on a theme.- Taxon-specific issues.- Exchanging assumptions for data.- Summary.
