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To swim up gradients of nutrients, E. coli senses nutrient concentrations within its periplasm. For small nutrient molecules, periplasmic concentrations typically match extracellular concentrations. However, this is not necessarily the case for saccharides, such as maltose, which are transported into the periplasm via a specific porin. Previous observations have shown that, under various conditions, E. coli limits maltoporin abundance so that, for extracellular micromolar concentrations of maltose, there are predicted to be only nanomolar concentrations of free maltose in the periplasm. Thus, in the micromolar regime, the total uptake of maltose from the external environment into the cytoplasm is limited not by the abundance of cytoplasmic transport proteins but by the abundance of maltoporins. Here, we present results from experiments and modeling suggesting that this porin-limited transport enables E. coli to sense micromolar gradients of maltose despite having a high-affinity ABC transport system that is saturated at these micromolar levels. We used microfluidic assays to study chemotaxis of E. coli in various gradients of maltose and methyl-aspartate and leveraged our experimental observations to develop a mechanistic transport-and-sensing chemotaxis model. Incorporating this model into agent-based simulations, we discover a trade-off between uptake and sensing: although high-affinity transport enables higher uptake rates at low nutrient concentrations, it severely limits the range of dynamic sensing. We thus propose that E. coli may limit periplasmic uptake to increase its chemotactic sensitivity, enabling it to use maltose as an environmental cue.

BioGeoSCAPES (BGS) is an international program being developed to understand controls on ocean productivity and metabolism by integrating systems biology (‘omics) and biogeochemistry (Figure 1). To ensure global input into the design of the BGS Program, countries interested in participating were tasked with holding an organizing meeting to discuss the country-specific research priorities. A United States BGS planning meeting, sponsored by the Ocean Carbon & Biogeochemistry (OCB) Project Office, was convened virtually November 10-12, 2021. The objectives of the meeting were to communicate the planning underway by international partners, engage the US community to explore possible national contributions to such a program, and build understanding, support, and momentum for US efforts towards BGS. The meeting was well-attended, with 154 participants and many fruitful discussions that are summarized in this document. Key outcomes from the meeting were the identification of additional programs and partners for BGS, a prioritization of measurements requiring intercalibration, and the development of a consensus around key considerations to be addressed in a science plan. Looking forward, the hope is that this workshop will serve as the foundation for future US and international discussions and planning for a BGS program, enabled by NSF funding for an AccelNet project (AccelNet - Implementation: Development of an International Network for the Study of Ocean Metabolism and Nutrient Cycles on a Changing Planet (BioGeoSCAPES)), beginning in 2022.

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.

Nitrification controls the oxidation state of bioavailable nitrogen. Distinct clades of chemoautotrophic microorganisms – predominantly ammonia-oxidizing archaea (AOA) and nitrite-oxidizing bacteria (NOB) – regulate the two steps of nitrification in the ocean, but explanations for their observed relative abundances and nitrification rates remain incomplete and their contributions to the global marine carbon cycle via carbon fixation remain unresolved. Using a mechanistic microbial ecosystem model with nitrifying functional types, we derive simple expressions for the controls on AOA and NOB in the deep, oxygenated open ocean. The relative biomass yields, loss rates, and cell quotas of AOA and NOB control their relative abundances, though we do not need to invoke a difference in loss rates to explain the observed relative abundances. The supply of ammonium, not the traits of AOA or NOB, controls the relatively equal ammonia and nitrite oxidation rates at steady state. The relative yields of AOA and NOB alone set their relative bulk carbon fixation rates in the water column. The quantitative relationships are consistent with multiple in situ datasets. In a complex global ecosystem model, nitrification emerges dynamically across diverse ocean environments, and ammonia and nitrite oxidation and their associated carbon fixation rates are decoupled due to physical transport and complex ecological interactions in some environments. Nevertheless, the simple expressions capture global patterns to first order. The model provides a mechanistic upper estimate on global chemoautotrophic carbon fixation of 0.2–0.5 Pg C yr−1, which is on the low end of the wide range of previous estimates. Modeled carbon fixation by AOA (0.2–0.3 Pg C yr−1) exceeds that of NOB (about 0.1 Pg C yr−1) because of the higher biomass yield of AOA. The simple expressions derived here can be used to quantify the biogeochemical impacts of additional metabolic pathways (i.e., mixotrophy) of nitrifying clades and to identify alternative metabolisms fueling carbon fixation in the deep ocean.

