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E-grāmata: Spatial Ecology and Conservation Modeling: Applications with R

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  • Formāts: EPUB+DRM
  • Izdošanas datums: 15-Feb-2019
  • Izdevniecība: Springer Nature Switzerland AG
  • Valoda: eng
  • ISBN-13: 9783030019891
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Spatial Ecology and Conservation Modeling: Applications with RPalielināt
  • Formāts: EPUB+DRM
  • Izdošanas datums: 15-Feb-2019
  • Izdevniecība: Springer Nature Switzerland AG
  • Valoda: eng
  • ISBN-13: 9783030019891
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This book provides a foundation for modern applied ecology. Much of current ecology research and conservation addresses problems across landscapes and regions, focusing on spatial patterns and processes. This book is aimed at teaching fundamental concepts and focuses on learning-by-doing through the use of examples with the software R. It is intended to provide an entry-level, easily accessible foundation for students and practitioners interested in spatial ecology and conservation.  
1 Introduction to Spatial Ecology and Its Relevance for Conservation
1(16)
1.1 What Is Spatial Ecology?
1(2)
1.2 The Importance of Space in Ecology
3(2)
1.3 The Importance of Space in Conservation
5(1)
1.4 The Growth of Frameworks for Spatial Modeling
6(2)
1.5 The Path Ahead
8(1)
References
9(8)
Part I Quantifying Spatial Pattern in Ecological Data
2 Scale
17(38)
2.1 Introduction
17(1)
2.2 Key Concepts and Approaches
18(10)
2.2.1 Scale Defined and Clarified
18(3)
2.2.2 Why Is Spatial Scale Important?
21(2)
2.2.3 Multiscale and Multilevel Quantitative Problems
23(2)
2.2.4 Spatial Scale and Study Design
25(3)
2.3 Examples in R
28(19)
2.3.1 Packages in R
28(1)
2.3.2 The Data
28(1)
2.3.3 A Simple Simulated Example
28(3)
2.3.4 Multiscale Species Response to Land Cover
31(16)
2.4 Next Steps and Advanced Issues
47(1)
2.4.1 Identifying Characteristic Scales Beyond Species-Environment Relationships
47(1)
2.4.2 Sampling and Scale
48(1)
2.5 Conclusions
48(1)
References
49(6)
3 Land-Cover Pattern and Change
55(46)
3.1 Introduction
55(1)
3.2 Key Concepts
56(9)
3.2.1 Land Use Versus Land Cover
56(1)
3.2.2 Conceptual Models for Land Cover and Habitat Change
56(4)
3.2.3 Habitat Loss and Fragmentation
60(2)
3.2.4 Quantifying Land-Cover Pattern
62(3)
3.3 Examples in R
65(25)
3.3.1 Packages in R
65(1)
3.3.2 The Data
65(1)
3.3.3 Quantifying Land-Cover Variation at Different Scales
66(20)
3.3.4 Simulating Land Cover: Neutral Landscapes
86(4)
3.4 Next Steps and Advanced Issues
90(2)
3.4.1 Testing for Pattern Differences Between Landscapes
90(1)
3.4.2 Land-Cover Quantification via Image Processing
91(1)
3.4.3 Categorical Versus Continuous Metrics
91(1)
3.5 Conclusions
92(1)
References
92(9)
4 Spatial Dispersion and Point Data
101(32)
4.1 Introduction
101(2)
4.2 Key Concepts and Approaches
103(10)
4.2.1 Characteristics of Point Patterns
103(2)
4.2.2 Summary Statistics for Point Patterns
105(2)
4.2.3 Common Statistical Models for Point Patterns
107(6)
4.3 Examples in R
113(15)
4.3.1 Packages in R
113(1)
4.3.2 The Data
113(1)
4.3.3 Creating Point Pattern Data and Visualizing It
114(2)
4.3.4 Univariate Point Patterns
116(4)
4.3.5 Marked Point Patterns
120(3)
4.3.6 Inhomogeneous Point Processes and Point Process Models
123(2)
4.3.7 Alternative Null Models
125(2)
4.3.8 Simulating Point Processes
