Phytoplankton temporal strategies increase entropy production in a marine food web model

TL;DR

This study introduces a trait-based model demonstrating that phytoplankton employing circadian strategies maximize entropy production in marine food webs, especially under seasonal conditions, revealing the importance of temporal strategies in ecological energetics.

Contribution

The paper presents a novel trait-based modeling approach where traits are optimized for maximum entropy production, highlighting the role of circadian strategies in marine ecosystems.

Findings

Circadian clock strategies increase entropy production compared to passive or fixed-growth strategies.

High growth rates favor ecotypes adapted to seasonal conditions, enhancing entropy production.

Trait optimization based on entropy production offers a new perspective for ecological modeling.

Abstract

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.