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Bayesian Statistics for Beginners is an entry-level book on Bayesian statistics. It is like no other math book you’ve read. It is written for readers who do not have advanced degrees in mathematics and who may struggle with mathematical notation, yet need to understand the basics of Bayesian inference for scientific investigations. Intended as a “quick read,” the entire book is written as an informal, humorous conversation between the reader and writer—a natural way to present material for those new to Bayesian inference. The most impressive feature of the book is the sheer length of the journey, from introductory probability to Bayesian inference and applications, including Markov Chain Monte Carlo approaches for parameter estimation, Bayesian belief networks, and decision trees. Detailed examples in each chapter contribute a great deal, where Bayes’ Theorem is at the front and center with transparent, step-by-step calculations. A vast amount of material is covered in a lighthearted manner; the journey is relatively pain-free. The book is intended to jump-start a reader’s understanding of probability, inference, and statistical vocabulary that will set the stage for continued learning. Other features include multiple links to web-based material, an annotated bibliography, and detailed, step-by-step appendices.

This book provides an introduction to three of the main tools used in the development of bioinformatics software — Perl, R, and MySQL — and explains how these can be used together to tackle the complex data-driven challenges that typify modern biology. The book is intended to provide the reader with the knowledge and confidence needed to create databases, to write programs to analyse and visualise data, and to develop interactive web-based applications. Platform-independent examples are provided throughout, making the book suitable for users of Windows, Mac OS or Linux.

Issues of regulation and control are central to the study of biological and biochemical systems. Thus it is not surprising that the tools of feedback control theory—engineering techniques developed to design and analyze self-regulating systems—have proven useful in the study of these biological mechanisms. Such interdisciplinary work requires knowledge of the results, tools, and techniques of another discipline, as well as an understanding of the culture of an unfamiliar research community. This book attempts to bridge the gap between disciplines by presenting applications of systems and control theory to cell biology that range from surveys of established material to descriptions of new developments in the field. The first chapter offers a primer on concepts from dynamical systems and control theory, which allows the life scientist with no background in control theory to understand the concepts presented in the rest of the book. Following the introduction of ordinary differential equation-based modeling in the first chapter, the second and third chapters discuss alternative modeling frameworks. The remaining chapters sample a variety of applications, considering such topics as quantitative measures of dynamic behavior, modularity, stoichiometry, robust control techniques, and network identification.

This book discusses the change in use of statistics in ecology—especially the increased use (over the last two decades) of more sophisticated statistical and computational methods. This book also highlights the contribution of statistical modeling to knowledge acquisition as an important way of abstracting ecological questions into mathematical models, and its role in the research cycle currently used by most ecologists. The book reviews briefly the logic and key parts of statistical linear models, as they form the conceptual foundation of most of the methods discussed in the book. Finally, it explains the book’s organization, the background required for readers, and strategies for getting the most out of this intermediate-level book.

Scientists must communicate their work through clear writing and publish it where it will be read. To succeed, you need method, but also need to understand the worlds of journals, publishers and science evaluation. The Editor’s Guide to Writing and Publishing Science provides a comprehensive approach to how to write engaging papers, and strategies for publishing where they will be read and have impact. Drawing on decades of experience as a scientist, mentor and chief editor, Michael Hochberg offers a unique, authoritative view on writing science and into the little-known worlds of journals and publication. Succeeding in science means being a citizen of science, and The Editor’s Guide educates the reader in some of the most pressing issues and possible solutions, and provides key references for deeper exploration. Developing one’s career does not mean careerism, and Hochberg provides guidelines and advice for young researchers to engage in the craft of science, forge collaborations, contribute to the scientific commons as a peer reviewer and interact through social media. Understanding the challenges and opportunities in publishing is only possible with knowledge of how science communication is changing, and the reader is introduced to the important, emerging world of Open Science. Written in a practical and accessible way for students, postdoctoral researchers, early-career scientists and professionals across a wide range of scientific fields, The Editor’s Guide is a powerful tool for learning and improving individual skills, and can be the basis for discussion groups, or used as a text for dedicated classroom courses.

