Stream-dwelling fungal decomposer communities along a gradient of eutrophication unraveled by 454 pyrosequencing
Bibliographic record
Abstract
Microbial decomposers, especially a fungal group called aquatic hyphomycetes, play a critical role in processing plant litter in freshwaters by increasing its palatability to invertebrate shredders. Traditionally, communities of aquatic hyphomycetes have been assessed through the identification of spores, which misses non-sporulating taxa. Among new technologies, 454 pyrosequencing stands out as most promising for large-scale species identification. However, very few attempts have been made to validate its effectiveness for assessing the diversity of stream-dwelling fungal communities. We attempted to gain greater insight into the diversity of aquatic fungal communities in streams exposed to various degrees of eutrophication by using the 454 pyrosequencing technology. A total of 173,889 ITS2 pyrosequencing reads with hits for fungi were obtained from the 5 investigated streams. The majority of operational taxonomic units (OTUs) belonged to Ascomycota and the identification to the genus level was achieved for 169 OTUs. Of the total, 135,257 reads (ca. 78 %) showed close affinities to aquatic hyphomycete species. Pyrosequencing showed declining fungal diversity in the most eutrophic streams, which was congruent with a reduced diversity found through spore identification. Dominance patterns revealed by connecting representative OTUs to ITS sequences from aquatic hyphomycetes were similar to those determined by traditional spore identification techniques. However, 454 pyrosequencing provided a more comprehensive view of fungal diversity; it captured almost twice as many taxa as spore counts. This study validates the effectiveness of 454 pyrosequencing for surveying the diversity of stream-dwelling fungal decomposer communities. Its application may accelerate the use of these communities for monitoring the integrity of freshwaters.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".