Predictive Species and Habitat Modeling in Landscape Ecology (eBook)

Foreword 8
Acknowledgments 10
Contents 12
Abbreviations 14
Chapter 1: Introduction. Landscape Modeling of Species and Their Habitats: History, Uncertainty, and Complexity 16
1.1 Where Do We Come from? 16
1.2 Where Are We Going? 17
1.3 Key Themes 18
1.4 Organization of the Book 20
References 21
Part I Current State of Knowledge 
22 
Chapter 2: Integrating Theory and Predictive Modeling for Conservation Research 23
2.1 Introduction 23
2.2 Ecological Theory and a Framework for Predictive Modeling 24
2.2.1 Integrating Data, Testing, and Theory for Predictive Modeling 24
2.2.2 Case Study: Butterfly Models Using Mechanistic Knowledge 25
2.3 Models Missing Mechanisms 26
2.3.1 Case Study: Madagascan Chameleons 27
2.4 Testing Spatial Models through Time 28
2.4.1 The “Space-for-Time” Assumption 29
2.4.2 Global Change as a Pseudo-experiment 29
2.5 Theoretical Perspectives on Predictive Modeling 30
2.5.1 Niches, Neutrality and Predictive Models 30
2.5.2 A Test of Neutral Theory in Fragmented Landscapes 32
2.5.3 Adding Niche into Neutral Models: Stochastic Niche Theory 32
2.5.4 Mechanistic Distribution Models – Early Developments 33
2.5.5 Integrating Detailed Mechanisms and Predictions: A Case Study for Malaria 36
References 39
Chapter 3: The State of Spatial and Spatio-Temporal Statistical Modeling 43
3.1 Introduction 43
3.1.1 Why Statistics? 43
3.1.2 Main Types of Data 44
3.2 Statistical Models 45
3.2.1 Parameters: Fixed or Random? 45
3.2.2 Naïve Models 46
3.2.3 Scientific Models 48
3.2.4 Hierarchical Models 50
3.2.5 Semi- and Non-parametric Models 51
3.3 Optimal Design 52
3.4 Conclusion 53
References 54
Part II Integration of Ecological Theory into Modeling Practice 56
Chapter 4: Proper Data Management as a Scientific Foundation for Reliable Species Distribution Modeling 57
4.1 Introduction 57
4.2 Management Challenge and Theoretical Framework 58
4.2.1 The Practical Aspects of Research and Sampling Design 58
4.2.2 Data Types 64
4.2.2.1 Occurrence Data 64
4.2.2.2 Occurrence Database Management 68
4.2.2.3 Presence-only Data 71
4.2.2.4 Presence-only Database Management 73
4.2.3 Data Availability and Dissemination 74
4.2.3.1 Data in a Digital Form 75
4.2.3.2 Online Data Dissemination 76
4.2.3.3 Metadata and Re-usability of Datasets 77
4.3 Past, Current, and Future Applications 78
References 79
Chapter 5: The Role of Assumptions in Predictions of Habitat Availability and Quality 83
5.1 Introduction 83
5.2 A Semantic Framework for Evaluating the Assumptions of Model Components and Data 85
5.3 Landscape Characterization 86
5.3.1 Discrete Patches Versus Continuous Gradients 87
5.3.2 Static Versus Dynamic Landscapes 88
5.3.3 Deterministic Versus Fuzzy Classification 90
5.4 Species Characterization Assumptions 91
5.4.1 Structural Versus Functional Connectivity 91
5.4.2 Single Species Versus Species in Community 92
5.4.3 Individual versus Population Habitat Associations 93
5.4.4 Perfect versus Imperfect Habitat Use 94
5.4.5 Demographically Closed versus Open Populations 95
5.4.6 Absence versus Non-detection 96
5.5 Conclusions: Evaluating Assumptions 97
References 99
Chapter 6: Insights from Ecological Theory on Temporal Dynamics and Species Distribution Modeling 103
6.1 Introduction 103
6.2 Management Challenge, Ecological Theory, and Statistical Framework 104
6.2.1 Perspectives from Habitat Selection and Metapopulation Theory 104
6.2.2 Statistical Framework 106
6.2.3 Comparing Models 108
