Sensitivity of gross primary production and evapotranspiration to heat and drought stress in a young temperate plantation in northern China
Bibliographic record
Abstract
Assessing the sensitivities of ecosystem functions to climatic factors is essential to understanding the response of ecosystems to environmental change. Temperate plantation forests contribute to global greening and climate change mitigation, yet little is known as to the sensitivity of gross primary production (GPP) and evapotranspiration (ET) of these forests to heat and drought stress. Based on near-continuous, eddy-covariance and hydrometeorological data from a young temperate plantation forest in Beijing, China (2012–2019), we used a sliding-window-fitting technique to assess the seasonal and interannual variation in ecosystem sensitivity (i.e., calculated slopes, S GPP-Ta , S ET-Ta , S GPP-EF , and S ET-EF ) in GPP and ET to anomalies in air temperature ( T a ) and evaporative fraction (EF). The EF was used here as an indicator of drought. Seasonally, daily S GPP-Ta , S ET-Ta , and S GPP-EF were greatest in summer, reaching maxima of 1.12 ± 0.56 g C·m −2 ·d −1 ⋅°C −1 , 1.36 ± 0.56 g H 2 O·m −2 ·d −1 ⋅°C −1 , and 0.37 ± 0.35 g C·m −2 ·d −1 , respectively. Evapotranspiration was constrained by drought, especially during the spring-to-summer period, S ET-EF reaching −0.51 ± 0.34 g H 2 O·m −2 ·d −1 . Variables EF, T a , soil water content (SWC), vapor pressure deficit (VPD), and precipitation (PPT) were the main controls of sensitivity, with S GPP-Ta and S ET-Ta increasing with T a , VPD, and PPT (<50 mm·d −1 ) during both spring and autumn. Increased drought stress during summer caused the positive response in GPP and ET to decrease with atmospheric warming. Variable S ET-EF intensified (i.e., became more negative) with decreasing EF and increasing T a . Interannually, annual S GPP-Ta and S ET-Ta were positive, S GPP-EF near-neutral, and S ET-EF negative. Interannual variability in S GPP-Ta , S ET-Ta , S ET-EF , and S GPP-EF was largely due to variations in bulk surface conductance. Our study suggests that the dynamics associated with the sensitivity of ecosystems to changes in climatic factors need to be considered in the management of plantation forests under future global climate change. • We assessed S GPP-Ta , S GPP-EF , S ET-Ta and S ET-EF in a plantation forest over 2012–2019. • EF, T a , SWC, VPD, and PPT mainly controlled seasonal S GPP-Ta , S GPP-EF , S ET-Ta and S ET-EF . • Annual S GPP-Ta and S ET-Ta were positive, S GPP-EF near 0.0 and S ET-EF was negative. • G s promoted S GPP-Ta , S GPP-EF , S ET-Ta and S ET-EF at interannual timescales. • Drought stress had greater negative effect on the plantation forest than heat stress.
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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 | 0.000 | 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".