Note: Intro -- Statistical Pattern Recognition -- Contents -- Preface -- Notation -- 1 Introduction to Statistical Pattern Recognition -- 1.1 Statistical Pattern Recognition -- 1.1.1 Introduction -- 1.1.2 The Basic Model -- 1.2 Stages in a Pattern Recognition Problem -- 1.3 Issues -- 1.4 Approaches to Statistical Pattern Recognition -- 1.5 Elementary Decision Theory -- 1.5.1 Bayes' Decision Rule for Minimum Error -- 1.5.2 Bayes' Decision Rule for Minimum Error - Reject Option -- 1.5.3 Bayes' Decision Rule for Minimum Risk -- 1.5.4 Bayes' Decision Rule for Minimum Risk - Reject Option -- 1.5.5 Neyman-Pearson Decision Rule -- 1.5.6 Minimax Criterion -- 1.5.7 Discussion -- 1.6 Discriminant Functions -- 1.6.1 Introduction -- 1.6.2 Linear Discriminant Functions -- 1.6.3 Piecewise Linear Discriminant Functions -- 1.6.4 Generalised Linear Discriminant Function -- 1.6.5 Summary -- 1.7 Multiple Regression -- 1.8 Outline of Book -- 1.9 Notes and References -- Exercises -- 2 Density Estimation - Parametric -- 2.1 Introduction -- 2.2 Estimating the Parameters of the Distributions -- 2.2.1 Estimative Approach -- 2.2.2 Predictive Approach -- 2.3 The Gaussian Classifier -- 2.3.1 Specification -- 2.3.2 Derivation of the Gaussian Classifier Plug-In Estimates -- 2.3.3 Example Application Study -- 2.4 Dealing with Singularities in the Gaussian Classifier -- 2.4.1 Introduction -- 2.4.2 Na¨ıve Bayes -- 2.4.3 Projection onto a Subspace -- 2.4.4 Linear Discriminant Function -- 2.4.5 Regularised Discriminant Analysis -- 2.4.6 Example Application Study -- 2.4.7 Further Developments -- 2.4.8 Summary -- 2.5 Finite Mixture Models -- 2.5.1 Introduction -- 2.5.2 Mixture Models for Discrimination -- 2.5.3 Parameter Estimation for Normal Mixture Models -- 2.5.4 Normal Mixture Model Covariance Matrix Constraints -- 2.5.5 How Many Components?. , 2.5.6 Maximum Likelihood Estimation via EM -- 2.5.7 Example Application Study -- 2.5.8 Further Developments -- 2.5.9 Summary -- 2.6 Application Studies -- 2.7 Summary and Discussion -- 2.8 Recommendations -- 2.9 Notes and References -- Exercises -- 3 Density Estimation - Bayesian -- 3.1 Introduction -- 3.1.1 Basics -- 3.1.2 Recursive Calculation -- 3.1.3 Proportionality -- 3.2 Analytic Solutions -- 3.2.1 Conjugate Priors -- 3.2.2 Estimating the Mean of a Normal Distribution with Known Variance -- 3.2.3 Estimating the Mean and the Covariance Matrix of a Multivariate Normal Distribution -- 3.2.4 Unknown Prior Class Probabilities -- 3.2.5 Summary -- 3.3 Bayesian Sampling Schemes -- 3.3.1 Introduction -- 3.3.2 Summarisation -- 3.3.3 Sampling Version of the Bayesian Classifier -- 3.3.4 Rejection Sampling -- 3.3.5 Ratio of Uniforms -- 3.3.6 Importance Sampling -- 3.4 Markov Chain Monte Carlo Methods -- 3.4.1 Introduction -- 3.4.2 The Gibbs Sampler -- 3.4.3 Metropolis-Hastings Algorithm -- 3.4.4 Data Augmentation -- 3.4.5 Reversible Jump Markov Chain Monte Carlo -- 3.4.6 Slice Sampling -- 3.4.7 MCMC Example - Estimation of Noisy Sinusoids -- 3.4.8 Summary -- 3.4.9 Notes and References -- 3.5 Bayesian Approaches to Discrimination -- 3.5.1 Labelled Training Data -- 