Note: Intro -- Preface -- Acknowledgements -- Contents -- Contributors -- An Overview of Conservation Paleobiology -- 1 Defining and Establishing Conservation Paleobiologyas a Discipline -- 2 Data in Conservation Paleobiology -- 3 Looking Forward -- References -- Should Conservation Paleobiologists Save the World on Their Own Time? -- 1 Always Academicize? -- 2 To Advocate, or Not to Advocate -- 3 Speaking Honestly to Power -- 4 From Pure Scientist to Honest Broker -- 5 Keeping It Real -- 6 Overcoming the Fear Factor -- 7 Later Is Too Late -- References -- Conceptions of Long-Term Data Among Marine Conservation Biologists and What Conservation Paleobiologists Need to Know -- 1 What is "Long Term"? -- 2 Survey Implementation -- 3 Survey Responses and What They Mean for Conservation Paleobiologists -- Conservation Goals -- Long-Term Data -- Environmental Stressors -- Baselines -- Challenges -- 4 Takeaways for Conservation Paleobiologists -- 5 Moving Forward -- Appendix 1: Survey Questions -- Appendix 2: Survey Population Selection -- Appendix 3: Categorization of Responses -- References -- Effectively Connecting Conservation Paleobiological Research to Environmental Management: Examples from Greater Everglades' Restoration of Southwest Florida -- 1 Introduction -- 2 Defining the Problem -- 3 Ensuring Success as a Conservation Paleobiologist -- Developing Partnerships and Collaborative Teams -- Becoming or Engaging a Liaison -- Participate in "Management Collaboratives" -- Compose Technical Reports in Addition to Peer-Reviewed Journal Articles -- Present Your Findings to Stake Holder Groups -- Attend and Present at Environmental Science and Restoration Conferences -- Train our Students -- Reward Faculty for Conducting Community-Engaged Scholarship -- Promote and Reward Community Service for Work with Environmental Agencies and NGOs. , 4 Case Studies from Greater Everglades' Restoration -- Case Study 1: Water Management of the Caloosahatchee River -- Case Study 2: Picayune Strand Restoration Project -- 5 Conclusions -- References -- Using the Fossil Record to Establish a Baseline and Recommendations for Oyster Mitigation in the Mid-Atlantic U.S. -- 1 Introduction -- 2 Methods -- Pleistocene Localities -- Field and Museum Sampling -- Oyster Size and Abundance Data -- Reconstructing Paleotemperature and Salinity -- Modern and Colonial Data -- 3 Results -- Paleoenvironmental Reconstruction of Holland Point -- Paleotemperature -- Paleosalinity -- Shell Height -- Growth Rate -- 4 Discussion -- Comparing Pleistocene to Modern Oysters -- Environmental Controls on Oyster Size -- Human Factors Influencing Oyster Size -- Implications for Restoration -- A Role for Conservation Paleobiology -- 5 Conclusion -- References -- Coral Reefs in Crisis: The Reliability of Deep-Time Food Web Reconstructions as Analogs for the Present -- 1 Introduction -- Preserving the Past -- Endangered Coral Reefs -- 2 Fossilizing a Coral Reef -- Dietary Breadth -- Trophic Chains and Levels -- Modularity -- 3 Guild Structure and Diversity -- Identifying Guilds in a Food Web -- 4 Reconstructing the Community -- Diversity and Evenness -- Simulated Food Webs -- 5 Summary -- Appendix 1 -- Hypergeometric Variance -- Appendix 2 -- References -- Exploring the Species -Area Relationship Within a Paleontological Context, and the Implications for Modern Conservation Biology -- 1 Introduction -- 2 Geological Setting -- 3 Methods -- 4 Results -- 5 Discussion -- 6 Conclusion -- References -- Marine Refugia Past, Present, and Future: Lessons from Ancient Geologic Crises for Modern Marine Ecosystem Conservation -- 1 Introduction -- 2 Defining Refugium. , A Species Must Have a Range Contraction, Range Shift, or Migration in Order to Escape the Onset of Global Environmental Degradation That Would Otherwise Cause Extinction of That Species -- Range Shifts -- Habitat Shifts -- Isolated Geographic Refugia -- Life History Refugia -- Cryptic Refugia -- Harvest Refugia -- The Environmental Conditions of a Refugium Are Sufficiently Habitable Such That the Species' Population Remains Viable During Its Time in the Refugium -- A Species' Population Is Smaller in the Refugium Than Its Pre-environmental Perturbation Size -- The Species Remains in the Refugium for Many Generations -- After the Environmental Crisis Ends, the Species Recovers by Inhabiting Newly Re-opened Habitats, Either Through Population Expansion or Through Adaptive Radiation -- Otherwise, the Refugium Became a Trap -- 3 Identifying Ancient Refugia -- Fossil Data -- Phylogeographic Studies -- Species Distribution Models -- 4 Lessons from the Past for Identifying Future Refugia -- As the Marine Environment Continues to Change, Refugia May Need to Shift -- Refugial Size and Connectivity Can Enhance Survivorship, But Can Also Have Evolutionary Consequences -- Conditions Inside Refugia May Not Necessarily Remain Pristine, But Will Need to Be of Sufficiently Lower Magnitude of Total Stress to Maintain Viable Populations -- Beware the Refugial Trap -- 5 Future Directions for Investigating Ancient Refugia -- 6 Conclusions -- Appendix -- References -- Training Tomorrow's Conservation Paleobiologists -- 1 Business As Usual Is Not Enough -- 2 A Call to Action -- 3 Bridging the Gap -- Recommendation 1 -- Recommendation 2 -- Recommendation 3 -- Recommendation 4 -- Recommendation 5 -- Recommendation 6 -- 4 Okay, But… -- 5 In the Meantime… -- 6 A Bright Future -- References -- A Conceptual Map of Conservation Paleobiology: Visualizinga Discipline. , 1 Determining the Current State and Structure of Conservation Paleobiology -- 2 Mapping a Discipline -- Bibliographic Co-Authorship Visualizations -- Text Co-Occurrence Visualizations -- Bibliographic Co-Citation Visualizations -- Bibliographic Coupling Visualizations -- 3 Bibliometric Networks -- Bibliographic Co-Authorship Networks -- Text Co-Occurrence Networks -- Bibliographic Co-Citation Networks -- Bibliometric Coupling Networks -- 4 The Intellectual Landscape -- 5 Emerging Frontiers -- 6 Conclusions -- References -- Index.

Note: Intro -- Contents -- Part I: Introduction -- Chapter 1: Ecological Informatics: An Introduction -- 1.1 Introduction -- 1.2 Data Management -- 1.3 Analysis and Synthesis -- 1.4 Communicating and Informing Decisions -- 1.5 Case Studies -- References -- Part II: Managing Ecological Data -- Chapter 2: Project Data Management Planning -- 2.1 Introduction -- 2.2 Components of a Data Management Plan -- 2.2.1 Context -- 2.2.2 Data Collection and Acquisition -- 2.2.3 Data Organization -- 2.2.4 Quality Assurance/Quality Control -- 2.2.5 Documentation -- 2.2.6 Storage and Preservation -- 2.2.7 Data Integration, Analysis, Modeling and Visualization -- 2.2.8 Data Policies -- Box 2.1 Recommended Data Citation Guidelines from Dryad Digital Repository (2016) -- 2.2.9 Communication and Dissemination of Research Outputs -- 2.2.10 Roles and Responsibilities -- 2.2.11 Budget -- 2.3 Developing and Using a Data Management Plan -- 2.3.1 Best Practices for Creating the Plan -- 2.3.2 Using the Plan -- 2.4 Conclusion -- References -- Chapter 3: Scientific Databases for Environmental Research -- 3.1 Introduction -- 3.2 Challenges for Scientific Databases -- 3.3 Examples of Scientific Databases -- 3.3.1 A Useful Analogy -- 3.3.2 Examples of Databases -- 3.4 Evolving a Database -- 3.4.1 A Strategy for Evolving a Database -- 3.4.2 Choosing Software -- 3.4.3 Database Management System (DBMS) Types -- 3.4.4 Data Models and Normalization -- 3.4.5 Advantages and Disadvantages of Using a DBMS -- 3.5 Interlinking Information Resources -- 3.5.1 A Database Related to the Human Genome Project -- 3.5.2 Environmental Databases for Sharing Data -- 