Introduction To Winbugs For Ecologists

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Introduction To Winbugs For Ecologists

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Author : Marc Kéry
language : en
Publisher: Academic Press
Release Date : 2010-07-19

Introduction To Winbugs For Ecologists written by Marc Kéry and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-07-19 with Science categories.

Introduction to WinBUGS for Ecologists introduces applied Bayesian modeling to ecologists using the highly acclaimed, free WinBUGS software. It offers an understanding of statistical models as abstract representations of the various processes that give rise to a data set. Such an understanding is basic to the development of inference models tailored to specific sampling and ecological scenarios. The book begins by presenting the advantages of a Bayesian approach to statistics and introducing the WinBUGS software. It reviews the four most common statistical distributions: the normal, the uniform, the binomial, and the Poisson. It describes the two different kinds of analysis of variance (ANOVA): one-way and two- or multiway. It looks at the general linear model, or ANCOVA, in R and WinBUGS. It introduces generalized linear model (GLM), i.e., the extension of the normal linear model to allow error distributions other than the normal. The GLM is then extended contain additional sources of random variation to become a generalized linear mixed model (GLMM) for a Poisson example and for a binomial example. The final two chapters showcase two fairly novel and nonstandard versions of a GLMM. The first is the site-occupancy model for species distributions; the second is the binomial (or N-) mixture model for estimation and modeling of abundance. - Introduction to the essential theories of key models used by ecologists - Complete juxtaposition of classical analyses in R and Bayesian analysis of the same models in WinBUGS - Provides every detail of R and WinBUGS code required to conduct all analyses - Companion Web Appendix that contains all code contained in the book and additional material (including more code and solutions to exercises)

Introduction To Bayesian Methods In Ecology And Natural Resources

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Author : Edwin J. Green
language : en
Publisher: Springer Nature
Release Date : 2020-11-26

Introduction To Bayesian Methods In Ecology And Natural Resources written by Edwin J. Green and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-26 with Science categories.

This book presents modern Bayesian analysis in a format that is accessible to researchers in the fields of ecology, wildlife biology, and natural resource management. Bayesian analysis has undergone a remarkable transformation since the early 1990s. Widespread adoption of Markov chain Monte Carlo techniques has made the Bayesian paradigm the viable alternative to classical statistical procedures for scientific inference. The Bayesian approach has a number of desirable qualities, three chief ones being: i) the mathematical procedure is always the same, allowing the analyst to concentrate on the scientific aspects of the problem; ii) historical information is readily used, when appropriate; and iii) hierarchical models are readily accommodated. This monograph contains numerous worked examples and the requisite computer programs. The latter are easily modified to meet new situations. A primer on probability distributions is also included because these form the basis of Bayesian inference. Researchers and graduate students in Ecology and Natural Resource Management will find this book a valuable reference.

Applied Statistical Modelling For Ecologists

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Author : Marc Kéry
language : en
Publisher: Elsevier
Release Date : 2024-07-18

Applied Statistical Modelling For Ecologists written by Marc Kéry and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-18 with Science categories.

**2025 PROSE Award Finalist in Environmental Science**Applied Statistical Modelling for Ecologists provides a gentle introduction to the essential models of applied statistics: linear models, generalized linear models, mixed and hierarchical models. All models are fit with both a likelihood and a Bayesian approach, using several powerful software packages widely used in research publications: JAGS, NIMBLE, Stan, and TMB. In addition, the foundational method of maximum likelihood is explained in a manner that ecologists can really understand.This book is the successor of the widely used Introduction to WinBUGS for Ecologists (Kéry, Academic Press, 2010). Like its parent, it is extremely effective for both classroom use and self-study, allowing students and researchers alike to quickly learn, understand, and carry out a very wide range of statistical modelling tasks.The examples in Applied Statistical Modelling for Ecologists come from ecology and the environmental sciences, but the underlying statistical models are very widely used by scientists across many disciplines. This book will be useful for anybody who needs to learn and quickly become proficient in statistical modelling, with either a likelihood or a Bayesian focus, and in the model-fitting engines covered, including the three latest packages NIMBLE, Stan, and TMB. - Contains a concise and gentle introduction to probability and applied statistics as needed in ecology and the environmental sciences - Covers the foundations of modern applied statistical modelling - Gives a comprehensive, applied introduction to what currently are the most widely used and most exciting, cutting-edge model fitting software packages: JAGS, NIMBLE, Stan, and TMB - Provides a highly accessible applied introduction to the two dominant methods of fitting parametric statistical models: maximum likelihood and Bayesian posterior inference - Details the principles of model building, model checking and model selection - Adopts a "Rosetta Stone" approach, wherein understanding of one software, and of its associated language, will be greatly enhanced by seeing the analogous code in other engines - Provides all code available for download for students, at https://www.elsevier.com/books-and-journals/book-companion/9780443137150

Bayesian Population Analysis Using Winbugs

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Author : Marc Kéry
language : en
Publisher: Academic Press
Release Date : 2012

Bayesian Population Analysis Using Winbugs written by Marc Kéry and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Computers categories.