Tropical forests are an important part of global water and energy cycles, but the mechanisms that drive seasonality of their land‐atmosphere exchanges have proven challenging to capture in models. Here, we (1) report the seasonality of fluxes of latent heat (LE), sensible heat (H), and outgoing short and longwave radiation at four diverse tropical forest sites across Amazonia ‐‐ along the equator from the Caxiuanã and Tapajós National Forests in the eastern Amazon to a forest near Manaus, and from the equatorial zone to the southern forest in Reserva Jaru; (2) investigate how vegetation and climate influence these fluxes; and (3) evaluate land surface model (LSM) performance by comparing simulations to observations. We found that previously identified failure of models to capture observed dry‐season increases in evapotranspiration was associated with model over‐estimations of (1) magnitude and seasonality of Bowen ratios (relative to aseasonal observations in which sensible was only 20‐30% of the latent heat flux) indicating model exaggerated water limitation, (2) canopy emissivity and reflectance (albedo was only 10 to 15% of incoming solar radiation, compared to 0.15‐0.22% simulated), and (3) vegetation temperatures (due to underestimation of dry‐season evapotranspiration and associated cooling). These partially compensating model‐observation discrepancies (e.g. higher temperatures expected from excess Bowen ratios were partially ameliorated by brighter leaves and more interception/evaporation) significantly biased seasonal model estimates of net radiation (Rn), the key driver of water and energy fluxes (LE ~ 0.6Rn and H ~ 0.15Rn). Though these biases varied among sites and models. A better representation of energy‐related parameters associated with dynamic phenology (e.g. leaf optical properties, canopy interception, and skin temperature) could improve simulations and benchmarking of current vegetation‐atmosphere exchange and reduce uncertainty of regional and global biogeochemical models.

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.

Ecological interactions between marine bacteria and phytoplankton play a pivotal role in governing the ocean’s major biogeochemical cycles. Among these, members of the marine Roseobacter Group (MRG) can establish mutualistic relationships with phytoplankton that are, in part, maintained by exchanges of the organosulfur compound, dimethylsulfoniopropionate (DMSP). Yet most of what is known about these interactions has been derived from culture-based laboratory studies. To investigate temporal and spatial co-occurrence patterns between members of the MRG and DMSP-producing phytoplankton we analysed 16S and 18S rRNA gene amplicon sequence variants (ASVs) derived from 5 years of monthly samples from seven environmentally distinct Australian oceanographic time-series. The MRG and DMSP-producer communities often displayed contemporaneous seasonality, which was greater in subtropical and temperate environments compared to tropical environments. The relative abundance of both groups varied latitudinally, displaying a poleward increase, peaking (MRG at 33% of total bacteria, DMSP producers at 42% of eukaryotic phototrophs) during recurrent spring-summer phytoplankton blooms in the most temperate site (Maria Island, Tasmania). Network analysis identified 20,140 significant positive correlations between MRG ASVs and DMSP producers and revealed that MRGs exhibit significantly stronger correlations to high DMSP producers relative to other DMSP-degrading bacteria (Pelagibacter, SAR86 and Actinobacteria). By utilising the power of a continental network of oceanographic time-series, this study provides in situ confirmation of interactions found in laboratory studies and demonstrates that the ecological dynamics of an important group of marine bacteria are shaped by the production of an abundant and biogeochemically significant organosulfur compound.

