Incluye bibliografía: páginas 338-357 e índice: páginas 358-365

Preface page.. 1 Introduction.. Introduction.. 1.1 Process and pattern.. 1.2 Spatial pattern: spatial dependence versus spatial autocorrelation.. 1.3 The concept of stationarity.. 1.4 Sampling.. 1.4.1 Ecological data.. 1.4.2 Sampling design.. 1.5 Spatial statistics.. 1.5.1 Significance testing of ecological data.. 1.6 Concluding remarks.. 2 Spatial analysis of population data.. Introduction.. 2.1 Mapped point data in two dimensions.. 2.1.1 Distance to neighbours methods.. 2.1.2 Refined nearest neighbour analysis.. 2.1.3 Second-order point pattern analysis.. 2.1.4 Bivariate data.. 2.1.5 Multivariate point pattern analysis.. 2.2 Mark correlation function.. 2.3 Networks of events.. 2.4 Network analysis of areal units.. 2.5 Point patterns in other dimensions.. 2.5.1 One dimension.. 2.5.2 Three or more dimensions.. 2.6 Contiguous units analysis.. 2.6.1 Quadrat variance methods.. 2.6.2 Significance tests for quadrat variance methods.. 2.6.3 Adaptations for two or more species.. 2.6.4 Two or more dimensions.. 2.6.5 Spectral analysis and related techniques.. 2.6.6 Wavelets.. 2.7 Circumcircle methods.. 2.7.1 Univariate analysis.. 2.7.2 Bivariate analysis.. 2.7.3 Multivariate analysis.. 2.8 Concluding remarks.. 3 Spatial analysis of sample data.. Introduction.. 3.1 How to determine 'nearby' relationships among sampling units.. 3.2 Join count statistics.. 3.2.1 Considerations and other join count statistics.. 3.3 Global spatial statistics.. 3.3.1 Spatial autocorrelation coefficients for one variable.. 3.3.2 Variography.. 3.3.3 Fractal dimension.. 3.3.4 Sampling design effects on the estimation of spatial pattern.. 3.3.5 Spatial relationship between two variables.. 3.3.6 Spatial relationships among several variables.. 3.4 Local spatial statistics.. 3.5 Interpolation and spatial models.. 3.5.1 Proximity polygons.. 3.5.2 Trend surface analysis.. 3.5.3 Inverse distance weighting.. 3.5.4 Kriging.. 3.6 Concluding remarks

4 Spatial partitioning of regions: patch and boundary.. Introduction.. 4.1 Patch identification.. 4.1.1 Patch properties.. 4.1.2 Spatial clustering.. 4.1.3 Fuzzy classification.. 4.2 Boundary delineation.. 4.2.1 Ecological boundaries.. 4.2.2 Boundary properties.. 4.2.3 Boundary detection based on several variables.. 4.2.4 Boundary statistics.. 4.2.5 Overlap statistics.. 4.2.6 Boundary detection based on one variable.. 4.3 Concluding remarks.. 5 Dealing with spatial autocorrelation.. Introduction.. 5.1 Solutions.. 5.1.1 Quick fixes.. 5.1.2 Adjusting the effective sample size.. 5.1.3 Other kinds of models.. 5.1.4 Particular examples.. 5.1.5 Restricted randomization and bootstrap.. 5.1.6 Model and Monte Carlo.. 5.2 More on induced autocorrelation and the relationships between variables.. 5.3 Models and reality.. 5.4 Considerations for sampling and experimental design.. 5.4.1 Sampling.. 5.4.2 Experimental design.. 5.5 Concluding remarks.. 6 Spatio-temporal analysis.. Introduction.. 6.1 Change in spatial statistics.. 6.2 Spatio-temporal join count.. 6.3 Spatio-temporal analysis of clusters and contagion.. 6.4 Polygon change analysis.. 6.5 Analysis of movement.. 6.6 Process and pattern.. 6.6.1 Tree regeneration, growth and mortality.. 6.6.2 Plant mobility.. 6.6.3 Lichen boundaries.. 6.7 Spatio-temporal orderliness and spatial synchrony.. 6.8 Chaos.. 6.9 Concluding remarks.. 6.9.1 Recommendations.. 7 Closing comments and future directions.. Back to basics.. 7.1 Programming skills.. 7.2 Stationarity.. 7.3 Null hypotheses.. 7.4 Numerical solutions.. 7.5 Statistical difficulties.. 7.6 Randomization and restricted randomization tests.. 7.7 Complementarity of methods.. 7.8 Future work.. Appendices.. References.. Index

The spatial and temporal dimensions of ecological phenomena have always been inherent in the conceptual framework of ecology, but only recently have they been incorporated explicitly into ecological theory, sampling design, experimental design and models. Statistical techniques for spatial analysis of ecological data are burgeoning and many ecologists are unfamiliar with what is available and how the techniques should be used correctly. Spatial Analysis: A Guide for Ecologists gives an overview of the wide range of spatial statistics available to analyse ecological data, and provides advice and guidance for graduate students and practising researchers who are either about to embark on spatial analysis in ecological studies or who have started but are unsure how to proceed. Only a basic understanding of statistics is assumed and many schematic illustrations are given to complement or replace mathematical technicalities, making Spatial Analysis: A Guide for Ecologists accessible to ecologists wishing to enter this important and fast-growing field for the first time. Inglés