Chapman & Hall/CRC Applied Environmental Statistics – serie

Many applied researchers equate spatial statistics with prediction or mapping, but this book naturally extends linear models, which includes regression and ANOVA as pillars of applied statistics, to achieve a more comprehensive treatment of the analysis of spatially autocorrelated data. Spatial Linear Models for Environmental Data, aimed at students and professionals with a master’s level training in statistics, presents a unique, applied, and thorough treatment of spatial linear models within a statistics framework. Two subfields, one called geostatistics and the other called areal or lattice models, are extensively covered. Zimmerman and Ver Hoef present topics clearly, using many examples and simulation studies to illustrate ideas. By mimicking their examples and R code, readers will be able to fit spatial linear models to their data and draw proper scientific conclusions.Topics covered include:Exploratory methods for spatial data including outlier detection, (semi)variograms, Moran’s I, and Geary’s c.Ordinary and generalized least squares regression methods and their application to spatial data.Suitable parametric models for the mean and covariance structure of geostatistical and areal data.Model-fitting, including inference methods for explanatory variables and likelihood-based methods for covariance parameters.Practical use of spatial linear models including prediction (kriging), spatial sampling, and spatial design of experiments for solving real world problems.All concepts are introduced in a natural order and illustrated throughout the book using four datasets. All analyses, tables, and figures are completely reproducible using open-source R code provided at a GitHub site. Exercises are given at the end of each chapter, with full solutions provided on an instructor’s FTP site supplied by the publisher.

Bringing Bayesian Models to Life empowers the reader to extend, enhance, and implement statistical models for ecological and environmental data analysis. We open the black box and show the reader how to connect modern statistical models to computer algorithms. These algorithms allow the user to fit models that answer their scientific questions without needing to rely on automated Bayesian software. We show how to handcraft statistical models that are useful in ecological and environmental science including: linear and generalized linear models, spatial and time series models, occupancy and capture-recapture models, animal movement models, spatio-temporal models, and integrated population-models. Features:R code implementing algorithms to fit Bayesian models using real and simulated data examples.A comprehensive review of statistical models commonly used in ecological and environmental science.Overview of Bayesian computational methods such as importance sampling, MCMC, and HMC.Derivations of the necessary components to construct statistical algorithms from scratch.Bringing Bayesian Models to Life contains a comprehensive treatment of models and associated algorithms for fitting the models to data. We provide detailed and annotated R code in each chapter and apply it to fit each model we present to either real or simulated data for instructional purposes. Our code shows how to create every result and figure in the book so that readers can use and modify it for their own analyses. We provide all code and data in an organized set of directories available at the authors' websites.

Sound forest management planning requires cost-efficient approaches to optimally utilize given resources. Emphasizing the mathematical and statistical features of forest sampling to assess classical dendrometrical quantities, Sampling Techniques for Forest Inventories presents the statistical concepts and tools needed to conduct a modern forest inventory.The book first examines design-based survey sampling and inference for finite populations, covering inclusion probabilities and the Horvitz–Thompson estimator, followed by more advanced topics, including three-stage element sampling and the model-assisted estimation procedure. The author then develops the infinite population model/Monte Carlo approach for both simple and complex sampling schemes. He also uses a case study to reveal a variety of estimation procedures, relies on anticipated variance to tackle optimal design for forest inventories, and validates the resulting optimal schemes with data from the Swiss National Forest Inventory. The last chapters outline facts pertaining to the estimation of growth and introduce transect sampling based on the stereological approach. Containing many recent developments available for the first time in book form, this concise and up-to-date work provides the necessary theoretical and practical foundation to analyze and design forest inventories.

Biometry for Forestry and Environmental Data with Examples in R focuses on statistical methods that are widely applicable in forestry and environmental sciences, but it also includes material that is of wider interest.Features:· Describes the theory and applications of selected statistical methods and illustrates their use and basic concepts through examples with forestry and environmental data in R.· Rigorous but easily accessible presentation of the linear, nonlinear, generalized linear and multivariate models, and their mixed-effects counterparts. Chapters on tree size, tree taper, measurement errors, and forest experiments are also included.· Necessary statistical theory about random variables, estimation and prediction is included. The wide applicability of the linear prediction theory is emphasized. · The hands-on examples with implementations using R make it easier for non-statisticians to understand the concepts and apply the methods with their own data. Lot of additional material is available at www.biombook.org.The book is aimed at students and researchers in forestry and environmental studies, but it will also be of interest to statisticians and researchers in other fields as well.

