Real World Ecology (eBook)

Foreword 6
Contents 10
Contributors 12
Introduction - Unprecedented Challenges in Ecological Research: Past and Present 15
1.1 Unprecedented Challenges in Ecological Research 15
1.2 Major Developments of Alternative Experimental Designs 20
1.3 Major Developments of Alternative Analytical Approaches 21
1.4 Ongoing Issues 23
1.5 Major Features of the Book 24
References 28
Structural Equation Modeling and Ecological Experiments 33
2.1 Introduction 33
2.2 What Is Structural Equation Modeling? 34
2.3 The Univariate Model and Analysis of Variance 37
2.4 SEM Example #1: The Factors Controlling Plant Diversity in Coastal Wetlands 41
2.4.1 Background 41
2.4.2 Methods 42
2.4.3 Results 42
2.4.4 Discussion 44
2.5 SEM Example #2: The Effects of Forest Treatments on Post-fire Mortality in Ponderosa Pine 46
2.5.1 Background 46
2.5.2 Field Methods 47
2.5.3 Univariate Analyses 48
2.5.4 Structural Equation Modeling 49
2.5.5 SEM Results 51
2.5.6 Implications of Findings 52
2.6 The Potential Utility of SEM in Experimental Studies 53
2.6.1 The Challenge of Exploring the Processes Behind Net Effects 53
2.6.2 Technical Issues Associated with Using SEM in Experimental Studies 55
2.7 Summary 56
References 57
Approaches to Predicting Broad-Scale Regime Shifts Using Changing Pattern-Process Relationships Across Scales 60
3.1 Introduction 60
3.2 The Shrinking Grasslands: Woody Plant Encroachment into Perennial Grasslands 62
3.3 Limits of Current Approaches 66
3.4 Cross-Scale Approach 67
3.5 Case Study: State Changes in the Chihuahuan Desert 67
3.5.1 Step 1. Identify patterns in broad-scale drivers 70
3.5.2 Step 2. Identify hierarchical levels of spatial units 73
3.5.3 Step 3. Stratify and map the areas of interest 73
3.5.4 Step 4. Sample and correlate attributes 76
3.5.5 Step 5. Experimental manipulations of drivers 76
3.5.6 Step 6. Simulation modeling of responses 77
3.6 Analytical Approaches to Identifying and Predicting Regime Shifts 78
3.7 Conclusions 79
References 80
Integrating Multiple Spatial Controls and Temporal Sampling Schemes To Explore Short- and Long-Term Ecosystem Response to Fire in an Everglades Wetland 86
4.1 Introduction 86
4.2 Rationale of a Large-Scale Fire Project 90
4.3 Spatial Features of the System Studied 91
4.4 Overall Experimental Design Incorporating Ecosystem Spatial and Temporal Features 93
4.5 Applying Moving Regressions to Determine Onset and Duration of Fire Impacts and Magnitude of Ecosystem Responses 103
4.6 Approaches to Determining the Onset and Duration of Downstream Impacts of Fire 110
4.7 Ecosystem Synthesis: Fire Effects on Wetland Phosphorus Pools 114
4.8 Summary 117
References 120
Bayesian Hierarchical/Multilevel Models for Inference and Prediction Using Cross-System Lake Data 123
5.1 Introduction 123
5.2 Multilevel/Hierarchical Models 127
5.3 Finding Groups in Data 130
5.3.1 Tree-Based Models 131
5.3.1.1 Bayesian CART and Bayesian Treed Models 133
5.4 Comparing Models 133
5.5 Our Analyses 135
5.5.1 Completely Pooled Model 136
5.5.2 Ecoregion Model 138
5.5.3 Bayesian Treed Model 142
5.6 Summary 145
References 146
Avian Spatial Responses to Forest Spatial Heterogeneity at the Landscape Level: Conceptual and Statistical Challenges 149
6.1 Introduction 149
6.2 Conceptual and Statistical Issues 150
6.2.1 Several Processes: Several Spatial Scales Versus Sub-regions 150
6.2.2 Which Methods? 152
6.3 Case Study: Distribution of S. aurocapilla in Relation to Forest Cover 153
6.3.1 Study Area 153
6.3.2 Bird Data 154
6.3.3 Forest Spatial Heterogeneity 155
6.3.4 Resolution and registration issues 156
6.4 Spatial Exploratory Data Analysis 157
6.4.1 Spatial Statistics 158
6.4.2 Delimiting Spatially Homogeneous Subregions 160
6.5 Spatially Explicit Regression Methods 163
6.6 Conclusion 167
References 169
The Role of Paleoecology in Whole-Ecosystem Science 173
7.1 Introduction 173
7.2 Case History 1 - Sources of Temporal Variability in Greenlandic Lake Ecosystems 175
7.2.1 Methodological Approach 177
7.2.1.1 Synchrony Analysis 179
7.2.1.2 Variance Partitioning Analysis 181
7.2.2 Statistical Issues, Caveats, and Future Directions 185
7.3 Case History 2 - Sockeye Salmon Ecology and Management in Bristol Bay, Alaska 186
7.3.1 Methodological Approach 188
7.3.1.1 Natural Variation in Sockeye Salmon Abundance 189