To swim up gradients of nutrients, E. coli senses nutrient concentrations within its periplasm. For small nutrient molecules, periplasmic concentrations typically match extracellular concentrations. However, this is not necessarily the case for saccharides, such as maltose, which is transported into the periplasm via a specific porin. Previous observations have shown that under various conditions E. coli limits maltoporin abundance so that, for extracellular micromolar concentrations of maltose, there are predicted to be only nanomolar concentrations of free maltose in the periplasm. Thus, in the micromolar regime, the total uptake of maltose from the external environment into the cytoplasm is limited not by the abundance of cytoplasmic transport proteins but by the abundance of maltoporins. Here we present results from experiments and modeling showing that this porin-limited transport enables E. coli to sense micromolar gradients of maltose despite having a high-affinity ABC transport system that is saturated at these micromolar levels. We used microfluidic assays to study chemotaxis of E. coli in various gradients of maltose and methyl-aspartate and leveraged our experimental observations to develop a mechanistic transport-and-sensing chemotaxis model. Incorporating this model into agent-based simulations, we discover a trade-off between uptake and sensing: although high-affinity transport enables higher uptake rates at low nutrient concentrations, it severely limits dynamic sensing range. We thus propose that E. coli may limit periplasmic uptake to increase its chemotactic sensitivity, enabling it to use maltose as an environmental cue.Statement of SignificanceBacterial chemotaxis is among the best-studied systems in biology and is paradigmatic of the mechanisms used by cells to link sensory inputs with regulated responses, thus providing insight into the ecological basis of cellular physiology. Here we present a mechanistic chemotaxis model that describes how the regulation of the transport of a sugar into and out of the cell’s periplasm affects the cell’s motile response to that sugar. Based on observations from population-level chemotaxis assays, we uncover an ecologically relevant trade-off between sensing and uptake. The general finding of this work is that, while high-affinity transport allows for higher uptake rates, it can severely limit the cell’s dynamic sensing range.

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.

Ocean phytoplankton play a critical role in the global carbon cycle, contributing ~50% of global photosynthesis. As planktonic organisms, phytoplankton encounter significant environmental variabili...

Sinking particulate organic carbon out of the surface ocean sequesters carbon on decadal to millennial timescales. Predicting the particulate carbon flux is therefore critical for understanding both global carbon cycling and the future climate. Microbes play a crucial role in particulate organic carbon degradation, but the impact of depth-dependent microbial dynamics on ocean-scale particulate carbon fluxes is poorly understood. Here we scale-up essential features of particle-associated microbial dynamics to understand the large-scale vertical carbon flux in the ocean. Our model provides mechanistic insight into the microbial contribution to the particulate organic carbon flux profile. We show that the enhanced transfer of carbon to depth can result from populations struggling to establish colonies on sinking particles due to diffusive nutrient loss, cell detachment, and mortality. These dynamics are controlled by the interaction between multiple biotic and abiotic factors. Accurately capturing particle-microbe interactions is essential for predicting variability in large-scale carbon cycling.

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.

The Ecosystem Demography model version 2.2 (ED-2.2) is a terrestrial biosphere model that simulates the biophysical, ecological, and biogeochemical dynamics of vertically and horizontally heterogeneous terrestrial ecosystems. In a companion paper (Longo et al., 2019a), we described how the model solves the energy, water, and carbon cycles, and verified the high degree of conservation of these properties in long-term simulations that include long-term (multi-decadal) vegetation dynamics. Here, we present a detailed assessment of the model's ability to represent multiple processes associated with the biophysical and biogeochemical cycles in Amazon forests. We use multiple measurements from eddy covariance towers, forest inventory plots, and regional remote-sensing products to assess the model's ability to represent biophysical, physiological, and ecological processes at multiple timescales, ranging from subdaily to century long. The ED-2.2 model accurately describes the vertical distribution of light, water fluxes, and the storage of water, energy, and carbon in the canopy air space, the regional distribution of biomass in tropical South America, and the variability of biomass as a function of environmental drivers. In addition, ED-2.2 qualitatively captures several emergent properties of the ecosystem found in observations, specifically observed relationships between aboveground biomass, mortality rates, and wood density; however, the slopes of these relationships were not accurately captured. We also identified several limitations, including the model's tendency to overestimate the magnitude and seasonality of heterotrophic respiration and to overestimate growth rates in a nutrient-poor tropical site. The evaluation presented here highlights the potential of incorporating structural and functional heterogeneity within biomes in Earth system models (ESMs) and to realistically represent their impacts on energy, water, and carbon cycles. We also identify several priorities for further model development.

