Temperature and light drive physiological and transcriptional responses, modulating infection outcomes in a freshwater diatom–chytrid system
Bibliographic record
Abstract
Abstract Stephanodiscus binderanus is a bloom‐forming diatom abundant in winter and persisting into spring in the Laurentian Great Lakes. Climate change impacts these blooms by altering ice cover, turbidity, light penetration, and water temperature. Fungal parasites, especially Chytridiomycota , can suppress phytoplankton growth and alter bloom succession. To address the effects of both biotic and abiotic factors on S. binderanus in the face of a changing climate, we tested a range of temperatures (9.4–24°C) and light intensities (15, 30, 50, 100 μ mol m −2 s −1 ) on infected and uninfected cultures. We also conducted an RNAseq analysis of both host and parasite across the described environmental parameters. Stephanodiscus binderanus can rapidly adapt to the above conditions, growing slowest at the lowest temperature and under low light, while adjusting its chlorophyll a (Chl a ) content in lower light treatments to be more efficient at light harvesting. Chytrid infections were more prevalent at either end of the tested temperature range, despite downregulation of zoospore mitotic cycle genes at elevated temperatures. Elevated temperatures also induced reproductive stress in S. binderanus , marked by downregulation of meiosis‐related genes. These effects, combined with nutrient depletion, likely contribute to seasonal declines in diatom populations as green algae and cyanobacteria emerge in late spring and early summer. It is anticipated that host response to lower light availability and the ability of the chytrid to infect under warming waters will contribute to a decline in filamentous diatom biomass in Lake Erie, especially as climate change increases the frequency of ice‐free winters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".