7.3.1.2 Effects of Fishing on Algal and Salmon Production 192
7.3.1.3 Relative Effects of Salmon and Climate on Lake Ecosystems 194
7.3.2 Management Insights, Caveats and Future Directions 196
7.4 Case History 3 - Water Quality Loss in Continental Lakes 199
7.4.1 Methodological Approach 200
7.4.1.1 Relative Effects of Climate, Land Use and Urbanization on Water Quality 204
7.4.1.2 Quantification of Nitrogen Effects on Water Quality 209
7.4.2 Management Insights, Caveats, and Future Directions 213
7.5 Summary 214
References 215
A Spatially Explicit, Mass-Balance Analysis of Watershed-Scale Controls on Lake Chemistry 221
8.1 Introduction 221
8.2 The Study Region 223
8.3 A Spatially Explicit, Mass-Balance Analysis of Lake Chemistry 224
8.3.1 Inputs 225
8.3.2 Losses 226
8.3.3 Interannual Variability in Watershed Loading 227
8.3.4 Data Sources 228
8.4 Statistical Analyses - Likelihood as a Basis for Linking Data and Models 230
8.5 Model Comparison as a Form of Hypothesis Testing and a Basis for Model Simplification 232
8.6 Goodness of Fit: How ‘‘Predictive’’ Are the Models, and Why Does This Matter? 235
8.7 Benefits of the Approach 235
8.7.1 Robust Empirical Estimates of Export from Different Source Areas in the Watershed 235
8.7.2 Quantifying Loss of Nutrients Along Flowpaths to a Lake 238
8.7.3 Giving Forests Their Due - the Power of Mass Balance 239
8.7.4 Assessing the Relative Importance of Direct Lake Inputs and in-Lake Processing 240
8.8 Refining the Approach 240
8.8.1 Incorporating the Effects of Nearshore and Riparian Zones on Watershed Loading 240
8.8.2 Quantifying the Effects of Regional Variation in N Deposition on Loading to Lakes 241
8.9 Summary 242
References 243
Forecasting and Assessing the Large-Scale and Long-Term Impacts of Global Environmental Change on Terrestrial Ecosystems in the United States and China 246
9.1 Introduction 247
9.2 Overview of the Regional Integration System for Earth’s Ecosystem (RISE) 249
9.2.1 Development of Spatially Explicit Ecosystem Model 250
9.2.1.1 Conceptualization and Formulation 250
9.2.1.2 Model Parameterization and Calibration 250
9.2.1.3 Model Validation and Evaluation 251
9.2.2 Development of Time Series Spatial Data Sets 251
9.2.2.1 Data Sources 251
9.2.2.2 Scaling Algorithms in the RISE 252
9.2.2.3 Application of Land Use and Climate Models in Generating Time Series Spatial Data Sets 253
9.2.3 Uncertainty Analysis 254
9.3 Case Studies 255
9.3.1 Case Study 1: Responses of Terrestrial Ecosystem of Southeastern U.S. to Future Climate Change 255
9.3.1.1 Model Description 256
9.3.1.2 Model Validation 258
9.3.1.3 Data Acquisition 258
9.3.1.4 Terrestrial Ecosystem Productivity and Carbon Storage in Southeastern U.S. 261
9.3.1.5 Response of Ecosystem Productivity to Climate Change from 2002 to 2050 262
9.3.1.6 Conclusions 265
9.3.1.7 Uncertainties 265
9.3.2 Case Study 2: Impacts of Tropospheric Ozone Pollution on Productivity and Carbon Storage of China’s Terrestrial Ecosystems from 1961 to 2000 266
9.3.2.1 Description of Input Data 266
9.3.2.2 Impact of Ozone on Carbon Storage and Flux in Terrestrial Ecosystems of China 268
9.3.2.3 Uncertainties 270
9.4 Summary and Perspectives 271
9.5 The Way Forward 272
References 273
Gradual Global Environmental Change in the Real World and Step Manipulative Experiments in Laboratory and Field: The Necessity of Inverse Analysis 278
10.1 Introduction 278
10.2 Gradual Increases in Global Environmental Variables in the Real World and Step Changes in Experiments 279
10.3 Modeled Ecosystem Responses to Gradual Versus Step Changes in CO2 280
10.4 Experimental Evidence of Different Responses to Gradual vs. Step Changes 285
10.5 Experimental Approaches to Approximate Gradual Change 289
10.6 New Approaches to Analysis of Data from Step Change Experiments 290
10.7 Applications of Inverse Analysis to Manipulative Experiments 296
10.8 Summary 297
References 298
Ecology in the Real World: How Might We Progress? 303
11.1 Introduction 303
11.2 A Framework for Thinking about Constraints and Alternative Approaches to the Study of Ecological Systems 304
11.3 The Variety of Statistical Methodologies 305
11.4 The Landscape of Methodologies 309
11.5 The Problem of Constraints on Sampling and Some Solutions 309
11.6 A Way Forward 310
References 312
Index 313