Earth system models (ESMs) have been developed to represent the role of terrestrial ecosystems on the energy, water, and carbon cycles. However, many ESMs still lack representation of within-ecosystem heterogeneity and diversity. In this paper, we present the Ecosystem Demography model version 2.2 (ED-2.2). In ED-2.2, the biophysical and physiological processes account for the horizontal and vertical heterogeneity of the ecosystem: the energy, water, and carbon cycles are solved separately for a series of vegetation cohorts (groups of individual plants of similar size and plant functional type) distributed across a series of spatially implicit patches (representing collections of micro-environments that have a similar disturbance history). We define the equations that describe the energy, water, and carbon cycles in terms of total energy, water, and carbon, which simplifies the differential equations and guarantees excellent conservation of these quantities in long-term simulation (< 0.1 % error over 50 years). We also show examples of ED-2.2 simulation results at single sites and across tropical South America. These results demonstrate the model's ability to characterize the variability of ecosystem structure, composition, and functioning both at stand and continental scales. A detailed model evaluation was conducted and is presented in a companion paper (Longo et al., 2019a). Finally, we highlight some of the ongoing model developments designed to improve the model's accuracy and performance and to include processes hitherto not represented in the model.

Significance Organic matter in the global ocean, soils, and sediments stores about five times more carbon than the atmosphere. Thus, the controls on the accumulation of organic matter are critical to global carbon cycling. However, we lack a quantitative understanding of these controls. This prevents meaningful descriptions of organic matter cycling in global climate models, which are required for understanding how changes in organic matter reservoirs provide feedbacks to past and present changes in climate. Currently, explanations for organic matter accumulation remain under debate, characterized by seemingly competing hypotheses. Here, we develop a quantitative framework for organic matter accumulation that unifies these hypotheses. The framework derives from the ecological dynamics of microorganisms, the dominant consumers of organic matter., Organic matter constitutes a key reservoir in global elemental cycles. However, our understanding of the dynamics of organic matter and its accumulation remains incomplete. Seemingly disparate hypotheses have been proposed to explain organic matter accumulation: the slow degradation of intrinsically recalcitrant substrates, the depletion to concentrations that inhibit microbial consumption, and a dependency on the consumption capabilities of nearby microbial populations. Here, using a mechanistic model, we develop a theoretical framework that explains how organic matter predictably accumulates in natural environments due to biochemical, ecological, and environmental factors. Our framework subsumes the previous hypotheses. Changes in the microbial community or the environment can move a class of organic matter from a state of functional recalcitrance to a state of depletion by microbial consumers. The model explains the vertical profile of dissolved organic carbon in the ocean and connects microbial activity at subannual timescales to organic matter turnover at millennial timescales. The threshold behavior of the model implies that organic matter accumulation may respond nonlinearly to changes in temperature and other factors, providing hypotheses for the observed correlations between organic carbon reservoirs and temperature in past earth climates.

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.

Significance Robust predictions of future changes in global biogeochemical cycling require an understanding of how microorganisms adapt to stressful and changing environments. In the ocean, rates of adaptation will be a function of both evolutionary timescales and physical dynamics. However, little is known about this interaction. We examined evolutionary dynamics of marine microbes by combining a model of microbial adaptation with varying selection pressures with a high-resolution ocean circulation model. A trade-off emerged between two evolutionary strategies: (i) ability to adapt plastically to short-term environmental fluctuations with delayed genetic adaptation and (ii) more rapid genetic adaptation with limited response to short-term environmental fluctuations. This trade-off determines evolutionary timescales and provides a foundation for understanding distributions of microbial traits and biogeochemistry., Marine microbes form the base of ocean food webs and drive ocean biogeochemical cycling. Yet little is known about the ability of microbial populations to adapt as they are advected through changing conditions. Here, we investigated the interplay between physical and biological timescales using a model of adaptation and an eddy-resolving ocean circulation climate model. Two criteria were identified that relate the timing and nature of adaptation to the ratio of physical to biological timescales. Genetic adaptation was impeded in highly variable regimes by nongenetic modifications but was promoted in more stable environments. An evolutionary trade-off emerged where greater short-term nongenetic transgenerational effects (low-γ strategy) enabled rapid responses to environmental fluctuations but delayed genetic adaptation, while fewer short-term transgenerational effects (high-γ strategy) allowed faster genetic adaptation but inhibited short-term responses. Our results demonstrate that the selective pressures for organisms within a single water mass vary based on differences in generation timescales resulting in different evolutionary strategies being favored. Organisms that experience more variable environments should favor a low-γ strategy. Furthermore, faster cell division rates should be a key factor in genetic adaptation in a changing ocean. Understanding and quantifying the relationship between evolutionary and physical timescales is critical for robust predictions of future microbial dynamics.

