WorldCat Linked Data Explorer

http://worldcat.org/entity/work/id/1029801

Multivariate analysis of ecological data using CANOCO

This book is primarily written for students and researchers dealing with complex ecological problems. Multivariate statistical methods are described, and advice is given on how best to apply these methods using Canoco software. Data sets and program files for the case studies are provided on a supporting website.

Open All Close All

http://schema.org/about

http://schema.org/description

  • "This book is primarily written for students and researchers dealing with complex ecological problems. Multivariate statistical methods are described, and advice is given on how best to apply these methods using Canoco software. Data sets and program files for the case studies are provided on a supporting website."@en
  • "Ordination, experimental design, gradient analysis, permutation, similarity."
  • "1: Introduction and data manipulation. Why ordination? Terminology. Types of analyses. Response variables. Explanatory variables. Handling missing values in data. Importing data from spreadsheets - WCanoImp program. Transformation of species data. Transformation of explanatory variables.2: Experimental design. Completely randomized design. Randomized complete blocks. Latin square design. Most frequent errors - pseudo replications. Combining more than one factor. Following the development of objects in time - repeated observations. Experimental and observational data. 3: Basics of gradient analysis.Techniques of gradient analysis. Models of species response to environmental gradients. Estimating species optima by the weighted averaging method. Calibration. Ordination.Constrained ordination. Basic ordination techniques. Ordination diagrams. Two approaches.Testing significance of the relation with environmental variables. Monte Carlo permutation tests for the significance of regression.4: Using the Canoco for Windows 4.5 package. Overview of the package. Typical flow-chart of data analysis with Canoco for Windows. Deciding on the ordination method: unimodal or linear? PCA or RDA ordination: centring and standardizing. DCA ordination: detrending. Scaling of ordination scores. Running CanoDraw for Windows. New analyses providing new views of our data sets.5: Constrained ordination and permutation tests. Linear multiple regression model. Constrained ordination model. RDA: constrained PCA. Monte Carlo permutation test: an introduction. Null hypothesis model. Test statistics. Spatial and temporal constraints. Split-plot constraints. Stepwise selection of the model. Variance partitioning procedure. 6: Similarity measures. Similarity measures for presence-absence data. Similarity measures for quantitative data. Similarity of samples versus similarity of communities. Principal coordinates analysis. Non-metric multidimensional scaling. Constrained principal coordinates analysis (db-RDA). Mantel test.7: Classification methods .Sample data set. Non-hierarchical classification (K-means clustering). Hierarchical classifications. TWINSPAN. 8: Regression methods. Regression models in general.General linear model: terms. Generalized linear models (GLM). Loess smoother. Generalized additive models (GAM). classification and regression trees. Modelling species response curves with CanoDraw. 9: Advanced use of ordination. Testing the significance of individual constrained ordination axes. Hierarchical analysis of community variation. Principal response curves (PRC) method. Linear discriminant analysis. 10: Visualizing multivariate data. What we can infer from ordination diagrams: linear methods. What we can infer from ordination diagrams: unimodal methods. Visualizing ordination results with statistical models. Ordination diagnostics. t-value biplot interpretation. 11: Case study 1: Variation in forest bird assemblages. Data manipulation. Deciding between linear and unimodal ordination. Indirect analysis: portraying variation in bird community. Direct gradient analysis: effect of altitude. 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.The unconstrained ordination. Constrained ordinations. Classification. Suggestions for additional analyses.13:Case study 3: Separating the effects of explanatory variables. Introduction. Data. Data analysis. 14:Case study 4: Evaluation of experiments in randomized complete blocks. Introduction. Data. Data analysis. 15: Case study 5: Analysis of repeated observations of species composition from a factorial experiment. Introduction . Experimental design. Sampling. Data analysis. Univariate analyses. Constrained ordinations. Further use of ordination results. Principal response curves.16:Case study 6: Hierarchical analysis of crayfish community variation. Data and design. 13:Case study 3: Separating the effects of explanatory variables. Introduction. Data. Data analysis. 14:Case study 4: Evaluation of experiments in randomized complete blocks. Introduction. Data. Data analysis. 15: Case study 5: Analysis of repeated observations of species composition from a factorial experiment. Introduction . Experimental design. Sampling. Data analysis. Univariate analyses. Constrained ordinations. Further use of ordination results. Principal response curves.16:Case study 6: Hierarchical analysis of crayfish community variation. Data and design.Differences among sampling locations. Hierarchical decomposition of community variation. 17: Case study 7: Differentiating two species and their hybrids witl discriminant analysis. Data. Stepwise selection of discriminating variables. Adjusting the discriminating variables. Displaying results."

http://schema.org/genre

  • "Electronic books"@en
  • "Case studies"@en
  • "Case studies"

http://schema.org/name

  • "Multivariate analysis of ecological data using CANOCO"@en
  • "Multivariate analysis of ecological data using CANOCO"