Suchergebnisse

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

The Eurasian otter Lutra lutra is semi-aquatic carnivore and apex predator in aquatic systems. Since Korean government has implemented comprehensive clean water plans (1989 ~ 1997), improved aquatic food-web structure sustains otter population recovery. In this study, using hybrid evolutionary algorithm (HEA), we would demonstrate the influential food chains to the otter populations. We used 22 chains of the food-web structures (11 water qualities measurements (BOD, NH3N, NO3N, TN, PO4P, TP, water temperature, dissolved oxygen (mg/l), pH, conductivity, and turbidity), Diatom, chlorophyll a, five Macro-Benthic Invertebrates categories (Mollusca, Anthropoda, Annelida, Nematomorpha, and Platyhelminthes), and four fish categories (benthivore, herbivore, planktivore, and piscivores)). According to the 22 chains, we investigated spraint densities (no. spraint per 600m) as otter population indices at 250 sites in Nakdong River basin (NR, 2014-2016, three years), 92 sites in Youngsan River basins (YR, 2016), and 83 sites in Seumjin River basin (SR, 2016). In NR of 2014, otter populations seemingly affected by both micro-invertebrates (r^2 = 0.32) and fish densities (r2 = 0.31). However, overall of NR (2014-16), otter populations have been more precisely expected by fish densities (r^2 = 0.41), other than water quality measurements (r^2 = 0.37), and macro-invertebrates (r^2 = 0.32). In Seumjin and Youngsan River basins, otter populations were more explained by macro-invertebrate (r^2 = 0.40), than fish (r^2 = 0.26) and water qualities measurements (r^2 = 0.28). Different river basins and years showed the different thresholds of different food chains. We concluded that otter population status could result in different sensitivity of chain of food-web structures.