How do seasonality, substrate, and management history influence macrofungal fruiting assemblages in a central Amazonian Forest?
Bibliographic record
Abstract
Abstract Worldwide, fungal richness peaks in tropical forest biomes where they are the primary drivers of decomposition. Understanding how environmental and anthropogenic factors influence tropical macrofungal fruiting patterns should provide insight as to how, for example, climate change and deforestation may impact their long‐term demographic stability and evolutionary potential. However, in Amazonia no studies have yet to disentangle the effects of substrate, seasonality and forest history on phenology. Here, we quantitate spatial and temporal variation in community structure of fruiting macrofungi in relation to these factors at a long‐term forest management research site in central Amazonia: the Biomass and Nutrients of Tropical Rain Forest ( BIONTE 's). Basidiome surveys of four substrate classes (leaves, soil, branches and trunks) were conducted along 250 m 2 transects in primary and secondary (managed) forests, between 2012–13. From the 669 basidiomes collected, 290 taxa were identified of which 44 percent were restricted to primary and 36 percent to secondary forests. Although species‐accumulation curves did not asymptote, rarefaction analyses and Fisher's alpha indicate contrasting differences in richness among forests in relation to substrate type. For example, leaf litter basidiome richness was higher in secondary forests, whereas the contrary was observed for soil communities, suggesting that variation in fruiting patterns in relation to disturbance is substrate‐dependent possibly due to differences in necromass quality and/or understory micro‐climates. Furthermore, secondary forests harbored significantly lower basidiome richness and abundance in dry months, suggesting synergistic impacts of seasonality and management history on fruiting regimes.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".