Evaluating Climate Change Impacts discusses assessing and quantifying climate change and its impacts from a multi-faceted perspective of ecosystem, social, and infrastructure resilience, given through a lens of statistics and data science. It provides a multi-disciplinary view on the implications of climate variability and shows how the new data science paradigm can help us to mitigate climate-induced risk and to enhance climate adaptation strategies.This book consists of chapters solicited from leading topical experts and presents their perspectives on climate change effects in two general areas: natural ecosystems and socio-economic impacts. The chapters unveil topics of atmospheric circulation, climate modeling, and long-term prediction; approach the problems of increasing frequency of extreme events, sea level rise, and forest fires, as well as economic losses, analysis of climate impacts for insurance, agriculture, fisheries, and electric and transport infrastructures. The reader will be exposed to the current research using a variety of methods from physical modeling, statistics, and machine learning, including the global circulation models (GCM) and ocean models, statistical generalized additive models (GAM) and generalized linear models (GLM), state space and graphical models, causality networks, Bayesian ensembles, a variety of index methods and statistical tests, and machine learning methods. The reader will learn about data from various sources, including GCM and ocean model outputs, satellite observations, and data collected by different agencies and research units. Many of the chapters provide references to open source software R and Python code that are available for implementing the methods.

Understand How to Analyze and Interpret Information in Ecological Point PatternsAlthough numerous statistical methods for analyzing spatial point patterns have been available for several decades, they haven’t been extensively applied in an ecological context. Addressing this gap, Handbook of Spatial Point-Pattern Analysis in Ecology shows how the techniques of point-pattern analysis are useful for tackling ecological problems. Within an ecological framework, the book guides readers through a variety of methods for different data types and aids in the interpretation of the results obtained by point-pattern analysis. Ideal for empirical ecologists who want to avoid advanced theoretical literature, the book covers statistical techniques for analyzing and interpreting the information contained in ecological patterns. It presents methods used to extract information hidden in spatial point-pattern data that may point to the underlying processes. The authors focus on point processes and null models that have proven their immediate utility for broad ecological applications, such as cluster processes.Along with the techniques, the handbook provides a comprehensive selection of real-world examples. Most of the examples are analyzed using Programita, a continuously updated software package based on the authors’ many years of teaching and collaborative research in ecological point-pattern analysis. Programita is tailored to meet the needs of real-world applications in ecology. The software and a manual are available online.

Making statistical modeling and inference more accessible to ecologists and related scientists, Introduction to Hierarchical Bayesian Modeling for Ecological Data gives readers a flexible and effective framework to learn about complex ecological processes from various sources of data. It also helps readers get started on building their own statistical models. The text begins with simple models that progressively become more complex and realistic through explanatory covariates and intermediate hidden states variables. When fitting the models to data, the authors gradually present the concepts and techniques of the Bayesian paradigm from a practical point of view using real case studies. They emphasize how hierarchical Bayesian modeling supports multidimensional models involving complex interactions between parameters and latent variables. Data sets, exercises, and R and WinBUGS codes are available on the authors’ website.This book shows how Bayesian statistical modeling provides an intuitive way to organize data, test ideas, investigate competing hypotheses, and assess degrees of confidence of predictions. It also illustrates how conditional reasoning can dismantle a complex reality into more understandable pieces. As conditional reasoning is intimately linked with Bayesian thinking, considering hierarchical models within the Bayesian setting offers a unified and coherent framework for modeling, estimation, and prediction.

