Temperature and Photoperiod Affect the Sensitivity of Biofilms to Nickel and its Accumulation
Bibliographic record
Abstract
Whereas metal impacts on fluvial communities have been extensively investigated, effects of abiotic parameters on community responses to contaminants are poorly documented. Variations in photoperiod and temperature commonly occur over the course of a season and could affect aquatic biofilm communities and their responses to contaminants. Our objective was to characterize the influence of environmental conditions (photoperiod and temperature) on nickel (Ni) bioaccumulation and toxicity using a laboratory-grown biofilm. Environmental parameters were chosen to represent variations that can occur over the summer season. Biofilms were exposed for 7 days to six dissolved Ni treatments (ranging from 6 to 115 µM) at two temperatures (14 and 20 °C) using two photoperiods (16:8 and 12:12-h light:dark cycle). Under these different scenarios, structural (dry weight biomass and chlorophyll-a) and functional biomarkers (photosynthetic yield and Ni content) were analyzed at four sampling dates, allowing us to evaluate Ni sensitivity of biofilms over time. The results highlight the effects of temperature on Ni accumulation and tolerance of biofilms. Indeed, biofilms exposed at 20 °C accumulated 1.6-4.2-fold higher concentrations of Ni and were characterized by a lower median effect concentration value using photosynthetic yield compared with those exposed at 14 °C. In terms of photoperiod, significantly greater rates of Ni accumulation were observed at the highest tested Ni concentration for biofilms exposed to a 12:12-h compared with a 16:8-h light:dark cycle. Our study demonstrates the influence of temperature on biofilm metabolism and illustrates that environmental factors may influence Ni accumulation response and thus Ni responses of phototrophic biofilms. Environ Toxicol Chem 2022;41:1649-1662. © 2022 SETAC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".