This book describes a range of models for ecological data sets using both classical and Bayesian methods of inference, and illustrates their use through a varied set of real examples. These examples exhibit a range of sampling schemes, applied to various taxa and in varying environmental conditions. The book also describes pitfalls to be avoided in the design and analysis of ecological surveys.
Models may be spatial and/or temporal, and often include covariates such as measures of habitat, temperature and rainfall. Covariate selection for given problems is discussed. Other topics include the construction of individual species spatial maps, how spatial distributions change over time, models for unmarked and marked/identifiable individuals, integrated analysis, and how methods can be used to estimate biodiversity. In addition, the book reviews emerging new technologies; for instance, remote sensing and the use of drones, which result in opportunities for new methods of analysis.
This book is a source of reference for postgraduates and research scientists in statistics and ecology. It may be used for graduate-level teaching, as well as a research reference. It critically and comprehensively presents the most up-to-date statistical modeling work in the area, recommending statistical methods. There is an extensive bibliography and multiple links to computer programs written in R, BUGS and other languages, as well as relevant computer packages; computing material may be accessed via download from the book website.
Part I Foundation.
Chapter 1 Introduction.
Chapter 2 Sampling schemes
and data collection.
Chapter 3 Models and inference.
Chapter 4 Model
classes.- Part II Analysing data on unmarked individuals.
Chapter 5 Models
for recorded/not recorded data.
Chapter 6 Point-process models and
presence-only data.
Chapter 7 A general framework for modelling animal
population survey data over time.
Chapter 8 N-mixture models for count
data.
Chapter 9 Models for the abundance, productivity, survival and
phenology of seasonal organisms.
Chapter 10 Distance sampling.- Part III
Analysing data on identifiable individuals.
Chapter 11 Models for data from
identifiable animals.- Part IV Combining methods.
Chapter 12 Integrated
analysis .
Chapter 13 Indices, Red Lists, biodiversity indicators and
turnover.- Part V Conclusions.
Chapter 14 Overview.
Byron J.T. Morgan, Ph.D., is Emeritus Professor at the University of Kent. His research interests include applied statistics, biometry, statistical ecology, parameter redundancy, population dynamics and applications of Bayesian methods.
Emily B. Dennis, Ph.D., is a Senior Ecological Statistician at Butterfly Conservation. Her research interests include modeling species abundance and distribution, citizen science data and biodiversity indicators, in particular with application to butterflies and moths.
Stephen T. Buckland, Ph.D., is Emeritus Professor of Statistics at the University of St. Andrews. His research interests include modeling population dynamics, wildlife resource management, wildlife population assessment, distance sampling and biodiversity monitoring