Statistical Methods for Field and Laboratory Studies in Behavioral Ecology focuses on how statistical methods may be used to make sense of behavioral ecology and other data. It presents fundamental concepts in statistical inference and intermediate topics such as multiple least squares regression and ANOVA. The objective is to teach students to recognize situations where various statistical methods should be used, understand the strengths and limitations of the methods, and to show how they are implemented in R code. Examples are based on research described in the literature of behavioral ecology, with data sets and analysis code provided. Features: This intermediate to advanced statistical methods text was written with the behavioral ecologist in mind Computer programs are provided, written in the R language. Datasets are also provided, mostly based, at least to some degree, on real studies. Methods and ideas discussed include multiple regression and ANOVA, logistic and Poisson regression, machine learning and model identification, time-to-event modeling, time series and stochastic modeling, game-theoretic modeling, multivariate methods, study design/sample size, and what to do when things go wrong. It is assumed that the reader has already had exposure to statistics through a first introductory course at least, and also has sufficient knowledge of R. However, some introductory material is included to aid the less initiated reader. Scott Pardo, Ph.D., is an accredited professional statistician (PStat®) by the American Statistical Association. Michael Pardo is a Ph.D. is a candidate in behavioral ecology at Cornell University, specializing in animal communication and social behavior.

Emphasizing the inductive nature of statistical thinking, Environmental and Ecological Statistics with R, Second Edition, connects applied statistics to the environmental and ecological fields. Using examples from published works in the ecological and environmental literature, the book explains the approach to solving a statistical problem, covering model specification, parameter estimation, and model evaluation. It includes many examples to illustrate the statistical methods and presents R code for their implementation. The emphasis is on model interpretation and assessment, and using several core examples throughout the book, the author illustrates the iterative nature of statistical inference.The book starts with a description of commonly used statistical assumptions and exploratory data analysis tools for the verification of these assumptions. It then focuses on the process of building suitable statistical models, including linear and nonlinear models, classification and regression trees, generalized linear models, and multilevel models. It also discusses the use of simulation for model checking, and provides tools for a critical assessment of the developed models. The second edition also includes a complete critique of a threshold model. Environmental and Ecological Statistics with R, Second Edition focuses on statistical modeling and data analysis for environmental and ecological problems. By guiding readers through the process of scientific problem solving and statistical model development, it eases the transition from scientific hypothesis to statistical model.

Evaluating Climate Change Impacts discusses assessing and quantifying climate change and its impacts from a multi-faceted perspective of ecosystem, social, and infrastructure resilience, given through a lens of statistics and data science. It provides a multi-disciplinary view on the implications of climate variability and shows how the new data science paradigm can help us to mitigate climate-induced risk and to enhance climate adaptation strategies.This book consists of chapters solicited from leading topical experts and presents their perspectives on climate change effects in two general areas: natural ecosystems and socio-economic impacts. The chapters unveil topics of atmospheric circulation, climate modeling, and long-term prediction; approach the problems of increasing frequency of extreme events, sea level rise, and forest fires, as well as economic losses, analysis of climate impacts for insurance, agriculture, fisheries, and electric and transport infrastructures. The reader will be exposed to the current research using a variety of methods from physical modeling, statistics, and machine learning, including the global circulation models (GCM) and ocean models, statistical generalized additive models (GAM) and generalized linear models (GLM), state space and graphical models, causality networks, Bayesian ensembles, a variety of index methods and statistical tests, and machine learning methods. The reader will learn about data from various sources, including GCM and ocean model outputs, satellite observations, and data collected by different agencies and research units. Many of the chapters provide references to open source software R and Python code that are available for implementing the methods.

Bringing Bayesian Models to Life empowers the reader to extend, enhance, and implement statistical models for ecological and environmental data analysis. We open the black box and show the reader how to connect modern statistical models to computer algorithms. These algorithms allow the user to fit models that answer their scientific questions without needing to rely on automated Bayesian software. We show how to handcraft statistical models that are useful in ecological and environmental science including: linear and generalized linear models, spatial and time series models, occupancy and capture-recapture models, animal movement models, spatio-temporal models, and integrated population-models. Features:R code implementing algorithms to fit Bayesian models using real and simulated data examples.A comprehensive review of statistical models commonly used in ecological and environmental science.Overview of Bayesian computational methods such as importance sampling, MCMC, and HMC.Derivations of the necessary components to construct statistical algorithms from scratch.Bringing Bayesian Models to Life contains a comprehensive treatment of models and associated algorithms for fitting the models to data. We provide detailed and annotated R code in each chapter and apply it to fit each model we present to either real or simulated data for instructional purposes. Our code shows how to create every result and figure in the book so that readers can use and modify it for their own analyses. We provide all code and data in an organized set of directories available at the authors' websites.

