Data_Sheet_1_Evapotranspiration and its partitioning during and following a mountain pine beetle infestation of a lodgepole pine stand in the interior of British Columbia, Canada.docx
Bibliographic record
Abstract
Introduction Massive tree mortality events in western Canada due to widespread infestation by mountain pine beetle (MPB) are expected to impact local-to-regional evapotranspiration (ET) dynamics during and after a disturbance. How ecosystem-level ET and its components may vary with canopy-tree mortality (treefall) and subsequent understory recovery remains unclear. Methods We used 10 years of continuous eddy-covariance and remote-sensing data (2007–2016) and machine-learning models based on random forest and xgboost to determine forest- and climate-driven effects at temporal scales appropriate for a lodgepole pine-dominated stand following a major, five-year MPB disturbance initiated in the summer of 2006. Results Total annual ET over the 10 years ranged from 207.2 to 384.6 mm, with annual plant transpiration (T) contributing to 57 ± 5.4% (mean ± standard deviation) of annual ET. Annual ET initially declined (2007–2011) and then increased (2011–2016), with ET and T/ET increasing at statistically non-significant rates of approximately 3.2 and 1.2% per year from 2007 to 2016. Air temperature (T<sub>a</sub>) and vapor pressure deficit (VPD) were the most important predictors of seasonal variation in ET and T/ET during the 10-year period, with high T<sub>a</sub>, VPD, and photosynthetically active radiation (PAR) causing ET and T/ET to increase. Annual ET increased with both increasing spring T<sub>a</sub> and decreasing VPD. Annual T/ET was shown to increase with increasing VPD and decrease with increasing volumetric soil water content at a 5-cm depth (VWC<sub>5</sub>). Enhanced vegetation index (EVI, an indicator of canopy greenness) lagged T and overstory tree mortality, whereas previous- and current-year values of EVI were shown to be poor predictors of annual ET and T/ET. Discussion and conclusions These findings suggest that the promotion of climate factors on forest ecosystem-level water vapor fluxes may offset reductions promoted by MPB outbreaks. Climate processes affected water vapor fluxes more than biotic factors, like stand greenness, highlighting the need to include climate-regulatory mechanisms in predictive models of ET dynamics during and subsequent to stand disturbance. Climate and forest-greenness effects on water vapor fluxes need to be explored at even longer time scales, e.g., at decadal scales, to capture long-drawn-out trends associated with stand disturbance and its subsequent recovery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".