Wind Is a Primary Driver of Fungal Dispersal Across a Mainland‐Island System
Bibliographic record
Abstract
Dispersal is one of the main processes shaping ecological communities. Yet, for species-rich communities in natural systems, the role of dispersal in community assembly remains relatively less studied compared to other processes. This is the case for fungal communities, for which predictable knowledge about where and how the dispersal propagules move across space is largely lacking. We sampled fungal communities at their dispersal stage in a lake mainland-island system in Finland, using a regular grid of 18 × 18 km, including sites on the mainland, islands and over the water. Fungal communities were screened by applying DNA barcoding to air samples. To assess the factors determining fungal dispersal, we modelled aerial fungal communities with a joint species distribution model, including spore traits, weather-related predictors, and spatial predictors. We found that the probability of occurrence of most species (and consequently species richness measured as the number of OTUs per sample) was lower in low-connectivity sites (water and isolated islands) compared to high-connectivity sites (mainland). There was a strong phylogenetic signal in how the fungal species responded to connectivity, indicating that some taxonomic groups are more dispersal limited than others, although such responses were not structured by their trophic guilds. Furthermore, wind speed influenced how species with different spore sizes responded to connectivity: in low-connectivity sites, species with large sexual spores were detected especially when wind was high, whereas, in high-connectivity sites, they were detected especially when wind was low. This study demonstrates that air fungal dispersal might be more predictable than previously considered and contributes to the mechanistic understanding of fungal air dispersal.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".