ALBERT

Anmerkung: 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.

Anmerkung: Beitr. teilw. dt., teilw. engl.