Spatial Ecology and Conservation Modeling
portes grátis
Spatial Ecology and Conservation Modeling
Applications with R
Fortin, Marie-Josee; Fletcher, Robert
Springer Nature Switzerland AG
02/2019
523
Dura
Inglês
9783030019884
15 a 20 dias
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.
Chapter 1: Introduction to spatial ecology and its relevance for conservation1.1 What is spatial ecology?1.2 The importance of space in ecology1.3 The importance of space in conservation 1.4 The growth of frameworks for spatial modeling1.5 The path aheadReferences Part I: Quantifying spatial pattern in ecological data Chapter 2: Scale2.1 Introduction2.2 Key concepts and approaches2.2.1 Scale defined and clarified2.2.2 Why is spatial scale important?2.2.3 Multi-scale and multi-level quantitative problems2.2.4 Spatial scale and study design2.3 Examples in R2.3.1 Packages in R2.3.2 The data2.3.3 A simple simulated example2.3.4 Multi-scale species response to land cover2.4 Next steps and advanced issues2.4.1 Identifying characteristic scales beyond species-environment relationships2.4.2 Sampling and scale2.5 ConclusionsReferences Chapter 3: Land-cover pattern and change3.1 Introduction3.2 Key concepts3.2.1 Land use versus land cover3.2.2 Conceptual models for land-cover and habitat change3.2.3 Habitat loss and fragmentation3.2.4 Quantifying land-cover pattern3.3 Examples in R3.3.1 Packages in R3.3.2 The data3.3.3 Quantifying land-cover variation at different scales3.3.4 Simulating land-cover: neutral landscapes3.4 Next steps and advanced issues3.4.1 Testing for pattern differences between landscapes3.4.2 Land-cover quantification via image processing3.4.3 Categorical versus continuous metrics3.5 ConclusionsReferences Chapter 4: Spatial dispersion and point data4.1 Introduction4.2 Key concepts and approaches4.2.1 Characteristics of point patterns4.2.2 Summary statistics for point patterns4.2.3 Common statistical models for point patterns4.3 Examples in R 4.3.1 Packages in R4.3.2 The data4.3.3 Creating point pattern data and visualizing it4.3.4 Univariate point patterns4.3.5 Marked point patterns4.3.6 Inhomogeneous point processes and point process models4.3.7 Alternative null models4.3.8 Simulating point processes4.4 Next steps and advanced issues4.4.1 Space-time analysis4.4.2 Replicated point patterns4.5 ConclusionsReferences Chapter 5: Spatial dependence and autocorrelation5.1 Introduction5.2 Key concepts and approaches5.2.1 The causes of spatial dependence5.2.2 Why spatial dependence matters5.2.3 Quantifying spatial dependence5.3 Examples in R5.3.1 Packages in R5.3.2 The data5.2.3 Correlograms5.3.3 Variograms5.3.4 Kriging5.3.5 Simulating spatially autocorrelated data5.3.6 Multiscale analysis5.4 Next steps and advanced issues5.4.1 Local spatial dependence5.4.2 Multivariate spatial dependence5.5 ConclusionsReferences Chapter 6: Accounting for spatial dependence in ecological data6.1 Introduction6.2 Key concepts and approaches6.2.1 The problem of spatial dependence in ecology and conservation6.2.2 The generalized linear model and its extensions6.2.3 General types of spatial models6.2.4 Common models that account for spatial dependence6.2.5 Inference versus prediction6. 3 Examples in R6.3.1 Packages in R6.3.2 The data6.3.3 Models that ignore spatial dependence6.3.4 Models that account for spatial dependence6.4 Next steps and advanced issues6.4.1 General Bayesian models for spatial dependence6.4.2 Detection errors and spatial dependence6.5 ConclusionsReferences Part II: Ecological responses to spatial pattern and conservation Chapter 7: Species distributions7.1 Introduction7.2 Key Concepts and approaches7.2.1 The niche concept7.2.2 Predicting distributions or niches?7.2.3 Mechanistic versus correlative distribution models7.2.4 Data for correlative distribution models7.2.5 Common types of distribution modeling techniques7.2.6 Combining models: ensembles7.2.7 Model evaluation7.3 Examples in R7.3.1 Packages in R7.3.2 The data7.3.3 