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Record W4404438179 · doi:10.1016/j.fecs.2024.100275

Sensitivity of gross primary production and evapotranspiration to heat and drought stress in a young temperate plantation in northern China

2024· article· en· W4404438179 on OpenAlexaff
Hongxian Zhao, Zeyuan Zhou, Feng Zhang, Charles P.‐A. Bourque, Xin Jia, Xinhao Li, Peng Liu, Haiqun Yu, Yun Tian, Chuan Jin, Shaorong Hao, Tianshan Zha

Bibliographic record

VenueForest Ecosystems · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsUniversity of New Brunswick
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsTemperate climateEvapotranspirationEnvironmental sciencePrimary productionChinaProduction (economics)Heat stressEcosystemForestryAgroforestryGeographyAtmospheric sciencesEcologyGeologyEconomics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.176

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.188
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2024
Admission routes1
Has abstractyes

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