Human‐induced changes in US biogenic volatile organic compound emissions: evidence from long‐term forest inventory data
Bibliographic record
Abstract
Abstract Volatile organic compounds (VOCs) emitted by woody vegetation influence global climate forcing and the formation of tropospheric ozone. We use data from over 250 000 re‐surveyed forest plots in the eastern US to estimate emission rates for the two most important biogenic VOCs (isoprene and monoterpenes) in the 1980s and 1990s, and then compare these estimates to give a decadal change in emission rate. Over much of the region, particularly the southeast, we estimate that there were large changes in biogenic VOC emissions: half of the grid cells (1°× 1°) had decadal changes in emission rate outside the range −2.3% to +16.8% for isoprene, and outside the range 0.2–17.1% for monoterpenes. For an average grid cell the estimated decadal change in heatwave biogenic VOC emissions (usually an increase) was three times greater than the decadal change in heatwave anthropogenic VOC emissions (usually a decrease, caused by legislation). Leaf‐area increases in forests, caused by anthropogenic disturbance, were the most important process increasing biogenic VOC emissions. However, in the southeast, which had the largest estimated changes, there were substantial effects of ecological succession (which decreased monoterpene emissions and had location‐specific effects on isoprene emissions), harvesting (which decreased monoterpene emissions and increased isoprene emissions) and plantation management (which increased isoprene emissions, and decreased monoterpene emissions in some states but increased monoterpene emissions in others). In any given region, changes in a very few tree species caused most of the changes in emissions: the rapid changes in the southeast were caused almost entirely by increases in sweetgum (Liquidambar styraciflua) and a few pine species. Therefore, in these regions, a more detailed ecological understanding of just a few species could greatly improve our understanding of the relationship between natural ecological processes, forest management, and biogenic VOC emissions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".