Measuring and predicting the influence of traits on the assembly processes of wood‐inhabiting fungi
Bibliographic record
Abstract
Summary The identification of traits that influence the responses of the species to environmental variation provides a mechanistic perspective on the assembly processes of ecological communities. While much research linking functional ecology with assembly processes has been conducted with animals and plants, the development of predictive or even conceptual frameworks for fungal functional community ecology remains poorly explored. Particularly, little is known about the contribution of traits to the occurrences of fungal species under different environmental conditions. Wood‐inhabiting fungi are known to strongly respond to habitat disturbance, and thus provide an interesting case study for investigating to what extent variation in occurrence patterns of fungi can be related to traits. We apply a trait‐based joint species distribution model to a data set consisting of fruit‐body occurrence data on 321 wood‐inhabiting fungal species collected in 22 460 dead wood units from managed and natural forest sites. Our results show that environmental filtering plays a big role on shaping wood‐inhabiting fungal communities, as different environments held different communities in terms of species and trait compositions. Most importantly, forest management selected against species with large and long‐lived fruit‐bodies as well as late decayers, and promoted the occurrences of species with small fruit‐bodies and early decayers. A strong phylogenetic signal in the data suggested the existence of also some other functionally important traits than the ones we considered. We found that those species groups that were more prevalent in natural conditions had more associations to other species than species groups that were tolerant to or benefitted from forest management. Therefore, the changes that forest management causes on wood‐inhabiting fungal communities influence ecosystem functioning through simplification of interactive associations among the fungal species. Synthesis. Our results show that functional traits are linked to the responses of wood‐inhabiting fungi to variation in their environment, and thus environmental changes alter ecosystem functions via promoting or reducing species with different fruit‐body types. However, further research is needed to identify other functional traits and to provide conclusive evidence for the adaptive nature of the links from traits to occurrence patterns found here.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".