Understory Community Assembly Following Wildfire in Boreal Forests: Shift From Stochasticity to Competitive Exclusion and Environmental Filtering
Bibliographic record
Abstract
Understory vegetation accounts for the majority of plant species diversity and serves as a driver of overstory succession and nutrient cycling in boreal forest ecosystems. However, investigations of the underlying assembly processes of understory vegetation associated with stand development following a wildfire disturbance are rare, particularly in Eurasian boreal forests. In this study, we measured the phylogenetic and functional diversity and trait dispersions of understory communities and tested how these patterns changed with stand age in the Great Xing'an Mountains of Northeastern China. Contrary to our expectation, we found that understory functional traits were phylogenetically convergent. We found that random patterns of phylogenetic, functional, and trait dispersions were dominant for most of our surveyed plots, indicating that stochastic processes may play a crucial role in the determination of understory community assembly. Yet, there was an evidence that understory community assembly was also determined by competitive exclusion and environmental filtering to a certain degree, which was demonstrated by the observed clustered phylogenetic and functional patterns in some plots. Our results showed that phylogenetic diversity significantly decreased, while functional diversity increased with stand age. The observed shift trends in phylogenetic and functional patterns between random to clustering along with stand age, which suggested that understory community assembly shifted from stochasticity to competitive exclusion and environmental filtering. Our study presented a difference to community assembly and species coexistence theories insisted solely on deterministic processes. These findings indicated that Eurasian boreal understory communities may be primarily regulated by stochastic processes, providing complementary evidence that stochastic processes are crucial in the determination of community assembly both in tropical and boreal forests.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".