Maximum canopy height is associated with community phylogenetic structure in boreal forests
Bibliographic record
Abstract
Abstract Understanding how maximum canopy height is related to forest community assembly is essential yet largely unexplored. Maximum canopy height is affected by competition and abiotic environmental factors through different ecological processes (e.g. niche differentiation and environmental filtering), as well as historical or stochastic factors. However, little has been done to empirically examine the ecological processes that influence maximum canopy height. We set out to examine the relationship between maximum canopy height and community phylogenic structure. We surveyed maximum canopy heights from a regional dataset of forest plots (466 sites of 50 m × 50 m) from the boreal forest of northeastern Alberta, Canada. We then explored the relationships between maximum canopy height as measured by airborne LiDAR (Light Detection and Ranging) and the phylogenetic structure of seed plants, represented by net relatedness index and nearest taxa index. We found stronger phylogenetic clustering among major evolutionary clades for communities with higher maximum canopy height, which implied that environmental filtering by abiotic factors is a driving factor for boreal forests. However, we also found stronger phylogenetic overdispersion within each clade for communities with higher maximum height, indicating more intense niche differentiation. Our results suggest that communities with higher maximum canopy height may have experienced more intense historical abiotic environmental filtering and recent niche differentiation in boreal forests. These findings will contribute to the monitoring and management of forest biodiversity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".