Taxa-area relationship of aquatic fungi on deciduous leaves
Bibliographic record
Abstract
One of the fundamental patterns in macroecology is the increase in the number of observed taxa with size of sampled area. For microbes, the shape of this relationship remains less clear. The current study assessed the diversity of aquatic fungi, by the traditional approach based on conidial morphology (captures reproducing aquatic hyphomycetes) and next generation sequencing (NGS; captures other fungi as well), on graded sizes of alder leaves (0.6 to 13.6 cm2). Leaves were submerged in two streams in geographically distant locations: the Oliveira Stream in Portugal and the Boss Brook in Canada. Decay rates of alder leaves and fungal sporulation rates did not differ between streams. Fungal biomass was higher in Boss Brook than in Oliveira Stream, and in both streams almost 100% of the reads belonged to active fungal taxa. In general, larger leaf areas tended to harbour more fungi, but these findings were not consistent between techniques. Morphospecies-based diversity increased with leaf area in Boss Brook, but not in Oliveira Stream; metabarcoding data showed an opposite trend. The higher resolution of metabarcoding resulted in steeper taxa-accumulation curves than morphospecies-based assessments (fungal conidia morphology). Fungal communities assessed by metabarcoding were spatially structured by leaf area in both streams. Metabarcoding promises greater resolution to assess biodiversity patterns in aquatic fungi and may be more accurate for assessing taxa-area relationships and local to global diversity ratios.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".