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浮游植物对生态系统模型的适应。

Phytoplankton adaptation in ecosystem models.

机构信息

AIONATEM, Hamburg, Germany.

IMF, CEN, Universität Hamburg, Olbersweg 24, Germany.

出版信息

J Theor Biol. 2019 May 7;468:60-71. doi: 10.1016/j.jtbi.2019.01.041. Epub 2019 Feb 20.

Abstract

We compare two different approaches to model adaptation of phytoplankton through trait value changes. Both consider mutation and selection (MuSe) but differ with respect to the underlying conceptual framework. The first one (MuSe-IBM) explicitly considers a population of individuals that are subject to random mutation during cell division. The second is a deterministic multi-compartment model (MuSe-MCM) that considers numerous genotypes of the population and where mutations are treated as a transfer of biomass between neighboring genotypes (i.e., a diffusion of characteristics in trait space). Focusing on the adaptation of optimal temperature, we show model results for different scenarios: a sudden change in environmental temperature, a seasonal variation and high frequency fluctuations. In addition, we investigate the effect of different shapes of thermal reaction norms as well as the role of alternating growth and resting phases on the adaptation process. For all cases, the differences between MuSe-IBM and MuSe-MCM are found to be negligible. Both models produce a number of well-known and plausible features. While the IBM has the advantage of including more mechanistic (i.e., probabilistic) processes, the MCM is much less computationally demanding and therefore suitable for implementation in three-dimensional ecosystem models.

摘要

我们比较了两种通过特征值变化来模拟浮游植物模型适应的不同方法。这两种方法都考虑了突变和选择(MuSe),但在基本概念框架上有所不同。第一个方法(MuSe-IBM)明确考虑了在细胞分裂过程中受到随机突变影响的个体群体。第二个方法是一个确定性的多隔室模型(MuSe-MCM),它考虑了种群的许多基因型,其中突变被视为相邻基因型之间生物量的转移(即特征在特征空间中的扩散)。我们专注于最适温度的适应,展示了不同场景下的模型结果:环境温度的突然变化、季节性变化和高频波动。此外,我们还研究了不同热反应规范形状的影响,以及生长和休眠阶段交替对适应过程的作用。对于所有情况,MuSe-IBM 和 MuSe-MCM 之间的差异可以忽略不计。两种模型都产生了许多众所周知且合理的特征。虽然 IBM 具有包含更多机械(即概率)过程的优势,但 MCM 的计算需求要低得多,因此适合在三维生态系统模型中实现。

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