Evolutionary genetics is the study of how genetic variation leads to evolutionary change. With the recent explosion in the availability of whole genome sequence data, vast quantities of genetic data are being generated at an ever-increasing pace with the result that programming has become an essential tool for researchers. Most importantly, a thorough understanding of evolutionary principles is essential for making sense of this genetic data. This up-to-date textbook covers all the major components of modern evolutionary genetics, carefully explaining fundamental processes such as mutation, natural selection, genetic drift, and speciation, together with their consequences. In addition to the text, study questions are provided to motivate the reader to think and reflect on the concepts in each chapter. Practical experience is essential when it comes to developing an understanding of how to use genetic data to analyze and address interesting questions in the life sciences and how to interpret results in meaningful ways. Throughout the book, a series of online, computer-based tutorials serves as an introduction to programming and analysis of evolutionary genetic data centered on the R programming language, which stands out as an ideal all-purpose platform to handle and analyze such data. The book and its online materials take full advantage of the authors’ own experience in working in a post-genomic revolution world, and introduce readers to the plethora of molecular and analytical methods that have only recently become available.

The notion of function is an integral part of thinking in both biology and technology; biological organisms and technical artifacts are both ascribed functionality. Yet the concept of function is notoriously obscure (with problematic issues regarding the normative and the descriptive nature of functions, for example) and demands philosophical clarification. So too the relationship between biological organisms and technical artifacts: although entities of one kind are often described in terms of the other—as in the machine analogy for biological organism or the evolutionary account of technological development—the parallels between the two break down at certain points. This book takes on both issues and examines the relationship between organisms and artifacts from the perspective of functionality. Believing that the concept of function is the root of an accurate understanding of biological organisms, technical artifacts, and the relation between the two, the chapters take an integrative approach, offering philosophical analyses that embrace both biological and technical fields of function ascription. They aim at a better understanding not only of the concept of function but also of the similarities and differences between organisms and artifacts as they relate to functionality. Their ontological, epistemological, and phenomenological comparisons will clarify problems that are central to the philosophies of both biology and technology.

Learning how to get answers from data is an integral part of modern training in the natural, physical, social, and engineering sciences. One of the most exciting changes in data management and analysis during the last decade has been the growth of open source software. The open source statistics and programming language R has emerged as a critical component of any researcher's toolbox. Indeed, R is rapidly becoming the standard software for analyses, graphical presentations, and programming in the biological sciences. This book provides a functional introduction to R. While teaching how to import, explore, graph, and analyse data, it keeps readers focused on their ultimate goals — communicating their data in oral presentations, posters, papers, and reports. It also provides a consistent method (workflow) for using R that is simple, efficient, reliable, accurate, and reproducible. The material in the book reproduces the engaging and sometimes humorous nature of the three-day course on which it is based.

Getting Started with R deals with learning how to get answers from data, an integral part of modern training in the natural, physical, social, and engineering sciences. One of the most exciting developments in data management, quantitative analysis, and visualization was the growth of the open source application R. This statistics and programming language has emerged as a critical component of biologists’, and many other scientists’, toolboxes. R is rapidly becoming standard software for data manipulation, visualization, and analysis. This book provides a functional introduction for biologists new to R. While teaching how to import, visualize, and analyse, it keeps readers focused on their ultimate goals … to communicate their data and analyses in presentations, posters, papers, websites, and reports. It provides a consistent approach and workflow for using R, one that is simple, efficient, intuitive, reliable, accurate, and reproducible. The material in the book reproduces the engaging and sometimes humorous nature of the three-day course on which it is based. What is different in the second edition? It has been entirely rewritten to accommodate several new developments in R and changes made in teaching the course. Chapters have been added on preparing data for R, on analyses of more experimental designs (regression and one-way and two-way ANOVA, in addition to the old ANCOVA example), and on generalized linear models. The book also uses as default a popular, new set of tools for managing data and producing graphs via the add-on packages dplyr and ggplot2. There are now three authors.

