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Novel Statistical Tools for Conserving and Managing Populations By gathering information on key demographic parameters, scientists can often predict how populations will develop in the future and relate these parameters to external influences, such as global warming. Because of their ability to easily incorporate random effects, fit state-space models, evaluate posterior model probabilities, and deal with missing data, modern Bayesian methods have become important in this area of statistical inference and forecasting. Emphasising model choice and model averaging, Bayesian Analysis for Population Ecology presents up-to-date methods for analysing complex ecological data. Leaders in the statistical ecology field, the authors apply the theory to a wide range of actual case studies and illustrate the methods using WinBUGS and R. The computer programs and full details of the data sets are available on the book’s website. The first part of the book focuses on models and their corresponding likelihood functions. The authors examine classical methods of inference for estimating model parameters, including maximum-likelihood estimates of parameters using numerical optimisation algorithms. After building this foundation, the authors develop the Bayesian approach for fitting models to data. They also compare Bayesian and traditional approaches to model fitting and inference. Exploring challenging problems in population ecology, this book shows how to use the latest Bayesian methods to analyse data. It enables readers to apply the methods to their own problems with confidence.
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BAYESIAN ANALYSIS for POPULATION ECOLOGY © 2010 by Taylor and Francis Group, LLC CHAPMAN & HALL/CRC Interdisciplinar y Statistics Series Series editors: N. Keiding, B.J.T. Morgan, C.K. Wikle, P. van der Heijden Published titles AN INVARIANT APPROACH TO STATISTICAL ANALYSIS OF SHAPES S. Lele and J. Richtsmeier ASTROSTATISTICS G. Babu and E. Feigelson BAYESIAN ANALYSIS FOR POPULATION ECOLOGY Ruth King, Byron J.T. Morgan, Olivier Gimenez, and Stephen P. Brooks BAYESIAN DISEASE MAPPING: HIERARCHICAL MODELING IN SPATIAL EPIDEMIOLOGY Andrew B. Lawson BIOEQUIVALENCE AND STATISTICS IN CLINICAL PHARMACOLOGY S. Patterson and B. Jones CLINICAL TRIALS IN ONCOLOGY SECOND EDITION J. Crowley, S. Green, and J. Benedetti CLUSTER RANDOMISED TRIALS R.J. Hayes and L.H. Moulton CORRESPONDENCE ANALYSIS IN PRACTICE, SECOND EDITION M. Greenacre DESIGN AND ANALYSIS OF QUALITY OF LIFE STUDIES IN CLINICAL TRIALS D.L. Fairclough DYNAMICAL SEARCH L. Pronzato, H. Wynn, and A. Zhigljavsky GENERALIZED LATENT VARIABLE MODELING: MULTILEVEL, LONGITUDINAL, AND STRUCTURAL EQUATION MODELS A. Skrondal and S. Rabe-Hesketh GRAPHICAL ANALYSIS OF MULTI-RESPONSE DATA K. Basford and J. Tukey INTRODUCTION TO COMPUTATIONAL BIOLOGY: MAPS, SEQUENCES, AND GENOMES M. Waterman © 2010 by Taylor and Francis Group, LLC Published titles MARKOV CHAIN MONTE CARLO IN PRACTICE W. Gilks, S. Richardson, and D. Spiegelhalter MEASUREMENT ERROR AND MISCLASSIFICATION IN STATISTICS AND EPIDEMIOLOGY: IMPACTS AND BAYESIAN ADJUSTMENTS P. Gustafson META-ANALYSIS OF BINARY DATA USING PROFILE LIKELIHOOD D. Böhning, R. Kuhnert, and S. Rattanasiri STATISTICAL ANALYSIS OF GENE EXPRESSION MICROARRAY DATA T. Speed STATISTICAL AND COMPUTATIONAL PHARMACOGENOMICS R. Wu and M. Lin STATISTICS IN MUSICOLOGY J. Beran STATISTICAL CONCEPTS AND APPLICATIONS IN CLINICAL MEDICINE J. Aitchison, J.W. Kay, and I.J. Lauder STATISTICAL AND PROBABILISTIC METHODS