Uncertainty quantification for ecological models with random parameters
Abstract There is often considerable uncertainty in parameters in ecological models. This uncertainty can be incorporated into models by treating parameters as random variables with distributions, rather than fixed quantities. Recent advances in uncertainty quantification methods, such as polynomial...
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crwiley:10.1111/ele.14095 2024-06-23T07:56:42+00:00 Uncertainty quantification for ecological models with random parameters Reimer, Jody R. Adler, Frederick R. Golden, Kenneth M. Narayan, Akil Division of Mathematical Sciences Office of Naval Research National Science Foundation of Sri Lanka National Institutes of Health National Institute of Biomedical Imaging and Bioengineering 2022 http://dx.doi.org/10.1111/ele.14095 https://onlinelibrary.wiley.com/doi/pdf/10.1111/ele.14095 https://onlinelibrary.wiley.com/doi/full-xml/10.1111/ele.14095 https://onlinelibrary.wiley.com/doi/am-pdf/10.1111/ele.14095 en eng Wiley http://onlinelibrary.wiley.com/termsAndConditions#am http://onlinelibrary.wiley.com/termsAndConditions#vor Ecology Letters volume 25, issue 10, page 2232-2244 ISSN 1461-023X 1461-0248 journal-article 2022 crwiley https://doi.org/10.1111/ele.14095 2024-06-11T04:43:47Z Abstract There is often considerable uncertainty in parameters in ecological models. This uncertainty can be incorporated into models by treating parameters as random variables with distributions, rather than fixed quantities. Recent advances in uncertainty quantification methods, such as polynomial chaos approaches, allow for the analysis of models with random parameters. We introduce these methods with a motivating case study of sea ice algal blooms in heterogeneous environments. We compare Monte Carlo methods with polynomial chaos techniques to help understand the dynamics of an algal bloom model with random parameters. Modelling key parameters in the algal bloom model as random variables changes the timing, intensity and overall productivity of the modelled bloom. The computational efficiency of polynomial chaos methods provides a promising avenue for the broader inclusion of parametric uncertainty in ecological models, leading to improved model predictions and synthesis between models and data. Article in Journal/Newspaper Sea ice Wiley Online Library Ecology Letters 25 10 2232 2244 |
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Wiley Online Library |
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crwiley |
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English |
description |
Abstract There is often considerable uncertainty in parameters in ecological models. This uncertainty can be incorporated into models by treating parameters as random variables with distributions, rather than fixed quantities. Recent advances in uncertainty quantification methods, such as polynomial chaos approaches, allow for the analysis of models with random parameters. We introduce these methods with a motivating case study of sea ice algal blooms in heterogeneous environments. We compare Monte Carlo methods with polynomial chaos techniques to help understand the dynamics of an algal bloom model with random parameters. Modelling key parameters in the algal bloom model as random variables changes the timing, intensity and overall productivity of the modelled bloom. The computational efficiency of polynomial chaos methods provides a promising avenue for the broader inclusion of parametric uncertainty in ecological models, leading to improved model predictions and synthesis between models and data. |
author2 |
Division of Mathematical Sciences Office of Naval Research National Science Foundation of Sri Lanka National Institutes of Health National Institute of Biomedical Imaging and Bioengineering |
format |
Article in Journal/Newspaper |
author |
Reimer, Jody R. Adler, Frederick R. Golden, Kenneth M. Narayan, Akil |
spellingShingle |
Reimer, Jody R. Adler, Frederick R. Golden, Kenneth M. Narayan, Akil Uncertainty quantification for ecological models with random parameters |
author_facet |
Reimer, Jody R. Adler, Frederick R. Golden, Kenneth M. Narayan, Akil |
author_sort |
Reimer, Jody R. |
title |
Uncertainty quantification for ecological models with random parameters |
title_short |
Uncertainty quantification for ecological models with random parameters |
title_full |
Uncertainty quantification for ecological models with random parameters |
title_fullStr |
Uncertainty quantification for ecological models with random parameters |
title_full_unstemmed |
Uncertainty quantification for ecological models with random parameters |
title_sort |
uncertainty quantification for ecological models with random parameters |
publisher |
Wiley |
publishDate |
2022 |
url |
http://dx.doi.org/10.1111/ele.14095 https://onlinelibrary.wiley.com/doi/pdf/10.1111/ele.14095 https://onlinelibrary.wiley.com/doi/full-xml/10.1111/ele.14095 https://onlinelibrary.wiley.com/doi/am-pdf/10.1111/ele.14095 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
Ecology Letters volume 25, issue 10, page 2232-2244 ISSN 1461-023X 1461-0248 |
op_rights |
http://onlinelibrary.wiley.com/termsAndConditions#am http://onlinelibrary.wiley.com/termsAndConditions#vor |
op_doi |
https://doi.org/10.1111/ele.14095 |
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Ecology Letters |
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25 |
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10 |
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2232 |
op_container_end_page |
2244 |
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1802649998753529856 |