Genetic\nCharacterization of Periphyton Communities\nAssociated with Selenium Bioconcentration and Trophic Transfer in\na Simple Food Chain
Bibliographic record
Abstract
A major source of uncertainty in\npredicting selenium (Se) distribution\nin aquatic food webs lies in the enrichment factor (EF), the ratio\nof Se bioconcentration in primary producers and microorganisms relative\nto the concentration of Se in the surrounding water. It has been well\ndemonstrated that EFs can vary dramatically among individual algal\ntaxa, but data are lacking regarding the influence of periphyton community\ncomposition on EFs for a given geochemical form of Se. Therefore,\nthe goals of this study were first to assess whether different periphyton\ncommunities could be established in aquaria with the same starting\ninoculum using different light and nutrient regimes, and second, to\ndetermine if the periphyton assemblage composition influences the\nuptake of waterborne Se (as selenite) and subsequent Se transfer to\na model macroinvertebrate primary consumer. Periphyton biofilms were\ngrown in aquaria containing filtered pond water (from Saskatoon, SK)\nspiked with approximately 20 μg Se/L (mean measured concentration\n21.0 ± 1.2 μg Se/L), added as selenite. Five different\nlight and nutrient regimes were applied to the aquaria (three replicates\nper treatment) to influence biofilm community development. After 6\nweeks of biofilm maturation, 40 to 80 immature cultured snails (Stagnicola elodes) were added to each aquarium. The\nbacterial and algal members of the periphyton community were characterized\nby targeted metagenomic analyses before and after addition of snails\nto ensure the snails themselves did not significantly alter the biofilm\ncommunity. Samples were collected for Se analysis of water, periphyton,\nand whole-body snail. The nutrient and light treatments resulted in\nsubstantially different compositions of the periphytic biofilms, with\neach being relatively consistent across replicates and throughout\nthe study. Although the aqueous concentration of dissolved Se administered\nto treatments was constant, uptake by the different periphytic biofilms\ndiffered significantly. Both the low-light (61.8 ± 12.1 μg\nSe/g d.w.) and high-light (30.5 ± 4.7 μg Se/g d.w.) biofilms,\nwhich were found to have high proportions of cyanobacteria, contained\nstatistically higher concentrations of Se relative to the other treatments.\nFurthermore, the concentration of Se in bulk periphyton was predictive\nof Se bioaccumulation in grazing snails but as an inverse relationship,\nopposite to expectations. The trophic transfer factor was inversely\ncorrelated with periphyton enrichment factor (<i>r</i> =\n−0.841). A number of different bacterial and algal taxa were\ncorrelated (either positively or negatively) with Se accumulation\nin periphyton biofilm and snails. Recent advancements in genetic methods\nmake it possible to conduct detailed characterization of periphyton\nassemblages and begin to understand the influence that periphyton\ncomposition has on Se biodynamics in aquatic systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".