Ecological Forecasting / / Michael Dietze.

• Superior document: Title is part of eBook package: De Gruyter Princeton University Press Complete eBook-Package 2017
• VerfasserIn: Dietze, Michael,
• Place / Publishing House: Princeton, NJ : : Princeton University Press, , [2017]; ©2017
• Year of Publication: 2017
• Language: English
• Physical Description: 1 online resource (288 p.) :; 1 halftone. 81 line illus. 6 tables.

An authoritative and accessible introduction to the concepts and tools needed to make ecology a more predictive scienceEcologists are being asked to respond to unprecedented environmental challenges. How can they provide the best available scientific information about what will happen in the future? Ecological Forecasting is the first book to bring together the concepts and tools needed to make ecology a more predictive science.Ecological Forecasting presents a new way of doing ecology. A closer connection between data and models can help us to project our current understanding of ecological processes into new places and times. This accessible and comprehensive book covers a wealth of topics, including Bayesian calibration and the complexities of real-world data; uncertainty quantification, partitioning, propagation, and analysis; feedbacks from models to measurements; state-space models and data fusion; iterative forecasting and the forecast cycle; and decision support.Features case studies that highlight the advances and opportunities in forecasting across a range of ecological subdisciplines, such as epidemiology, fisheries, endangered species, biodiversity, and the carbon cycle Presents a probabilistic approach to prediction and iteratively updating forecasts based on new dataDescribes statistical and informatics tools for bringing models and data together, with emphasis on:Quantifying and partitioning uncertaintiesDealing with the complexities of real-world data Feedbacks to identifying data needs, improving models, and decision supportNumerous hands-on activities in R available online

Table of Contents

Frontmatter -- Contents -- Preface -- Acknowledgments -- 1. Introduction -- 2. From Models to Forecasts -- 3. Data, Large and Small -- 4. Scientific Workflows and the Informatics of Model- Data Fusion -- 5. Introduction to Bayes -- 6. Characterizing Uncertainty -- 7. Case Study: Biodiversity, Populations, and Endangered Species -- 8. Latent Variables and State- Space Models -- 9. Fusing Data Sources -- 10. Case Study: Natural Resources -- 11. Propagating, Analyzing, and Reducing Uncertainty -- 12. Case Study: Carbon Cycle -- 13. Data Assimilation 1: Analytical Methods -- 14. Data Assimilation 2: Monte Carlo Methods -- 15. Epidemiology -- 16. Assessing Model Performance -- 17. Projection and Decision Support -- 18. Final Thoughts -- References -- Index