Cumulative disturbance converts regional forests into a substantial carbon source
Bibliographic record
Abstract
Abstract British Columbia’s interior forests (∼400 000 km2) have experienced severe cumulative disturbance from harvesting, wildfires, and mountain pine beetle (MPB). Estimating their impacts on carbon dynamics is critical for effective forest management and climate-change mitigation strategies. This study quantifies the magnitude of historical cumulative forest disturbances and models the effect on regional carbon stocks and emissions using the Carbon Budget Model of the Canadian Forest Service from 1980 to 2018. The study region has been a sustained carbon source since 2003, with an estimated net biome production of −18.6 ± 4.6 gC m−2 yr−1 from 2003 to 2016, dropping to −90.4 ± 8.6 gC m−2 yr−1 in 2017 and 2018 due to large-scale wildfires. MPB affected areas emitted an estimated 268 ± 28 Mt C from 2000 to 2018. Harvesting transferred an estimated 153 ± 14 Mt C to forest products and these areas also emitted 343 ± 27 Mt C in 2000–2018. Areas disturbed by wildfire from 2000 to 2018 generated an estimated 100 ± 8 Mt C of emissions, 73% of which were from 2017 and 2018. Of the area burned between 2014 and 2018, 38% had been previously affected by MPB, highlighting landscape-level interactions of cumulative forest disturbance. Approximately half of decomposition carbon emissions from disturbances in 2000–2018 were calculated as incremental to the decomposition that would have otherwise occurred without MPB disturbance. The average net primary production was reduced by 10% to 335 ± 31 gC m−2 yr−1 from 2000 to 2018. We conclude that cumulative forest disturbance has driven the region’s forests to become a sustained carbon source over the past two decades. While MPB and harvesting were dominant and consistent drivers, recent severe wildfires have prolonged and strengthened the carbon source. Increased disturbances, driven in part by climate change, may limit the ability of regional forests to meet national carbon emission reduction targets.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".