Vallino Joseph J, Tsakalakis Ioannis
Marine Biological Laboratory, Woods Hole, MA 02543, USA.
Department of Earth, Atmosphere and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Entropy (Basel). 2020 Nov 3;22(11):1249. doi: 10.3390/e22111249.
We develop a trait-based model founded on the hypothesis that biological systems evolve and organize to maximize entropy production by dissipating chemical and electromagnetic free energy over longer time scales than abiotic processes by implementing temporal strategies. A marine food web consisting of phytoplankton, bacteria, and consumer functional groups is used to explore how temporal strategies, or the lack thereof, change entropy production in a shallow pond that receives a continuous flow of reduced organic carbon plus inorganic nitrogen and illumination from solar radiation with diel and seasonal dynamics. Results show that a temporal strategy that employs an explicit circadian clock produces more entropy than a passive strategy that uses internal carbon storage or a balanced growth strategy that requires phytoplankton to grow with fixed stoichiometry. When the community is forced to operate at high specific growth rates near 2 d, the optimization-guided model selects for phytoplankton ecotypes that exhibit complementary for winter versus summer environmental conditions to increase entropy production. We also present a new type of trait-based modeling where trait values are determined by maximizing entropy production rather than by random selection.
我们基于这样一种假设开发了一种基于特征的模型，即生物系统通过实施时间策略，在比非生物过程更长的时间尺度上耗散化学和电磁自由能，从而进化并组织起来以最大化熵产生。一个由浮游植物、细菌和消费者功能组组成的海洋食物网被用来探索时间策略（或缺乏时间策略）如何改变一个浅池塘中的熵产生，该池塘接收连续流动的还原有机碳加无机氮，并受到具有昼夜和季节动态的太阳辐射光照。结果表明，采用明确生物钟的时间策略比使用内部碳储存的被动策略或要求浮游植物以固定化学计量比生长的平衡生长策略产生更多的熵。当群落被迫在接近2天的高比生长速率下运行时，优化引导模型会选择出对冬季和夏季环境条件具有互补性的浮游植物生态型，以增加熵产生。我们还提出了一种新型的基于特征的建模方法，其中特征值由最大化熵产生而不是随机选择来确定。
