Impacts of vegetation greening and climate change on trend and interannual variability in vegetation productivity in the Wuyi Mountain region
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Gross Primary Productivity (GPP) is an important component in regional and global carbon budgets. Southeastern China has experienced vegetation greening and climate change. Yet, it remains unclear how these changes have impacted GPP in this region. As one of six special national parks in China with complex topography, high biodiversity, and little destructive human activities to the ecosystems, the Wuyi Mountain region is selected to study these impacts. In this study, we use a hydroecological model (BEPS-TerrainLab V2.0) to simulate the spatial and temporal variations of GPP in the Wuyi Mountain region over 2001–2018. We quantitatively separate the effects of vegetation greening and climate change on the trend and interannual variation in GPP through sensitivity experiments. The results show a significant increasing trend in Leaf Area Index (LAI) in the region over 2001–2018 (0.06 m2 m−2 yr−1, p < 0.01). For climate, a significant warming trend (0.03°C yr−1, p = 0.06) and an insignificant wetting trend are found, companied with large interannual variations. The sensitivity experiments suggest that the combined effect of vegetation greening and climate change makes the annual GPP increase significantly over 2001–2018 (14.41 g C m−2 yr−2, p < 0.01). Vegetation greening plays a dominant role in the GPP increasing trend with a positive contribution of 15.76 g C m−2 yr−2. Climate change only makes an insignificant negative contribution (−0.43 g C m−2 yr−2), mainly due to warming. However, the climate modulates the interannual variation of GPP dominantly, with temperature being the most influential climate factor. Our results underscore the critical impact of vegetation greening on the GPP trend and the impact of climate on the GPP interannual variation in this subtropical forest region over east coast of China.
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.
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 | 0.002 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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 it