Miao / Carstenn / Nungesser Real World Ecology

Large-Scale and Long-Term Case Studies and Methods
1. Auflage 2008
ISBN: 978-0-387-77942-3
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 312 Seiten

Ecological and environmental research has increased in scope and complexity in the last few decades, from simple systems with a few managed variables to complex ecosystems with many uncontrolled variables. These issues encompass problems that are inadequately addressed using the types of carefully controlled experiments that dominate past ecological research. Contemporary challenges facing ecologists include whole ecosystem responses to planned restoration activities and ecosystem modifications, as well as unplanned catastrophic events such as biological invasions, natural disasters, and global climate changes. Major perturbations implicated in large-scale ecological alterations share important characteristics that challenge traditional experimental design and statistical analyses. These include: * Lack of randomization, replication and independence
* Multiple scales of spatial and temporal variability
* Complex interactions and system feedbacks. In real world ecology, standard replicated designs are often neither practical nor feasible for large-scale experiments, yet ecologists continue to cling to these same standard designs and related statistical analyses. Case studies that fully elucidate the currently available techniques for conducting large-scale unreplicated analyses are lacking. Real World Ecology: Large-Scale and Long-Term Case Studies and Methods is the first to focus on case studies to demonstrate how ecologists can investigate complex contemporary problems using new and powerful experimental approaches. This collection of case studies showcases innovative experimental designs, analytical options, and interpretation possibilities currently available to theoretical and applied ecologists, practitioners, and biostatisticians. By illustrating how scientists have answered pressing questions about ecosystem restoration, impact and recovery, global warming, conservation, modeling, and biological invasions, this book will broaden the acceptance and application of modern approaches by scientists and encourage further methodological development.

Autoren/Hrsg.