Prepping the data for modeling7.3.4 Contrasting models7.3.5 Interpreting environmental relationships7.3.6 Model evaluation7.3.7 Combining models: ensembles7.4 Next steps and advanced issues7.4.1 Incorporating dispersal7.4.2 Integrating multiple data sources7.4.3 Dynamic models7.4.4 Multi-species models7.4.5 Sampling error and distribution models7.5 ConclusionsReferences Chapter 8: Space use and resource selection8.1 Introduction8.2 Key concepts and approaches8.2.1 Distinguishing among the diversity of habitat-related concepts and terms8.2.2 Habitat selection theory8.2.3 General types of habitat use and selection data8.2.4 Home range and space use approaches8.2.5 Resource selection functions at different scales8.3 Examples in R8.3.1 Packages in R8.3.1 The data8.3.2 Prepping the data for modeling8.3.3 Home range analysis8.3.4 Resource selection functions8.4 Next steps and advanced issues8.4.1 Mechanistic models and the identification of hidden states8.4.2 Biotic interactions8.4.3 Sampling error and resource selection models8.5 ConclusionsReferences Chapter 9: Connectivity9.1 Introduction9.2 Key concepts and approaches9.2.1 The multiple meanings of connectivity9.2.2 The connectivity concept9.2.3 Factors limiting connectivity9.2.4 Three common perspectives on quantifying connectivity9.3 Examples in R9.3.1 Packages in R9.3.2 The data9.3.3 Functional connectivity among protected areas for Florida panthers9.3.4 Patch-based networks and graph theory9.3.5 Combining connectivity mapping with graph theory9.4 Next steps and advanced issues9.4.1 Connectivity in space and time9.4.2 Individual-based models9.4.3 Diffusion models9.4.4 Spatial capture-recapture9.5 ConclusionsReferences Chapter 10: Population dynamics in space10.1 Introduction10.2 Key concepts and approaches10.2.1 Foundational population concepts10.2.2 Spatial population concepts10.2.3 Population viability analysis10.2.4 Common types of spatial population models10.3 Examples in R10.3.1 Packages in R10.3.2 The data10.3.3 Spatial correlation and synchrony10.3.4 Metapopulation metrics10.3.5 Estimating colonization-extinction dynamics10.3.6 Projecting dynamics10.3.7 Metapopulation viability and environmental change10.4 Next steps and advanced issues10.4.1 Spatial population matrix models10.4.2 Diffusion and spatial dynamics10.4.3 Agent-based models10.4.4 Integrated population models10.5 ConclusionsReferences Chapter 11: Spatially structured communities11.1 Introduction11.2 Key concepts and approaches11.2.1 Spatial community concepts11.2.2 Common approaches to understanding community-environment relationships11.2.3 Spatial models for communities11.3 Examples in R11.3.1 Packages in R11.3.2 The data11.3.3 Modeling communities and extrapolating in space11.3.4 Spatial dependence in communities11.3.5 Community models with explicit accounting for space11.4 Next steps and advanced issues11.4.1 Decomposition of space-environment effects11.4.2 Accounting for dependence among species11.4.3 Spatial networks11.5 ConclusionsReferences Chapter 12: What have we learned? Looking back and pressing forward12.1 The impact of spatial ecology and conservation12.2 Looking forward: frontiers for spatial ecology and conservation12.3 Where to go from here for advanced spatial modeling?12.4 Beyond R12.5 ConclusionsReferences Appendix: An introduction to RA.1 IntroductionA.2 R beginnings: before any analysisA.2.1 R packagesA.2.2 Editors for RA.2.3 The R prompt, console, and editorA.2.4 Getting help in RA.2.5 R classesA.2.6 Getting data into and out of RA.2.7 Functions in RA.3 Data access, management and manipulation in RA.3.1 Accessing dataA.3.2 Merging, appending, and removingA.3.3 Data subsetting and summariesA.3.4 Reformatting dataA.4 Graphics in RA.5 Spatial data in RA.5.1 Using spatial classesA.5.2 Projections and transformationsA.6 Next steps: where to for further R mastery?References
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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.