3.5.2 Unlabelled Training Data -- 3.6 Sequential Monte Carlo Samplers -- 3.6.1 Introduction -- 3.6.2 Basic Methodology -- 3.6.3 Summary -- 3.7 Variational Bayes -- 3.7.1 Introduction -- 3.7.2 Description -- 3.7.3 Factorised Variational Approximation -- 3.7.4 Simple Example -- 3.7.5 Use of the Procedure for Model Selection -- 3.7.6 Further Developments and Applications -- 3.7.7 Summary -- 3.8 Approximate Bayesian Computation -- 3.8.1 Introduction -- 3.8.2 ABC Rejection Sampling -- 3.8.3 ABC MCMC Sampling -- 3.8.4 ABC Population Monte Carlo Sampling -- 3.8.5 Model Selection -- 3.8.6 Summary. , 3.9 Example Application Study -- 3.10 Application Studies -- 3.11 Summary and Discussion -- 3.12 Recommendations -- 3.13 Notes and References -- Exercises -- 4 Density Estimation - Nonparametric -- 4.1 Introduction -- 4.1.1 Basic Properties of Density Estimators -- 4.2 k-Nearest-Neighbour Method -- 4.2.1 k-Nearest-Neighbour Classifier -- 4.2.2 Derivation -- 4.2.3 Choice of Distance Metric -- 4.2.4 Properties of the Nearest-Neighbour Rule -- 4.2.5 Linear Approximating and Eliminating Search Algorithm -- 4.2.6 Branch and Bound Search Algorithms: kd-Trees -- 4.2.7 Branch and Bound Search Algorithms: Ball-Trees -- 4.2.8 Editing Techniques -- 4.2.9 Example Application Study -- 4.2.10 Further Developments -- 4.2.11 Summary -- 4.3 Histogram Method -- 4.3.1 Data Adaptive Histograms -- 4.3.2 Independence Assumption (Na¨ıve Bayes) -- 4.3.3 Lancaster Models -- 4.3.4 Maximum Weight Dependence Trees -- 4.3.5 Bayesian Networks -- 4.3.6 Example Application Study - Na¨ıve Bayes Text Classification -- 4.3.7 Summary -- 4.4 Kernel Methods -- 4.4.1 Biasedness -- 4.4.2 Multivariate Extension -- 4.4.3 Choice of Smoothing Parameter -- 4.4.4 Choice of Kernel -- 4.4.5 Example Application Study -- 4.4.6 Further Developments -- 4.4.7 Summary -- 4.5 Expansion by Basis Functions -- 4.6 Copulas -- 4.6.1 Introduction -- 4.6.2 Mathematical Basis -- 4.6.3 Copula Functions -- 4.6.4 Estimating Copula Probability Density Functions -- 4.6.5 Simple Example -- 4.6.6 Summary -- 4.7 Application Studies -- 4.7.1 Comparative Studies -- 4.8 Summary and Discussion -- 4.9 Recommendations -- 4.10 Notes and References -- Exercises -- 5 Linear Discriminant Analysis -- 5.1 Introduction -- 5.2 Two-Class Algorithms -- 5.2.1 General Ideas -- 5.2.2 Perceptron Criterion -- 5.2.3 Fisher's Criterion -- 5.2.4 Least Mean-Squared-Error Procedures -- 5.2.5 Further Developments -- 5.2.6 Summary. , 5.3 Multiclass Algorithms -- 5.3.1 General Ideas -- 5.3.2 Error-Correction Procedure -- 5.3.3 Fisher's Criterion - Linear Discriminant Analysis -- 5.3.4 Least Mean-Squared-Error Procedures -- 5.3.5 Regularisation -- 5.3.6 Example Application Study -- 5.3.7 Further Developments -- 5.3.8 Summary -- 5.4 Support Vector Machines -- 5.4.1 Introduction -- 5.4.2 Linearly Separable Two-Class Data -- 5.4.3 Linearly Nonseparable Two-Class Data -- 5.4.4 Multiclass SVMs -- 5.4.5 SVMs for Regression -- 5.4.6 Implementation -- 5.4.7 Example Application Study -- 5.4.8 Summary -- 5.5 Logistic Discrimination -- 5.5.1 Two-Class Case -- 5.5.2 Maximum Likelihood