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E-grāmata: Spatial Analysis: A Guide For Ecologists

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(University of Toronto), (University of Northern British Columbia)
  • Formāts: PDF+DRM
  • Izdošanas datums: 11-Sep-2014
  • Izdevniecība: Cambridge University Press
  • Valoda: eng
  • ISBN-13: 9781139989114
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Spatial Analysis: A Guide For EcologistsPalielināt
  • Formāts: PDF+DRM
  • Izdošanas datums: 11-Sep-2014
  • Izdevniecība: Cambridge University Press
  • Valoda: eng
  • ISBN-13: 9781139989114
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Nowadays, ecologists worldwide recognize the use of spatial analysis as essential. However, because of the fast-growing range of methods available, even an expert might occasionally find it challenging to choose the most appropriate one. Providing the ecological and statistical foundations needed to make the right decision, this second edition builds and expands upon the previous one by: Encompassing the basic methods for spatial analysis, for both complete census and sample data Investigating updated treatments of spatial autocorrelation and spatio-temporal analysis Introducing detailed explanations of currently developing approaches, including spatial and spatio-temporal graph theory, scan statistics, fibre process analysis, and Hierarchical Bayesian analysis Offering practical advice for specific circumstances, such as how to analyze forest Permanent Sample Plot data and how to proceed with transect data when portions of the data series are missing. Written for graduates, researchers and professionals, this book will be a valuable source of reference for years to come.

Recenzijas

'Spatial Analysis: A Guide for Ecologists is every bit the reference book it was crafted to be, and will be a useful reference for students, researchers and practitioners with all levels of experience.' Ricardo A. Correia, The Biologist

