Aboveground Biomass Allocation of Boreal Shrubs and Short-Stature Trees in Northwestern Canada
Bibliographic record
Abstract
In this follow-on study on aboveground biomass of shrubs and short-stature trees, we provide plant component aboveground biomass (herein ‘AGB’) as well as plant component AGB allometric models for five common boreal shrub and four common boreal short-stature tree genera/species. The analyzed plant components consist of stem, branch, and leaf organs. We found similar ratios of component biomass to total AGB for stems, branches, and leaves amongst shrubs and deciduous tree genera/species across the southern Northwest Territories, while the evergreen Picea genus differed in the biomass allocation to aboveground plant organs compared to the deciduous genera/species. Shrub component AGB allometric models were derived using the three-dimensional variable volume as predictor, determined as the sum of line-intercept cover, upper foliage width, and maximum height above ground. Tree component AGB was modeled using the cross-sectional area of the stem diameter as predictor variable, measured at 0.30 m along the stem length. For shrub component AGB, we achieved better model fits for stem biomass (60.33 g ≤ RMSE ≤ 163.59 g; 0.651 ≤ R2 ≤ 0.885) compared to leaf biomass (12.62 g ≤ RMSE ≤ 35.04 g; 0.380 ≤ R2 ≤ 0.735), as has been reported by others. For short-stature trees, leaf biomass predictions resulted in similar model fits (18.21 g ≤ RMSE ≤ 70.0 g; 0.702 ≤ R2 ≤ 0.882) compared to branch biomass (6.88 g ≤ RMSE ≤ 45.08 g; 0.736 ≤ R2 ≤ 0.923) and only slightly better model fits for stem biomass (30.87 g ≤ RMSE ≤ 11.72 g; 0.887 ≤ R2 ≤ 0.960), which suggests that leaf AGB of short-stature trees (<4.5 m) can be more accurately predicted using cross-sectional area as opposed to diameter at breast height for tall-stature trees. Our multi-species shrub and short-stature tree allometric models showed promising results for predicting plant component AGB, which can be utilized for remote sensing applications where plant functional types cannot always be distinguished. This study provides critical information on plant AGB allocation as well as component AGB modeling, required for understanding boreal AGB and aboveground carbon pools within the dynamic and rapidly changing Taiga Plains and Taiga Shield ecozones. In addition, the structural information and component AGB equations are important for integrating shrubs and short-stature tree AGB into carbon accounting strategies in order to improve our understanding of the rapidly changing boreal ecosystem function.
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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".