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Abstract Prochlorococcus is found throughout the euphotic zone in the oligotrophic open ocean. Deep mixing and sinking while attached to particles can, however, transport Prochlorococcus cells below this sunlit zone, depriving them of light for extended periods of time. Previous work has shown that Prochlorococcus by itself cannot survive extended periods of darkness. However, when co-cultured with a heterotrophic microbe and subjected to repeated periods of extended darkness, Prochlorococcus cells develop an epigenetically inherited dark-tolerant phenotype that can survive longer periods of darkness. Here we examine the metabolic and physiological changes underlying this adaptation using co-cultures of dark-tolerant and parental strains of Prochlorococcus, each grown with the heterotroph Alteromonas under diel light:dark conditions. The relative abundance of Alteromonas was higher in dark-tolerant than parental co-cultures, while dark-tolerant Prochlorococcus cells were larger, contained less chlorophyll, and were less synchronized to the light:dark cycle. Meta-transcriptome analysis revealed that dark-tolerant co-cultures undergo a joint change, in which Prochlorococcus undergoes a relative shift from photosynthesis to respiration, while Alteromonas shifts toward using more organic acids instead of sugars. Furthermore, the transcriptome data suggested enhanced biosynthesis of amino acids and purines in dark-tolerant Prochlorococcus and enhanced degradation of these compounds in Alteromonas. Collectively, our results demonstrate that dark adaptation involves a strengthening of the metabolic coupling between Prochlorococcus and Alteromonas, presumably mediated by an enhanced, and compositionally modified, carbon exchange between the two species. 

Abstract The daunting complexity of ecosystems has led ecologists to use mathematical modelling to gain understanding of ecological relationships, processes and dynamics. In pursuit of mathematical tractability, these models use simplified descriptions of key patterns, processes and relationships observed in nature. In contrast, ecological data are often complex, scale‐dependent, space‐time correlated, and governed by nonlinear relations between organisms and their environment. This disparity in complexity between ecosystem models and data has created a large gap in ecology between model and data‐driven approaches. Here, we explore data assimilation (DA) with the Ensemble Kalman filter to fuse a two‐predator‐two‐prey model with abundance data from a 2600+ day experiment of a plankton community. We analyse how frequently we must assimilate measured abundances to predict accurately population dynamics, and benchmark our population model's forecast horizon against a simple null model. Results demonstrate that DA enhances the predictability and forecast horizon of complex community dynamics.