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关于冯·贝塔朗菲生长模型中的指数。

On the exponent in the Von Bertalanffy growth model.

作者信息

Renner-Martin Katharina, Brunner Norbert, Kühleitner Manfred, Nowak Werner Georg, Scheicher Klaus

机构信息

Department of Integrative Biology and Biodiversity, Institute of Mathematics, Universität für Bodenkultur Wien, Vienna, Austria.

出版信息

PeerJ. 2018 Jan 4;6:e4205. doi: 10.7717/peerj.4205. eCollection 2018.

Abstract

Von Bertalanffy proposed the differential equation '() =  × ()  -  × () for the description of the mass growth of animals as a function () of time . He suggested that the solution using the metabolic scaling exponent = 2/3 (Von Bertalanffy growth function VBGF) would be universal for vertebrates. Several authors questioned universality, as for certain species other models would provide a better fit. This paper reconsiders this question. Based on 60 data sets from literature (37 about fish and 23 about non-fish species) it optimizes the model parameters, in particular the exponent 0 ≤  < 1, so that the model curve achieves the best fit to the data. The main observation of the paper is the large variability in the exponent, which can vary over a very large range without affecting the fit to the data significantly, when the other parameters are also optimized. The paper explains this by differences in the data quality: variability is low for data from highly controlled experiments and high for natural data. Other deficiencies were biologically meaningless optimal parameter values or optimal parameter values attained on the boundary of the parameter region (indicating the possible need for a different model). Only 11 of the 60 data sets were free of such deficiencies and for them no universal exponent could be discerned.

摘要

冯·贝塔朗菲提出了微分方程“() = × () - × ()”,用于描述动物质量增长随时间()的函数关系。他认为,使用代谢标度指数 = 2/3的解(冯·贝塔朗菲生长函数VBGF)对脊椎动物具有普遍性。一些作者对其普遍性提出质疑,因为对于某些物种,其他模型拟合效果更好。本文重新审视了这个问题。基于文献中的60个数据集(37个关于鱼类,23个关于非鱼类物种),对模型参数进行了优化,特别是指数0 ≤ < 1,以使模型曲线与数据达到最佳拟合。本文的主要观察结果是指数存在很大变异性,当其他参数也进行优化时,指数可以在很大范围内变化而不会显著影响对数据的拟合。本文通过数据质量的差异来解释这一点:来自高度控制实验的数据变异性低,而自然数据的变异性高。其他不足之处包括生物学上无意义的最优参数值或在参数区域边界获得的最优参数值(表明可能需要不同的模型)。60个数据集中只有11个没有这些缺陷,对于它们无法识别出通用指数。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a20/5756614/00e07f244e97/peerj-06-4205-g001.jpg

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