Estimation -- 5.5.3 Multiclass Logistic Discrimination -- 5.5.4 Example Application Study -- 5.5.5 Further Developments -- 5.5.6 Summary -- 5.6 Application Studies -- 5.7 Summary and Discussion -- 5.8 Recommendations -- 5.9 Notes and References -- Exercises -- 6 Nonlinear Discriminant Analysis - Kernel and Projection Methods -- 6.1 Introduction -- 6.2 Radial Basis Functions -- 6.2.1 Introduction -- 6.2.2 Specifying the Model -- 6.2.3 Specifying the Functional Form -- 6.2.4 The Positions of the Centres -- 6.2.5 Smoothing Parameters -- 6.2.6 Calculation of the Weights -- 6.2.7 Model Order Selection -- 6.2.8 Simple RBF -- 6.2.9 Motivation -- 6.2.10 RBF Properties -- 6.2.11 Example Application Study -- 6.2.12 Further Developments -- 6.2.13 Summary -- 6.3 Nonlinear Support Vector Machines -- 6.3.1 Introduction -- 6.3.2 Binary Classification -- 6.3.3 Types of Kernel -- 6.3.4 Model Selection -- 6.3.5 Multiclass SVMs -- 6.3.6 Probability Estimates -- 6.3.7 Nonlinear Regression -- 6.3.8 Example Application Study -- 6.3.9 Further Developments -- 6.3.10 Summary -- 6.4 The Multilayer Perceptron -- 6.4.1 Introduction -- 6.4.2 Specifying the MLP Structure -- 6.4.3 Determining the MLP Weights. , 6.4.4 Modelling Capacity of the MLP -- 6.4.5 Logistic Classification -- 6.4.6 Example Application Study -- 6.4.7 Bayesian MLP Networks -- 6.4.8 Projection Pursuit -- 6.4.9 Summary -- 6.5 Application Studies -- 6.6 Summary and Discussion -- 6.7 Recommendations -- 6.8 Notes and References -- Exercises -- 7 Rule and Decision Tree Induction -- 7.1 Introduction -- 7.2 Decision Trees -- 7.2.1 Introduction -- 7.2.2 Decision Tree Construction -- 7.2.3 Selection of the Splitting Rule -- 7.2.4 Terminating the Splitting Procedure -- 7.2.5 Assigning Class Labels to Terminal Nodes -- 7.2.6 Decision Tree Pruning - Worked Example -- 7.2.7 Decision Tree Construction Methods -- 7.2.8 Other Issues -- 7.2.9 Example Application Study -- 7.2.10 Further Developments -- 7.2.11 Summary -- 7.3 Rule Induction -- 7.3.1 Introduction -- 7.3.2 Generating Rules from a Decision Tree -- 7.3.3 Rule Induction Using a Sequential Covering Algorithm -- 7.3.4 Example Application Study -- 7.3.5 Further Developments -- 7.3.6 Summary -- 7.4 Multivariate Adaptive Regression Splines -- 7.4.1 Introduction -- 7.4.2 Recursive Partitioning Model -- 7.4.3 Example Application Study -- 7.4.4 Further Developments -- 7.4.5 Summary -- 7.5 Application Studies -- 7.6 Summary and Discussion -- 7.7 Recommendations -- 7.8 Notes and References -- Exercises -- 8 Ensemble Methods -- 8.1 Introduction -- 8.2 Characterising a Classifier Combination Scheme -- 8.2.1 Feature Space -- 8.2.2 Level -- 8.2.3 Degree of Training -- 8.2.4 Form of Component Classifiers -- 8.2.5 Structure -- 8.2.6 Optimisation -- 8.3 Data Fusion -- 8.3.1 Architectures -- 8.3.2 Bayesian Approaches -- 8.3.3 Neyman-Pearson Formulation -- 8.3.4 Trainable Rules -- 8.3.5 Fixed Rules -- 8.4 Classifier Combination Methods -- 8.4.1 Product Rule -- 8.4.2 Sum Rule -- 8.4.3 Min, Max and Median Combiners -- 8.4.4 Majority Vote -- 8.4.5 Borda Count. , 8.4.6 Combiners Trained on Class Predictions.