6.3 Model and Model Validation Techniques 109
6.3.1 Modeling Database 109
6.3.2 Focal Species 110
6.3.3 Environmental Covariates 111
6.3.4 Model Development 111
6.3.5 Model Results 113
6.3.5.1 Past, Current, and Future Applications 115
6.4 Data Availability and Suitability 116
References 117
Part III Simplicity, Complexity, and Uncertainty in Applied Models 120
Chapter 7: Focused Assessment of Scale-Dependent Vegetation Pattern 121
7.1 Introduction 121
7.2 Management Challenge, Ecological Theory, and Empirical Framework 124
7.2.1 Management Challenge 124
7.2.2 Ecological Theory 125
7.2.3 Empirical Framework 126
7.3 Data Availability and Suitability 127
7.4 Model and Model Validation Techniques 128
7.5 Case Studies in Western US Forests 131
7.5.1 Predicting Spatial Shifts in Old-Growth Forest Habitat 131
7.5.2 Predicting Herbaceous Response to Prescribed Fire 136
7.6 Conclusions 142
References 143
Chapter 8: Modeling Species Distribution and Change Using Random Forest 149
8.1 Introduction 149
8.2 Ecological Theory and Statistical Framework 150
8.3 Random Forest 152
8.3.1 Classification and Regression Trees 152
8.3.2 Random Forest Algorithm 153
8.3.3 Model Selection 155
8.3.4 Imbalanced Data 156
8.3.5 Model Validation 157
8.3.6 Visualization 157
8.3.7 Spatial Structure 159
8.4 Data Suitability 159
8.4.1 Dependent Variable 159
8.4.2 Independent (Predictor) Variables 160
8.5 Prediction of Current and Future Species Distributions 160
8.5.1 Study Area 161
8.6 Methods 161
8.6.1 Uncertainty 162
8.7 Results 162
8.8 Discussion 165
References 166
Chapter 9: Genetic Patterns as a Function of Landscape Process: Applications of Neutral Genetic Markers for Predictive Modeling 170
9.1 Introduction 170
9.2 Management, Communication, Analytical, and Data Availability Challenges 171
9.2.1 Management Challenges 171
9.2.2 Communication Challenges 171
9.2.3 Analytical Challenges 172
9.2.4 Challenges Acquiring Data 173
9.3 Species Identification 175
9.3.1 Motivations for Application of Genetic Data for Species Identification 175
9.3.2 Suitable Data and Availability 176
9.3.3 Past, Current, and Future Applications 176
9.4 Genetic Diversity 180
9.4.1 Motivations for Application of Genetic Data to Assess Genetic Diversity 180
9.4.2 Suitable Data and Availability 180
9.5 Past, Current, and Future Applications 181
9.6 Functional Connectivity 181
9.6.1 Motivations for Application of Genetic Data for Quantifying Functional Connectivity 181
9.6.2 Suitable Data and Availability 182
9.6.3 Past, Present and Future Applications for Predicting Functional Connectivity 183
9.7 Case Study: Predictive Landscape Genetics of the Boreal Toad (Bufo boreas) in Yellowstone National Park 186
9.7.1 Overview of the Questions and Study Motivation 186
9.7.2 Suitable Data and Availability 186
9.7.3 Models Applied and Validation Techniques 187
9.8 Conclusions 189
Glossary 189
References 192
Chapter 10: Simplicity, Model Fit, Complexity and Uncertainty in Spatial Prediction Models Applied Over Time: We Are Quite Sure, Aren't We? 198
10.1 Introduction 198
10.2 How Do Temporal Dynamics Create Challenges in Predictive Landscape Ecology? How Are They Addressed? 201

10.4 How Important Is Temporal Variability and Timingin Predictive Modeling, or: How Important to Predict Where and What, but Without When? 207
10.5 What Information, Model Algorithms, Statisticsand Ecological Theory Are Available and Being Usedto Address Temporal and Predictive Concerns? 209
10.5.1 Information Used to Assess Predictive Models 209
