Abstract
Arctic phytoplankton and their response to future conditions shape one of the most rapidly changing ecosystems on the planet. We tested how much the phenotypic responses of strains from the same Arctic diatom population diverge and whether the physiology and intraspecific composition of multistrain populations differs from expectations based on single strain traits. To this end, we conducted incubation experiments with the diatom Thalassiosira hyalina under present‐day and future temperature and pCO2 treatments. Six fresh isolates from the same Svalbard population were incubated as mono‐ and multistrain cultures. For the first time, we were able to closely follow intraspecific selection within an artificial population using microsatellites and allele‐specific quantitative PCR. Our results showed not only that there is substantial variation in how strains of the same species cope with the tested environments but also that changes in genotype composition, production rates, and cellular quotas in the multistrain cultures are not predictable from monoculture performance. Nevertheless, the physiological responses as well as strain composition of the artificial populations were highly reproducible within each environment. Interestingly, we only detected significant strain sorting in those populations exposed to the future treatment. This study illustrates that the genetic composition of populations can change on very short timescales through selection from the intraspecific standing stock, indicating the potential for rapid population level adaptation to climate change. We further show that individuals adjust their phenotype not only in response to their physicochemical but also to their biological surroundings. Such intraspecific interactions need to be understood in order to realistically predict ecosystem responses to global change.
Highlights
Marine phytoplankton are the base of the oceanic food web and the main driver of the biological carbon pump, which strongly influences the biogeochemical cycles in the oceans (Geider et al, 2001)
Diatoms play a central role in these processes as they are the most important primary producers in the present‐day oceans and contribute disproportionally to the vertical carbon flux, especially during highly productive bloom events (Sarthou, Timmermans, Blain, & Tréguer, 2005)
With the recognition that trait diversity can be considerable within species, we have to understand how knowledge gained in single strain studies can be applied in an ecological context that assumes or models mul‐ tistrain communities (Follows & Dutkiewicz, 2011; Fontana, Thomas, Moldoveanu, Spaak, & Pomati, 2017; Kiørboe, Visser, & Andersen, 2018)
Summary
Marine phytoplankton are the base of the oceanic food web and the main driver of the biological carbon pump, which strongly influences the biogeochemical cycles in the oceans (Geider et al, 2001). Our attempts to understand and predict future phytoplank‐ ton community productivity and species composition often rely on the upscaling of single strain responses to environmental driv‐ ers as measured in laboratory experiments (e.g., Dutkiewicz et al, 2015) Such laboratory setups, have yielded varying re‐ sults (Gao & Campbell, 2014), especially when compared with ob‐ servations from studies using more complex assemblages (Sommer, Paul, & Moustaka‐Gouni, 2015; Tatters et al, 2018). From for‐ mer experiments with this species (Wolf et al, 2018), we expected responses often found in diatoms: increased growth and productivity under higher temperature and variable, strain‐specific effects in the interaction with elevated pCO2 We combined these six strains into artificial multistrain assemblages and used microsatellite markers to measure the relative strain frequencies in the assemblages over time. This enabled us to evaluate the predictability of population productivity and bulk trait values of multistrain assem‐ blages from monoculture traits and to compare the selection dy‐ namics that occurred in the multistrain assemblage with the predictions of population composition based on measurements made in monocultures
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