Note: Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- List of Tables -- List of Illustrations -- Preface -- 1 Introduction -- 1.1 Satellite Meteorology -- 1.2 Overview of Numerical Weather Prediction Modeling -- 1.3 Evolution of Observational Data for Numerical Weather Prediction Modeling -- 1.4 Additional Applications of Meteorological Satellite Data -- 1.4.1 Sea Surface Temperature Analyses -- 1.4.2 Climate Modeling -- 1.4.3 Tropical Storm Monitoring -- 1.4.4 Satellite-Derived Wind Fields -- 2 Meteorological Satellite Systems -- 2.1 Evolution of Satellites and Sensors -- 2.1.1 Polar-orbiting Operational Environmental Satellite Systems -- 2.1.1.1 The Advanced TIROS-N Satellite Series -- 2.1.1.2 Defense Meteorological Satellite Program -- 2.1.1.3 NASA Earth Observing System Program -- 2.1.1.4 Other Polar-orbiting Meteorological Satellite Systems -- 2.1.1.5 The Proposed EUMETSAT Meteorological Operational Series -- 2.1.2 Geostationary Meteorological Satellite Systems -- 2.2 The National Polor-orbiting Operational Environmental Satellite System -- 3 VIIRS Imagery Design Analysis -- 3.1 VIIRS Environmental Data Record Requirements Overview -- 3.2 VIIRS Imagery Requirements -- 3.3 Cloud Applications-Related Imagery Requirements -- 3.3.1 Cloud Cover -- 3.3.2 Cloud Type -- 3.4 Value of Manually Generated Cloud Analyses -- 3.4.1 Performance Verification of Automated Cloud Models -- 3.4.2 Quality Control of Automated Cloud Analyses -- 4 VIIRS Imagery Requirements Analysis -- 4.1 Theoretical Basis for Manual Cloud Analyses -- 4.2 Overview of Approach to Instrument Design -- 4.3 Cloud Truth Data Sets to Flowdown Sensor Requirements -- 4.3.1 Cloud Truth from Manual Interpretation of Multispectral Imagery -- 4.3.2 Cloud Truth in Simulated Imagery -- 4.4 Derivation of Sensing Requirements from Analysis Requirements. , 4.5 Overview of VIIRS Hardware Design -- 4.5.1 VIIRS Sensor Overview -- 4.5.2 Detailed VIIRS Design Capabilities -- 4.5.2.1 VIIRS Spectral Design Requirements -- 4.5.2.2 VIIRS Spatial Capabilities -- 4.5.2.3 VIIRS Horizontal Sampling Interval -- 4.5.2.4 VIIRS Dynamic Range Capability -- 4.5.2.5 VIIRS Sensitivity Capability -- 4.5.2.6 VIIRS Sensor Polarization Sensitivity -- 4.5.2.7 Detector Performance -- 4.5.2.8 Band-to-Band Registration or Coregistration -- 4.5.2.9 VIIRS Calibration -- 5 Principles in Image Interpretation -- 5.1 Introduction -- 5.2 VIIRS Imagery Data -- 5.2.1 VIIRS Imagery Band I1 (0.64 ± 0.040-μm) -- 5.2.1.1 Theoretical Basis for Band Interpretation -- 5.2.1.2 Representative Imagery of the VIIRS I1 Band (0.640- ± 0.040-μm) -- 5.2.2 VIIRS I2 Band (0.865- ± 0.020-μm) -- 5.2.2.1 Theoretical Basis for Band Interpretation -- 5.2.2.2 Representative Imagery of the VIIRS I2 Band (0.865- ± 0.020-μm) -- 5.2.3 VIIRS I3 Band (1.61- ± 0.03-μm) -- 5.2.3.1 Theoretical Basis for Band Interpretation -- 5.2.3.2 Representative Imagery of the VIIRS I3/M10 Band (1.61-μm ± 0.03-μm) -- 5.2.4 VIIRS I4 Band (3.74-μm ± 0.19-μm) -- 5.2.4.1 Theoretical Basis for Band Interpretation -- 5.2.4.2 Representative Imagery of VIIRS I4 Band (3.74- ± 0.19-μm) -- 5.2.5 VIIRS I5 Band (11.45 ± 0.95-μm) -- 5.2.5.1 Theoretical Basis for Band Interpretation -- 5.2.5.2 Representative Imagery of the VIIRS I5 Band (11.45- ± 0.95-μm) -- 5.2.6 VIIRS Day-Night Band -- 5.2.6.1 Theoretical Basis for Band Interpretation -- 5.2.6.2 Representative Imagery of the VIIRS DNB (0.7- ± 0.2-μm) -- 