127(1)
4.4 Next Steps and Advanced Issues
128(1)
4.4.1 Space-Time Analysis
128(1)
4.4.2 Replicated Point Patterns
128(1)
4.5 Conclusions
129(1)
References
129(4)
5 Spatial Dependence and Autocorrelation
133(36)
5.1 Introduction
133(1)
5.2 Key Concepts and Approaches
134(8)
5.2.1 The Causes of Spatial Dependence
134(1)
5.2.2 Why Spatial Dependence Matters
135(2)
5.2.3 Quantifying Spatial Dependence
137(5)
5.3 Examples in R
142(23)
5.3.1 Packages in R
142(1)
5.3.2 The Data
143(1)
5.3.3 Correlograms
144(6)
5.3.4 Variograms
150(5)
5.3.5 Kriging
155(2)
5.3.6 Simulating Spatially Autocorrelated Data
157(2)
5.3.7 Multiscale Analysis
159(6)
5.4 Next Steps and Advanced Issues
165(1)
5.4.1 Local Spatial Dependence
165(1)
5.4.2 Multivariate Spatial Dependence
165(1)
5.5 Conclusions
166(1)
References
166(3)
6 Accounting for Spatial Dependence in Ecological Data
169(44)
6.1 Introduction
169(1)
6.2 Key Concepts and Approaches
170(13)
6.2.1 The Problem of Spatial Dependence in Ecology and Conservation
170(1)
6.2.2 The Generalized Linear Model and Its Extensions
171(4)
6.2.3 General Types of Spatial Models
175(1)
6.2.4 Common Models that Account for Spatial Dependence
176(7)
6.2.5 Inference Versus Prediction
183(1)
6.3 Examples in R
183(21)
6.3.1 Packages in R
183(1)
6.3.2 The Data
184(1)
6.3.3 Models that Ignore Spatial Dependence
185(9)
6.3.4 Models that Account for Spatial Dependence
194(10)
6.4 Next Steps and Advanced Issues
204(1)
6.4.1 General Bayesian Models for Spatial Dependence
204(1)
6.4.2 Detection Errors and Spatial Dependence
204(1)
6.5 Conclusions
205(1)
References
206(7)
Part II Ecological Responses to Spatial Pattern and Conservation
7 Species Distributions
213(58)
7.1 Introduction
213(1)
7.2 Key Concepts and Approaches
214(21)
7.2.1 The Niche Concept
214(5)
7.2.2 Predicting Distributions or Niches?
219(1)
7.2.3 Mechanistic Versus Correlative Distribution Models
219(1)
7.2.4 Data for Correlative Distribution Models
220(2)
7.2.5 Common Types of Distribution Modeling Techniques
222(7)
7.2.6 Combining Models: Ensembles
229(1)
7.2.7 Model Evaluation
230(5)
7.3 Examples in R
235(23)
7.3.1 Packages in R
235(1)
7.3.2 The Data
235(1)
7.3.3 Propping the Data for Modeling
236(4)
7.3.4 Contrasting Models
240(11)
7.3.5 Interpreting Environmental Relationships
251(2)
7.3.6 Model Evaluation
253(4)
7.3.7 Combining Models: Ensembles
257(1)
7.4 Next Steps and Advanced Issues
258(2)
7.4.1 Incorporating Dispersal
258(1)
7.4.2 Integrating Multiple Data Sources
258(1)
7.4.3 Dynamic Models
259(1)
7.4.4 Multi-species Models
259(1)
7.4.5 Sampling Error and Distribution Models
259(1)
7.5 Conclusions
260(1)
References
261(10)
8 Space Use and Resource Selection
271(50)
8.1 Introduction
271(1)
8.2 Key Concepts and Approaches
271(15)
8.2.1 Distinguishing Among the Diversity of Habitat-Related Concepts and Terms
271(2)
8.2.2 Habitat Selection Theory
273(5)
8.2.3 General Types of Habitat Use and Selection Data
278(1)
8.2.4 Home Range and Space Use Approaches
278(3)
8.2.5 Resource Selection Functions at Different Scales
281(5)
8.3 Examples in R
286(24)
8.3.1 Packages in R
286(1)
8.3.2 The Data
286(1)
8.3.3 Propping the Data for Modeling
287(2)
8.3.4 Home Range Analysis
289(9)
8.3.5 Resource Selection Functions
298(12)
8.4 Next Steps and Advanced Issues
310(1)
8.4.1 Mechanistic Models and the Identification of Hidden States
310(1)
8.4.2 Biotic Interactions
311(1)
8.4.3 Sampling Error and Resource Selection Models
