浮游植物时间策略增加海洋食物网〈动〉<动>模型的熵产生
Phytoplankton temporal strategies increase entropy production in a marine food web model
摘要：
我们开发了一个基于特征的模型，该模型建立在生物系统进化和组织的假设上，通过实施时间策略，在较长的时间尺度上耗散化学和电磁势，以最大限度地提高熵的产生。一个海洋食物网〈动〉<动>组成的浮游植物，细菌和消费者的功能群，用来探讨如何时间的策略，或缺乏，改变熵产生在一个浅池塘，接收到一个连续的流减少有机碳加上无机氮和太阳辐射与昼夜和季节动态的照明。结果表明，采用明确的生物钟的时间策略产生更多的熵比被动的策略，使用内部碳储存或平衡的增长策略，需要浮游植物生长与固定的化学计量。当社区被迫在高比增长率2 d-1附近运行时，优化引导模型选择表现出冬季与夏季环境条件互补的浮游植物生态型，以增加熵产生。我们还提出了一种新型的基于特质的建模，其中特质值是由最大化熵产生，而不是随机选择。
英文摘要：
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 potentials 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 there of, 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-1, 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.
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