Dataset Information

Rapid photoacclimation during episodic deep mixing augments the biological carbon pump.

ABSTRACT: Episodic deep mixing events are one component of the biological carbon pump that physically transports organic carbon into the mesopelagic. Episodic deep mixing also disrupts summertime thermal stratification thereby changing the light field and nutrient concentrations available for phytoplankton growth. Phytoplankton survival and growth below the mixed layer following restratification depends on how rapidly cells can employ a variety of photoacclimation processes in response to the environmental changes. To compare the relative timescales of summertime episodic deep mixing events with the timescales of phytoplankton photoacclimation processes, we first analyzed autonomous float data to survey the frequency and magnitude of deep mixing events in the western North Atlantic Ocean. Next, we simulated a sustained deep mixing event in the laboratory and measured rates of acclimation processes ranging from light harvesting to growth in a model diatom and green alga. In both algae increases in chlorophyll (Chl) were coupled to growth, but growth of the green alga lagged the diatom by about a day. In float profiles, significant increases in Chl and phytoplankton carbon (C _phyto) were detected below the mixed layer following episodic deep mixing events. These events pose a previously unrecognized source of new production below the mixed layer that can significantly boost the amount of carbon available for export to the deep ocean.

SUBMITTER: Penta WB

PROVIDER: S-EPMC8252461 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundModeling a dynamical biological system is often a difficult task since the a priori unknown parameters of such models are not always directly given by the experiments. Despite the lack of experimental quantitative knowledge, one can see a dynamical biological system as (i) the combined evolution tendencies (increase or decrease) of the biological compound concentrations, and: (ii) the temporal features, such as delays between two concentration peaks (i.e. the times when one of the components completes an increase (resp. decrease) phase and starts a decrease (resp. increase) phase).ResultsWe propose herein a new hybrid modeling framework that follows such biological assumptions. This hybrid approach deals with both a qualitative structure of the system and a quantitative structure. From a theoretical viewpoint, temporal specifications are expressed as equality or inequality constraints between delay parameters, while the qualitative specifications are expressed as an ordered pattern of the concentrations peaks of the components. Using this new hybrid framework, the temporal specifications of a biological system can be obtained from incomplete experimental data. The model may be processed by a hybrid model-checker (e.g. Phaver) which is able to give some new constraints on the delay parameters (e.g. the delay for a given transition is exactly 5 hours after the later peak of a gene product concentration). Furthermore, by using a constraint solver on the previous results, it becomes possible to get the set of parameters settings which are consistent with given specifications. Such a modeling approach is particularly accurate for modeling oscillatory biological behaviors like those observed in the Drosophila circadian cycles. The achieved results concerning the parameters of this oscillatory system formally confirm the several previous studies made by numerical simulations. Moreover, our analysis makes it possible to propose an automatic investigation of the respective impact of per and tim on the circadian cycle.ConclusionsA new hybrid technique for an automatic formal analysis of biological systems is developed with a special emphasis on their oscillatory behaviors. It allows the use of incomplete and empirical biological data.

Dataset Information

Rapid photoacclimation during episodic deep mixing augments the biological carbon pump.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets