318 research outputs found

    Multi-nutrient, multi-group model of present and future oceanic phytoplankton communities

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    International audiencePhytoplankton community composition profoundly affects patterns of nutrient cycling and the dynamics of marine food webs; therefore predicting present and future phytoplankton community structure is crucial to understand how ocean ecosystems respond to physical forcing and nutrient limitations. We develop a mechanistic model of phytoplankton communities that includes multiple taxonomic groups (diatoms, coccolithophores and prasinophytes), nutrients (nitrate, ammonium, phosphate, silicate and iron), light, and a generalist zooplankton grazer. Each taxonomic group was parameterized based on an extensive literature survey. We test the model at two contrasting sites in the modern ocean, the North Atlantic (North Atlantic Bloom Experiment, NABE) and subarctic North Pacific (ocean station Papa, OSP). The model successfully predicts general patterns of community composition and succession at both sites: In the North Atlantic, the model predicts a spring diatom bloom, followed by coccolithophore and prasinophyte blooms later in the season. In the North Pacific, the model reproduces the low chlorophyll community dominated by prasinophytes and coccolithophores, with low total biomass variability and high nutrient concentrations throughout the year. Sensitivity analysis revealed that the identity of the most sensitive parameters and the range of acceptable parameters differed between the two sites. We then use the model to predict community reorganization under different global change scenarios: a later onset and extended duration of stratification, with shallower mixed layer depths due to increased greenhouse gas concentrations; increase in deep water nitrogen; decrease in deep water phosphorus and increase or decrease in iron concentration. To estimate uncertainty in our predictions, we used a Monte Carlo sampling of the parameter space where future scenarios were run using parameter combinations that produced acceptable modern day outcomes and the robustness of the predictions was determined. Change in the onset and duration of stratification altered the timing and the magnitude of the spring diatom bloom in the North Atlantic and increased total phytoplankton and zooplankton biomass in the North Pacific. Changes in nutrient concentrations in some cases changed dominance patterns of major groups, as well as total chlorophyll and zooplankton biomass. Based on these scenarios, our model suggests that global environmental change will inevitably alter phytoplankton community structure and potentially impact global biogeochemical cycles

    Cell size, genome size, and maximum growth rate are near-independent dimensions of ecological variation across bacteria and archaea.

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    Among bacteria and archaea, maximum relative growth rate, cell diameter, and genome size are widely regarded as important influences on ecological strategy. Via the most extensive data compilation so far for these traits across all clades and habitats, we ask whether they are correlated and if so how. Overall, we found little correlation among them, indicating they should be considered as independent dimensions of ecological variation. Nor was correlation evident within particular habitat types. A weak nonlinearity (6% of variance) was found whereby high maximum growth rates (temperature-adjusted) tended to occur in the midrange of cell diameters. Species identified in the literature as oligotrophs or copiotrophs were clearly separated on the dimension of maximum growth rate, but not on the dimensions of genome size or cell diameter

    Multi-nutrient, multi-group model of present and future oceanic phytoplankton communities

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    Phytoplankton community composition profoundly affects patterns of nutrient cycling and the dynamics of marine food webs; therefore predicting present and future phytoplankton community structure is crucial to understand how ocean ecosystems respond to physical forcing and nutrient limitations. We develop a mechanistic model of phytoplankton communities that includes multiple taxonomic groups (diatoms, coccolithophores and prasinophytes), nutrients (nitrate, ammonium, phosphate, silicate and iron), light, and a generalist zooplankton grazer. Each taxonomic group was parameterized based on an extensive literature survey. We test the model at two contrasting sites in the modern ocean, the North Atlantic (North Atlantic Bloom Experiment, NABE) and subarctic North Pacific (ocean station Papa, OSP). The model successfully predicts general patterns of community composition and succession at both sites: In the North Atlantic, the model predicts a spring diatom bloom, followed by coccolithophore and prasinophyte blooms later in the season. In the North Pacific, the model reproduces the low chlorophyll community dominated by prasinophytes and coccolithophores, with low total biomass variability and high nutrient concentrations throughout the year. Sensitivity analysis revealed that the identity of the most sensitive parameters and the range of acceptable parameters differed between the two sites. We then use the model to predict community reorganization under different global change scenarios: a later onset and extended duration of stratification, with shallower mixed layer depths due to increased greenhouse gas concentrations; increase in deep water nitrogen; decrease in deep water phosphorus and increase or decrease in iron concentration. To estimate uncertainty in our predictions, we used a Monte Carlo sampling of the parameter space where future scenarios were run using parameter combinations that produced acceptable modern day outcomes and the robustness of the predictions was determined. Change in the onset and duration of stratification altered the timing and the magnitude of the spring diatom bloom in the North Atlantic and increased total phytoplankton and zooplankton biomass in the North Pacific. Changes in nutrient concentrations in some cases changed dominance patterns of major groups, as well as total chlorophyll and zooplankton biomass. Based on these scenarios, our model suggests that global environmental change will inevitably alter phytoplankton community structure and potentially impact global biogeochemical cycles

