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A new framework for growth curve fitting based on the von Bertalanffy Growth Function
All organisms grow. Numerous growth functions have been applied to a wide taxonomic range of organisms, yet some of these models have poor fits to empirical data and lack of flexibility in capturing variation in growth rate. We propose a new VBGF framework that broadens the applicability and increas...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7224396/ https://www.ncbi.nlm.nih.gov/pubmed/32409646 http://dx.doi.org/10.1038/s41598-020-64839-y |
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author | Lee, Laura Atkinson, David Hirst, Andrew G. Cornell, Stephen J. |
author_facet | Lee, Laura Atkinson, David Hirst, Andrew G. Cornell, Stephen J. |
author_sort | Lee, Laura |
collection | PubMed |
description | All organisms grow. Numerous growth functions have been applied to a wide taxonomic range of organisms, yet some of these models have poor fits to empirical data and lack of flexibility in capturing variation in growth rate. We propose a new VBGF framework that broadens the applicability and increases flexibility of fitting growth curves. This framework offers a curve-fitting procedure for five parameterisations of the VBGF: these allow for different body-size scaling exponents for anabolism (biosynthesis potential), besides the commonly assumed 2/3 power scaling, and allow for supra-exponential growth, which is at times observed. This procedure is applied to twelve species of diverse aquatic invertebrates, including both pelagic and benthic organisms. We reveal widespread variation in the body-size scaling of biosynthesis potential and consequently growth rate, ranging from isomorphic to supra-exponential growth. This curve-fitting methodology offers improved growth predictions and applies the VBGF to a wider range of taxa that exhibit variation in the scaling of biosynthesis potential. Applying this framework results in reliable growth predictions that are important for assessing individual growth, population production and ecosystem functioning, including in the assessment of sustainability of fisheries and aquaculture. |
format | Online Article Text |
id | pubmed-7224396 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-72243962020-05-20 A new framework for growth curve fitting based on the von Bertalanffy Growth Function Lee, Laura Atkinson, David Hirst, Andrew G. Cornell, Stephen J. Sci Rep Article All organisms grow. Numerous growth functions have been applied to a wide taxonomic range of organisms, yet some of these models have poor fits to empirical data and lack of flexibility in capturing variation in growth rate. We propose a new VBGF framework that broadens the applicability and increases flexibility of fitting growth curves. This framework offers a curve-fitting procedure for five parameterisations of the VBGF: these allow for different body-size scaling exponents for anabolism (biosynthesis potential), besides the commonly assumed 2/3 power scaling, and allow for supra-exponential growth, which is at times observed. This procedure is applied to twelve species of diverse aquatic invertebrates, including both pelagic and benthic organisms. We reveal widespread variation in the body-size scaling of biosynthesis potential and consequently growth rate, ranging from isomorphic to supra-exponential growth. This curve-fitting methodology offers improved growth predictions and applies the VBGF to a wider range of taxa that exhibit variation in the scaling of biosynthesis potential. Applying this framework results in reliable growth predictions that are important for assessing individual growth, population production and ecosystem functioning, including in the assessment of sustainability of fisheries and aquaculture. Nature Publishing Group UK 2020-05-14 /pmc/articles/PMC7224396/ /pubmed/32409646 http://dx.doi.org/10.1038/s41598-020-64839-y Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Lee, Laura Atkinson, David Hirst, Andrew G. Cornell, Stephen J. A new framework for growth curve fitting based on the von Bertalanffy Growth Function |
title | A new framework for growth curve fitting based on the von Bertalanffy Growth Function |
title_full | A new framework for growth curve fitting based on the von Bertalanffy Growth Function |
title_fullStr | A new framework for growth curve fitting based on the von Bertalanffy Growth Function |
title_full_unstemmed | A new framework for growth curve fitting based on the von Bertalanffy Growth Function |
title_short | A new framework for growth curve fitting based on the von Bertalanffy Growth Function |
title_sort | new framework for growth curve fitting based on the von bertalanffy growth function |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7224396/ https://www.ncbi.nlm.nih.gov/pubmed/32409646 http://dx.doi.org/10.1038/s41598-020-64839-y |
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