Long‐term trend and interannual variation in evapotranspiration of a young temperate Douglas‐fir stand over 2002–2022 reveals the impacts of climate change
Bibliographic record
Abstract
Abstract The shortage of decades‐long continuous measurements of ecosystem processes limits our understanding of how changing climate impacts forest ecosystems. We used continuous eddy‐covariance and hydrometeorological data over 2002–2022 from a young Douglas‐fir stand on Vancouver Island, Canada to assess the long‐term trend and interannual variability in evapotranspiration (ET) and transpiration (T). Collectively, annual T displayed a decreasing trend over the 21 years with a rate of 1% yr−1, which is attributed to the stomatal downregulation induced by rising atmospheric CO2 concentration. Similarly, annual ET also showed a decreasing trend since evaporation stayed relatively constant. Variability in detrended annual ET was mostly controlled by the average soil water storage during the growing season (May–October). Though the duration and intensity of the drought did not increase, the drought‐induced decreases in T and ET showed an increasing trend. This pattern may reflect the changes in forest structure, related to the decline in the deciduous understory cover during the stand development. These results suggest that the water‐saving effect of stomatal regulation and water‐related factors mostly determined the trend and variability in ET, respectively. This may also imply an increase in the limitation of water availability on ET in young forests, associated with the structural and compositional changes related to forest growth.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".