5.3 VIIRS Imagery Assist Data -- 5.3.1 VIIRS M1-M4 Bands (0.412 ± 0.010, 0.445 ± 0.009, 0.488 ± 0.010, 0.555 ± 0.010-μm) -- 5.3.1.1 Theoretical Basis for Band Interpretation -- 5.3.1.2 Representative Imagery of the VIIRS M1 Band (0.412- ± 0.05-μm) -- 5.3.2 VIIRS M9 Band (1.378 ± 0.0075-μm). , 5.3.2.1 Theoretical Basis for Band Interpretation -- 5.3.2.2 Representative Imagery of the VIIRS M9 Band (1.378- ± 0.075-μm) -- 6 Multicolor Composites of Multispectral Imagery -- 6.1 Introduction -- 6.2 Color Composites of (0.645-, 0.865-, and 12.0-μm) Surface Vegetation and Cloud Classifications -- 6.3 Color Composites (3.7-μm albedo, 0.865μm, 12.0μm) for Snow Detection -- 6.4 Color Composites of (0.645-μm, 0.645-μm, 3.7-μm albedo) Snow Mapping Through Thin Cirrus Clouds -- 6.5 Color Composites of (0.412-, 0.865-, and 0.645-μm) Clouds Over Arid Regions -- 7 Case Studies in the Use of Multicolor Composites for Scene Interpretation -- 7.1 Overview -- 7.2 MODIS Airborne Simulation Data Over Alaska -- 7.2.1 Color Composite 1: Identification of Vegetated Surfaces -- 7.2.2 Color Composite 2: Identification of Snow and Ice Features -- 7.2.3 Color Composite 3: Cloud Type Classification Part I -- 7.2.4 Color Composite 4: Cloud Type Classification Part II -- 7.3 MODIS Airborne Simulation Success Data Collected Over Colorado -- 7.3.1 Color Composite 1: Identification of Vegetated Surfaces -- 7.3.2 Color Composite 2: Identification of Snow and Ice Features -- 7.3.3 Color Composite 3: Cloud Type Classification Part I -- 7.3.4 Color Composite 4: Cloud Type Classification Part II -- 8 Automated 3-D Cloud Analyses from NPOESS -- 8.1 Architecture for 3-D Cloud Analyses -- 8.2 Automated Cloud Detection -- 8.2.1 Single-Channel Cloud Detection Algorithms -- 8.2.2 Multispectral Channel Cloud Detection Algorithms -- 8.2.3 Spatial Cloud Classifier Algorithms -- 8.3 Cloud Top Phase Classifications -- 8.4 Cloud Optical (Thickness and Particle Size) Properties -- 8.4.1 Retrieval for Water Clouds During Daytime Conditions -- 8.4.2 Retrieval for Ice Cloud Microphysical Properties -- 8.4.2.1 Retrieval of Ice Cloud Properties in Daytime Imagery. , 8.4.2.2 Retrieval of Ice Cloud Properties in Nighttime Imagery -- 8.5 Cloud Top (Temperature, Pressure, and Height) Parameters -- 8.6 Cloud Base Heights -- 8.6.1 Cloud Base Heights Retrieved for Water Clouds -- 8.6.2 Cloud Base Heights Retrieved for Ice Clouds -- 8.6.3 Ancillary Data and Products from Other Sensors -- 8.6.4 Integration of VIIRS, CMIS, and Conventional Cloud Base Observations -- References -- Index.

Note: THE IGBP SERIES Paleoclimate, Global Change and the Future -- Paleoclimate, Global Change and the Future -- Copyright -- Preface -- Contents -- Contributors -- The Societal Relevance of Paleoenvironmental Research -- The Late Quaternary History of Atmospheric Trace Gases and Aerosols: Interactions Between Climate and Biogeochemical Cycles -- The History of Climate Dynamics in the Late Quaternary -- The Late Quaternary History of Biogeochemical Cycling of Carbon -- Terrestrial Biosphere Dynamics in the Climate System: Past and Future -- The Climate of the Last Millennium -- The Role of Human Activities in Past Environmental Change -- Challenges of a Changing Earth: Past Perspectives, Future Concerns -- Appendix A- The Past Global Changes (PAGES) Program -- Appendix B- The PAGES Data System -- References -- Subject Index -- Acknowledgements.

