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E-grāmata: Spatial Analysis: A Guide for Ecologists

3.56/5 (14 ratings by Goodreads)
(University of Alberta), (University of Toronto)
  • Formāts: PDF+DRM
  • Izdošanas datums: 21-Apr-2005
  • Izdevniecība: Cambridge University Press
  • Valoda: eng
  • ISBN-13: 9780511110030
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Spatial Analysis: A Guide for EcologistsPalielināt
  • Formāts: PDF+DRM
  • Izdošanas datums: 21-Apr-2005
  • Izdevniecība: Cambridge University Press
  • Valoda: eng
  • ISBN-13: 9780511110030
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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. This book 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 the book accessible to ecologists wishing to enter this important and fast-growing field for the first time.

Recenzijas

"The overall result is a book that should be well received and prove highly useful to its target audience of graduate students and researchers in ecology, both as an introductory course text in spatial analysis and also for subsequent reference." Trevor Bailey, Biometrics "Spatial Analysis is a guide, not a recipe book. In today's quest for ecological understanding of spatial patterns, recipes of methods clutter bookshelves, and an important guide like this one is much in need. Without any hesitation, I highly recommend this book to anyone who is interested in spatial analysis in ecology and environmental sciences." Jianguo Wu, BioScience

Papildus informācija

An overview of the wide range of spatial statistics available to analyse ecological data.
Preface xi
1 Introduction 1(31)
Introduction
1(1)
1.1 Process and pattern
2(4)
1.2 Spatial pattern: spatial dependence versus spatial autocorrelation
6(5)
1.3 The concept of stationarity
11(2)
1.4 Sampling
13(12)
1.4.1 Ecological data
14(4)
1.4.2 Sampling design
18(7)
1.5 Spatial statistics
25(5)
1.5.1 Significance testing of ecological data
26(4)
1.6 Concluding remarks
30(2)
2 Spatial analysis of population data 32(79)
Introduction
32(1)
2.1 Mapped point data in two dimensions
33(22)
2.1.1 Distance to neighbours methods
33(2)
2.1.2 Refined nearest neighbour analysis
35(2)
2.1.3 Second-order point pattern analysis
37(6)
2.1.4 Bivariate data
43(4)
2.1.5 Multivariate point pattern analysis
47(8)
2.2 Mark correlation function
55(2)
2.3 Networks of events
57(7)
2.4 Network analysis of areal units
64(11)
2.5 Point patterns in other dimensions
75(7)
2.5.1 One dimension
75(6)
2.5.2 Three or more dimensions
81(1)
2.6 Contiguous units analysis
82(16)
2.6.1 Quadrat variance methods
82(3)
2.6.2 Significance tests for quadrat variance methods
85(3)
2.6.3 Adaptations for two or more species
88(3)
2.6.4 Two or more dimensions
91(4)
2.6.5 Spectral analysis and related techniques
95(1)
2.6.6 Wavelets
96(2)
2.7 Circumcircle methods
98(5)
2.7.1 Univariate analysis
99(1)
2.7.2 Bivariate analysis
100(3)
2.7.3 Multivariate analysis
103(1)
2.8 Concluding remarks
103(8)
3 Spatial analysis of sample data 111(63)
Introduction
111(2)
3.1 How to determine 'nearby' relationships among sampling units
113(5)
3.2 Join count statistics
118(4)
3.2.1 Considerations and other join count statistics
120(2)
3.3 Global spatial statistics
122(31)
3.3.1 Spatial autocorrelation coefficients for one variable
124(8)
3.3.2 Variography
132(7)
3.3.3 Fractal dimension
139(3)
3.3.4 Sampling design effects on the estimation of spatial pattern
142(5)
3.3.5 Spatial relationship between two variables
147(1)
3.3.6 Spatial relationships among several variables
147(6)
3.4 Local spatial statistics
153(6)
3.5 Interpolation and spatial models
159(11)
3.5.1 Proximity polygons
160(1)
3.5.2 Trend surface analysis
161(3)
3.5.3 Inverse distance weighting
164(1)
3.5.4 Kriging
165(5)
3.6 Concluding remarks
170(4)
4 Spatial partitioning of regions: patch and boundary 174(38)
Introduction
174(1)
4.1 Patch identification
175(9)
4.1.1 Patch properties
175(1)
4.1.2 Spatial clustering
176(4)
4.1.3 Fuzzy classification
180(4)
4.2 Boundary delineation
184(26)
4.2.1 Ecological boundaries
184(1)
4.2.2 Boundary properties
184(2)
4.2.3 Boundary detection based on several variables
186(13)
4.2.4 Boundary statistics
199(3)
4.2.5 Overlap statistics
202(3)
4.2.6 Boundary detection based on one variable
205(5)
4.3 Concluding remarks
210(2)
5 Dealing with spatial autocorrelation 212(44)
Introduction
212(9)
5.1 Solutions
221(22)
5.1.1 Quick fixes
221(1)
5.1.2 Adjusting the effective sample size
222(7)
5.1.3 Other kinds of models
229(5)
5.1.4 Particular examples
234(5)
5.1.5 Restricted randomization and bootstrap
239(3)
5.1.6 Model and Monte Carlo
242(1)
5.2 More on induced autocorrelation and the relationships between variables
243(4)
5.3 Models and reality
247(1)
5.4 Considerations for sampling and experimental design
248(6)
5.4.1 Sampling
248(5)
5.4.2 Experimental design
253(1)
5.5 Concluding remarks
254(2)
6 Spatio-temporal analysis 256(61)
Introduction
256(5)
6.1 Change in spatial statistics
261(2)
6.2 Spatio-temporal join count
263(3)
6.3 Spatio-temporal analysis of clusters and contagion
266(3)
6.4 Polygon change analysis
269(5)
6.5 Analysis of movement
274(17)
6.6 Process and pattern
291(8)
6.6.1 Tree regeneration, growth and mortality
291(1)
6.6.2 Plant mobility
292(1)
6.6.3 Lichen boundaries
293(6)
6.7 Spatio-temporal orderliness and spatial synchrony
299(6)
6.8 Chaos
305(10)
6.9 Concluding remarks
315(2)
6.9.1 Recommendations
316(1)
7 Closing comments and future directions 317(19)
Back to basics
317(3)
7.1 Programming skills
320(1)
7.2 Stationarity
320(2)
7.3 Null hypotheses
322(1)
7.4 Numerical solutions
323(2)
7.5 Statistical difficulties
325(1)
7.6 Randomization and restricted randomization tests
326(2)
7.7 Complementarity of methods
328(5)
7.8 Future work
333(3)
Appendices 336(2)
References 338(20)
Index 358


Marie-Josée Fortin is an Associate Professor in Spatial Ecology at the Department of Zoology, University of Toronto, Ontario, Canada. Mark Dale is Professor in Plant Ecology at the Department of Biological Science, University of Alberta, Edmonton, Canada.
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