Future Sustainable Ecosystems: Complexity, Risk, Uncertainty provides an interdisciplinary, integrative overview of environmental problem-solving using statistics. It shows how statistics can be used to solve diverse environmental and socio-economic problems involving food, water, energy scarcity, and climate change risks. It synthesizes interdisciplinary theory, concepts, definitions, models and findings involved in complex global sustainability problem-solving, making it an essential guide and reference. It includes real-world examples and applications making the book accessible to a broader interdisciplinary readership.Discussions include a broad, integrated perspective on sustainability, integrated risk, multi-scale changes and impacts taking place within ecosystems worldwide. State-of-the-art statistical techniques, including Bayesian hierarchical, spatio-temporal, agent-based and game-theoretic approaches are explored. The author then focuses on the real-world integration of observational and experimental data and its use within statistical models.

Modern ecological and environmental sciences are dominated by observational data. As a result, traditional statistical training often leaves scientists ill-prepared for the data analysis tasks they encounter in their work. Bayesian methods provide a more robust and flexible tool for data analysis, as they enable information from different sources to be brought into the modelling process. Bayesian Applications in Evnironmental and Ecological Studies with R and Stan provides a Bayesian framework for model formulation, parameter estimation, and model evaluation in the context of analyzing environmental and ecological data.Features:An accessible overview of Bayesian methods in environmental and ecological studiesEmphasizes the hypothetical deductive process, particularly model formulationNecessary background material on Bayesian inference and Monte Carlo simulationDetailed case studies, covering water quality monitoring and assessment, ecosystem response to urbanization, fisheries ecology, and moreAdvanced chapter on Bayesian applications, including Bayesian networks and a change point modelComplete code for all examples, along with the data used in the book, are available via GitHubThe book is primarily aimed at graduate students and researchers in the environmental and ecological sciences, as well as environmental management professionals. This is a group of people representing diverse subject matter fields, who could benefit from the potential power and flexibility of Bayesian methods.

Written by renowned experts in the field, Sampling Strategies for Natural Resources and the Environment covers the sampling techniques used in ecology, forestry, environmental science, and natural resources. The book presents methods to estimate aggregate characteristics on a per unit area basis as well as on an elemental basis. In addition to common sampling designs such as simple random sampling and list sampling, the authors explore more specialized designs for sampling vegetation, including randomized branch sampling and 3P sampling.One of the book's unique features is the emphasis on areal sampling designs, including plot/quadrat sampling, Bitterlich sampling, line intersect sampling, and several lesser known designs. The book also provides comprehensive solutions to the problem of edge effect. Another distinguishing aspect is the inclusion of sampling designs for continuums, focusing on the methods of Monte Carlo integration.By presenting a conceptual understanding of each sampling design and estimation procedure as well as mathematical derivations and proofs in the chapter appendices, this text promotes a deep understanding of the underpinnings of sampling theory, estimation, and inference. Moreover, it will help you reliably sample natural populations and continuums.

When interpreting environmental data, scientists and engineers first must select the correct statistical tool to use for their analysis. By doing this they will be able to make sound decisions in their efforts to solve environmental problems. They need a detailed reference that points out the subtle differences between statistical procedures, making clear what procedure to use when trying to find the answer to a specific problem.Statistical Tools for Environmental Quality Measurement provides a detailed review of statistical tools used in analyzing and addressing environmental issues. This book examines commonly-used techniques found in USEPA guidelines and discusses their potential impact on decision-making. The authors are not constrained by statistical formalism; they advise when to go outside of standard statistical models when making difficult decisions. The content is presented in a practical style that prioritizes methods that work, based upon the authors' extensive experience.The text points out that simplicity facilitates effective communication of an analysis and decision to a "consumer" of statistics. The book emphasizes the exact question that each procedure addresses, so that environmental scientists and engineers can clearly identify precisely the question they want to ask, and correctly interpret the results.