Bayesian statistics has exploded into biology and its sub-disciplines, such as ecology, over the past decade. The free software program WinBUGS, and its open-source sister OpenBugs, is currently the only flexible and general-purpose program available with which the average ecologist can conduct standard and non-standard Bayesian statistics. Comprehensive and richly commented examples illustrate a wide range of models that are most relevant to the research of a modern population ecologist All WinBUGS/OpenBUGS analyses are completely integrated in software R Includes complete documentation of all R and WinBUGS code required to conduct analyses and shows all the necessary steps from having the data in a text file out of Excel to interpreting and processing the output from WinBUGS in R

Bayesian Models

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Author : N. Thompson Hobbs
language : en
Publisher: Princeton University Press
Release Date : 2025-06-03

Bayesian Models written by N. Thompson Hobbs and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-03 with Science categories.

A fully updated and expanded edition of the essential primer on Bayesian modeling for ecologists Uniquely suited to deal with complexity in a statistically coherent way, Bayesian modeling has become an indispensable tool for ecological research. This book teaches the basic principles of mathematics and statistics needed to apply Bayesian models to the analysis of ecological data, using language non-statisticians can understand. Deemphasizing computer coding in favor of a clear treatment of model building, it starts with a definition of probability and proceeds step-by-step through distribution theory, likelihood, simple Bayesian models, and hierarchical Bayesian models. Now revised and expanded, Bayesian Models enables students and practitioners to gain new insights from ecological models and data properly tempered by uncertainty. Covers the basic rules of probability needed to model diverse types of ecological data in the Bayesian framework Shows how to write proper mathematical expressions for posterior distributions using directed acyclic graphs as templates Explains how to use the powerful Markov chain Monte Carlo algorithm to find posterior distributions of model parameters, latent states, and missing data Teaches how to check models to assure they meet the assumptions of model-based inference Demonstrates how to make inferences from single and multiple Bayesian models Provides worked problems for practicing and strengthening modeling skills Features new chapters on spatial models and modeling missing data

Camera Trapping For Wildlife Research

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Author : Francesco Rovero
language : en
Publisher: Pelagic Publishing Ltd
Release Date : 2016-06-18

Camera Trapping For Wildlife Research written by Francesco Rovero and has been published by Pelagic Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-06-18 with Photography categories.

Camera trapping is a powerful and now widely used tool in scientific research on wildlife ecology and management. It provides a unique opportunity for collecting knowledge, investigating the presence of animals, or recording and studying behaviour. Its visual nature makes it easy to successfully convey findings to a wide audience. This book provides a much-needed guide to the sound use of camera trapping for the most common ecological applications to wildlife research. Each phase involved in the use of camera trapping is covered: - Selecting the right camera type - Set-up and field deployment of your camera trap - Defining the sampling design: presence/absence, species inventory, abundance; occupancy at species level; capture-mark-recapture for density estimation; behavioural studies; community-level analysis - Data storage, management and analysis for your research topic, with illustrative examples for using R and Excel - Using camera trapping for monitoring, conservation and public engagement. Each chapter in this edited volume is essential reading for students, scientists, ecologists, educators and professionals involved in wildlife research or management.

Bayesian Statistics For Beginners

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Author : Therese M. Donovan
language : en
Publisher: Oxford University Press
Release Date : 2019-05-23

Bayesian Statistics For Beginners written by Therese M. Donovan and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-23 with Mathematics categories.