Chapter 1: Introduction to spatial ecology and its relevance for conservation1.1 What is spatial ecology?1.2 The importance of space in ecology1.3 The importance of space in conservation 1.4 The growth of frameworks for spatial modeling1.5 The path aheadReferences Part I: Quantifying spatial pattern in ecological data Chapter 2: Scale2.1 Introduction2.2 Key concepts and approaches2.2.1 Scale defined and clarified2.2.2 Why is spatial scale important?2.2.3 Multi-scale and multi-level quantitative problems2.2.4 Spatial scale and study design2.3 Examples in R2.3.1 Packages in R2.3.2 The data2.3.3 A simple simulated example2.3.4 Multi-scale species response to land cover2.4 Next steps and advanced issues2.4.1 Identifying characteristic scales beyond species-environment relationships2.4.2 Sampling and scale2.5 ConclusionsReferences Chapter 3: Land-cover pattern and change3.1 Introduction3.2 Key concepts3.2.1 Land use versus land cover3.2.2 Conceptual models for land-cover and habitat change3.2.3 Habitat loss and fragmentation3.2.4 Quantifying land-cover pattern3.3 Examples in R3.3.1 Packages in R3.3.2 The data3.3.3 Quantifying land-cover variation at different scales3.3.4 Simulating land-cover: neutral landscapes3.4 Next steps and advanced issues3.4.1 Testing for pattern differences between landscapes3.4.2 Land-cover quantification via image processing3.4.3 Categorical versus continuous metrics3.5 ConclusionsReferences Chapter 4: Spatial dispersion and point data4.1 Introduction4.2 Key concepts and approaches4.2.1 Characteristics of point patterns4.2.2 Summary statistics for point patterns4.2.3 Common statistical models for point patterns4.3 Examples in R 4.3.1 Packages in R4.3.2 The data4.3.3 Creating point pattern data and visualizing it4.3.4 Univariate point patterns4.3.5 Marked point patterns4.3.6 Inhomogeneous point processes and point process models4.3.7 Alternative null models4.3.8 Simulating point processes4.4 Next steps and advanced issues4.4.1 Space-time analysis4.4.2 Replicated point patterns4.5 ConclusionsReferences Chapter 5: Spatial dependence and autocorrelation5.1 Introduction5.2 Key concepts and approaches5.2.1 The causes of spatial dependence5.2.2 Why spatial dependence matters5.2.3 Quantifying spatial dependence5.3 Examples in R5.3.1 Packages in R5.3.2 The data5.2.3 Correlograms5.3.3 Variograms5.3.4 Kriging5.3.5 Simulating spatially autocorrelated data5.3.6 Multiscale analysis5.4 Next steps and advanced issues5.4.1 Local spatial dependence5.4.2 Multivariate spatial dependence5.5 ConclusionsReferences Chapter 6: Accounting for spatial dependence in ecological data6.1 Introduction6.2 Key concepts and approaches6.2.1 The problem of spatial dependence in ecology and conservation6.2.2 The generalized linear model and its extensions6.2.3 General types of spatial models6.2.4 Common models that account for spatial dependence6.2.5 Inference versus prediction6. 3 Examples in R6.3.1 Packages in R6.3.2 The data6.3.3 Models that ignore spatial dependence6.3.4 Models that account for spatial dependence6.4 Next steps and advanced issues6.4.1 General Bayesian models for spatial dependence6.4.2 Detection errors and spatial dependence6.5 ConclusionsReferences Part II: Ecological responses to spatial pattern and conservation Chapter 7: Species distributions7.1 Introduction7.2 Key Concepts and approaches7.2.1 The niche concept7.2.2 Predicting distributions or niches?7.2.3 Mechanistic versus correlative distribution models7.2.4 Data for correlative distribution models7.2.5 Common types of distribution modeling techniques7.2.6 Combining models: ensembles7.2.7 Model evaluation7.3 Examples in R7.3.1 Packages in R7.3.2 The data7.3.3 Prepping the data for modeling7.3.4 Contrasting models7.3.5 Interpreting environmental relationships7.3.6 Model evaluation7.3.7 