Metabarcoding-based fungal diversity on coarse and fine particulate organic matter in a first-order stream in Nova Scotia, Canada
Bibliographic record
Abstract
Most streams receive substantial inputs of allochthonous organic material in the form of leaves and twigs (CPOM , coarse particulate organic matter). Mechanical and biological processing converts this into fine particulate organic matter (FPOM). Other sources of particles include flocculated dissolved matter and soil particles. Fungi are known to play a role in the CPOM conversion process, but the taxonomic affiliations of these fungi remain poorly studied. The present study seeks to shed light on the composition of fungal communities on FPOM and CPOM as assessed in a natural stream in Nova Scotia, Canada. Maple leaves were exposed in a stream for four weeks and their fungal community evaluated through pyrosequencing. Over the same period, four FPOM size fractions were collected by filtration and assessed. Particles had much lower ergosterol contents than leaves, suggesting major differences in the extent of fungal colonization. Pyrosequencing documented a total of 821 fungal operational taxonomic units (OTU), of which 726 were exclusive to particles and 47 to leaf samples. Most fungal phyla were represented, including yeast lineages (e.g., Taphrinaceae and Saccharomycotina), Basidiomycota, Chytridiomycota and Cryptomycota, but several classes of Pezizomycontina (Ascomycota) dominated. Cluster dendrograms clearly separated fungal communities from leaves and from particles. Characterizing fungal communities may shed some light on the processing pathways of fine particles in streams and broadens our view of the phylogenetic composition of fungi in freshwater ecosystems.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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; both teacher heads agree on what is shown here.
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".