Miao, Shili

Carstenn, Susan

Nungesser, Martha

Inhaltsverzeichnis

1;Foreword;6
2;Contents;10
3;Contributors;12
4;Introduction - Unprecedented Challenges in Ecological Research: Past and Present;15
4.1;1.1 Unprecedented Challenges in Ecological Research;15
4.2;1.2 Major Developments of Alternative Experimental Designs;20
4.3;1.3 Major Developments of Alternative Analytical Approaches;21
4.4;1.4 Ongoing Issues;23
4.5;1.5 Major Features of the Book;24
4.6;References;28
5;Structural Equation Modeling and Ecological Experiments;33
5.1;2.1 Introduction;33
5.2;2.2 What Is Structural Equation Modeling?;34
5.3;2.3 The Univariate Model and Analysis of Variance;37
5.4;2.4 SEM Example #1: The Factors Controlling Plant Diversity in Coastal Wetlands;41
5.4.1;2.4.1 Background;41
5.4.2;2.4.2 Methods;42
5.4.3;2.4.3 Results;42
5.4.4;2.4.4 Discussion;44
5.5;2.5 SEM Example #2: The Effects of Forest Treatments on Post-fire Mortality in Ponderosa Pine;46
5.5.1;2.5.1 Background;46
5.5.2;2.5.2 Field Methods;47
5.5.3;2.5.3 Univariate Analyses;48
5.5.4;2.5.4 Structural Equation Modeling;49
5.5.5;2.5.5 SEM Results;51
5.5.6;2.5.6 Implications of Findings;52
5.6;2.6 The Potential Utility of SEM in Experimental Studies;53
5.6.1;2.6.1 The Challenge of Exploring the Processes Behind Net Effects;53
5.6.2;2.6.2 Technical Issues Associated with Using SEM in Experimental Studies;55
5.7;2.7 Summary;56
5.8;References;57
6;Approaches to Predicting Broad-Scale Regime Shifts Using Changing Pattern-Process Relationships Across Scales;60
6.1;3.1 Introduction;60
6.2;3.2 The Shrinking Grasslands: Woody Plant Encroachment into Perennial Grasslands;62
6.3;3.3 Limits of Current Approaches;66
6.4;3.4 Cross-Scale Approach;67
6.5;3.5 Case Study: State Changes in the Chihuahuan Desert;67
6.5.1;3.5.1 Step 1. Identify patterns in broad-scale drivers;70
6.5.2;3.5.2 Step 2. Identify hierarchical levels of spatial units;73
6.5.3;3.5.3 Step 3. Stratify and map the areas of interest;73
6.5.4;3.5.4 Step 4. Sample and correlate attributes;76
6.5.5;3.5.5 Step 5. Experimental manipulations of drivers;76
6.5.6;3.5.6 Step 6. Simulation modeling of responses;77
6.6;3.6 Analytical Approaches to Identifying and Predicting Regime Shifts;78
6.7;3.7 Conclusions;79
6.8;References;80
7;Integrating Multiple Spatial Controls and Temporal Sampling Schemes To Explore Short- and Long-Term Ecosystem Response to Fire in an Everglades Wetland;86
7.1;4.1 Introduction;86
7.2;4.2 Rationale of a Large-Scale Fire Project;90
7.3;4.3 Spatial Features of the System Studied;91
7.4;4.4 Overall Experimental Design Incorporating Ecosystem Spatial and Temporal Features;93
7.5;4.5 Applying Moving Regressions to Determine Onset and Duration of Fire Impacts and Magnitude of Ecosystem Responses;103
7.6;4.6 Approaches to Determining the Onset and Duration of Downstream Impacts of Fire;110
7.7;4.7 Ecosystem Synthesis: Fire Effects on Wetland Phosphorus Pools;114
7.8;4.8 Summary;117
7.9;References;120
8;Bayesian Hierarchical/Multilevel Models for Inference and Prediction Using Cross-System Lake Data;123
8.1;5.1 Introduction;123
8.2;5.2 Multilevel/Hierarchical Models;127
8.3;5.3 Finding Groups in Data;130
8.3.1;5.3.1 Tree-Based Models;131
8.3.1.1;5.3.1.1 Bayesian CART and Bayesian Treed Models;133
8.4;5.4 Comparing Models;133
8.5;5.5 Our Analyses;135
8.5.1;5.5.1 Completely Pooled Model;136
8.5.2;5.5.2 Ecoregion Model;138
8.5.3;5.5.3 Bayesian Treed Model;142
8.6;5.6 Summary;145
8.7;References;146
9;Avian Spatial Responses to Forest Spatial Heterogeneity at the Landscape Level: Conceptual and Statistical Challenges;149
9.1;6.1 Introduction;149
9.2;6.2 Conceptual and Statistical Issues;150
9.2.1;6.2.1 Several Processes: Several Spatial Scales Versus Sub-regions;150
9.2.2;6.2.2 Which Methods?;152
9.3;6.3 Case Study: Distribution of S. aurocapilla in Relation to Forest Cover;153
9.3.1;6.3.1 Study Area;153
9.3.2;6.3.2 Bird Data;154
9.3.3;6.3.3 Forest Spatial Heterogeneity;155
9.3.4;6.3.4 Resolution and registration issues;156
9.4;6.4 Spatial Exploratory Data Analysis;157
9.4.1;6.4.1 Spatial Statistics;158
9.4.2;6.4.2 Delimiting Spatially Homogeneous Subregions;160
9.5;6.5 Spatially Explicit Regression Methods;163
9.6;6.6 Conclusion;167
9.7;References;169
10;The Role of Paleoecology in Whole-Ecosystem Science;173
10.1;7.1 Introduction;173
10.2;7.2 Case History 1 - Sources of Temporal Variability in Greenlandic Lake Ecosystems;175
10.2.1;7.2.1 Methodological Approach;177
10.2.1.1;7.2.1.1 Synchrony Analysis;179
10.2.1.2;7.2.1.2 Variance Partitioning Analysis;181
10.2.2;7.2.2 Statistical Issues, Caveats, and Future Directions;185
10.3;7.3 Case History 2 - Sockeye Salmon Ecology and Management in Bristol Bay, Alaska;186
10.3.1;7.3.1 Methodological Approach;188