311(1)
8.5 Conclusions
311(1)
References
312(9)
9 Connectivity
321(48)
9.1 Introduction
321(1)
9.2 Key Concepts and Approaches
321(14)
9.2.1 The Multiple Meanings of Connectivity
322(2)
9.2.2 The Connectivity Concept
324(2)
9.2.3 Factors Limiting Connectivity
326(2)
9.2.4 Three Common Perspectives on Quantifying Connectivity
328(7)
9.3 Examples in R
335(23)
9.3.1 Packages in R
335(1)
9.3.2 The Data
336(1)
9.3.3 Functional Connectivity Among Protected Areas for Florida Panthers
336(9)
9.3.4 Patch-Based Networks and Graph Theory
345(11)
9.3.5 Combining Connectivity Mapping with Graph Theory
356(2)
9.4 Next Steps and Advanced Issues
358(2)
9.4.1 Connectivity in Space and Time
358(1)
9.4.2 Individual-Based Models
359(1)
9.4.3 Diffusion Models
359(1)
9.4.4 Spatial Capture--Recapture
359(1)
9.5 Conclusions
360(1)
References
360(9)
10 Population Dynamics in Space
369(50)
10.1 Introduction
369(2)
10.2 Key Concepts and Approaches
371(16)
10.2.1 Foundational Population Concepts
371(1)
10.2.2 Spatial Population Concepts
372(10)
10.2.3 Population Viability Analysis
382(2)
10.2.4 Common Types of Spatial Population Models
384(3)
10.3 Examples in R
387(21)
10.3.1 Packages in R
387(1)
10.3.2 The Data
388(3)
10.3.3 Spatial Correlation and Synchrony
391(3)
10.3.4 Metapopulation Metrics
394(1)
10.3.5 Estimating Colonization--Extinction Dynamics
395(8)
10.3.6 Projecting Dynamics
403(3)
10.3.7 Metapopulation Viability and Environmental Change
406(2)
10.4 Next Steps and Advanced Issues
408(1)
10.4.1 Spatial Population Matrix Models
408(1)
10.4.2 Diffusion and Spatial Dynamics
408(1)
10.4.3 Agent-Based Models
408(1)
10.4.4 Integrated Population Models
409(1)
10.5 Conclusions
409(1)
References
410(9)
11 Spatially Structured Communities
419(56)
11.1 Introduction
419(1)
11.2 Key Concepts and Approaches
420(17)
11.2.1 Spatial Community Concepts
420(8)
11.2.2 Common Approaches to Understanding Community--Environment Relationships
428(3)
11.2.3 Spatial Models for Communities
431(6)
11.3 Examples in R
437(26)
11.3.1 Packages in R
437(1)
11.3.2 The Data
438(1)
11.3.3 Modeling Communities and Extrapolating in Space
438(22)
11.3.4 Spatial Dependence in Communities
460(1)
11.3.5 Community Models with Explicit Accounting for Space
461(2)
11.4 Next Steps and Advanced Issues
463(1)
11.4.1 Decomposition of Space--Environment Effects
463(1)
11.4.2 Accounting for Dependence Among Species
463(1)
11.4.3 Spatial Networks
463(1)
11.5 Conclusions
464(1)
References
464(11)
12 What Have We Learned? Looking Back and Pressing Forward
475(14)
12.1 The Impact of Spatial Ecology and Conservation
475(2)
12.2 Looking Forward: Frontiers for Spatial Ecology and Conservation
477(1)
12.3 Where to Go from Here for Advanced Spatial Modeling?
478(1)
12.4 Beyond R
479(1)
12.5 Conclusions
480(1)
References
481(8)
Appendix A An Introduction to R 489(24)
Index 513
Robert Fletcher is an associate professor of Landscape and Spatial Ecology at the University of Florida. His research interests include landscape and spatial ecology, conservation biology, animal behavior, population biology, and quantitative modeling.





Marie-Josée Fortin is professor of Spatial Ecology at the University of Toronto, fellow of the Royal Society of Canada, and Web of Science Highly Cited Researcher in Environment/Ecology. Her research interests are at the interface of ecology, conservation, and spatial statistics.
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