Data Analysis in Vegetation Ecology

About Vegetation Ecology This book covers central issues of plant ecology: Knowing what the environmental conditions are when recognizing specific species; Knowing which species are most likely to establish when knowing the environmental conditions; Knowing how vegetation is likely to change as the environment changes. Emerging from years of teaching, basic principles of how to handle measurements and observations by plant ecologists is described. Examples are drawn from experience in the authors own field of research: Wetland ecology, forest ecology, vegetation succession including short- and long-term change. Abstract Data Analysis in Vegetation Ecology aims to explain the background and basics of mathematical (mainly multivariate) analysis of vegetation data. The book lays out the basic operations involved in the analysis, the underlying hypotheses, aims and points of views in the subject. It conveys the message that each step in the calculations has a specific, straightforward meaning and that patterns and processes known by ecologists often find their counterpart in mathematical operations and functions. The first chapters introduce the elementary concepts and operations and relate them to real-world phenomena and problems. Later chapters concentrate on combinations of methods to reveal surprising features in data sets. Showing how to find patterns in time series, how to generate simple dynamic models, how to reveal spatial patterns and related occurrence probability maps. Reference Wildi, Otto 2010. Data Analysis in Vegetation Ecology. Wiley-Blackwell, Chichester, UK. 211pp. Get a copy from amazon

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Supplementary material

• Data sets listed in Appendix B

Overview by Chapter

	1 Introduction Real-, Data-, Model-world, World of values.
	2 Patterns in vegetation ecology Pattern recognition, Multiple patterns, Sampling design supplementary material
	3 Transformation Data types, Scalar-, Vector-transformation, Cover-abundance score transformation
	4 Multivariate comparison Geometric indices, Contingency measures, Product moments, Resemblance matrices
	5 Ordination Eigenanalysis, Metric multidimensional scaling, Detrending, Orthogonal ranking
	6 Classification Group patterns, Agglomerative clustering, Group number, Synoptic tables
	7 Joining ecological patterns Ecological response, Analysis of variance, Correlating spaces, Autocorrelation, Contingency, Constrained ordination
	8 Static explanatory modelling Bayes probability model, Predicting wetland vegetation.
	9 Assessing vegetation change in time Time series analysis. Markov models, Space-for-time substitution, Pollen data
	10 Dynamic modelling Differential equations, Numerical integration, Space-time process, Alpine pasture succession supplementary material
	11 Large data sets: wetland patterns Sub-sampling, Outlier detection, Phytosociology, Indicator values, Synoptic tables
	12 Swiss forests: a case study Systematic sampling, Scale effect, Comparison of data spaces, Fuzzy mapping, Intra-species trait supplementary material

Extended keyword list

Acceleration, Agglomerative clustering, Alliances, Altitude, Analysis of concentration, Anisotropy, Attributes, Bayes, Biological space, Canonical analysis, Canonical correlation, Canonical correspondence analysis, Centroid clustering, Chi squared, Chord distance, Classification, Climate, Cluster analysis, Colonization, Component coefficient, Constrained ordination, Contingency coeffient, Contingency table, Continuous model, Correlation, Correlogram, Correspondence analysis, Covariance, Cover-abundance, Cross-product, Database, Data organization, Data space, Data type, Degrees of freedom, Dendrogram, Dependent variables, Deterministic, Detrending, Differential equation, Digital terrain model, Discrete model, Dispersion, Distance, Divisive clustering, Dynamic modelling, Ecogram, Ecological pattern, Eigenanalysis, Eigenvalue, Eigenvector, Euclidean distance, Euler’s rule, Expectation, Explanatory modelling, Exponential growth, Flexible shortest-path adjustment, Floristic space, Forecasting, Forest, Fuzzy classification, F-value, Gaussian function, Geographical distribution, Geometric approach, Group structure, Hierarchy, Homogeneity, Horseshoe effect, Independent variables, Isotropy, Jaccard coefficient, Linkage clustering, Logical models, Logistic growth, Maarel’s similarity ratio, Manhattan distance, Mantel test, Markov model, Mean square contingency, Metric data, Multidimensional scaling, Minimum spanning tree, Minimum-variance clustering, Monoclimax, Moran’s I, Multivariate analysis, Nearest-neighbour analysis, Noise, Nominal data, Nonlinearity, Non-metric multidimensional scaling, Normalizing, Numerical integration, Ochiai’s coefficient, Ordinal data, Ordination, Orthogonal component, Oscillation, Outlier, Pasture, Pattern, Physical space, Phytosociology, Pollen diagram, Population, Potential natural vegetation, Predictive modelling, Preferential sampling, Principal axis analysis, Principal component analysis, Principal coordinates analysis, Probability, Product moment, Random sampling, Randomization test, Range adjustment, Ranking, Redundancy analysis, Reforestation, Regression, Resemblance space, Sample size, Sampling plan, Sampling unit, Scalar product, Scale effect, Scatter diagram, Similarity space, Similarity matrix, Soerensen coefficient, Spatial process, Space-for-time substitution, Spatial autocorrelation, Spatial dependence, Species performance, Standardization, Static modelling, Statistical sampling, Sum-of-squares clustering, Synoptic tables, Systematic sampling, Taxonomy, Temporal dependence, Time series, Transformation, Transition matrix, Trend, Variance, Vector transformation, Vegetation map, Velocity, Wetland