10.3.1.1;7.3.1.1 Natural Variation in Sockeye Salmon Abundance;189
10.3.1.2;7.3.1.2 Effects of Fishing on Algal and Salmon Production;192
10.3.1.3;7.3.1.3 Relative Effects of Salmon and Climate on Lake Ecosystems;194
10.3.2;7.3.2 Management Insights, Caveats and Future Directions;196
10.4;7.4 Case History 3 - Water Quality Loss in Continental Lakes;199
10.4.1;7.4.1 Methodological Approach;200
10.4.1.1;7.4.1.1 Relative Effects of Climate, Land Use and Urbanization on Water Quality;204
10.4.1.2;7.4.1.2 Quantification of Nitrogen Effects on Water Quality;209
10.4.2;7.4.2 Management Insights, Caveats, and Future Directions;213
10.5;7.5 Summary;214
10.6;References;215
11;A Spatially Explicit, Mass-Balance Analysis of Watershed-Scale Controls on Lake Chemistry;221
11.1;8.1 Introduction;221
11.2;8.2 The Study Region;223
11.3;8.3 A Spatially Explicit, Mass-Balance Analysis of Lake Chemistry;224
11.3.1;8.3.1 Inputs;225
11.3.2;8.3.2 Losses;226
11.3.3;8.3.3 Interannual Variability in Watershed Loading;227
11.3.4;8.3.4 Data Sources;228
11.4;8.4 Statistical Analyses - Likelihood as a Basis for Linking Data and Models;230
11.5;8.5 Model Comparison as a Form of Hypothesis Testing and a Basis for Model Simplification;232
11.6;8.6 Goodness of Fit: How ‘‘Predictive’’ Are the Models, and Why Does This Matter?;235
11.7;8.7 Benefits of the Approach;235
11.7.1;8.7.1 Robust Empirical Estimates of Export from Different Source Areas in the Watershed;235
11.7.2;8.7.2 Quantifying Loss of Nutrients Along Flowpaths to a Lake;238
11.7.3;8.7.3 Giving Forests Their Due - the Power of Mass Balance;239
11.7.4;8.7.4 Assessing the Relative Importance of Direct Lake Inputs and in-Lake Processing;240
11.8;8.8 Refining the Approach;240
11.8.1;8.8.1 Incorporating the Effects of Nearshore and Riparian Zones on Watershed Loading;240
11.8.2;8.8.2 Quantifying the Effects of Regional Variation in N Deposition on Loading to Lakes;241
11.9;8.9 Summary;242
11.10;References;243
12;Forecasting and Assessing the Large-Scale and Long-Term Impacts of Global Environmental Change on Terrestrial Ecosystems in the United States and China;246
12.1;9.1 Introduction;247
12.2;9.2 Overview of the Regional Integration System for Earth’s Ecosystem (RISE);249
12.2.1;9.2.1 Development of Spatially Explicit Ecosystem Model;250
12.2.1.1;9.2.1.1 Conceptualization and Formulation;250
12.2.1.2;9.2.1.2 Model Parameterization and Calibration;250
12.2.1.3;9.2.1.3 Model Validation and Evaluation;251
12.2.2;9.2.2 Development of Time Series Spatial Data Sets;251
12.2.2.1;9.2.2.1 Data Sources;251
12.2.2.2;9.2.2.2 Scaling Algorithms in the RISE;252
12.2.2.3;9.2.2.3 Application of Land Use and Climate Models in Generating Time Series Spatial Data Sets;253
12.2.3;9.2.3 Uncertainty Analysis;254
12.3;9.3 Case Studies;255
12.3.1;9.3.1 Case Study 1: Responses of Terrestrial Ecosystem of Southeastern U.S. to Future Climate Change;255
12.3.1.1;9.3.1.1 Model Description;256
12.3.1.2;9.3.1.2 Model Validation;258
12.3.1.3;9.3.1.3 Data Acquisition;258
12.3.1.4;9.3.1.4 Terrestrial Ecosystem Productivity and Carbon Storage in Southeastern U.S.;261
12.3.1.5;9.3.1.5 Response of Ecosystem Productivity to Climate Change from 2002 to 2050;262
12.3.1.6;9.3.1.6 Conclusions;265
12.3.1.7;9.3.1.7 Uncertainties;265
12.3.2;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
12.3.2.1;9.3.2.1 Description of Input Data;266
12.3.2.2;9.3.2.2 Impact of Ozone on Carbon Storage and Flux in Terrestrial Ecosystems of China;268
12.3.2.3;9.3.2.3 Uncertainties;270
12.4;9.4 Summary and Perspectives;271
12.5;9.5 The Way Forward;272
12.6;References;273
13;Gradual Global Environmental Change in the Real World and Step Manipulative Experiments in Laboratory and Field: The Necessity of Inverse Analysis;278
13.1;10.1 Introduction;278
13.2;10.2 Gradual Increases in Global Environmental Variables in the Real World and Step Changes in Experiments;279
13.3;10.3 Modeled Ecosystem Responses to Gradual Versus Step Changes in CO2;280
13.4;10.4 Experimental Evidence of Different Responses to Gradual vs. Step Changes;285
13.5;10.5 Experimental Approaches to Approximate Gradual Change;289
13.6;10.6 New Approaches to Analysis of Data from Step Change Experiments;290
13.7;10.7 Applications of Inverse Analysis to Manipulative Experiments;296
13.8;10.8 Summary;297
13.9;References;298
14;Ecology in the Real World: How Might We Progress?;303
14.1;11.1 Introduction;303
14.2;11.2 A Framework for Thinking about Constraints and Alternative Approaches to the Study of Ecological Systems;304
14.3;11.3 The Variety of Statistical Methodologies;305
14.4;11.4 The Landscape of Methodologies;309
14.5;11.5 The Problem of Constraints on Sampling and Some Solutions;309
14.6;11.6 A Way Forward;310
14.7;References;312
15;Index;313