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The interplay of phytoplankton competition and adaptation affects how phytoplankton, and ultimately marine ecosystems, respond to global warming. However, current ecosystem models do not consider both processes simultaneously. To study how the interplay of competition and adaptation affects phytoplankton responses to global warming, we developed an innovative ecosystem model for the Baltic Sea that simulates competition between three functional phytoplankton groups and allows for adaptation to changing temperatures. We found that competition and adaptation influence each other, with the outcome depending on environmental conditions. In a steady environment, competition drives adaptation to individual niches to reduce competition pressure. In a changing environment, adaptation enhances the competition pressure by allowing inferior competitors to mitigate the dominance of pre-adapted superior competitors. Our results demonstrate that by neglecting adaptation, models can overestimate warming-related changes in species dominance. Ecosystem models should include both competition and adaptation to accurately simulate phytoplankton responses to global warming.

Marine viruses have been identified as key players in biogeochemical cycles and in the termination of phytoplankton blooms; however, most models of biogeochemical processes have yet to resolve viral dynamics. Here, we incorporate a viral component into a 1D ecosystem model for the Baltic Sea to explore the influence of viruses on ecosystem dynamics under current and future climatic conditions. Virus host interactions and zooplankton grazing were mechanistically described through size-based contact rates. The model demonstrated that the presence of viruses increased nutrient retention in the upper water column. This corresponded to a reduction in phytoplankton biomass, production of dead organic matter and transfer of biomass to higher trophic levels. Viral presence played a key role in deeper water layers, near the thermocline. While warming alone reversed these trends, the combination of warming and viral presence enhanced the effect of viruses. Our results illustrate that marine ecosystem models need to incorporate viral dynamics to better predict system responses to climate change.

Unicellular photosynthetic marine microbes, or phytoplankton, make up the base of marine food webs and drive global nutrient cycles. Despite their key roles in ecology and biogeochemistry, we have a limited understanding of how the basic features of their demographics along with dynamic environments affect trait evolution. A key feature of diatom ecology is frequent extreme reductions in population size, both as part of their bloom-and-bust growth dynamics, and as a result of living within ocean currents. Here, we use experimental evolution to understand which metabolic pathways and functions readily diversify in diatom populations following population bottleneck events. We subjected replicate populations of six genetically distinct diatom strains to population bottlenecks and then subsequently allowed them to evolve as large populations in the absence of environmental change. Phylogenies and global expression of orthologs were generally strain-specific, indicating that vertical (inherited) evolutionary constraints largely determine the occupation of specific locations in the transcriptional landscape (i.e. tran-scape). Following bottlenecks and subsequent evolution as large populations, transcriptional networks of most populations returned to those of the ancestral population. However, at least one replicate population per lineage migrated in the tran-scape, demonstrating that evolutionary changes in gene expression patterns and transcriptional relationships can be driven by population bottlenecks even in the absence of environmental change. Importantly, the orthologs dominating transcriptional diversification resided in common, central metabolic pathways. These data advance our understanding of constraints and patterns of transcriptional relationships underlying trait evolution in microbes that drive global food webs and elemental cycles.

Phytoplankton are photosynthetic marine microbes that affect food webs, nutrient cycles, and climate regulation. Their roles are determined by a correlated set of phytoplankton functional traits including cell size, chlorophyll content, and cellular composition. Here, we explore how interrelated trait values and correlations evolve. Because both chance events and natural selection contribute to phytoplankton trait evolution, we used population bottlenecks to diversify six genotypes of Thalassiosirid diatoms. We then evolved them in two environments where natural selection could act on this diversity. Interspecific variation and within-species evolution were visualized for nine traits and their correlations using reduced axes (a trait-scape). Shifts in both trait values and correlations, resulting in movement of evolving populations on the trait-scape, occurred in both environments, and were more frequent under environmental change. Which trait correlations evolved was strain-specific, but greater departures from ancestral trait correlations were associated with lower population growth rates. There was no single master trait that could be used to understand multitrait evolution. Instead, repeatable multitrait evolution occurred along a major axis of variation defined by several diatom functional traits and trait relationships. Because trait-scapes capture changes in trait correlations and values together, they offer an insightful way to study multitrait variation.

