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Which of Our Modeling Predictions Are Robust?
In theoretical ecology it is well known that the steady state expressions of the variables in a food chain crucially depend on the parity of the length of the chain. This poses a major problem for modeling real food webs because it is difficult to establish their true number of trophic levels, with...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Public Library of Science
2012
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3405990/ https://www.ncbi.nlm.nih.gov/pubmed/22844235 http://dx.doi.org/10.1371/journal.pcbi.1002593 |
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author | De Boer, Rob J. |
author_facet | De Boer, Rob J. |
author_sort | De Boer, Rob J. |
collection | PubMed |
description | In theoretical ecology it is well known that the steady state expressions of the variables in a food chain crucially depend on the parity of the length of the chain. This poses a major problem for modeling real food webs because it is difficult to establish their true number of trophic levels, with sometimes rare predators and often rampant pathogens. Similar problems arise in the modeling of chronic viral infections. We review examples where seemingly general interpretations strongly depend on the number of levels in a model, and on its specific equations. This Perspective aims to open the discussion on this problem. |
format | Online Article Text |
id | pubmed-3405990 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-34059902012-07-27 Which of Our Modeling Predictions Are Robust? De Boer, Rob J. PLoS Comput Biol Perspective In theoretical ecology it is well known that the steady state expressions of the variables in a food chain crucially depend on the parity of the length of the chain. This poses a major problem for modeling real food webs because it is difficult to establish their true number of trophic levels, with sometimes rare predators and often rampant pathogens. Similar problems arise in the modeling of chronic viral infections. We review examples where seemingly general interpretations strongly depend on the number of levels in a model, and on its specific equations. This Perspective aims to open the discussion on this problem. Public Library of Science 2012-07-26 /pmc/articles/PMC3405990/ /pubmed/22844235 http://dx.doi.org/10.1371/journal.pcbi.1002593 Text en Rob J. De Boer. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Perspective De Boer, Rob J. Which of Our Modeling Predictions Are Robust? |
title | Which of Our Modeling Predictions Are Robust? |
title_full | Which of Our Modeling Predictions Are Robust? |
title_fullStr | Which of Our Modeling Predictions Are Robust? |
title_full_unstemmed | Which of Our Modeling Predictions Are Robust? |
title_short | Which of Our Modeling Predictions Are Robust? |
title_sort | which of our modeling predictions are robust? |
topic | Perspective |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3405990/ https://www.ncbi.nlm.nih.gov/pubmed/22844235 http://dx.doi.org/10.1371/journal.pcbi.1002593 |
work_keys_str_mv | AT deboerrobj whichofourmodelingpredictionsarerobust |