One-quarter of photosynthesis-derived carbon on Earth rapidly cycles through a set of short-lived seawater metabolites that are generated from the activities of marine phytoplankton, bacteria, grazers and viruses. Here we discuss the sources of microbial metabolites in the surface ocean, their roles in ecology and biogeochemistry, and approaches that can be used to analyse them from chemistry, biology, modelling and data science. Although microbial-derived metabolites account for only a minor fraction of the total reservoir of marine dissolved organic carbon, their flux and fate underpins the central role of the ocean in sustaining life on Earth.

The cyanobacterium Trichodesmium fixes as much as half of the nitrogen (N2) that supports tropical open-ocean biomes, but its growth is frequently limited by iron (Fe) availability1,2. How future ocean warming may interact with this globally widespread Fe limitation of Trichodesmium N2 fixation is unclear3. Here, we show that the optimum growth temperature of Fe-limited Trichodesmium is ~5 °C higher than for Fe-replete cells, which results in large increases in growth and N2 fixation under the projected warmer Fe-deplete sea surface conditions. Concurrently, the cellular Fe content decreases as temperature rises. Together, these two trends result in thermally driven increases of ~470% in Fe-limited cellular iron use efficiencies (IUEs), defined as the molar quantity of N2 fixed by Trichodesmium per unit time per mole of cellular Fe (mol N2 fixed h–1 mol Fe–1), which enables Trichodesmium to much more efficiently leverage the scarce available Fe supplies to support N2 fixation. Modelling these results in the context of the IPCC representative concentration pathway (RCP) 8.5 global warming scenario4 predicts that IUEs of N2 fixers could increase by ~76% by 2100, and largely alleviate the prevailing Fe limitation across broad expanses of the tropical Pacific and Indian Oceans. Thermally enhanced cyanobacterial IUEs could increase future global marine N2 fixation by ~22% over the next century, and thus profoundly alter the biology and biogeochemistry of open-ocean ecosystems. The growth of nitrogen-fixing marine cyanobacteria Trichodesmium is limited by iron availability under current conditions. However warmer temperatures reduce the iron requirement, allowing greater growth rates and increased nitrogen fixation.

Marine bacterial diversity is immense and believed to be driven in part by trade-offs in metabolic strategies. Here we consider heterotrophs that rely on organic carbon as an energy source and present a molecular-level model of cell metabolism that explains the dichotomy between copiotrophs—which dominate in carbon-rich environments—and oligotrophs—which dominate in carbon-poor environments—as the consequence of trade-offs between nutrient transport systems. While prototypical copiotrophs, like Vibrios, possess numerous phosphotransferase systems (PTS), prototypical oligotrophs, such as SAR11, lack PTS and rely on ATP-binding cassette (ABC) transporters, which use binding proteins. We develop models of both transport systems and use them in proteome allocation problems to predict the optimal nutrient uptake and metabolic strategy as a function of carbon availability. We derive a Michaelis–Menten approximation of ABC transport, analytically demonstrating how the half-saturation concentration is a function of binding protein abundance. We predict that oligotrophs can attain nanomolar half-saturation concentrations using binding proteins with only micromolar dissociation constants and while closely matching transport and metabolic capacities. However, our model predicts that this requires large periplasms and that the slow diffusion of the binding proteins limits uptake. Thus, binding proteins are critical for oligotrophic survival yet severely constrain growth rates. We propose that this trade-off fundamentally shaped the divergent evolution of oligotrophs and copiotrophs., PLoS Computational Biology, 17 (5), ISSN:1553-734X, ISSN:1553-7358