Papildus informācija

An essential guide for graduates, researchers and professionals to spatial analysis and the fast-growing range of methods available.
Preface xi
1 Spatial concepts and notions
1(31)
Introduction
1(2)
1.1 The spatial context
3(1)
1.2 Ecological data
4(1)
1.3 Spatial structure: spatial dependence and spatial autocorrelation
4(5)
1.4 Spatial scales
9(2)
1.5 Sampling design
11(5)
1.5.1 The sample size (the number of observations 'n')
11(1)
1.5.2 Spatial resolution
11(1)
1.5.3 The size of the study area: extent
12(2)
1.5.4 The location in the landscape
14(1)
1.5.5 The size of the sampling or observational units: grain
14(1)
1.5.6 The shape of the sampling or observational units
14(1)
1.5.7 The spatial sampling design
14(1)
1.5.8 Spatial lag
15(1)
1.5.9 Edge effect
16(1)
1.6 Stationarity
16(5)
1.7 Spatial statistics
21(1)
1.7.1 First-order statistics
21(1)
1.7.2 Second-order statistics
21(1)
1.8 Ecological hypotheses and spatial analysis
22(3)
1.9 Randomization tests for spatially structured ecological data
25(5)
1.9.1 Restricted randomizations
27(3)
1.10 In conclusion: what is space?
30(2)
2 Ecological and spatial processes
32(14)
Introduction
32(1)
2.1 Ecological processes and spatial structure
32(8)
2.2 Spatial processes by species level of organization
40(3)
2.3 Spatial process
43(3)
3 Points, lines and graphs
46(42)
Introduction
46(4)
3.1 Points: spatial patterns of point events
50(11)
3.1.1 Topological neighbours
50(5)
3.1.2 Distance-based spatial neighbours
55(4)
3.1.3 Directional angle-based spatial neighbours
59(2)
3.2 Lines: fibre pattern analysis
61(6)
3.2.1 Aggregation and overdispersion of fibres
62(3)
3.2.2 Fibres with properties
65(1)
3.2.3 Curving fibres
65(1)
3.2.4 Branching curved fibres
66(1)
3.2.5 Congruence and parallelism of curved fibres
67(1)
3.3 Points and lines together
67(2)
3.4 Points and lines: spatial graphs
69(3)
3.4.1 Signed and directed graphs and networks
70(1)
3.4.2 How to create subgraphs
71(1)
3.4.3 Graph models
72(1)
3.5 Network analysis of areal units
72(5)
3.6 Spatial analysis of movement
77(3)
3.6.1 Transport and gravity models
77(1)
3.6.2 Least-cost paths
77(2)
3.6.3 Circuit theory
79(1)
3.6.4 Spatial graphs and movement
79(1)
3.6.5 Corridors
79(1)
3.7 Testing hypotheses with graphs
80(4)
3.7.1 Comment on spatial graph randomization
83(1)
3.8 Concluding remarks
84(4)
Glossary: graph definitions and properties
84(4)
4 Spatial analysis of complete point location data
88(35)
Introduction
88(1)
4.1 Mapped point data in two dimensions
88(17)
4.1.0 Introduction: three pattern types
88(1)
4.1.1 Distance to neighbours methods
89(1)
4.1.2 Refined nearest neighbour analysis
90(1)
4.1.3 Second-order point pattern analysis
91(5)
4.1.4 Bivariate data
96(2)
4.1.5 Multivariate point pattern analysis data
98(7)
4.2 Mark correlation function
105(1)
4.3 Ripley's iT-function for inhomogeneous point pattern analysis
106(5)
4.3.1 Bivariate and multivariate non-stationary point patterns
109(1)
4.3.2 Quantitative marks: mark correlation
110(1)
4.4 Point patterns in other dimensions
111(5)
4.4.1 One dimension
111(1)
4.4.2 Lacunarity
112(3)
4.4.3 Three dimensions
115(1)
4.5 Circumcircle methods
116(3)
4.5.1 Univariate analysis
116(1)
4.5.2 Bivariate analysis
117(2)
4.5.3 Multivariate analysis
119(1)
4.6 Concluding remarks
119(4)
5 Contiguous units analysis
123(17)
Introduction
123(1)
5.1 Quadrat variance methods
123(3)
5.2 Significance tests for quadrat variance methods
126(1)
5.3 Adaptations for two or more species
127(2)
5.4 Two or more dimensions
129(4)
5.5 Spectral analysis and related techniques
133(1)
5.6 Wavelets
134(1)
5.7 Concluding remarks
135(5)
6 Spatial analysis of sample data
140(42)
Introduction
140(1)
6.1 Join count statistics
141(3)
6.1.1 Join count statistics for k-categories
142(2)
6.2 Global spatial statistics
144(15)
6.2.1 Spatial covariance
144(1)
6.2.2 Spatial autocorrelation coefficients for one variable
145(7)
6.2.3 Variography
152(6)
6.2.4 Fractal dimension
158(1)
6.3 Sampling design effects on the estimation of spatial pattern
159(4)
6.4 Spatial relationship between two variables
163(1)
6.5 Local spatial statistics
164(4)
6.6 Spatial scan statistics
168(2)
6.7 Interpolation and spatial models
170(8)
6.7.1 Proximity polygons
171(1)
6.7.2 Trend surface analysis
172(1)
6.7.3 Inverse distance weighting
172(1)
6.7.4 Kriging
173(5)
6.8 Concluding remarks
178(4)
7 Spatial relationship and multiscale analysis
182(24)
Introduction
182(1)
7.1 Correlation between spatially autocorrelated variables
182(1)
7.2 Correlation of distance matrices
183(9)
7.2.1 Mantel test
183(6)
7.2.2 Partial Mantel tests and multiple-matrix regression
189(3)
7.3 Canonical (constrained) ordination
192(2)
7.4 Multiscale analysis
194(9)
7.4.1 Generalized Moran's eigenvector maps
195(3)
7.4.2 Multiresolution spectral decomposition analysis based on wavelets
198(5)
7.5 Concluding remarks
203(3)
8 Spatial autocorrelation and inferential tests
206(38)
Introduction
206(1)
8.1 Models dealing with one-dimensional autocorrelated data
207(6)
8.2 Dealing with spatial autocorrelation in inferential models
