UID:
kobvindex_GFZ354136712
Format:
XI, 269 S.
,
graph. Darst.
,
26 cm
Edition:
Transferred to digital printing 2009
ISBN:
052181409X (hb)
,
0521891086 (pb.)
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.
Language:
English
Subjects:
Geography
,
Biology
URL:
http://www.gbv.de/dms/hebis-darmstadt/toc/111779995.pdf
