ALBERT

Note: Introduction.The role of compliance in an evolving climate regime / Lavanya Rajamani, Jutta Brunnée, and Meinhard Doelle -- The emerging post-Cancun climate regime / Jennifer Morgan -- Promoting compliance with multilateral environmental agreements / Jutta Brunnée -- Compliance regimes in multilateral environmental agreements / Jane Bulmer -- Key features of the Kyoto Protocol's compliance system / René Lefeber and Sebastian Oberthür -- Experience with the facilitative and enforcement branches of the Kyoto compliance system / Meinhard Doelle -- Experiences with Articles 5, 7, and 8 defining the monitoring, reporting and verification system under the Kyoto Protocol / Anke Herold -- The role of non-state actors in climate compliance / Eric Dannenmaier -- Facilitation of compliance / Catherine Redgwell -- Enforcing compliance in an evolving climate regime / Michael Mehling -- Financial mechanisms under the climate regime / Haroldo Machado-Filho -- Post-2012 compliance and carbon markets / Franceso Sindico -- Compliance and the use of trade measures / Jacob Werksman -- 'Comparability of efforts' among developed country parties and the post-2012 compliance system / M.J. Mace -- From the Kyoto Protocol compliance system to MRV : what is at stake for the European Union? / Sandrine Maljean-Dubois and Anne-Sophie Tabau -- Compliance in transition countries / Christina Voigt -- Developing countries and compliance in the climate regime / Lavanya Rajamani -- The role of dispute settlement in the climate regime / Ruth Mackenzie -- Depoliticizing compliance / Geir Ulfstein -- Conclusion.Promoting compliance in an evolving climate regime / Meinhard Doelle, Jutta Brunnée, and Lavanya Rajamani..

Note: Machine generated contents note: Preface 1. Introduction 2. Whole Earth morphology 3. Large scale morphology: the roles of geophysics 4. Tectonic geomorphology 5. Atmospheric processes and climate 6. Establishing timing in the landscape: dating methods 7. Weathering 8. Glaciers and glacial geology 9. Periglacial forms and processes 10. Hillslopes 11. Water in the landscape 12. Rivers 13. Bedrock channel incision 14. Sediment transport mechanics 15. Eolian processes and forms 16. Coastal geomorphology 17. Geomorphology of big floods 18. Whole landscapes Appendix A. Physics Appendix B. Math References Index

Note: Contents: 1. Agents, institutions, and the political economy of performance ; 2. Career theories of monetary policy ; 3. Careers and inflation in industrial democracies ; 4. Careers and the monetary policy process ; 5. Careers and inflation in developing countries ; 6. The uses of autonomy: what independence really means ; 7. Partisan governments, labor unions, and monetary policy ; 8. The politics of central banker appointment ; 9. The politics of central banker tenure ; 10. Conclusion: the dilemma of discretion.