Note: Intro -- Title Page -- Copyright Page -- Contents -- List of contributors -- Preface -- Chapter 1 Integrating biological control into a conservation context: why is it necessary? -- Potential problems if integration is lacking -- Book organization -- Acknowledgments -- References -- Chapter 2 Designing restoration programs based on understanding the drivers of ecological change -- Overview of concepts -- Introduction -- Ecological restoration planning process -- Invasive species and system change -- Ranking invasive species for classical biological control -- Designing a restoration plan using Connecticut River floodplain forests as a model -- Restoring physical processes to suppress invasive plants -- Assessing ecological impact of invasive species -- Eradiation and containment of a serious invader -- Biological control and breeding host resistance against pests and pathogens -- Holistic ecological restoration and invasive species management -- Biological control agents from other regions -- Conclusion for Connecticut River watershed case study -- Acknowledgments -- References -- Chapter 3 Matching tools to management goals -- Introduction -- Eradication -- Limiting spread -- Local, or area-wide, temporary suppression of invaders -- Manual or mechanical removal -- Mass trapping -- Hunting and bounties -- Pesticides -- Behavior-modifying chemicals -- Area-wide, permanent suppression through modification of ecosystem processes -- Changes in fire regimes -- Changes in flood level or duration -- Changes in grazing regimes -- Changes in soil fertility levels -- Replanting with native plants -- Area-wide, permanent control through natural enemy introductions -- Factors affecting control efficacy -- Invader biology -- Ecological or geographic features of the invaded ecosystem -- Effects of treatment scale on control operations -- Cost per unit area. , Conflicts with abutters and other groups -- Scale-related issues for biological control -- When is biological control the right choice? -- When biological control is the right choice -- When biological control may not be a good fit -- Key messages -- Acknowledgments -- References -- Chapter 4 Tools in action: understanding tradeoffs through case histories -- Risk tradeoffs in invasive species management -- When is no action the right choice? -- Strawberry guava -- Garlic mustard -- Is herbicide use a good option for invasive plant control? -- Phragmites australis (common reed) -- Melaleuca -- Are biological control projects high or low risk? -- Prickly pear cactus in Australia in the 1920s (Rank 1: little to no risk) -- Melaleuca in Florida, 1986 to present (Rank 1: little to no risk) -- Saltcedar (tamarix) in the United States, 1990-2010 (Rank 2: low to moderate risk) -- Emerald ash borer, North America, 2002 to present (Rank 2: low to moderate risk) -- Invasive thistles, North America, 1960s (Rank 3: moderate to high risk) -- Opuntia cacti, Caribbean Islands in the 1950s (Rank 4: high risk) -- Spotted-wing drosophila, in Hawaii, a hypothetical case (Rank 5: unacceptably high risk) -- Acknowledgments -- References -- Chapter 5 Benefit-risk assessment of biological control in wildlands -- Who gets to decide which species are targeted -- Evidence needed to justify use of biological control -- What principles and processes should guide decision-making? -- Openness -- Insulation from urgency -- Separation of functions -- Risk assessment of biological control agents -- Differences in risk assessment of herbivorous and carnivorous biological control agents -- The US review system - a discussion -- International coordination of reviews of release petitions -- Trouble spots: bad practice -- Cases of unsafe biological control introductions. , Post-release risk evaluation -- Overview -- General review of risks of past introductions -- Improving post-release monitoring -- Conclusions -- Acknowledgments -- References -- Chapter 6 Systematics and biological control -- Introduction -- Identification's effect on biological control service -- Misidentification of target pests -- Misidentification of natural enemies -- Using classifications to make biological predictions -- Using phylogenies to predict the native range -- Using phylogenies and classifications to predict host ranges -- Environmental niche modeling (ENM) -- The importance of voucher specimens -- Molecular methods -- DNA "barcodes" -- Overview of fragment length analyses -- Amplified fragment length polymorphisms (AFLP) -- Microsatellites -- Methods of classification using genomic methods -- Conclusions -- Acknowledgments -- References -- Chapter 7 Forecasting unintended effects of natural enemies used for classical biological