Ecological dynamics are tremendously complicated and are studied at a variety of spatial and temporal scales. Ecologists often simplify analysis by describing changes in density of individuals across a landscape, and statistical methods are advancing rapidly for studying spatio-temporal dynamics. However, spatio-temporal statistics is often presented using a set of principles that may seem very distant from ecological theory or practice. This book seeks to introduce a minimal set of principles and numerical techniques for spatio-temporal statistics that can be used to implement a wide range of real-world ecological analyses regarding animal movement, population dynamics, community composition, causal attribution, and spatial dynamics. We provide a step-by-step illustration of techniques that combine core spatial-analysis packages in R with low-level computation using Template Model Builder. Techniques are showcased using real-world data from varied ecological systems, providing a toolset for hierarchical modelling of spatio-temporal processes. Spatio-Temporal Models for Ecologists is meant for graduate level students, alongside applied and academic ecologists.Key Features:Foundational ecological principles and analysesThoughtful and thorough ecological examplesAnalyses conducted using a minimal toolbox and fast computationCode using R and TMB included in the book and available online

Fisheries science is an applied field. A biologist working on a depleted population might need to forecast the population trajectory or examine which factors are limiting recovery. For an exploited population, a biologist might investigate whether the fishery or the stock would be improved through harvest regulation, such as a quota or size limit. Addressing those sorts of questions generally involves estimating parameters such as population size or survival rate. An essential step is characterizing the degree of uncertainty in results, which can be substantial for fisheries models. Bayesian methods and current software provide a flexible and powerful framework for addressing these challenges. Models developed using Bayesian software can be tailored to the unique aspects of each field study.A Bayesian Introduction to Fish Population Analysis is aimed at advanced undergraduate and graduate students as well as working professionals interested in a hands-on introduction to Bayesian approaches for fitting fisheries models. Chapters address key aspects of population dynamics: abundance, mortality, growth, and recruitment. The book includes complete R code for simulating each study design and JAGS Bayesian code for model fitting; code files are also available online. No prior knowledge of R or JAGS is assumed and new commands are introduced gradually through the sequence of examples. There is emphasis throughout the book on how to vary simulation settings to develop intuition about fisheries models (e.g., how many fish should be tagged in order to obtain usefully precise results). Additional topics include development of integrated population models, model checking, model selection, and uninformative and informative prior distributions.Key features include: The book begins with a focus on ecologically relevant probability distributions (e.g. binomial, Poisson, normal, lognormal) as a foundation for the applied fisheries chapters.Subsequent chapters demonstrate Bayesian approaches for estimating abundance, mortality, growth, and recruitment.Full open-source code is provided for simulation and plotting using R and Bayesian model fitting using JAGS.The book demonstrates how simulation can be used to gain a deeper understanding of analytical methods and for planning field studies.Joseph E. Hightower is a professor emeritus in the Department of Applied Ecology at NCState University. His research interests focus on fish population dynamics, especially field andanalytical methods for estimating population parameters. His primary teaching role was agraduate course in quantitative fisheries management, including Bayesian methods that serveas the foundation for this book.

Fisheries science is an applied field. A biologist working on a depleted population might need to forecast the population trajectory or examine which factors are limiting recovery. For an exploited population, a biologist might investigate whether the fishery or the stock would be improved through harvest regulation, such as a quota or size limit. Addressing those sorts of questions generally involves estimating parameters such as population size or survival rate. An essential step is characterizing the degree of uncertainty in results, which can be substantial for fisheries models. Bayesian methods and current software provide a flexible and powerful framework for addressing these challenges. Models developed using Bayesian software can be tailored to the unique aspects of each field study.A Bayesian Introduction to Fish Population Analysis is aimed at advanced undergraduate and graduate students as well as working professionals interested in a hands-on introduction to Bayesian approaches for fitting fisheries models. Chapters address key aspects of population dynamics: abundance, mortality, growth, and recruitment. The book includes complete R code for simulating each study design and JAGS Bayesian code for model fitting; code files are also available online. No prior knowledge of R or JAGS is assumed and new commands are introduced gradually through the sequence of examples. There is emphasis throughout the book on how to vary simulation settings to develop intuition about fisheries models (e.g., how many fish should be tagged in order to obtain usefully precise results). Additional topics include development of integrated population models, model checking, model selection, and uninformative and informative prior distributions.Key features include: The book begins with a focus on ecologically relevant probability distributions (e.g. binomial, Poisson, normal, lognormal) as a foundation for the applied fisheries chapters.Subsequent chapters demonstrate Bayesian approaches for estimating abundance, mortality, growth, and recruitment.Full open-source code is provided for simulation and plotting using R and Bayesian model fitting using JAGS.The book demonstrates how simulation can be used to gain a deeper understanding of analytical methods and for planning field studies.Joseph E. Hightower is a professor emeritus in the Department of Applied Ecology at NCState University. His research interests focus on fish population dynamics, especially field andanalytical methods for estimating population parameters. His primary teaching role was agraduate course in quantitative fisheries management, including Bayesian methods that serveas the foundation for this book.

