Data Analysis in Community and Landscape EcologyR. H. Jongman, C. J. F. Ter Braak, O. F. R. van Tongeren Ecologists need to analyze their field data to interpret relationships within plant and animal communities and with their environments. The purpose of this book is to show ecologists and environmental scientists what numerical and statistical methods are most useful, how to use them and interpret the results from them, and what pitfalls to avoid. Subjects treated include data requirements, regression analysis, calibration (or inverse regression), ordination techniques, cluster analysis, and spatial analysis of ecological data. The authors take pains to use only elementary mathematics and explain the ecological models behind the techniques. Exercises and solution are provided for practice. This is the only book written specifically for ecologists that explains such techniques as logistic regression, canonical correspondence analysis, and kriging (statistical manipulation of data). This is a reissue of a book first published in 1987 by Pudoc (The Netherlands). |
Contents
1 Introduction | 1 |
2 Data collection | 10 |
3 Regression | 29 |
4 Calibration | 78 |
5 Ordination | 91 |
6 Cluster analysis | 174 |
7 Spatial aspects of ecological data | 213 |
casestudies | 252 |
| 275 | |
| 292 | |
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Common terms and phrases
abundance values algorithm ANOVA b₁ b₂ biplot Braak calculated canonical ordination classification cluster analysis correlation correspondence analysis covariance detrending deviance dissimilarity Dune Meadow Data ecological eigenvalue eigenvector environmental variables Equation error estimated Euclidean Distance example Exercise expected response explanatory variables Figure fitted gradient gradient analysis groups hayfield heathlands indicator values iteration least-squares regression linear logit logit regression matrix maximum likelihood mean methods moisture multidimensional scaling multiple regression normal distribution number of species obtained optimum ordination axes ordination diagram parameters plot points Poisson distribution probability of occurrence quantitative regression analysis relation relevés residual sum response curve response model sample scale second axis semivariance semivariogram shows site scores soil type spatial species composition species data species scores standard deviation statistical Step straight line Subsection sum of squares techniques transformation trend surfaces TWINSPAN unimodal variance variation vegetation weighted average x₁