control of invasive species -- Introduction -- Forecasting non-trophic effects -- Theoretical basis for non-trophic effects -- Examples of non-trophic effects from classical biological control -- Forecasting unwanted trophic effects -- Trophic effects of weed biological control agents -- Predicting unwanted trophic effects of carnivorous biological control agents -- Predicting unwanted trophic effects of pathogens used for biological control -- Conclusions -- Acknowledgments -- References -- Chapter 8 Measuring and evaluating ecological outcomes of biological control introductions -- Introduction -- Setting the stage -- Defining success in biological control programs -- Biological success -- Ecological success -- Economic success -- Democratic or societal success -- Criteria not useful to measure success -- Evidence for success or failure -- Evaluation of biological and ecological success. , Ecological success: avoidance of non-target effects -- Evidence for economic success -- Evidence for societal success -- Population growth rates and demographic modeling -- Principles of stewardship -- Conclusions -- Acknowledgments -- References -- Chapter 9 Methods for evaluation of natural enemy impacts on invasive pests of wildlands -- Types of outcomes used as measures of impact -- Evaluation methods for invasive insects -- Natural enemy evaluation through experimental comparisons of pest density -- Natural enemy evaluation using life tables -- Evaluation methods for invasive plants -- Overview -- Measuring agents' effects on plant performance -- Assessing agent impacts on plant populations: attendant issues -- Assessing agent effects on plant populations: methods -- Changes in the invaded community -- Conclusions -- Acknowledgments -- References -- Chapter 10 Cases of biological control restoring natural systems -- The value of case histories -- Successes -- Everglades preservation through biological control of Melaleuca quinquenervia -- Recovery of native vegetation in New Zealand forests following control of mist flower -- Protecting Galápagos plants from cottony cushion scale -- Preserving Hawaiian dry forests by protecting wiliwili trees from erythrina gall wasps -- Failures -- Destruction of Fraser fir by balsam woolly adelgid in Appalachian "sky-island" forests -- A century of unsuccessful Lantana camara biological control effort in Australia -- Projects still unfolding -- Preserving ash-dependent biodiversity through biological control of emerald ash borer -- Restoring native vegetation in Tahitian forests dominated by Miconia calvescens -- Acknowledgments -- References -- Chapter 11 Societal values expressed through policy and regulations concerning biological control releases -- Introduction. , National regulatory processes and differences -- Australia -- New Zealand -- South Africa -- United States -- Canada -- European Union -- Scientists and regulators: different perspectives -- Differences in national approaches and associated cultural values -- Where can process improvements be made? -- Weed biological control risk-assessment processes -- Insect biological control risk-assessment processes -- International sharing of biodiversity for public benefit -- Conclusions -- Acknowledgments -- References -- Chapter 12 Managing conflict over biological control: the case of strawberry guava in Hawaii -- Introduction -- Strawberry guava in Hawaii -- Regulatory review of strawberry guava biological control -- Lessons learned about our public -- Opponents -- Supporters of biological control -- Lessons learned about the process -- Conclusions -- Acknowledgments -- References -- Chapter 13 An ethical framework for integrating biological control into conservation practice -- Why integration of biological control into conservation practice is an ethical issue -- How environmental thought evolved to encompass classical biological control as an ethical issue -- Risk, precaution, prudence, and policy -- A framework to improve ethics of biological control decisions -- Public advocacy for invasive species control should be separate from selection of specific control strategies -- Decision-making criteria and processes should be transparent -- Beneficiaries (stakeholders, not researchers) should explain why control of the invasive species is in the public's interest -- Beneficiaries should present an ecologically-based risk/cost : benefit analysis justifying biological control on conservation grounds -- Public agencies should gather stakeholder input on how their criteria apply to a specific permit application -- Conclusion -- Acknowledgments -- References. , Chapter 14 Economics of biological control for species invading wildlands.

