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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.

This book is a practical guide designed for students and researchers with an existing knowledge of R who wish to learn how to apply it in an epidemiological context and exploit its versatility. It also serves as a broader introduction to the quantitative aspects of modern practical epidemiology. The standard tools used in epidemiology are described and the practical use of R for these is explained and laid out. R code examples, many with output, are embedded throughout the text. Epidemiology with R is an advanced textbook suitable for senior undergraduate and graduate students, professional researchers, and practitioners in the fields of human and non-human epidemiology, public health, veterinary science, and biostatistics.

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.

Game theory in biology seeks to predict social behaviour and other traits that influence how individuals interact. It does this by tentatively assuming that current traits are stable endpoints of evolution by natural selection. The theory is used to model aggressive behaviour, cooperation, negotiation, and signalling, as well as phenotypic attributes like an individual’s sex and mating type. This book covers the basic concepts and the traditional examples of biological game theory. It expands the frontiers of the field, emphasizing the importance of the co-evolution of traits and the implications of variation for reputation, markets, negotiation, and other social phenomena. It also highlights that it can be important to embed game interactions in the environment and an individual’s life. A major new direction developed in the book is that game theory can be extended by incorporating behavioural mechanisms, including mechanisms of reinforcement learning. By doing this the theory can successfully describe important phenomena like social dominance in group-living animals that previously have been difficult to model. By focusing on behavioural mechanisms, game theory can also make closer contact with empirical observation and with current research in fields like animal psychology and neuroscience.

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.

Habitat is crucial to the survival and reproduction of individual organisms as well as persistence of populations. As such, species-habitat relationships have long been studied, particularly in the field of wildlife ecology and to a lesser extent in the more encompassing discipline of ecology. The habitat requirements of a species largely determine its spatial distribution and abundance in nature. One way to recognize and appreciate the over-riding importance of habitat is to consider that a young organism must find and settle into the appropriate type of habitat as one of the first challenges of life. This process can be cast in a probabilistic framework and used to better understand the mechanisms behind habitat preferences and selection. There are at least six distinctly different statistical approaches to conducting a habitat analysis – that is, identifying and quantifying the environmental variables that a species most strongly associates with. These are (1) comparison among group means (e.g., ANOVA), (2) multiple linear regression, (3) multiple logistic regression, (4) classification and regression trees, (5) multivariate techniques (Principal Components Analysis and Discriminant Function Analysis), and (6) occupancy modelling. Each of these is lucidly explained and demonstrated by application to a hypothetical dataset. The strengths and weaknesses of each method are discussed. Given the ongoing biodiversity crisis largely caused by habitat destruction, there is a crucial and general need to better characterize and understand the habitat requirements of many different species, particularly those that are threatened and endangered.

Knowledge of how to get useful information from data is essential in the life and environmental sciences. This book provides learners with knowledge, experience, and confidence about how to efficiently and reliably discover useful information from data. The content is developed from first- and second-year undergraduate-level courses taught by the authors. It charts the journey from question, to raw data, to clean and tidy data, to visualizations that provide insights. This journey is presented as a repeatable workflow fit for use with many types of question, study, and data. Readers discover how to use R and RStudio, and learn key concepts for drawing appropriate conclusions from patterns in data. The book focuses on providing learners with a solid foundation of skills for working with data, and for getting useful information from data summaries and visualizations. It focuses on the strength of patterns (i.e. effect sizes) and their meaning (e.g. correlation or causation). It purposefully stays away from statistical tests and p-values. Concepts covered include distribution, sample, population, mean, median, mode, variance, standard deviation, correlation, interactions, and non-independence. The journey from data to insight is illustrated by one workflow demonstration in the book, and three online. Each involves data collected in a real study. Readers can follow along by downloading the data, and learning from the descriptions of each step in the journey from the raw data to visualizations that show the answers to the questions posed in the original studies.

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 is a quantitative exposition of our current understanding of the dynamics of plant and animal populations, with the goal that readers will be able to understand, and participate in the management of populations in the wild. The book uses mathematical models to establish the basic principles of population behaviour. It begins with a philosophical approach to mathematical models of populations. It then progresses from a description of models with a single variable, abundance, to models that describe changes in the abundance of individuals at each age, then similar models that describe populations in terms of the abundance over size, life stage, and space. The book assumes a knowledge of basic calculus, but explains more advanced mathematical concepts such as partial derivatives, matrices, and random signals, as it makes use of them. The book explains the basis of the principles underlying important population processes, such as the mechanism that allow populations to persist, rather than go extinct, the way in which populations respond to variable environments, and the origin of population cycles.The next two chapters focus on application of the principles of population dynamics to manage for the prevention of extinction, as well as the management of fisheries for sustainable, high yields. The final chapter recapitulates how different population behaviors arise in situations with different levels of density dependence and replacement (the potential lifetime reproduction per individual), and how variability arises at different time scales set by a species’ life history.

Population genetics is an inherently quantitative discipline. Because the focus of population genetics studies is usually on abstract concepts like the frequencies of genetic variants over time, it can at first glance be difficult to conceptualize and appropriately visualize. As more and more quantitative models and methods have become established in the discipline, it has become necessary for people just entering the field to quickly develop a good understanding of the many layers of complex approaches, so as to correctly interpret even basic results. An unfortunate side effect of the widespread implementation of ready-to-use quantitative software packages is that some facets of analysis can become rote, which at best might lead to implementation without the full understanding of the user and at worst, inappropriate application leading to misguided conclusions. In this book a “learning by doing” approach is employed to encourage readers to begin developing an intuitive understanding of population genetics concepts. The analytical software R, which has increasingly been the program of choice for early exposure to basic statistical programming, is freely available online, has cross-platform compatibility (Windows, Mac, and Linux all support distributions of R), and offers the potential for hands-on implementation by the students, in addition to using pre-packaged functions.