ALBERT

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: Erscheinungsjahr in Vorlageform:2001

Note: Contents:PART I THE IMPORTANCE OF IMAGE REGISTRATION FOR REMOTE SENSING. - 1 Introduction. - 2 Influence of image registration on validation efforts. - 3 Survey of image registration methods. - PART II SIMILARITY METRICS FOR IMAGE REGISTRATION. - 4 Fast correlation and phase correlation. - 5 Matched filtering techniques. - 6 Image registration using mutual information. - PART III FEATURE MATCHING AND STRATEGIES FOR IMAGE REGISTRATION. - 7 Registration of multiview images. - 8 New approaches to robust, point-based image registration. - 9 Condition theory for image registration and post-registration error estimation. - 10 Feature-based image to image registration. - 11 On the use of wavelets for image registration. - 12 Gradient descent approaches to image registration. - 13 Bounding the performance of image registration. - PART IV APPLICATIONS AND OPERATIONAL SYSTEMS. - 14 Multitemporal and multisensor image registration. - 15 Georegistration of meteorological images. - 16 Challenges, solutions, and applications of accurate multiangle image registration: lessons learned from MISR. - 17 Automated AVHRR image navigation. - 18 Landsat image geocorrection and registration. - 19 Automatic and precise orthorectification of SPOT images. - 20 Geometry of the VEGETATION sensor. - 21 Accurate MODIS global geolocation through automated ground control image matching. - 22 Sea WiFS operational geolocation assessment system. - PART V CONCLUSION. - 23 Concluding remarks.

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: Contents: Preface. - 1. Introduction and data manipulation. - 1.1. Why ordination?. - 1.2. Terminology. - 1.3. Types of analyses. - 1.4. Response variables. - 1.5. Explanatory variables. - 1.6. Handling missing values in data. - 1.7. Importing data from spreadsheets - WCanoImp program. - 1.8. Transformation of species data. - 1.9. Transformation of explanatory variables. - 2. Experimental design. - 2.1. Completely randomized design. - 2.2. Randomized complete blocks. - 2.3. Latin square design. - 2.4. Most frequent errors - pseudoreplications. - 2.5. Combining more than one factor. - 2.6. Following the development of objects in time - repeated observations. - 2.7. Experimental and observational data. - 3. Basics of gradient analysis. - 3.1. Techniques of gradient analysis. - 3.2. Models of species response to environmental gradients. - 3.3. Estimating species optima by the weighted averaging method. - 3.4. Calibration. - 3.5. Ordination. - 3.6. Constrained ordination. - 3.7. Basic ordination techniques. - 3.8. Ordination diagrams. - 3.9. Two approaches. - 3.10. Testing significance of the relation with environmental variables. - 3.11. Monte Carlo permutation tests for the significance of regression. - 4. Using the Canoco for Windows 4.5 package. - 4.1. Overview of the package. - 4.2. Typical flow-chart of data analysis with Canoco for Windows. - 4.3. Deciding on the ordination method: unimodal or linear?. - 4.4. PCA or RDA ordination: centring and standardizing. - 4.5. DCA ordination: detrending. - 4.6. Scaling of ordination scores. - 4.7. Running CanoDraw for Windows 4.0. - 4.8. New analyses providing new views of our data sets. - 5. Constrained ordination and permutation tests. - 5.1. Linear multiple regression model. - 5.2. Constrained ordination model. - 5.3. RDA: constrained PCA. - 5.4. Monte Carlo permutation test: an introduction. - 5.5. Null hypothesis model. - 5.6. Test statistics. - 5.7. Spatial and temporal constraints. - 5.8. Split-plot constraints. - 5.9. Stepwise selection of the model. - 5.10. Variance partitioning procedure. - 6. Similarity measures. - 6.1. Similarity measures for presence-absence data. - 6.2. Similarity measures for quantitative data. - 6.3. Similarity of samples versus similarity of communities. - 6.4. Principal coordinates analysis. - 6.5. Non-metric multidimensional scaling. - 6.6. Constrained principal coordinates analysis (db-RDA). - 6.7. Mantel test. - 7. Classification methods. - 7.1. Sample data set. - 7.2. Non-hierarchical classification (K-means clustering). - 7.3. Hierarchical classifications. - 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. Classification and regression trees. - 8.7. Modelling species response curves with CanoDraw. - 9. Advanced use of ordination. - 9.1. Testing the significance of individual constrained ordination axes. - 9.2. Hierarchical analysis of community variation. - 9.3. Principal response curves (PRC) method. - 9.4. Linear discriminant analysis. - 10. Visualizing multivariate data. - 10.1. What we can infer from ordination diagrams: linear methods. - 10.2. What we can infer from ordination diagrams: unimodal methods. - 10.3. Visualizing ordination results with statistical models. - 10.4. Ordination diagnostics. - 10.5. t-value biplot interpretation. - 11. Case study 1: Variation in forest bird assemblages. - 11.1. Data manipulation. - 11.2. Deciding between linear and unimodal ordination. - 11.3. Indirect analysis: portraying variation in bird community. - 11.4. Direct gradient analysis: effect of altitude. - 11.5.Direct gradient analysis: additional effect of other habitat characteristics. - 12. Case study 2: Search for community composition patterns and their environmental correlates: vegetation of spring meadows. - 12.1. The unconstrained ordination. - 12.2. Constrained ordinations. - 12.3. Classification. - 12.4. Suggestions for additional analyses. - 13. Case study 3: Separating the effects of explanatory variables. - 13.1. Introduction. - 13.2. Data. - 13.3. Data analysis. - 14. Case study 4: Evaluation of experiments in randomized complete blocks. - 14.1. Introduction. - 14.2. Data. - 14.3. Data analysis. - 15. Case study 5: Analysis of repeated observations of species composition from a factorial experiment. - 15.1. Introduction. - 15.2. Experimental design. - 15.3. Sampling. - 15.4. Data analysis. - 15.5. Univariate analyses. - 15.6. Constrained ordinations. - 15.7. Further use of ordination results. - 15.8. Principal response curves. - 16. Case study 6: Hierarchical analysis of crayfish community variation. - 16.1. Data and design. - 16.2. Differences among sampling locations. - 16.3. Hierarchical decomposition'of community variation. - 17. Case study 7: Differentiating two species and their hybrids with discriminant analysis. - 17.1. Data. - 17.2. Stepwise selection of discriminating variables. - 17.3. Adjusting the discriminating variables. - 17.4. Displaying results. - Appendix A: Sample datasets and projects. - Appendix B: Vocabulary. - Appendix C: Overview of available software. - References. - Index.

Note: Erscheinungsjahr in Vorlageform:2004