Department of Evolution, Ecology and Behaviour, University of Liverpool, Liverpool, UK.
School of Environmental Sciences, University of Liverpool, Liverpool, UK.
Sci Rep. 2020 May 14;10(1):7953. doi: 10.1038/s41598-020-64839-y.
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.
所有生物都会生长。众多的生长功能已被应用于广泛的生物分类群，但其中一些模型与经验数据拟合不良，并且在捕捉生长速率的变化方面缺乏灵活性。我们提出了一个新的 VBGF 框架，该框架拓宽了应用范围并提高了生长曲线拟合的灵活性。该框架为 VBGF 的五种参数化提供了曲线拟合程序：这些参数允许对合成代谢（生物合成潜力）的体型缩放指数进行不同的设定，除了通常假设的 2/3 幂缩放，并且允许超指数生长，有时会观察到这种情况。该程序应用于十二种不同水生无脊椎动物的物种，包括浮游生物和底栖生物。我们揭示了生物合成潜力和生长速率的体型缩放的广泛变化，范围从同形到超指数生长。这种曲线拟合方法提供了改进的生长预测，并将 VBGF 应用于表现出生物合成潜力缩放变化的更广泛的分类群。应用该框架可得出可靠的生长预测，这对于评估个体生长、种群产量和生态系统功能非常重要，包括评估渔业和水产养殖的可持续性。