Note: Intro -- Preface -- Chapter 1 -- Inhibition of Extracellular Polymeric Substances to Reduce Bacteria Pathogenicity -- Abstract -- Introduction -- Problems Associated with Biofilm Formation -- Biofilms in the Food Industry -- Fresh Produce -- Seafood -- Dairy Products -- Meat -- Biofilms in the Healthcare Environment -- Composition of Extracellular Polymeric Substances of Pathogenic Bacteria -- Strategies to Inhibit the Synthesis of Extracellular Polymeric Substances -- Natural Sources -- Enzymatic Treatments -- Chemical Synthetic Treatments -- Nanoparticles -- Conclusion -- References -- Chapter 2 -- Biophysical Tools to Explore the Anti-Virulence Mode of Action of Phytochemicals Against Pathogenic Bacteria -- Abstract -- Introduction -- Biophysical Methods to Evidence the Interaction of Phytochemicals with Lipid Membrane -- Langmuir Monolayers -- Fluorescence Polarization -- Biophysical Methods to Validate the Interaction of Phytochemicals with Virulence Proteins -- Tools Aimed to Determine Binding Affinity -- X-Ray Crystallography -- Isothermal Calorimetry (ITC) -- Surface Plasmon Resonance (SPR) -- Nuclear Magnetic Resonance (NMR) -- Methods to Evaluate Conformational Changes in Target Proteins -- Circular Dichroism -- Ultraviolet and Visible Light Spectroscopy -- Fluorescence Spectroscopy -- Conclusion and Future Trends -- References -- Chapter 3 -- Antivirulence Mechanisms of Plant Terpenes against Pathogenic Bacteria -- Abstract -- Introduction -- Effect of Terpenes on Biofilm Formation -- Adhesion and Motility -- Biofilm Development -- Quorum Sensing System -- Intercellular Damages -- Efflux Pump Inhibition -- Cell Membrane Damage -- Interactions with Bacterial Cell Proteins and Nucleic Acid -- Bacterial Cell Proteins -- Nucleic Acids -- Bacteriostatic Terpenes Mode of Action -- Conclusion -- References -- Chapter 4. , Antipathogenic Potential of Ferula asafoetida Essential Oil -- Abstract -- Introduction -- A Brief Review of Antimicrobial Activity of F. asafoetida -- Our Work on Antimicrobial Potential of F. Asafoetida Essential Oil -- Plant Material and Extraction -- Test Organisms -- Bacteria -- Nematode Worm -- Antimicrobial Assay -- In Vivo Assay -- Results and Discussion -- Kabuli Variety -- Irani Variety -- Uzbeki Variety -- Shiro Variety -- Chemical Composition -- Final Comments -- References -- Chapter 5 -- Sonic Stimulation at Certain Frequencies Can Confer Limited Protection on Nematode Host Infected with Serratia marcescens -- Abstract -- Background -- Methods -- Organisms -- Sonic Frequencies -- Poly-Frequency Sound -- Mono-Frequency Sound -- Sonic Stimulation of the Infected Worms -- Statistical Analysis -- Results -- Conclusion -- Acknowledgments -- References -- Index -- Blank Page.

Note: Includes bibliographical references (p. 181 - 216) and index , A set of color overhead transparencies based on the figures in the book is available free on the PAGES website (www.pages-igbp.org) for use in teaching and lecturing.

Note: The chapters in this volume represent an extensive review of the material presented by the invited speakers at a two-day short course on 'Minerals, Inclusions and Volcanic Processes' held prior to the annual fall American Geophysical Union meeting in San Francisco, California (December 13 - 14, 2008) , Introduction to minerals, inclusions and volcanic processes , Experimental studies of the kinetics and energetics of magma crystallization , Thermometers and barometers for volcanic systems , Thermometers and thermobarometers in granitic systems , Fluid inclusion thermobarometry as a tracer for magmatic processes , Petrologic reconstruction of magmatic system variables and processes , Magma ascent rates , Melt inclusions in basaltic and related volcanic rocks , Interpreting H₂O and CO₂ contents in melt inclusions : constraints from solubility experiments and modeling , Volatile abundances in basaltic magmas and their degassing paths tracked by melt inclusions , Inter- and intracrystalline isotopic disequilibria : techniques and applications , Oxygen Isotopes in mantle and crustal magmas as revealed by single crystal analysis , Uranium-series crystal ages , Time scales of magmatic processes from modeling the zoning patterns of crystals , Mineral textures and zoning as evidence for open system process , Decryption of igneous rock textures : crystal size distribution tools , Deciphering magma chamber dynamics from styles of compositional zoning in large silicic ash flow sheets

Note: Literaturverz. S. [199] - 225