2nd Edition

Statistics for Environmental Science and Management

By Bryan F.J. Manly Copyright 2008
    310 Pages 81 B/W Illustrations
    by Chapman & Hall

    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 Objectives
    • Generalized Linear Models
    • Spatial Data Analysis
    • Censored Data
    • Monte Carlo Risk Assessment

    There 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.

    The Role of Statistics in Environmental Science

    Introduction

    Some Examples

    The Importance of Statistics in the Examples

    Chapter Summary

    Exercises

    Environmental Sampling

    Introduction

    Simple Random Sampling

    Estimation of Population Means

    Estimation of Population Totals

    Estimation of Proportions

    Sampling and Nonsampling Errors

    Stratified Random Sampling

    Post-Stratification

    Systematic Sampling

    Other Design Strategies

    Ratio Estimation

    Double Sampling0

    Choosing Sample Sizes1

    Unequal-Probability Sampling

    The Data Quality Objectives Process

    Chapter Summary

    Exercises

    Models for Data

    Statistical Models

    Discrete Statistical Distributions

    Continuous Statistical Distributions

    The Linear Regression Model8

    Factorial Analysis of Variance

    Generalized Linear Models

    Chapter Summary

    Exercises

    Drawing Conclusions from Data

    Introduction

    Observational and Experimental Studies

    True Experiments and Quasi-Experiments

    Design-Based and Model-Based Inference

    Tests of Significance and Confidence Intervals

    Randomization Tests

    Bootstrapping

    Pseudoreplication

    Multiple Testing

    Meta-Analysis

    Bayesian Inference

    Chapter Summary

    Exercises

    Environmental Monitoring

    Introduction

    Purposely Chosen Monitoring Sites

    Two Special Monitoring Designs6

    Designs Based on Optimization

    Monitoring Designs Typically Used

    Detection of Changes by Analysis of Variance

    Detection of Changes Using Control Charts

    Detection of Changes Using CUSUM Charts

    Chi-Squared Tests for a Change in a Distribution

    Chapter Summary

    Exercises

    Impact Assessment

    Introduction

    The Simple Difference Analysis with BACI Designs

    Matched Pairs with a BACI Design.

    Impact-Control Designs

    Before–After Designs

    Impact-Gradient Designs

    Inferences from Impact Assessment Studies

    Chapter Summary

    Exercises

    Assessing Site Reclamation

    Introduction

    Problems with Tests of Significance

    The Concept of Bioequivalence

    Two-Sided Tests of Bioequivalence

    Chapter Summary

    Exercises

    Time Series Analysis

    Introduction

    Components of Time Series

    Serial Correlation

    Tests for Randomness

    Detection of Change Points and Trends

    More-Complicated Time Series Models

    Frequency Domain Analysis

    Forecasting

    Chapter Summary

    Exercises

    Spatial-Data Analysis

    Introduction

    Types of Spatial Data

    Spatial Patterns in Quadrat Counts

    Correlation between Quadrat Counts

    Randomness of Point Patterns

    Correlation between Point Patterns

    Mantel Tests for Autocorrelation

    The Variogram

    Kriging

    Correlation between Variables in Space

    Chapter Summary

    Exercises

    Censored Data

    Introduction

    Single Sample Estimation

    Estimation of Quantiles

    Comparing the Means of Two or More Samples

    Regression with Censored Data

    Chapter Summary

    Exercises

    Monte Carlo Risk Assessment

    Introduction

    Principles for Monte Carlo Risk Assessment

    Risk Analysis Using a Spreadsheet

    Chapter Summary

    Exercises

    Final Remarks

    Appendices

    References

    Index

    Biography

    Bryan F.J. Manly