Note: Contents: Preface. - 1 Introduction and datatypes. - 1.1 Why ordination?. - 1.2 Datatypes. - 1.3 Data transformation and standardisation. - 1.4 Missing values. - 1.5 Types of analyses. - 2 Using Canoco 5. - 2.1 Philosophy of Canoco 5. - 2.2 Data import and editing. - 2.3 Defining analyses. - 2.4 Visualising results. - 2.5 Beware, CANOCO 4.x users!. - 3 Experimental design. - 3.1 Completely randomised design. - 3.2 Randomised complete blocks. - 3.3 Latin square design. - 3.4 Pseudo replicates. - 3.5 Combining more than one factor. - 3.6 Following the development of objects in time: repeated observations. - 3.7 Experimental and observational data. - 4 Basics of gradient analysis. - 4.1 Techniques of gradient analysis. - 4.2 Models of response to gradients. - 4.3 Estimating species optima by weighted averaging. - 4.4 Calibration. - 4.5 Unconstrained ordination. - 4.6 Constrained ordination. - 4.7 Basic ordination techniques. - 4.8 Ordination axes as optimal predictors. - 4.9 Ordination diagrams. - 4.10 Two approaches. - 4.11 Testing significance of the relation with explanatory variables. - 4.12 Monte Carlo permutation tests for the significance of regression. - 4.13 Relating two biotic communities. - 4.14 Community composition as a cause: using reverse analysis. - 5.1 Permutation tests: the philosophy. - 5.2 Pseudo-F statistics and significance. - 5.3 Testing individual constrained axes. - 5.4 Tests with spatial or temporal constraints. - 5.5 Tests with hierarchical constraints. - 5.6 Simple versus conditional effects and stepwises election. - 5.7 Variation partitioning. - 5.8 Significance adjustment for multiple tests. - 6 Similarity measures and distance-based methods. - 6.1 Similarity measures for presence-absence data. - 6.2 Similarity measures for quantitative data. - 6.3 Similarity of cases versus similarity of communities. - 6.4 Similarity between species in trait values. - 6.5 Principal coordinates analysis. - 6.6 Constrained principal coordinates analysis (db-RDA). - 6.7 Non-metric multidimensional scaling. - 6.8 Mantel test. - 7.1 Example data set properties. - 7.2 Non-hierarchical classification (K-means clustering). - 7.3 Hierarchical classification. - 7.4 TWINSPAN. - 8 Regression methods. - 8.1 Regression models in general. - 8.2 General linear model: terms. - 8.3 Generalized linear models (GLM). - 8.4 Loess smoother. - 8.5 Generalized additive models (GAM). - 8.6 Mixed-effect models (LMM, GLMM and GAMM). - 8.7 Classification and regression trees (CART). - 8.8 Modelling species response curves with Canoco. - 9 Interpreting community composition with functional traits. - 9.1 Required data. - 9.2 Two approaches in traits - environment studies. - 9.3 Community-based approach. - 9.4 Species-based approach. - 10 Advanced use of ordination. - 10.1 Principal response curves (PRC). - 10.2 Separating spatial variation. - 10.3 Linear discriminant analysis. - 10.4 Hierarchical analysis of community variation. - 10.5 Partitioning diversity indices into alpha and beta components. - 10.6 Predicting community composition. - 11 Visualising multivariate data. - 11.1 Reading ordination diagrams of linear methods. - 11.2 Reading ordination diagrams of unimodal methods. - 11.3 Attribute plots. - 11.4 Visualising classification, groups, and sequences. - 11.5 T-value biplot. - 12 Case study 1: Variation in forest bird assemblages. - 12.1 Unconstrained ordination: portraying variation in bird community. - 12.2 Simple constrained ordination: the effect of altitude on bird community. - 12.3 Partial constrained ordination: additional effect of other habitat characteristics. - 12.4 Separating and testing alpha and beta diversity. - 13 Case study 2: Search for community composition patterns and their environmental correlates: vegetation of spring meadows. - 13.1 Unconstrained ordination. - 13.2 Constrained ordination. - 13.3 Classification. - 13.4 Suggestions for additional analyses. - 13.5 Comparing two communities. - 14 Case study 3: Separating the effects of explanatory variables. - 14.1 Introduction. - 14.2 Data. - 14.3 Changes in species richness and composition. - 14.4 Changes in species traits. - 15 Case study 4: Evaluation of experiments in randomised complete blocks. - 15.1 Introduction. - 15.2 Data. - 15.3 Analysis. - 15.4 Calculating ANOVA using constrained ordination. - 16 Case study 5: Analysis of repeated observations of species composition from a factorial experiment. - 16.1 Introduction. - 16.2 Experimental design. - 16.3 Data coding and use. - 16.4 Univariate analyses. - 16.5 Constrained ordinations. - 16.6 Principal response curves. - 16.7 Temporal changes across treatments. - 16.8 Changes in composition of functional traits. - 17 Case study 6: Hierarchical analysis of crayfish community variation. - 17.1 Data and design. - 17.2 Differences among sampling locations. - 17.3 Hierarchical decomposition of community variation. - 18 Case study 7: Analysis of taxonomic data with discriminant analysis and distance-based ordination. - 18.1 Data. - 18.2 Summarising morphological data with PCA. - 18.3 Linear discriminant analysis of morphological data. - 18.4 Principal coordinates analysis of AFLP data. - 18.5 Testing taxon differences in AFLP data using db-RDA. - 18.6 Taking populations into account. - 19 Case study 8: Separating effects of space and environment on oribatid community with PCNM. - 19.1 Ignoring the space. - 19.2 Detecting spatial trends. - 19.3 All-scale spatial variation of community and environment. - 19.4 Variation partitioning with spatial predictors. - 19.5 Visualising spatial variation. - 20 Case study 9: Performing linear regression with redundancy analysis. - 20.1 Data. - 20.2 Linear regression using program R. - 20.3 Linear regression with redundancy analysis. - 20.4 Fitting generalized linear models in Canoco. - Appendix A Glossary. - Appendix B Sample data sets and projects. - Appendix C Access to Canoco and overview of other software. - Appendix D Working with R. - References. - Index to useful tasks in Canoco 5. - Subject index.

Note: Machine generated contents note: Preface; 1. Introduction; Part I. Fundamental Concepts: 2. Fundamental concepts and terminology; 3. Modeling and computational simulation; Part II. Code Verification: 4. Software engineering; 5. Code verification; 6. Exact solutions; Part III. Solution Verification: 7. Solution verification; 8. Discretization error; 9. Solution adaptation; Part IV. Model Validation and Prediction: 10. Model validation fundamentals; 11. Design and execution of validation experiments; 12. Model accuracy assessment; 13. Predictive capability; Part V. Planning, Management, and Implementation Issues: 14. Planning and prioritization in modeling and simulation; 15. Maturity assessment of modeling and simulation; 16. Development and responsibilities for verification, validation and uncertainty quantification; Appendix. Programming practices; Index..