Combining models: ensembles7.4 Next steps and advanced issues7.4.1 Incorporating dispersal7.4.2 Integrating multiple data sources7.4.3 Dynamic models7.4.4 Multi-species models7.4.5 Sampling error and distribution models7.5 ConclusionsReferences Chapter 8: Space use and resource selection8.1 Introduction8.2 Key concepts and approaches8.2.1 Distinguishing among the diversity of habitat-related concepts and terms8.2.2 Habitat selection theory8.2.3 General types of habitat use and selection data8.2.4 Home range and space use approaches8.2.5 Resource selection functions at different scales8.3 Examples in R8.3.1 Packages in R8.3.1 The data8.3.2 Prepping the data for modeling8.3.3 Home range analysis8.3.4 Resource selection functions8.4 Next steps and advanced issues8.4.1 Mechanistic models and the identification of hidden states8.4.2 Biotic interactions8.4.3 Sampling error and resource selection models8.5 ConclusionsReferences Chapter 9: Connectivity9.1 Introduction9.2 Key concepts and approaches9.2.1 The multiple meanings of connectivity9.2.2 The connectivity concept9.2.3 Factors limiting connectivity9.2.4 Three common perspectives on quantifying connectivity9.3 Examples in R9.3.1 Packages in R9.3.2 The data9.3.3 Functional connectivity among protected areas for Florida panthers9.3.4 Patch-based networks and graph theory9.3.5 Combining connectivity mapping with graph theory9.4 Next steps and advanced issues9.4.1 Connectivity in space and time9.4.2 Individual-based models9.4.3 Diffusion models9.4.4 Spatial capture-recapture9.5 ConclusionsReferences Chapter 10: Population dynamics in space10.1 Introduction10.2 Key concepts and approaches10.2.1 Foundational population concepts10.2.2 Spatial population concepts10.2.3 Population viability analysis10.2.4 Common types of spatial population models10.3 Examples in R10.3.1 Packages in R10.3.2 The data10.3.3 Spatial correlation and synchrony10.3.4 Metapopulation metrics10.3.5 Estimating colonization-extinction dynamics10.3.6 Projecting dynamics10.3.7 Metapopulation viability and environmental change10.4 Next steps and advanced issues10.4.1 Spatial population matrix models10.4.2 Diffusion and spatial dynamics10.4.3 Agent-based models10.4.4 Integrated population models10.5 ConclusionsReferences Chapter 11: Spatially structured communities11.1 Introduction11.2 Key concepts and approaches11.2.1 Spatial community concepts11.2.2 Common approaches to understanding community-environment relationships11.2.3 Spatial models for communities11.3 Examples in R11.3.1 Packages in R11.3.2 The data11.3.3 Modeling communities and extrapolating in space11.3.4 Spatial dependence in communities11.3.5 Community models with explicit accounting for space11.4 Next steps and advanced issues11.4.1 Decomposition of space-environment effects11.4.2 Accounting for dependence among species11.4.3 Spatial networks11.5 ConclusionsReferences Chapter 12: What have we learned? Looking back and pressing forward12.1 The impact of spatial ecology and conservation12.2 Looking forward: frontiers for spatial ecology and conservation12.3 Where to go from here for advanced spatial modeling?12.4 Beyond R12.5 ConclusionsReferences Appendix: An introduction to RA.1 IntroductionA.2 R beginnings: before any analysisA.2.1 R packagesA.2.2 Editors for RA.2.3 The R prompt, console, and editorA.2.4 Getting help in RA.2.5 R classesA.2.6 Getting data into and out of RA.2.7 Functions in RA.3 Data access, management and manipulation in RA.3.1 Accessing dataA.3.2 Merging, appending, and removingA.3.3 Data subsetting and summariesA.3.4 Reformatting dataA.4 Graphics in RA.5 Spatial data in RA.5.1 Using spatial classesA.5.2 Projections and transformationsA.6 Next steps: where to for further R mastery?References
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