Trophic state and phytoplankton composition shape lake mycoplankton diversity
Bibliographic record
Abstract
Aquatic fungi play key roles in organic matter decomposition and nutrient cycling, but the effects of lake conditions and food web interactions on fungal diversity are still largely unknown. Our study is the first to assess mycoplankton along a broad lake trophic gradient based on total phosphorus (TP) (2-2500 μg/L) using DNA metabarcoding data from 369 Canadian lakes. Zoosporic fungi, Chytridiomycota in particular, dominated mycoplankton assemblages. Mycoplankton diversity declined ∼15 % along the trophic gradient. Community composition varied the most between oligotrophic and hypereutrophic lakes, with pH, TP and water temperature as main drivers. Notably, mycoplankton communities showed stronger correlations with eukaryotic phytoplankton than with physicochemical variables, underlining the importance of phytoplankton hosts and substrates. Chytrid taxa associated with Chrysophyta in acidic lakes differed from those associated with Chlorophyta and Cryptophyta in lakes within agricultural lands. Overall, our study highlights the essential role of phytoplankton in shaping mycoplankton diversity and communities.
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 | 0.001 | 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".