Bayesian statistics is currently undergoing something of a renaissance. At its heart is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. It is an approach that is ideally suited to making initial assessments based on incomplete or imperfect information; as that information is gathered and disseminated, the Bayesian approach corrects or replaces the assumptions and alters its decision-making accordingly to generate a new set of probabilities. As new data/evidence becomes available the probability for a particular hypothesis can therefore be steadily refined and revised. It is very well-suited to the scientific method in general and is widely used across the social, biological, medical, and physical sciences. Key to this book's novel and informal perspective is its unique pedagogy, a question and answer approach that utilizes accessible language, humor, plentiful illustrations, and frequent reference to on-line resources. Bayesian Statistics for Beginners is an introductory textbook suitable for senior undergraduate and graduate students, professional researchers, and practitioners seeking to improve their understanding of the Bayesian statistical techniques they routinely use for data analysis in the life and medical sciences, psychology, public health, business, and other fields.

Environmental Data Analysis

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Author : Carsten Dormann
language : en
Publisher: Springer Nature
Release Date : 2020-12-20

Environmental Data Analysis written by Carsten Dormann and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-20 with Medical categories.

Environmental Data Analysis is an introductory statistics textbook for environmental science. It covers descriptive, inferential and predictive statistics, centred on the Generalized Linear Model. The key idea behind this book is to approach statistical analyses from the perspective of maximum likelihood, essentially treating most analyses as (multiple) regression problems. The reader will be introduced to statistical distributions early on, and will learn to deploy models suitable for the data at hand, which in environmental science are often not normally distributed. To make the initially steep learning curve more manageable, each statistical chapter is followed by a walk-through in a corresponding R-based how-to chapter, which reviews the theory and applies it to environmental data. In this way, a coherent and expandable foundation in parametric statistics is laid, which can be expanded in advanced courses.The content has been “field-tested” in several years of courses on statistics for Environmental Science, Geography and Forestry taught at the University of Freiburg.

Integrated Population Models

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Author : Michael Schaub
language : en
Publisher: Academic Press
Release Date : 2021-11-12

Integrated Population Models written by Michael Schaub and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-12 with Science categories.

Integrated Population Models: Theory and Ecological Applications with R and JAGS is the first book on integrated population models, which constitute a powerful framework for combining multiple data sets from the population and the individual levels to estimate demographic parameters, and population size and trends. These models identify drivers of population dynamics and forecast the composition and trajectory of a population. Written by two population ecologists with expertise on integrated population modeling, this book provides a comprehensive synthesis of the relevant theory of integrated population models with an extensive overview of practical applications, using Bayesian methods by means of case studies. The book contains fully-documented, complete code for fitting all models in the free software, R and JAGS. It also includes all required code for pre- and post-model-fitting analysis. Integrated Population Models is an invaluable reference for researchers and practitioners involved in population analysis, and for graduate-level students in ecology, conservation biology, wildlife management, and related fields. The text is ideal for self-study and advanced graduate-level courses. - Offers practical and accessible ecological applications of IPMs (integrated population models) - Provides full documentation of analyzed code in the Bayesian framework - Written and structured for an easy approach to the subject, especially for non-statisticians

Capture Recapture Parameter Estimation For Open Animal Populations

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Author : George A. F. Seber
language : en
Publisher: Springer
Release Date : 2019-08-13

Capture Recapture Parameter Estimation For Open Animal Populations written by George A. F. Seber and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-13 with Mathematics categories.

This comprehensive book, rich with applications, offers a quantitative framework for the analysis of the various capture-recapture models for open animal populations, while also addressing associated computational methods. The state of our wildlife populations provides a litmus test for the state of our environment, especially in light of global warming and the increasing pollution of our land, seas, and air. In addition to monitoring our food resources such as fisheries, we need to protect endangered species from the effects of human activities (e.g. rhinos, whales, or encroachments on the habitat of orangutans). Pests must be be controlled, whether insects or viruses, and we need to cope with growing feral populations such as opossums, rabbits, and pigs. Accordingly, we need to obtain information about a given population’s dynamics, concerning e.g. mortality, birth, growth, breeding, sex, and migration, and determine whether the respective population is increasing , static, or declining. There are many methods for obtaining population information, but the most useful (and most work-intensive) is generically known as “capture-recapture,” where we mark or tag a representative sample of individuals from the population and follow that sample over time using recaptures, resightings, or dead recoveries. Marks can be natural, such as stripes, fin profiles, and even DNA; or artificial, such as spots on insects. Attached tags can, for example, be simple bands or streamers, or more sophisticated variants such as radio and sonic transmitters. To estimate population parameters, sophisticated and complex mathematical models have been devised on the basis of recapture information and computer packages. This book addresses the analysis of such models. It is primarily intended for ecologists and wildlife managers who wish to apply the methods to the types of problems discussed above, though it will also benefit researchers and graduate students in ecology. Familiarity with basic statistical concepts is essential.