10.5.2 Statistical Models and Algorithms Used to Forecast 209
10.5.3 Statistics Used to Assess Predictive Models 210
10.5.4 Use of Underlying Ecological Theory 211
10.6 Open Questions Yet to Be Addressed 211
10.7 Toward A Modeling Culture: On the Use of Digital Data and Models in Court Cases, for Adaptive Management,in Risk Assessment, Cumulative Impact Studies, and Elsewhere 212
10.8 Conclusion 213
References 214
Chapter 11: Variation, Use, and Misuse of Statistical Models: A Review of the Effects on the Interpretation of Research Results 218
11.1 Introduction 218
11.1.1 Predictive Habitat Modeling: No Unifying Route 218
11.2 Management Challenge, Ecological Theory, and Statistical Framework 223
11.2.1 Statistical Models 225
11.2.1.1 Distance-Based Models 225
11.2.1.2 Tree-Based Models 225
11.2.1.3 Regression-Based Models 226
11.2.2 Model Considerations 227
11.3 Model and Model Validation Techniques 229
11.4 Data Availability and Suitability 231
11.5 Past, Current, and Future Applications 232
References 233
Chapter 12: Expert Knowledge as a Basis for Landscape Ecological Predictive Models 237
12.1 Introduction 237
12.2 Who Is An Expert and What Is An Expert-Based Model? 239
12.3 Evidence of Expert-Based Model Strengths and Weaknesses 240
12.4 Strategies for Sampling Expertise 241
12.4.1 Identifying and Calibrating Experts 242
12.4.2 Where Did Experts Acquire Knowledge? 242
12.4.3 When Did Experts Acquire Knowledge? 243
12.4.4 How Did Experts Acquire Knowledge? 244
12.4.5 Challenges Unique to Sampling Expertise 245
12.4.5.1 Miscommunication During Elicitation 245
12.4.5.2 Cognitive Limitations and Group Dynamics 246
12.5 Methods and Tools for Eliciting Knowledge 247
12.5.1 Elicitation Framework 247
12.5.2 Combining Knowledge of Multiple Experts 248
12.6 Landscape Ecological Theory in Expert-Based Models of Conservation 250
12.7 Conclusions and Recommendations 251
References 253
Part IV Designing Models for Increased Utility 257
Chapter 13: Choices and Strategies for Using a Resource Inventory Database to Support Local Wildlife Habitat Monitoring 258
13.1 Introduction 258
13.2 Management Challenge, Ecological Theory, and Statistical Framework 260
13.3 Model and Model Validation Techniques 262
13.3.1 Model Results 267
13.4 Data Availability and Suitability 272
13.5 Past, Current, and Future Applications 274
References 275
Chapter 14: Using Species Distribution Models for Conservation Planning and Ecological Forecasting 278
14.1 Introduction 278
14.2 Management Challenge, Ecological Theory, and Statistical Framework 279
14.2.1 Conservation Planning 279
14.2.2 Forecasting 280
14.3 Modeling and Model Validation Techniques 281
14.3.1 Empirical Models 282
14.3.2 Mechanistic Models 284
14.3.3 Model Validation 284
14.4 Past, Current, and Future Applications 285
14.4.1 Systematic Reserve Selection Based on Species Distribution Modeling 285
14.4.2 Forecasting Climate-Induced Range Shifts for Three North American Vertebrates 288
14.4.3 Future Improvements for Applications 290
References 292
Chapter 15: Conclusion: An Attempt to Describe the State of Habitat and Species Modeling Today 298
15.1 Emerging Themes: Theoretical 299
15.1.1 The Value of Realism Versus Utility 299
15.1.2 Modeling Uncertain Futures 300
15.2 Emerging Themes: Technical 301
15.2.1 Effective Data Management is Essential 301
15.2.2 Transitioning from a Data-Poor to a Data-Rich Modeling Environment 302
15.2.3 Recognizing the True Costs (and Benefits) of Good Models 303
15.3 Summary and Future Directions 304
Author Bios 306
Index 309