Temperature variability interacts with mean temperature to influence the predictability of microbial phenotypes

Abstract

Despite their relatively high thermal optima (T opt ), tropical taxa may be particularly vulnerable to a rising baseline and increased temperature variation because they live in relatively stable temperatures closer to their T opt . We examined how microbial eukaryotes with differing thermal histories responded to temperature fluctuations of different amplitudes (0 control, ±2, ±4°C) around mean temperatures below or above their T opt . Cosmopolitan dinoflagellates were selected based on their distinct thermal traits and included two species of the same genus (tropical and temperate Coolia spp.), and two strains of the same species maintained at different temperatures for >500 generations (tropical Amphidinium massartii control temperature and high temperature, CT and HT, respectively). There was a universal decline in population growth rate under temperature fluctuations, but strains with narrower thermal niche breadth (temperate Coolia and HT) showed ~10% greater reduction in growth. At suboptimal mean temperatures, cells in the cool phase of the fluctuation stopped dividing, fixed less carbon (C) and had enlarged cell volumes that scaled positively with elemental C, N, and P and C:Chlorophyll-a. However, at a supra-optimal mean temperature, fixed C was directed away from cell division and novel trait combinations developed, leading to greater phenotypic diversity. At the molecular level, heat-shock proteins, and chaperones, in addition to transcripts involving genome rearrangements, were upregulated in CT and HT during the warm phase of the supra-optimal fluctuation (30 ± 4°C), a stress response indicating protection. In contrast, the tropical Coolia species upregulated major energy pathways in the warm phase of its supra-optimal fluctuation (25 ± 4°C), indicating a broadscale shift in metabolism. Our results demonstrate divergent effects between taxa and that temporal variability in environmental conditions interacts with changes in the thermal mean to mediate microbial responses to global change, with implications for biogeochemical cycling.

Heatwave responses of Arctic phytoplankton communities are driven by combined impacts of warming and cooling

“…Further physiological studies on single species under heatwaves might help to shed light on this question, but here, compositional shifts were apparently driven more by the fluctuation of temperature than its absolute value. This in mind, note that responses to fluctuating temperature are not independent of the thermal history and the optimal temperatures of different species, which is why elevated future mean temperatures likely also have an effect on heatwave responses ( 58 ).…”

Responses of globally important phytoplankton species to olivine dissolution products and implications for carbon dioxide removal via ocean alkalinity enhancement

“…Dilutions were calculated to bring the cultures back down to the biomass levels that were recorded after the previous day's dilution. In this way, cultures were allowed to determine their own growth rates under each set of experimental conditions, without ever nearing stationary phase, significantly depleting nutrients or self-shading (Fu et al, 2022). For all experiments, cultures were sampled for a basic set of core biomass and physiological parameters, including cell counts, CO2 fixation, particulate organic carbon (POC), particulate organic nitrogen (PON), particulate organic phosphorus (POP) and biogenic silica (BSi, diatoms only) once steady-state growth was obtained for each growth condition (typically after 8−10 generations).…”

Responses of globally important phytoplankton species to olivine dissolution products and implications for carbon dioxide removal via ocean alkalinity enhancement

“…Dilutions were calculated to bring the cultures back down to the biomass levels that were recorded after the previous day's dilution. In this way, cultures were allowed to determine their own growth rates under each set of experimental conditions, without ever nearing a stationary phase, significantly depleting nutrients, or self-shading (Fu et al, 2022). For all experiments, cultures were sampled for a basic set of core biomass and physiological parameters, including cell counts, CO 2 fixation, particulate organic carbon (POC), particulate organic nitrogen (PON), particulate Table 1.…”