Invasion Dynamics depicts how non-native species spread and perform in their novel ranges and how recipient socio-ecological systems are reshaped and how they respond to the new incursions. It collects evidence for grouping patterns of spread into four types and three associated phenomena, discusses candidate explanations for each pattern, and introduces analytic tools for capturing and forecasting invasion dynamics. Special attention is given to the potential mechanisms of boosted range expansion and nonequilibrium demographic dynamics during invasion. The diverse mechanisms that drive direct and mediated biotic interactions between invaders and resident species are elucidated, and triggers of potential regime shifts in recipient ecosystems are identified. It further explores the ways in which local and regional species assemblages are reshuffled and reorganized. Efficient management of invasions requires not only insights on invasion dynamics across scales but also objective assessment of ecological and economic impacts, as well as sound protocols for prioritizing and optimizing management effort. Biological invasions, therefore, involve more than the actions of invaders and reactions of invaded ecosystems; they represent a co-evolving complex adaptive system with emergent features of network complexity and invasibility. Invasions are thus a formidable force that acts in concert with other facets of global change to initiate the adaptive wheel of panarchy and shape the altered biosphere in the Anthropocene.

Measuring Metabolic Rates demystifies the field of metabolic rate measurement, explaining every common variation of the art, from century-old manometric methods through ingenious syringe-based techniques, direct calorimetry, aquatic respirometry, stable-isotope metabolic measurement, and every type of flow-through respirometry. Each variation is described in enough detail to allow it to be applied in practice. Special chapters are devoted to metabolic phenotyping and human metabolic measurement, including room calorimetry. Background information on different analyzer and equipment types allows users to choose the best instruments for their application. Respirometry equations—normally a topic of terror and confusion to researchers—are derived and described in enough detail to make their selection and use effortless. Tools and skills—many of them open source—that will amplify the innovative researcher’s capabilities are described. Vital topics such as manual and automated baselining, implementing multi-animal systems, common pitfalls, and the correct analysis and presentation of metabolic data are covered in enough detail to turn a respirometry neophyte into a hardened metabolic warrior, ready to take on the task of publication in peer-reviewed journals with confidence.

This book summarizes the statistical models and computational algorithms for comparative analysis of genetic sequence data in the fields of molecular evolution, molecular phylogenetics, and statistical phylogeography. The book presents and explains the models of nucleotide, amino acid, and codon substitution, and their use in calculating pairwise sequence distances and in reconstruction of phylogenetic trees. All major methods for phylogeny reconstruction are covered in detail, including neighbour joining, maximum parsimony, maximum likelihood, and Bayesian methods. Using motivating examples, the book includes a comprehensive introduction to Bayesian computation using Markov chain Monte Carlo (MCMC). Advanced topics include estimation of species divergence times using the molecular clock, detection of molecular adaptation, simulation of molecular evolution, as well as species tree estimation and species delimitation using genomic sequence data.

Statistics is a fundamental component of the scientific toolbox, but learning the basics of this area of mathematics is one of the most challenging parts of a research training. This book gives an up-to-date introduction to the classical techniques and modern extensions of linear model analysis—one of the most useful approaches in the analysis of scientific data in the life and environmental sciences. The book emphasizes an estimation-based approach that takes account of recent criticisms of over-use of probability values and introduces the alternative approach using information criteria. The book is based on the use of the open-source R programming language for statistics and graphics that is rapidly becoming the lingua franca in many areas of science. Statistics is introduced through worked analyses performed in R using interesting data sets from ecology, evolutionary biology, and environmental science. The data sets and R scripts are available as supporting material.

The book presents a hands-on introductory guide to DNA sequence analysis. This can be depicted as a linear map of As, Cs, Gs, and Ts; however, such a map only hints at the varied contours and crevices, twists, kinks, loops, and nodes of the extraordinary double helix. The book uncovers why Perl is the language of choice when identifying patterns in strings of text. It offers a simplified approach to programming that is applicable to biological sequence analysis, especially geared to those who do not have prior programming experience. Concepts include good programming practices, creative approaches to teaching and working with strings and files of sequence data, and sequence related applications of regular expressions, control structures, arrays, and hash tables. A linguistic metaphor is used throughout the text to complement an exceptionally friendly and pedagogically sound introduction to sequence analysis via Perl programming.

This book presents an integrative approach tomathematical and statistical modelling in ecology and evolutionary biology. After an introductory chapter, the book devotes one chapter for movement ecology, one for population ecology, one for community ecology, and one for genetics and evolutionary ecology. Each chapter starts with a conceptual section, which provides the necessary biological background and motivates the modelling approaches. The next three sections present mathematical modelling approaches, followed by one section devoted to statistical approaches. Each chapter ends with a perspectives section, which summarizes the key messages and discusses the limitations of the approaches considered. To illustrate how the very same modelling approaches apply in different fields of ecology and evolutionary biology, the book uses movement models as a building block to construct single-species models of population dynamics, the models of which are further expanded to models of species communities and to models of evolutionary dynamics. In all chapters, the book starts by making assumptions at the level of individuals, leading to individual-based simulationmodels. To derive analytical insights and to compare the behaviours of different types of models, the book shows how the individual-based models can be simplified, e.g. to yield models formulated directly at the population level. The book has a special emphasis on the integration of models with data. To achieve this, it applies statistical methods to data generated by mathematical models, and thus asks to what extent does the data contain signals of the underlying mechanisms.

This book intends to familiarise prospective users in the environmental community with satellite remote sensing technology and its applications, introducing terminology and principles behind satellite remote sensing data and analyses. It provides a detailed overview of the possible applications of satellite data in natural resource management, demonstrating how ecological knowledge and satellite-based information can be effectively combined to address a wide array of current natural resource management needs. Topics considered include the use of satellite data to monitor the various dimensions of biodiversity; the use of this technology to track pressures on biodiversity such as invasive species, pollution, and illegal fishing; the utility of satellite remote sensing to inform the management of protected areas, translocation, and habitat restoration; and the contribution of satellite remote sensing towards the monitoring of ecosystem services and wellbeing. The intended audience is ecologists and environmental scientists; the book is targeted as a handbook and is therefore also suitable for advanced undergraduate and postgraduate students in the biological and ecological sciences, as well as policy makers and specialists in the fields of conservation biology, biodiversity monitoring, and natural resource management. The book assumes no prior technical knowledge of satellite remote sensing systems and products. It is written so as to generate interest in the ecological, environmental management, and remote sensing communities, highlighting issues associated with the emergence of truly synergistic approaches between these disciplines.

Findings from the Human Genome Project and from Genome-Wide Association (GWA) studies indicate that many diseases and traits manifest a more complex genomic pattern than previously assumed. These findings, and advances in high-throughput sequencing, suggest that there are many sources of influence—genetic, epigenetic, and environmental. This volume investigates the role of the interactions of genes and environment (G × E) in diseases and traits (referred to by the contributors as complex phenotypes) including depression, diabetes, obesity, and substance use.  The contributors first present different statistical approaches or strategies to address G × E and G × G interactions with high-throughput sequenced data, including two-stage procedures to identify G × E and G × G interactions, marker-set approaches to assessing interactions at the gene level, and the use of a partial-least square (PLS) approach. The contributors then turn to specific complex phenotypes, research designs, or combined methods that may advance the study of G × E interactions, considering such topics as randomized clinical trials in obesity research, longitudinal research designs and statistical models, and the development of polygenic scores to investigate G × E interactions. Contributors Fatima Umber Ahmed, Yin-Hsiu Chen, James Y. Dai, Caroline Y. Doyle, Zihuai He, Li Hsu, Shuo Jiao, Erin Loraine Kinnally, Yi-An Ko, Charles Kooperberg, Seunggeun Lee, Arnab Maity, Jeanne M. McCaffery, Bhramar Mukherjee, Sung Kyun Park, Duncan C. Thomas, Alexandre Todorov, Jung-Ying Tzeng, Tao Wang, Michael Windle, Min Zhang

Evolutionary genomics is a relatively new research field with the ultimate goal of understanding the underlying evolutionary and genetic mechanisms for the emergence of genome complexity under changing environments. It stems from an integration of high throughput data from functional genomics, statistical modelling and bioinformatics, and the procedure of phylogeny-based analysis. This book summarises the statistical framework of evolutionary genomics, and illustrates how statistical modelling and testing can enhance our understanding of functional genomic evolution. The book reviews the recent developments in methodology from an evolutionary perspective of genome function, and incorporates substantial examples from high throughput data in model organisms. In addition to phylogeny-based functional analysis of DNA sequences, the book includes discussion on how new types of functional genomic data (e.g., microarray) can provide exciting new insights into the evolution of genome function, which can lead in turn to an understanding of the emergence of genome complexity during evolution.