In 1904, medievalist critic William Henry Schofield declared that the fourteenth-century poem Pearl was not an elegy, overturning an assumption that had persisted since the poem's first publication in 1864. This article focuses on the question of Pearl's genre and its relation to the Victorian literary culture into which the poem was reborn. I argue that Victorian critics did not read Pearl simply as an elegy, but as a Victorian elegy, a genre with a very particular cluster of thematic and formal attributes – and, indeed, a heightened sensitivity to the fit between theme and form. Although Pearl is five centuries older than In Memoriam, its long latency as a manuscript and its subsequent revival fourteen years into the In Memoriam craze created the impression that the medieval poem followed and was somehow derived from the Victorian one. This article proposes that Victorian models of form and genre were powerful enough to work backward. Pearl's late-century reception demonstrates how thoroughly In Memoriam defined Victorian poetics not only by instigating new prosodic fashions, but also by shaping the reading practices with which Victorians approached their literary historical past.

Remineralization of organic matter by heterotrophic organisms regulates the biological sequestration of carbon, thereby mediating atmospheric CO2. While surface nutrient supply impacts the elemental ratios of primary production, stoichiometric control by remineralization remains unclear. Here we develop a mechanistic description of remineralization and its stoichiometry in a marine microbial ecosystem model. The model simulates the observed elemental plasticity of phytoplankton and the relatively constant, lower C:N of heterotrophic biomass. In addition, the model captures the observed decreases in DOC:DON and the C:N remineralization ratio with depth for more labile substrates, which are driven by a switch in the dominant source of labile DOM from phytoplankton to heterotrophic biomass. Only a model version with targeted remineralization of N-rich components is able to simulate the observed profiles of preferential remineralization of DON relative to DOC and the elevated C:N of bulk DOM. The model suggests that more labile substrates are associated with C-limited heterotrophic growth and not with preferential remineralization, while more recalcitrant substrates are associated with growth limited by processing rates and with preferential remineralization. The resulting patterns of variable remineralization stoichiometry mediate the extent to which a proportional increase in carbon production resulting from changes in phytoplankton stoichiometry can increase the efficiency of the biological pump. Results emphasize the importance of understanding the physiology of both phytoplankton and heterotrophs for anticipating changes in biologically driven ocean carbon storage.

Marine microbes form the base of ocean food webs and drive ocean biogeochemical cycling. Yet little is known about how microbial populations will evolve due to global change-driven shifts in ocean dynamics. Understanding adaptive timescales is critical where long-term trends (e.g. warming) are coupled to shorter-term advection dynamics that move organisms rapidly between ecoregions. Here we investigated the interplay between physical and biological timescales using a model of adaptation and an eddy-resolving ocean circulation climate model. Two criteria (α and β) were identified that relate physical and biological timescales and determine the timing and nature of adaptation. Genetic adaptation was impeded in highly variable regimes (α1). An evolutionary trade-off emerged where greater short-term transgenerational effects (low-β-strategy) enabled rapid responses to environmental fluctuations but delayed genetic adaptation, while fewer short-term transgenerational effects (high-β-strategy) allowed faster genetic adaptation but inhibited short-term responses. Our results suggest that organisms with faster growth rates are better positioned to adapt to rapidly changing ocean conditions and that more variable environments will favor a bet-hedging, low-β-strategy. Understanding the relationship between evolutionary and physical timescales is critical for robust predictions of future microbial dynamics.

The Ecosystem Demography Model version 2.2 (ED-2.2) is a terrestrial biosphere model that simulates the biophysical and biogeochemical cycles of dynamic ecosystems while considering the role of vertical structure of plant communities and the heterogeneity of such structures across the landscape. In a companion paper, we described in detail how the model solves the energy, water, and carbon cycles, and verified the excellent conservation of such properties in long-term simulation. Here, we present a thorough assessment of the model's ability to represent multiple processes associated with the biophysical and biogeochemical cycles, with focus on the Amazon forest. We used multiple measurements from eddy covariance towers, forest inventory plots and regional remote-sensing products to assess the model's ability to represent biophysical, physiological, and ecological processes at multiple time scales ranging from sub-daily to century-long. The ED-2.2 model accurately describes the vertical distribution of light, water fluxes and the storage of water, energy and carbon in the canopy air space, the regional distribution of biomass in tropical South America, and the variability of biomass as a function of environmental drivers. In addition, ED-2.2 also simulates emerging properties of the ecosystem found in observations, such as the relationship between biomass and mortality rates and wood density, although the relationships predicted by the model were biased. We also identified some of the model limitations, such as the model's tendency to overestimate the magnitude and seasonality of heterotrophic respiration, and to overestimate growth rates in a nutrient-poor tropical site. The evaluation presented here highlights the potential of incorporating structural and functional heterogeneity within biomes in ESMs, to realistically represent the role of forest structure and composition on energy, water, and carbon cycles, as well as the priority areas for further model development.

