Ecological Forecasting / / Michael Dietze.
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?...
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Place / Publishing House: | Princeton, NJ : : Princeton University Press, , [2017] ©2017 |
Year of Publication: | 2017 |
Language: | English |
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Physical Description: | 1 online resource (288 p.) :; 1 halftone. 81 line illus. 6 tables. |
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Dietze, Michael, author. aut http://id.loc.gov/vocabulary/relators/aut Ecological Forecasting / Michael Dietze. Princeton, NJ : Princeton University Press, [2017] ©2017 1 online resource (288 p.) : 1 halftone. 81 line illus. 6 tables. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda 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 restricted access http://purl.org/coar/access_right/c_16ec online access with authorization star 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 Issued also in print. Mode of access: Internet via World Wide Web. In English. Description based on online resource; title from PDF title page (publisher's Web site, viewed 30. Aug 2021) Ecology Forecasting. Ecosystem health Forecasting. SCIENCE / Life Sciences / Ecology. bisacsh Title is part of eBook package: De Gruyter Princeton University Press Complete eBook-Package 2017 9783110543322 print 9780691160573 https://doi.org/10.1515/9781400885459?locatt=mode:legacy https://www.degruyter.com/isbn/9781400885459 Cover https://www.degruyter.com/cover/covers/9781400885459.jpg |
language |
English |
format |
eBook |
author |
Dietze, Michael, Dietze, Michael, |
spellingShingle |
Dietze, Michael, Dietze, Michael, Ecological Forecasting / 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 |
author_facet |
Dietze, Michael, Dietze, Michael, |
author_variant |
m d md m d md |
author_role |
VerfasserIn VerfasserIn |
author_sort |
Dietze, Michael, |
title |
Ecological Forecasting / |
title_full |
Ecological Forecasting / Michael Dietze. |
title_fullStr |
Ecological Forecasting / Michael Dietze. |
title_full_unstemmed |
Ecological Forecasting / Michael Dietze. |
title_auth |
Ecological Forecasting / |
title_alt |
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 |
title_new |
Ecological Forecasting / |
title_sort |
ecological forecasting / |
publisher |
Princeton University Press, |
publishDate |
2017 |
physical |
1 online resource (288 p.) : 1 halftone. 81 line illus. 6 tables. Issued also in print. |
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 |
isbn |
9781400885459 9783110543322 9780691160573 |
callnumber-first |
Q - Science |
callnumber-subject |
QH - Natural History and Biology |
callnumber-label |
QH541 |
callnumber-sort |
QH 3541.15 E265 D54 42017EB |
url |
https://doi.org/10.1515/9781400885459?locatt=mode:legacy https://www.degruyter.com/isbn/9781400885459 https://www.degruyter.com/cover/covers/9781400885459.jpg |
illustrated |
Illustrated |
dewey-hundreds |
500 - Science |
dewey-tens |
570 - Life sciences; biology |
dewey-ones |
577 - Ecology |
dewey-full |
577/.0112 |
dewey-sort |
3577 3112 |
dewey-raw |
577/.0112 |
dewey-search |
577/.0112 |
doi_str_mv |
10.1515/9781400885459?locatt=mode:legacy |
oclc_num |
983474406 |
work_keys_str_mv |
AT dietzemichael ecologicalforecasting |
status_str |
n |
ids_txt_mv |
(DE-B1597)479682 (OCoLC)983474406 |
carrierType_str_mv |
cr |
hierarchy_parent_title |
Title is part of eBook package: De Gruyter Princeton University Press Complete eBook-Package 2017 |
is_hierarchy_title |
Ecological Forecasting / |
container_title |
Title is part of eBook package: De Gruyter Princeton University Press Complete eBook-Package 2017 |
_version_ |
1770176762705084416 |
fullrecord |
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