Trait-based approaches to phytoplankton ecology have gained traction in recent decades as phenotypic traits are incorporated into ecological and biogeochemical models. Here, we use high-throughput phenotyping to explore both intra- and interspecific constraints on trait combinations that are expressed in the cosmopolitan marine diatom genus Thalassiosira. We demonstrate that within Thalassiosira, phenotypic diversity cannot be predicted from genotypic diversity, and moreover, plasticity can create highly divergent phenotypes that are incongruent with taxonomic grouping. Significantly, multivariate phenotypes can be represented in reduced dimensional space using principal component analysis with 77.7% of the variance captured by two orthogonal axes, here termed a ‘trait-scape’. Furthermore, this trait-scape can be recovered with a reduced set of traits. Plastic responses to the new environments expanded phenotypic trait values and the trait-scape, however, the overall pattern of response to the new environments was similar between strains and many trait correlations remained constant. These findings demonstrate that trait-scapes can be used to reveal common constraints on multi-trait plasticity in phytoplankton with divergent underlying phenotypes. Understanding how to integrate trait correlational constraints and trade-offs into theoretical frameworks like biogeochemical models will be critical to predict how microbial responses to environmental change will impact elemental cycling now and into the future.

Microbes form the base of food webs and drive biogeochemical cycling. Predicting the effects of microbial evolution on global elemental cycles remains a significant challenge due to the sheer number of interacting environmental and trait combinations. Here, we present an approach for integrating multivariate trait data into a predictive model of trait evolution. We investigated the outcome of thousands of possible adaptive walks parameterized using empirical evolution data from the alga Chlamydomonas exposed to high CO 2 . We found that the direction of historical bias (existing trait correlations) influenced both the rate of adaptation and the evolved phenotypes (trait combinations). Critically, we use fitness landscapes derived directly from empirical trait values to capture known evolutionary phenomena. This work demonstrates that ecological models need to represent both changes in traits and changes in the correlation between traits in order to accurately capture phytoplankton evolution and predict future shifts in elemental cycling.

Due to its crucial role in the ecosystem, phytoplankton is incorporated in marine ecosystem models. Most models however neglect the evolutionary potential of phytoplankton. Previous resurrection experiments with a spring bloom dinoflagellate suggest that the past century of global warming has caused an adaptive response in an important life cycle trait, the encystment rate. Here, based on this resurrection case study, we apply an advanced ecosystem model including selection and mutation, to test whether a temperature increase could induce a change in encystment. In line with the findings from resurrection experiments, our results show that in warmer waters strains with a lower encystment rate benefit over those with a higher encystment rate. The magnitude of change in encystment rate is however only reproduced, if additional factors, like eutrophication and a cyst mortality that increases with temperature, are considered. By using this ecosystem model including selection and mutation, we demonstrate that ecosystem modeling represents a powerful approach to investigate the adaptive potential of phytoplankton.

Microbes form the base of food webs and drive biogeochemical cycling. Predicting the effects of microbial evolution on global elemental cycles remains a significant challenge due to the sheer number of interacting environmental and trait combinations. Here we present an approach for modeling the interactive effects of de novo biological change and multivariate trait correlation evolution using principal component axes. We investigated the outcome of thousands of possible adaptive walks parameterized using empirical evolution data from the alga Chlamydomonas exposed to high CO2. We found that only a limited number of phenotypes emerged. Applying adaptive trait correlations to the starting population (historical bias) accelerated adaptation while highly convergent, nonrandom phenotypic solutions emerged irrespective of bias. These findings are consistent with a limited set of evolutionary trajectories underlying the vast amount of possible trait combinations (phenotypes). Critically, we demonstrate that these dynamics emerge in an empirically defined multidimensional trait space and show that trait correlations, in addition to trait values, must evolve to explain multi-trait adaptation. Identifying high probability high-fitness outcomes based on trait correlations is necessary in order to connect microbial evolutionary responses to biogeochemical cycling, thereby enabling the incorporation of these dynamics into global ecosystem models.

Cryopreservation and thawing protocol for Thallasiosira diatoms.

The history of expansion of bloom-forming cold water dinoflagellates in the Northern Baltic Sea was studied using 100-year-old sediment archives of their resting cysts. Vertical cyst distributions ...

We simulate pH-dependent growth of cyanobacteria with an ecosystem model for the central Baltic Sea. Four model components—a life cycle model of cyanobacteria, a biogeochemical model, a carbonate chemistry model and a water column model—are coupled via the framework for aquatic biogeochemical models. The coupled model is forced by the output of a regional climate model, based on the A1B emission scenario. With this coupled model, we perform simulations for the period 1968–2098. Our simulation experiments suggest that in the future, cyanobacteria growth is hardly affected by the projected pH decrease. However, in the simulation phase prior to 1980, cyanobacteria growth and N2-fixation are limited by the relatively high pH. The observed absence of cyanobacteria before the 1960s may thus be explained not only by lower eutrophication levels, but also by a higher alkalinity.