An Easy-to-Understand Treatment of Ecological Sampling Methods and Data AnalysisIncluding only the necessary mathematical derivations, Introduction to Ecological Sampling shows how to use sampling procedures for ecological and environmental studies. It incorporates both traditional sampling methods and recent developments in environmental and ecological sampling methods.After an introduction, the book presents standard sampling methods and analyses. Subsequent chapters delve into specialized topics written by well-known researchers. These chapters cover adaptive sampling methods, line transect sampling, removal and change-in-ratio methods, plotless sampling, mark-recapture sampling of closed and open populations, occupancy models, sampling designs for environmental modeling, and trend analysis. The book explains the methods as simply as possible, keeping equations and their derivations to a minimum. It provides references to important, more advanced sampling methods and analyses. It also directs readers to computer programs that can be used to perform the analyses.Accessible to biologists, the text only assumes a basic knowledge of statistical methods. It is suitable for an introductory course on methods for collecting and analyzing ecological and environmental data.

This book shows how to use sampling procedures for ecological and environmental studies. It incorporates both traditional sampling methods and recent developments in environmental and ecological sampling methods, including mark-recapture, adaptive, and removal sampling. The book explains the methods as simply as possible, keeping equations and their derivations to a minimum. Accessible to biologists, the text only assumes a basic knowledge of statistical methods. Data sets and R code are available on a supplementary website.

This handbook shows how the techniques of point-pattern analysis are useful for tackling ecological problems. Within an ecological framework, it guides readers through a variety of methods for different data types and aids in the interpretation of the results obtained by point-pattern analysis. Along with the techniques, the book provides a comprehensive selection of real-world examples. Most of the examples are analyzed using the authors' software package Programita. The software and a manual are available online.

Making statistical modeling and inference more accessible to ecologists and related scientists, Introduction to Hierarchical Bayesian Modeling for Ecological Data gives readers a flexible and effective framework to learn about complex ecological processes from various sources of data. It also helps readers get started on building their own statisti

Statistical Geoinformatics for Human Environment Interface presents two paradigms for studying both space and interface with regard to human/environment: localization and multiple indicators. The first approach localizes thematic targets by treating space as a pattern of vicinities, with the pattern being a square grid and the placement of viciniti

When interpreting environmental data, scientists and engineers first must select the correct statistical tool to use for their analysis. By doing this they will be able to make sound decisions in their efforts to solve environmental problems. They need a detailed reference that points out the subtle differences between statistical procedures, makin

Statistical Geoinformatics for Human Environment Interface presents two paradigms for studying both space and interface with regard to human/environment: localization and multiple indicators. The first approach localizes thematic targets by treating space as a pattern of vicinities, with the pattern being a square grid and the placement of vicinities centrically referenced. The second approach explores human/environment interface as an abstraction through indicators, neutralizing the common conundrum of how to reconcile disparate spatial structures such as points, lines, and polygons. These paired paradigms enable: The capacity to cope with complexity Systematic surveillanceVisualization and communication Preliminary prioritizationCoupling of GIS and statistical softwareAvenues for automationIllustrating the interdisciplinary nature of geoinformatics, this book offers a novel approach to the spatial analysis of human influences and environmental resources. It includes practical strategies for statistical and spatial analysis.