    Clot Characterization in Acute Ischemic Stroke

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    Background: In the treatment of acute ischemic stroke (AIS) with mechanical thrombectomy, revascularization depends upon integration of the thrombus into the retrieval device. The histologic and mechanical characteristics of thrombi are key determinants of effective thrombus-device interaction. Thrombi with greater calcium and fibrin content have been associated with more challenging thrombus retrievals. Objective: To develop thrombus analogs with histologic and mechanical characteristics similar to those of challenging clinical thrombi for thrombectomy device testing. Methods: Fifty thrombi were retrieved from twenty-nine patients with AIS. Clinical thrombi underwent histologic analysis to determine erythrocyte and fibrin content. Nine clinical thrombi underwent dynamic mechanical analysis to determine thrombus stiffness, which was defined as a function of stress variation at low and high strains. Results from the clinical thrombi were used to determine the key mechanical characteristics of the challenging thrombus analogs, the calcium apatite-rich and fibrin-rich thrombus analogs. Results: Of the twenty-nine AIS cases, fifteen required multiple pass attempts. The average histologic composition of the challenging clinical thrombi was 26% erythrocyte, 54% fibrin, and 20% mixed. The average stiffness of the challenging clinical thrombi was found to be similar to that of the fibrin-rich thrombus analogs. Addition of calcium apatite increased the stiffness of the thrombus analogs at low strain approximately five-fold. Conclusions: Thrombus analogs with mechanical characteristics similar to those of challenging clinical thrombi were successfully developed. The calcium apatite-rich thrombus analogs were found to be stiffer than the fibrin-rich red thrombus analogs

    Modeling Algae Self-Replenishment

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    This paper presents a sunlight-dependent algae growth model. Driven by the circumstances surrounding Lake Chapala, Mexico, this theoretical model is an endeavor to understand the resilient sustainability of algae that threatens the area’s ecosystem. In this paper, free-floating algae (phytoplankton) are treated as two distinct populations according to their location in the body of water: the vibrant sunlit upper region and the stagnate lower region where photosynthesis is not possible. The numerical solution for the model is analyzed and results are discussed in light of previous studies and the state of Lake Chapala

    Fluctuation induces evolutionary branching in a modeled microbial ecosystem

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    The impact of environmental fluctuation on species diversity is studied with a model of the evolutionary ecology of microorganisms. We show that environmental fluctuation induces evolutionary branching and assures the consequential coexistence of multiple species. Pairwise invasibility analysis is applied to illustrate the speciation process. We also discuss how fluctuation affects species diversity.Comment: 4 pages, 4 figures. Submitted to Physical Review Letter

    Potential of microbiome-based solutions for agrifood systems

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    Host-associated microbiomes are central to food production systems and human nutrition and health. Harnessing the microbiome may help increase food and nutrient security, enhance public health, mitigate climate change and reduce land degradation. Although several microbiome solutions are currently under development or commercialized in the agrifood, animal nutrition, biotechnology, diagnostics, pharmaceutical and health sectors , fewer products than expected have been successfully commercialized beyond food processing, and fewer still have achieved wider adoption by farming, animal husbandry and other end-user communities. This creates concerns about the translatability of microbiome research to practical applications. Inconsistent efficiency and reliability of microbiome solutions are major constraints for their commercialization and further development, and demands urgent attention

    A synthesis of bacterial and archaeal phenotypic trait data

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    A synthesis of phenotypic and quantitative genomic traits is provided for bacteria and archaea, in the form of a scripted, reproducible workflow that standardizes and merges 26 sources. The resulting unified dataset covers 14 phenotypic traits, 5 quantitative genomic traits, and 4 environmental characteristics for approximately 170,000 strain-level and 15,000 species-aggregated records. It spans all habitats including soils, marine and fresh waters and sediments, host-associated and thermal. Trait data can find use in clarifying major dimensions of ecological strategy variation across species. They can also be used in conjunction with species and abundance sampling to characterize trait mixtures in communities and responses of traits along environmental gradients

    Toward a stoichiometric framework for evolutionary biology. Oikos

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    2005. Toward a stoichiometric framework for evolutionary biology. Á/ Oikos 109: 6 Á/17. Ecological stoichiometry, the study of the balance of energy and materials in living systems, may serve as a useful synthetic framework for evolutionary biology. Here, we review recent work that illustrates the power of a stoichiometric approach to evolution across multiple scales, and then point to important open questions that may chart the way forward in this new field. At the molecular level, stoichiometry links hereditary changes in the molecular composition of organisms to key phenotypic functions. At the level of evolutionary ecology, a simultaneous focus on the energetic and material underpinnings of evolutionary tradeoffs and transactions highlights the relationship between the cost of resource acquisition and the functional consequences of biochemical composition. At the macroevolutionary level, a stoichiometric perspective can better operationalize models of adaptive radiation and escalation, and elucidate links between evolutionary innovation and the development of global biogeochemical cycles. Because ecological stoichiometry focuses on the interaction of energetic and multiple material currencies, it should provide new opportunities for coupling evolutionary dynamics across scales from genomes to the biosphere
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