213(11)
8.2.1 Simple adjustments
213(1)
8.2.2 Adjusting the effective sample size
214(4)
8.2.3 More on induced autocorrelation and the relationships between variables
218(2)
8.2.4 Correlation and related methods
220(4)
8.3 Randomization procedures
224(3)
8.3.1 Restricted randomization and bootstrap
224(2)
8.3.2 Markov Chain Monte Carlo
226(1)
8.4 Spatial regressions
227(12)
8.4.1 Spatial filtering using autoregressive models
230(2)
8.4.2 Spatial filtering using moving average models
232(1)
8.4.3 Spatial filtering using Moran's eigenvector maps
233(1)
8.4.4 Spatial error regression
233(1)
8.4.5 Geographically weighted regression
234(1)
8.4.6 Remove spatial autocorrelation from the residuals
234(2)
8.4.7 Example of the use of non-spatial and spatial regressions
236(3)
8.5 Considerations for sampling and experimental design
239(2)
8.5.1 Sampling
239(2)
8.5.2 Experimental design
241(1)
8.6 Concluding remarks
241(3)
9 Spatial partitioning: spatial clusters and boundary detection
244(34)
Introduction
244(1)
9.1 Patch identification
244(7)
9.1.1 Patch properties
244(1)
9.1.2 Spatial clustering
245(4)
9.1.3 Fuzzy classification
249(2)
9.2 Boundary delineation
251(19)
9.2.1 Ecological boundaries
251(1)
9.2.2 Boundary properties
251(2)
9.2.3 Boundary detection and analysis for one-dimensional transect data
253(9)
9.2.4 Boundary detection based on two-dimensional data
262(8)
9.3 Boundary statistics
270(1)
9.4 Boundary overlap statistics
271(2)
9.5 Hierarchical spatial partitioning
273(2)
9.5.1 Edge enhancement with kernel filters
274(1)
9.6 Concluding remarks
275(3)
10 Spatial diversity analysis
278(41)
Introduction
278(1)
10.1 Space in diversity analysis
278(5)
10.1.1 Spatial heterogeneity
279(1)
10.1.2 Spatial location and environmental gradients
280(1)
10.1.3 Spatial scale
280(1)
10.1.4 Propinquity and spatial dependence
281(2)
10.2 First-order diversity
283(12)
10.2.1 α-diversity
284(3)
10.2.2 β-diversity
287(6)
10.2.3 γ-diversity
293(1)
10.2.4 Why space in first-order diversity analysis?
293(2)
10.3 Species combinations and composition: agreement and complementarity
295(16)
10.3.1 Species combinations
296(6)
10.3.2 Comments on species compositional diversity
302(1)
10.3.3 Nested subsets, constraining compositional diversity
303(8)
10.4 Multiple classifications
311(3)
10.5 Spatial diversity: putting it all together with spatial graphs
314(1)
10.6 Temporal aspects of spatial diversity
315(2)
10.7 Concluding remarks
317(2)
11 Spatio-temporal analysis
319(42)
Introduction
319(4)
11.1 Change in spatial statistics
323(1)
11.2 Spatio-temporal join count
324(1)
11.3 Spatio-temporal analysis of clusters and contagion
325(4)
11.4 Spatio-temporal scan statistics
329(1)
11.5 Polygon change analysis
329(4)
11.6 Analysis of movement
333(8)
11.7 Process and pattern
341(9)
11.7.1 Tree regeneration, growth and mortality
341(1)
11.7.2 Plant mobility
342(1)
11.7.3 Population synchrony
343(3)
11.7.4 Spatio-temporal chaos
346(4)
11.8 Spatio-temporal graphs
350(10)
11.8.1 Characteristics and classification
351(2)
11.8.2 Animal movement with spatio-temporal graphs
353(2)
11.8.3 Other applications
355(4)
11.8.4 Final comment on spatio-temporal graphs
359(1)
11.9 Concluding remarks
360(1)
11.9.1 Recommendations
360(1)
12 Closing comments and future directions
361(37)
Introduction: myths, misunderstandings and challenges
361(6)
12.1 Back to basics
367(1)
12.2 Numerical solutions: software programs and programming
368(2)
12.3 Statistical and ecological tests
370(1)
12.4 Complementarity of current methods
371(2)
12.5 Analyses in both space and time
373(15)
12.5.1 Analysis of permanent sample plot data
373(6)
12.5.2 Spatially linked time series
379(3)
12.5.3 Spatial analysis of animal-vegetation according to data types
382(6)
12.6 Future work
388(8)
12.6.1 Ongoing development
388(1)
12.6.2 The hierarchical Bayesian approach
389(6)
12.6.3 Hypothesis testing with spatio-temporal graphs
395(1)
12.7 Other future directions
396(2)
References 398(27)
Index 425
Mark R. T. Dale is the Provost of the University of Northern British Columbia, and Professor in the Ecosystem Science and Management Program. His research concerns the effects of interactions between plants on the spatial relationships of plants of different species in a community and the effects of population processes on the development of spatial pattern in the vegetation, as during succession. One main focus of research in the past twenty years has been the analysis of spatial structure in plant communities. Marie-Josée Fortin is Professor of Spatial Ecology in the Department of Ecology and Evolutionary Biology, University of Toronto. Her research focuses on the application of spatial ecology to fields of research such as the conservation biology, ecotone detection, disturbance ecology, organismal dispersal, landscape genetics, and functional connectivity of landscapes. She has been awarded the 2013 Distinguished Landscape Ecologist Award by the United States Chapter of the International Association for Landscape Ecology (US-IALE).
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