Modern ecological and environmental sciences are dominated by observational data. As a result, traditional statistical training often leaves scientists ill-prepared for the data analysis tasks they encounter in their work. Bayesian methods provide a more robust and flexible tool for data analysis, as they enable information from different sources to be brought into the modelling process. Bayesian Applications in Evnironmental and Ecological Studies with R and Stan provides a Bayesian framework for model formulation, parameter estimation, and model evaluation in the context of analyzing environmental and ecological data.Features:An accessible overview of Bayesian methods in environmental and ecological studiesEmphasizes the hypothetical deductive process, particularly model formulationNecessary background material on Bayesian inference and Monte Carlo simulationDetailed case studies, covering water quality monitoring and assessment, ecosystem response to urbanization, fisheries ecology, and moreAdvanced chapter on Bayesian applications, including Bayesian networks and a change point modelComplete code for all examples, along with the data used in the book, are available via GitHubThe book is primarily aimed at graduate students and researchers in the environmental and ecological sciences, as well as environmental management professionals. This is a group of people representing diverse subject matter fields, who could benefit from the potential power and flexibility of Bayesian methods.

Statistical Methods for Field and Laboratory Studies in Behavioral Ecology focuses on how statistical methods may be used to make sense of behavioral ecology and other data. It presents fundamental concepts in statistical inference and intermediate topics such as multiple least squares regression and ANOVA. The objective is to teach students to recognize situations where various statistical methods should be used, understand the strengths and limitations of the methods, and to show how they are implemented in R code. Examples are based on research described in the literature of behavioral ecology, with data sets and analysis code provided. Features: This intermediate to advanced statistical methods text was written with the behavioral ecologist in mind Computer programs are provided, written in the R language. Datasets are also provided, mostly based, at least to some degree, on real studies. Methods and ideas discussed include multiple regression and ANOVA, logistic and Poisson regression, machine learning and model identification, time-to-event modeling, time series and stochastic modeling, game-theoretic modeling, multivariate methods, study design/sample size, and what to do when things go wrong.It is assumed that the reader has already had exposure to statistics through a first introductory course at least, and also has sufficient knowledge of R. However, some introductory material is included to aid the less initiated reader. Scott Pardo, Ph.D., is an accredited professional statistician (PStat®) by the American Statistical Association. Michael Pardo is a Ph.D. is a candidate in behavioral ecology at Cornell University, specializing in animal communication and social behavior.

Revised, expanded, and updated, this second edition of Statistics for Environmental Science and Management is that rare animal, a resource that works well as a text for graduate courses and a reference for appropriate statistical approaches to specific environmental problems. It is uncommon to find so many important environmental topics covered in one book. Its strength is author Bryan Manly’s ability to take a non-mathematical approach while keeping essential mathematical concepts intact. He clearly explains statistics without dwelling on heavy mathematical development. The book begins by describing the important role statistics play in environmental science. It focuses on how to collect data, highlighting the importance of sampling and experimental design in conducting rigorous science. It presents a variety of key topics specifically related to environmental science such as monitoring, impact assessment, risk assessment, correlated and censored data analysis, to name just a few. Revised, updated or expanded material on: Data Quality ObjectivesGeneralized Linear ModelsSpatial Data AnalysisCensored DataMonte Carlo Risk AssessmentThere are numerous books on environmental statistics; however, while some focus on multivariate methods and others on the basic components of probability distributions and how they can be used for modeling phenomenon, most do not include the material on sampling and experimental design that this one does. It is the variety of coverage, not sacrificing too much depth for breadth, that sets this book apart.

Understand How to Analyze and Interpret Information in Ecological Point PatternsAlthough numerous statistical methods for analyzing spatial point patterns have been available for several decades, they haven’t been extensively applied in an ecological context. Addressing this gap, Handbook of Spatial Point-Pattern Analysis in Ecology shows how the techniques of point-pattern analysis are useful for tackling ecological problems. Within an ecological framework, the book guides readers through a variety of methods for different data types and aids in the interpretation of the results obtained by point-pattern analysis. Ideal for empirical ecologists who want to avoid advanced theoretical literature, the book covers statistical techniques for analyzing and interpreting the information contained in ecological patterns. It presents methods used to extract information hidden in spatial point-pattern data that may point to the underlying processes. The authors focus on point processes and null models that have proven their immediate utility for broad ecological applications, such as cluster processes.Along with the techniques, the handbook provides a comprehensive selection of real-world examples. Most of the examples are analyzed using Programita, a continuously updated software package based on the authors’ many years of teaching and collaborative research in ecological point-pattern analysis. Programita is tailored to meet the needs of real-world applications in ecology. The software and a manual are available online.