7 pages, 5 figures, supplementary material http://advances.sciencemag.org/cgi/content/full/5/8/eaaw8855/DC1, All known phototrophic metabolisms on Earth rely on one of three categories of energy-converting pigments: chlorophyll-a (rarely -d), bacteriochlorophyll-a (rarely -b), and retinal, which is the chromophore in rhodopsins. While the significance of chlorophylls in solar energy capture has been studied for decades, the contribution of retinal-based phototrophy to this process remains largely unexplored. We report the first vertical distributions of the three energy-converting pigments measured along a contrasting nutrient gradient through the Mediterranean Sea and the Atlantic Ocean. The highest rhodopsin concentrations were observed above the deep chlorophyll-a maxima, and their geographical distribution tended to be inversely related to that of chlorophyll-a. We further show that proton-pumping proteorhodopsins potentially absorb as much light energy as chlorophyll-a–based phototrophy and that this energy is sufficient to sustain bacterial basal metabolism. This suggests that proteorhodopsins are a major energy-transducing mechanism to harvest solar energy in the surface ocean, This project was founded by the Marie Curie Actions-International Outgoing Fellowships (project 253970), the U.S. National Science Foundation grant OCE1335269, the Simons Foundation award 509727, and the Gordon and Betty Moore Foundation Marine Microbiology Initiative award 3779. J.A. and J.M.G. were supported by the Spanish project HOTMIX (CTM2011-30010-C02-MAR). The University of Dundee is a registered Scottish charity, no. SC015096

In the subtropical ocean, physical and biogeochemical patchiness exists across a range of spatial and temporal scales. As ocean surface temperatures rise, climate models suggest that the strength of the basin-scale biological carbon pump may diminish due to increased stratification and depleted surface nutrients. However, global model predictions often cannot account for climate-physical-ecosystem interactions occurring at finer spatial scales (e.g., less than a few tens of kilometers). In order to study such fine-scale interactions, we developed a computationally tractable, idealized modeling framework that allows for a systematic assessment of the impact of fine-scale frontal disturbances (i.e., short-lived upwelling and downwelling events) on biogeochemical and ecosystem dynamics. The model successfully captured both the mean dynamics and the range of ecosystem variability observed at the Hawaii Ocean Time-series (HOT) site in the oligotrophic North Pacific Subtropical Gyre. Fine-scale frontal disturbances were shown to impact both phytoplankton assemblages and carbon cycling. Specifically, disturbances of shorter duration but higher intensity favored the growth of large phytoplankton and contributed to elevated carbon export efficiency. However, for disturbances of longer duration and higher intensity, a decoupling between physical changes and biological responses led to a reduced utilization efficiency of upwelled nutrients and thus a reduction in new production. Our results emphasize that future changes in both large and fine-scale physical dynamics may play an important role in shaping marine ecosystems, and could be accounted for in the next-generation global climate models using this type of parameterization.

Subtropical gyres contribute significantly to global ocean productivity. As the climate warms, the strength of these gyres as a biological carbon pump is predicted to diminish due to increased stratification and depleted surface nutrients. We present results suggesting that the impact of submesoscale physics on phytoplankton in the oligotrophic ocean is substantial and may either compensate or exacerbate future changes in carbon cycling. A new statistical tool was developed to quantify surface patchiness from sea surface temperatures. Chlorophyll concentrations in the North Pacific Subtropical Gyre were shown to be enhanced by submesoscale frontal dynamics with an average increase of 38% (maximum of 83%) during late winter. The magnitude of this enhancement is comparable to the observed decline in chlorophyll due to a warming of ~1.1°C. These results highlight the need for an improved understanding of fine-scale physical variability in order to predict the response of marine ecosystems to projected climate changes.

