Idiosyncratic responses to biotic and environmental filters in wood‐inhabiting fungal communities
Bibliographic record
Abstract
Quantification of different processes affecting the assembly of ecological communities remains challenging, especially in species-rich communities. While the role of environmental filtering has generally been well established, fewer studies have experimentally shown how other ecological assembly processes, such as biotic filtering, structure species-rich communities. Here, we studied the relative roles of biotic and environmental filtering in the colonization of wood-inhabiting fungi, a species-rich, highly interactive, and environment-sensitive group of species. We conducted a field experiment where we simulated colonization with inoculations of nine fungal species in habitat patches (i.e., logs) with varying biotic and abiotic conditions. We characterized the local resident communities before the inoculations and the colonization success of the inoculated species after one and two years using DNA metabarcoding. We asked what determined the colonization success of the inoculated species by comparing the predictive performance of alternative models. These models included either only abiotic environmental predictors (i.e., physical log properties) or additionally different aspects of the resident fungal communities (i.e., resident fungal species richness, community composition, and DNA amount) as biotic predictors. While all nine species successfully colonized the logs, the rate of success and the factors explaining their colonization success varied among species. The colonization success of four of the inoculated species was explained mostly by the abiotic environmental variables, while the colonization success of three species was additionally explained by the resident communities. The influential biotic predictors varied from the presence of individual species to the collective presence of multiple species. Finally, for two of the inoculated species, all the models showed poor predictive performance. Our results indicate how environmental and biotic filtering may jointly structure species-rich communities. Overall, the results show that species vary idiosyncratically in their response to biotic and environmental factors, highlighting the need to consider the complexity of species-level responses when predicting community-level changes.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".