Increasing net ecosystem biomass production of Canada's boreal and temperate forests despite decline in dry climates
Bibliographic record
Abstract
Abstract Repeated measurements of tree biomass at field plots describe recovery from disturbances, sampling artifacts, and potential effects of environmental change on forest ecosystems. Challenges in differentiating between intrinsic and extrinsic sources of variation, both in theory and in practice, continue to confound claims of an anthropogenic carbon sink in forest biomass. Here we analyzed observations at 10,307 plots across southern ecozones of Canada to investigate temporal trends in stand‐level biomass growth (G), biomass loss due to mortality (M), and net ecosystem biomass production (NEBP) of intact stands. Net extrinsic forcing (Fex) was expressed by the collective dependence of biomass fluxes on climate anomalies, nitrogen deposition (N), and atmospheric carbon dioxide concentration (C). Inferences drawn directly from linear mixed‐effects model coefficients only reflect the static behavior of the model specifically at field plot locations. We, therefore, defined a dynamic landscape‐scale net extrinsic forcing (Fex′), which additionally accounted for potential negative feedback responses to anthropogenic growth enhancement. Simulations were performed over 1501–2012 to estimateFex′. Overall,Fex′ was positive, suggesting that environmental changes drove a 90% increase in NEBP. The increase in NEBP was confined to wet regions, while the biomass sink in dry regions decreased, suggesting that large expanses of northern forests, historically located near the boundary between wet and dry climates, may be at high risk of decline under continued increases in evaporative demand. These results have important implications for the greenhouse gas balance of Canada's forest sector.
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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 | 0.000 | 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".