© 2018 The Authors. New Phytologist © 2018 New Phytologist Trust The impact of increases in drought frequency on the Amazon forest's composition, structure and functioning remain uncertain. We used a process- and individual-based ecosystem model (ED2) to quantify the forest's vulnerability to increased drought recurrence. We generated meteorologically realistic, drier-than-observed rainfall scenarios for two Amazon forest sites, Paracou (wetter) and Tapajós (drier), to evaluate the impacts of more frequent droughts on forest biomass, structure and composition. The wet site was insensitive to the tested scenarios, whereas at the dry site biomass declined when average rainfall reduction exceeded 15%, due to high mortality of large-sized evergreen trees. Biomass losses persisted when year-long drought recurrence was shorter than 2–7 yr, depending upon soil texture and leaf phenology. From the site-level scenario results, we developed regionally applicable metrics to quantify the Amazon forest's climatological proximity to rainfall regimes likely to cause biomass loss > 20% in 50 yr according to ED2 predictions. Nearly 25% (1.8 million km2) of the Amazon forests could experience frequent droughts and biomass loss if mean annual rainfall or interannual variability changed by 2σ. At least 10% of the high-emission climate projections (CMIP5/RCP8.5 models) predict critically dry regimes over 25% of the Amazon forest area by 2100.

All known phototrophic metabolisms on Earth are based on one of three energy-converting pigments: chlorophyll- a , bacteriochlorophyll- a , and retinal, which is the chromophore in rhodopsins. While the significance of chlorophylls in global energy flows and marine carbon cycling has been studied for decades, the contribution of retinal-based phototrophy remains largely unexplored. This is despite the fact that hundreds of rhodopsins (including proteorhodopsins) have been identified across a broad phylogenetic range of microorganisms including euryarchaea, proteobacteria, cyanobacteria, fungi, dinoflagellates, and green algae. Most importantly, quantitative field estimates of proteorhodopsin contributions to the solar energy flux captured by marine microorganisms are still lacking. Here, we report the first vertical distributions of the three energy-converting pigments (chlorophyll- a , bacteriochlorophyll- a and retinal) measured along a contrasting nutrient gradient through the Mediterranean Sea and the Eastern Atlantic Ocean. The highest concentrations of proteorhodopsin were observed above the deep chlorophyll- a maxima, and their geographical distribution tended to be inversely related to that of chlorophyll- a , with highest levels in the oligotrophic Eastern Mediterranean. We further show that proteorhodopsins potentially absorb as much or more light energy than chlorophyll- a -based phototrophy and that their cellular energy yield was sufficient to sustain bacterial basal metabolism. Our results suggest that ubiquitous proteorhodopsin-containing heterotrophs are important contributors to the light energy captured in the sea. Given the predicted expansion of oligotrophy in response to global warming, the ecological role of marine proteorhodopsins could even increase in the future oceans.

Oceanic time series have been instrumental in providing an understanding of biological, physical, and chemical dynamics in the oceans and how these processes change over time. However, the extrapolation of these results to larger oceanographic regions requires an understanding and characterization of local versus regional drivers of variability. Here we use high-frequency spatial and temporal glider data to quantify variability at the coastal San Pedro Ocean Time-series (SPOT) site in the San Pedro Channel (SPC) and provide insight into the underlying oceanographic dynamics for the site. The dataset could be described by a combination of four water column profile types that typified active upwelling, a surface bloom, warm-stratified low-nutrient conditions, and a subsurface chlorophyll maximum. On weekly timescales, the SPOT station was on average representative of 64 % of profiles taken within the SPC. In general, shifts in water column profile characteristics at SPOT were also observed across the entire channel. On average, waters across the SPC were most similar to offshore profiles, suggesting that SPOT time series data would be more impacted by regional changes in circulation than local coastal events. These results indicate that high-resolution in situ glider deployments can be used to quantify major modes of variability and provide context for interpreting time series data, allowing for broader application of these datasets and greater integration into modeling efforts.