3.5.3 Tools for Interlinking Information -- 3.6 Conclusions -- References -- Chapter 4: Quality Assurance and Quality Control (QA/QC) -- 4.1 Introduction -- 4.2 Quality Assurance -- 4.3 Quality Control -- 4.3.1 Data Filters. , 4.3.2 Graphical QC -- 4.3.3 Statistical QC -- 4.3.4 Treatment of Errors and Outliers -- 4.4 Implementing QA/QC -- 4.5 Conclusion -- References -- Chapter 5: Creating and Managing Metadata -- 5.1 Introduction -- 5.2 Metadata Descriptors -- 5.3 Metadata Standards -- 5.3.1 Dublin Core Metadata Initiative -- 5.3.2 Darwin Core -- 5.3.3 Ecological Metadata Language -- 5.3.4 GBIF Metadata Profile -- 5.3.5 FGDC CSDGM -- 5.3.6 ISO 19115 -- 5.4 Metadata Management -- 5.4.1 Metadata Tools -- 5.4.2 Best Practices for Creating and Managing Metadata -- 5.5 Conclusion -- References -- Chapter 6: Preserve: Protecting Data for Long-Term Use -- 6.1 Introduction -- 6.1.1 Preservation and Its Benefits -- 6.2 Practices for Preserving Ecological Data -- 6.2.1 Define the Contents of Your Data Files -- 6.2.2 Define the Parameters -- 6.2.3 Use Consistent Data Organization -- 6.2.4 Use Stable File Formats -- 6.2.5 Specify Spatial Information -- 6.2.6 Assign Descriptive File Names -- 6.2.7 Document Processing Information -- 6.2.8 Perform Quality Assurance -- 6.2.9 Provide Documentation -- 6.2.10 Protect Your Data -- 6.3 Prepare Your Data for Archival -- 6.4 What the Archive Does -- 6.4.1 Quality Assurance -- 6.4.2 Documentation and Metadata -- 6.4.3 Release of a Data Set -- 6.5 Data Users -- 6.6 Conclusions -- Appendix: Example R-Script for Processing Data -- References -- Chapter 7: Data Discovery -- 7.1 Introduction -- 7.2 Discovering Data Created by Others -- 7.2.1 Internet Search Engines -- 7.2.2 Data Repositories -- 7.2.3 Data Directories -- 7.2.4 Data Aggregators -- 7.3 Best Practices for Promoting Data Discovery and Reuse -- 7.3.1 Data Products -- Box 7.1 DataCite Recommendations for Data Citation -- Box 7.2 Dryad Digital Repository Data Citation Recommendations -- 7.3.2 Scientific Code -- References -- Chapter 8: Data Integration: Principles and Practice. , 8.1 Introduction -- 8.2 Essential Characteristics of All Data -- 8.3 Data as Records About Reality -- 8.4 Record-Keeping and Prose Documents as Data Integration Challenges -- 8.5 Formal Data Structures Facilitate Integration -- 8.5.1 Sets and Sequences -- 8.5.2 Matrices -- 8.5.3 Cross-classifications -- 8.5.4 Tables -- 8.5.5 Tables or Spreadsheets? -- 8.5.6 Tables or Cross-classifications? -- 8.5.7 Modeling True Tables -- 8.5.8 Need for Global Keys -- 8.6 Merging or JOINing Tables -- 8.6.1 APPENDING or Unioning -- 8.6.2 JOINs -- 8.7 The Datum Is the Atom -- 8.8 Conclusion -- References -- Part III: Analysis, Synthesis and Forecasting of Ecological Data -- Chapter 9: Inferential Modelling of Population Dynamics -- 9.1 Introduction -- 9.2 Inferential Modelling of Ecological Data by the Hybrid Evolutionary Algorithm -- 9.2.1 Population Dynamics of the Cyanobacterium Microcystis in Lake Müggelsee (Germany) -- 9.2.2 Meta-Analysis of Population Dynamics of the Cyanobacterium -- 9.3 Inferential Modelling of Ecological Data by Regression Trees -- 9.3.1 Induction Algorithm of Regression Trees -- 9.3.2 Pruning of Regression Trees -- 9.3.3 Diatom Populations in Lake Prespa (Mazedonia) -- 9.3.4 Vegetation Status of Selected Land Sites in Victoria (Australia) -- 9.4 Conclusions -- References -- Chapter 10: Process-Based Modeling of Nutrient Cycles and Food-Web Dynamics -- 10.1 Introduction -- 10.2 Zero- and One-Dimensional Lake Models -- 10.2.1 Zero-Dimensional Model for the Phosphorus Cycle in a Hypereutrophic Wetland -- 10.2.2 One-Dimensional Model for Nutrient Cycles and Plankton Dynamics in Lakes and Reservoirs -- 10.3 Multi-dimensional Lake Models -- 10.3.1 Horizontal and Vertical Transport of Nutrients and Organisms -- 10.3.2 Multi-segment