Environmental landscape determinants of maximum forest canopy height of boreal forests
Bibliographic record
Abstract
Canopy height is a key driver of forest biodiversity and carbon cycling. Accurate estimates of canopy height are needed for assessing mechanisms relating to ecological patterns and processes of tree height limitations. At global scales forest canopy height patterns are largely controlled by climate, while local variation at fine scales is due to differences in disturbance history and local patterns in environmental conditions. The relative effect of local environmental drivers on canopy height is poorly understood partly due to gaps in data on canopy height and methods for examining limiting factors. Here, we used airborne laser scanning (ALS) data on vegetation structure of boreal forests to examine the effects of environmental factors on potential maximum forest canopy height. Relationships between maximum canopy height from ALS measures and environmental variables were examined to assess factors limiting tree height. Specifically, we used quantile regression at the 0.90 quantile to relate maximum canopy height with environmental characteristics of climate (i.e. mean annual temperature [MAT] and mean annual precipitation), terrain (i.e. slope) and depth-to-water (DTW) across a 33000 km2 multiple use boreal forest landscape in northeast Alberta, Canada. Maximum canopy height was positively associated with MAT, terrain slope and terrain-derived DTW, collectively explaining 33.2% of the variation in heights. The strongest explanatory variable was DTW explaining 26% of canopy height variation with peatland forests having naturally shorter maximum canopy heights, but also more sites currently at their maximum potential height. In contrast, the most productive forests (i.e. mesic to xeric upland forests) had the fewest sites at their potential maximum height, illustrating the effects of long-term forest management, wildfires and general anthropogenic footprints on reducing the extent and abundance of older, taller forest habitat in Alberta’s boreal forest.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".