Dramatic increase in water use efficiency with cumulative forest disturbance at the large forested watershed scale
Bibliographic record
Abstract
Abstract Background Forest disturbance induced changes in the coupling of forest carbon and water have important implications for ecosystem functioning and sustainable forest management. However, this is rarely investigated at the large watershed scale with cumulative forest disturbance. We used a combination of techniques including modeling, statistical analysis, and machine learning to investigate the effects of cumulative forest disturbance on water use efficiency (WUE, a proxy for carbon and water coupling) in the 19,200 km 2 Chilcotin watershed situated in the central interior of British Columbia, Canada. Harvesting, wildfire, and a severe Mountain Pine Beetle (MPB) infestation have gradually cumulated over the 45-year study period, and the watershed reached a cumulative equivalent clear-cut area of 10% in 1999 and then 40% in 2016. Results Surprisingly, with the dramatic forest disturbance increase from 2000 to 2016 which was mainly due to MPB, watershed-level carbon stocks and sequestration showed an insignificant reduction. This resilience was mainly due to landscape-level carbon dynamics that saw a balance between a variety of disturbance rates and types, an accumulation of older stand types, and fast growing young regenerated forests. Watershed-level carbon sequestration capacity was sustained, measured by Net Primary Production (NPP). A concurrent significant decrease in annual evapotranspiration (ET), led to a 19% increase in WUE (defined as the ratio of NPP to ET), which is contrary to common findings after disturbance at the forest stand-level. During this period of high disturbance, ET was the dominant driver of the WUE increase. Conclusions We conclude that disturbance-driven forest dynamics and the appropriate scale must be considered when investigating carbon and water relationship. In contrast to the stand-level trade-off relationship between carbon and water, forested watersheds may be managed to maintain timber, carbon and water resources across large landscapes.
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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 | 0.000 | 0.001 |
| 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".