Publications

Export 3 results:
Sort by: Author Title Type [ Year  (Desc)]
2017
Stuart, RK, Bundy R, Buck K, Ghassemain M, Barbeau K, Palenik B.  2017.  Copper toxicity response influences mesotrophic Synechococcus community structure. Environmental Microbiology. 19:756-769.   10.1111/1462-2920.13630   AbstractWebsite

Picocyanobacteria from the genus Synechococcus are ubiquitous in ocean waters. Their phylogenetic and genomic diversity suggests ecological niche differentiation, but the selective forces influencing this are not well defined. Marine picocyanobacteria are sensitive to Cu toxicity, so adaptations to this stress could represent a selective force within, and between, species', also known as clades. Here, we compared Cu stress responses in cultures and natural populations of marine Synechococcus from two co-occurring major mesotrophic clades (I and IV). Using custom microarrays and proteomics to characterize expression responses to Cu in the lab and field, we found evidence for a general stress regulon in marine Synechococcus. However, the two clades also exhibited distinct responses to copper. The Clade I representative induced expression of genomic island genes in cultures and Southern California Bight populations, while the Clade IV representative downregulated Fe-limitation proteins. Copper incubation experiments suggest that Clade IV populations may harbour stress-tolerant subgroups, and thus fitness tradeoffs may govern Cu-tolerant strain distributions. This work demonstrates that Synechococcus has distinct adaptive strategies to deal with Cu toxicity at both the clade and subclade level, implying that metal toxicity and stress response adaptations represent an important selective force for influencing diversity within marine Synechococcus populations.

2015
Pizeta, I, Sander SG, Hudson RJM, Omanovic D, Baars O, Barbeau KA, Buck KN, Bundy RM, Carrasco G, Croot PL, Garnier C, Gerringa LJA, Gledhill M, Hirose K, Kondo Y, Laglera LM, Nuester J, Rijkenberg MJA, Takeda S, Twining BS, Wells M.  2015.  Interpretation of complexometric titration data: An intercomparison of methods for estimating models of trace metal complexation by natural organic ligands. Marine Chemistry. 173:3-24.   10.1016/j.marchem.2015.03.006   AbstractWebsite

With the common goal of more accurately and consistently quantifying ambient concentrations of free metal ions and natural organic ligands in aquatic ecosystems, researchers from 15 laboratories that routinely analyze trace metal speciation participated in an intercomparison of statistical methods used to model their most common type of experimental dataset, the complexometric titration. All were asked to apply statistical techniques that they were familiar with to model synthetic titration data that are typical of those obtained by applying state-of-the-art electrochemical methods - anodic stripping voltammetry (ASV) and competitive ligand equilibration-adsorptive cathodic stripping voltammetry (CLE-ACSV) - to the analysis of natural waters. Herein, we compare their estimates for parameters describing the natural ligands, examine the accuracy of inferred ambient free metal ion concentrations (]M-f]), and evaluate the influence of the various methods and assumptions used on these results. The ASV-type titrations were designed to test each participant's ability to correctly describe the natural ligands present in a sample when provided with data free of measurement error, i.e., random noise. For the three virtual samples containing just one natural ligand, all participants were able to correctly identify the number of ligand classes present and accurately estimate their parameters. For the four samples containing two or three ligand classes, a few participants detected too few or too many classes and consequently reported inaccurate 'measurements' of ambient [M-f]. Since the problematic results arose from human error rather than any specific method of analyzing the data, we recommend that analysts should make a practice of using one's parameter estimates to generate simulated (back-calculated) titration curves for comparison to the original data. The root-mean-squared relative error between the fitted observations and the simulated curves should be comparable to the expected precision of the analytical method and upon visual inspection the distribution of residuals should not be skewed. Modeling the synthetic, CLE-ACSV-type titration dataset, which comprises 5 titration curves generated at different analytical-windows or levels of competing ligand added to the virtual sample, proved to be more challenging due to the random measurement error that was incorporated. Comparison of the submitted results was complicated by the participants' differing interpretations of their task. Most adopted the provided 'true' instrumental sensitivity in modeling the CLE-ACSV curves, but several estimated sensitivities using internal calibration, exactly as is required for actual samples. Since most fitted sensitivities were biased low, systematic error in inferred ambient [M-f] and in estimated weak ligand (L-2) concentrations resulted. The main distinction between the mathematical approaches taken by participants lies in the functional form of the speciation model equations, with their implicit definition of independent and dependent or manipulated variables. In 'direct modeling', the dependent variable is the measured [M-f] (or I-p) and the total metal concentration ([M](T)) is considered independent In other, much more widely used methods of analyzing titration data - classical linearization, best known as van den Berg/Ruzic and isotherm fitting by nonlinear regression, best known as the langmuir or Gerringa methods - [M-f] is defined as independent and the dependent variable calculated from both [M](T) and [M-f]. Close inspection of the biases and variability in the estimates of ligand parameters and in predictions of ambient [M-f] revealed that the best results were obtained by the direct approach. Linear regression of transformed data yielded the largest bias and greatest variability, while non-linear isotherm fitting generated results with mean bias comparable to direct modeling, but also with greater variability. Participants that performed a unified analysis of ACSV titration curves at multiple detection windows for a sample improved their results regardless of the basic mathematical approach taken. Overall, the three most accurate sets of results were obtained using direct modeling of the unified multiwindow dataset, while the single most accurate set of results also included simultaneous calibration. We therefore recommend that where sample volume and time permit, titration experiments for all natural water samples be designed to include two or more detection windows, especially for coastal and estuarine waters. It is vital that more practical experimental designs for multi-window titrations be developed. Finally, while all mathematical approaches proved to be adequate for some datasets, matrix-based equilibrium models proved to be most naturally suited for the most challenging cases encountered in this work, i.e., experiments where the added ligand in ACSV became titrated. The ProMCC program (Omanovic et al., this issue) as well as the Excel Add-in based KINETEQL Multiwindow Solver spreadsheet (Hudson, 2014) have this capability and have been made available for public use as a result of this intercomparison exercise. (C) 2015 The Authors. Published by Elsevier B.V.

2001
Barbeau, K, Kujawinski EB, Moffett JW.  2001.  Remineralization and recycling of iron, thorium and organic carbon by heterotrophic marine protists in culture. Aquatic Microbial Ecology. 24:69-81.   10.3354/ame024069   AbstractWebsite

To characterize trace metal cycling in marine systems as mediated by heterotrophic protists, we conducted a series of laboratory experiments in 2-organism model systems consisting of bacteria and protistan grazers. Trace metal isotopes (Fe-59 and Th-234),C-14, and bulk organic carbon measurements were used to follow the chemical transformation of bacterial carbon and associated trace metals by several different grazer species. Results indicate that grazers were able to cause repartitioning of Th and regeneration of Fe from bacterial prey into the dissolved phase (<0.2 m), even in particle-rich laboratory cultures. For both Th and Fe, protist grazing led to the formation of relatively stable dissolved and colloidal metal-organic species. Metal/carbon ratios of the particle pool in some model systems with grazers were significantly altered, indicating a decoupling of trace metal and organic carbon cycling through the grazing process. Different protist species exhibited substantial variation (up to a factor of 10) in their ability to quantitatively remobilize trace metals from bacterial prey. The implications of these findings for trace metal cycling in marine systems are discussed.