Lake Model for Studying Dreissenids and Macrophytes -- 10.4 Concluding Remarks -- References. , Chapter 11: Uncertainty Analysis by Bayesian Inference -- 11.1 Does Uncertainty Really Matter? -- 11.2 Hamilton Harbour -- 11.2.1 Introduction -- 11.2.2 Eutrophication Modeling to Elucidate the Role of Lower Food Web -- 11.2.3 Nutrient Export Modeling for the Hamilton Harbour Watershed -- 11.3 Bay of Quinte -- 11.3.1 Introduction -- 11.3.2 Modeling the Relationship Among Watershed Physiography, Land Use Patterns, and Phosphorus Loading -- 11.3.3 Eutrophication Risk Assessment with Process-Based Modeling and Determination of Water Quality Criteria -- 11.4 Concluding Remarks -- References -- Chapter 12: Multivariate Data Analysis by Means of Self-Organizing Maps -- 12.1 Introduction -- 12.2 Properties of a Self-Organizing Map -- 12.3 Data Preparation -- 12.3.1 Missing Values and Outliers -- 12.3.2 Data Transformation -- 12.3.3 Distance Measure -- 12.4 Self-Organizing Maps -- 12.4.1 Architecture -- 12.4.2 Learning Algorithm -- Box 12.1 Sequential Learning Algorithm of an SOM -- 12.4.3 Evaluation of Trained Map Quality -- 12.4.4 Optimum Map Size -- 12.4.5 Clustering SOM Units -- 12.4.6 Evaluation of Input Variables -- 12.4.7 Relations Between Biological and Environmental Variables -- 12.5 Application in Ecological Modelling -- 12.6 SOM Tools -- 12.7 Example of SOM Application -- 12.8 Advantages and Disadvantages -- 12.8.1 Utility for Training and Information Extraction -- 12.8.2 Visualization and Recognition -- 12.8.3 Architecture Flexibility -- 12.8.4 Flexibility in Combining with Other Models -- 12.8.5 Constraints on Measure Consistency and Output Variability -- 12.8.6 Necessity of Sufficient Data -- 12.9 Future Development -- 12.10 Conclusions -- References -- Chapter 13: GIS-Based Data Synthesis and Visualization -- 13.1 Introduction -- 13.2 Synthesizing Species Distributions by Virtual Species. , 13.3 Cartograms to Synthesize and Visualize Sampling Effort Bias -- 13.4 Fuzzy Methods to Synthesize Species Distribution Uncertainty -- 13.5 Synthesis of Remote Sensing Data -- 13.5.1 Exploratory Data Analysis -- 13.5.1.1 Correlation of Remotely Sensed Bands by Hexagon Binning -- 13.5.1.2 Correlation Among Several Layers by Texture Measures -- 13.5.2 Fourier Transformations -- 13.6 Synthesizing Diversity Measurements from Space: The Case of Generalized Entropy -- 13.7 Neutral Landscapes -- 13.8 Conclusions -- References -- Part IV: Communicating and Informing Decisions -- Chapter 14: Communicating and Disseminating Research Findings -- 14.1 Introduction -- 14.2 Publishing Research Findings -- 14.2.1 Scholarly Publications -- 14.2.1.1 Journal Articles -- 14.2.1.2 Abstracts -- 14.2.1.3 Technical Reports -- 14.2.1.4 Books and Book Chapters -- 14.2.2 Newspaper and Magazine Articles for General Audiences -- 14.2.3 Designing Effective Figures -- 14.3 Communicating Research Findings Outside of Publications -- 14.3.1 Simple Steps for Giving an Effective Presentation -- 14.3.2 Best Practices for Slides -- 14.3.2.1 Slide Design -- 14.3.2.2 Text Slides -- 14.3.2.3 Graphics -- 14.3.3 Handouts -- 14.3.4 Posters -- 14.4 Communication in a Virtual Environment -- 14.4.1 Websites -- 14.4.2 Types and Uses of Different Social Media -- 14.4.3 Simple Steps for Effective Use of Social Media -- 14.4.4 Understanding Your Social Media Impact -- 14.5 Metrics and Altmetrics -- 14.6 Conclusion -- References -- Chapter 15: Operational Forecasting in Ecology by Inferential Models and Remote Sensing -- 15.1 Introduction -- 15.2 Early Warning of HABs Based on Inferential Modelling -- 15.2.1 Cyanobacterium Cylindrospermopsis in Lake Wivenhoe (Australia) -- 15.2.2 Cyanotoxin Microcystins in Lake Vaal (South Africa) -- 15.3 Early Warning of HABs Based on Remotely-Sensed Data. , 15.3.1 Earth Observation of Water Quality Parameters.