Changes and responses of GPP among different plant functional types in a savanna ecosystem under future climate scenarios
Bibliographic record
Abstract
The carbon cycling within savanna ecosystem (SE) is highly sensitive to climate change, and the impact of future climate on the gross primary productivity (GPP) of various plant functional types (PFTs) remains unclear. However, there is a lack of effective methods for simulating the GPP of different PFTs within the SE currently. We employ a method, based on eddy covariance GPP of the Yuanjiang savanna ecosystem (YJSE) and the BIOME-BGCMuSo model, that simultaneously calibrates the parameters of the four PFTs: deciduous shrubs (shrub_dc), evergreen shrubs (shrub_eg), grasses (grass), and deciduous broadleaf forests (dbf), to investigate the impact of future climate scenarios on the GPP of the YJSE and its individual PFTs. The results indicate: 1) Under the SSP1−2.6 scenario, YJSE GPP tends to stabilise, peaking at 1032.03 gC m −2 yr −1 by 2068, whereas YJSE GPP may increase with rainfall under the SSP2−4.5 and SSP5−8.5 scenarios. 2) Under the same scenarios, evergreen shrubs display lower sensitivity to climate change compared to the other two deciduous vegetation types. Additionally, climate change under the SSP2−4.5 scenario has a more significant impact on GPP in this region. 3) In the future SSP2−4.5 and SSP5−8.5 scenarios, the GPP contribution rate of grasses shows an upward trend, while those of the other three PFTs are declining, which potentially indicates YJSE may exhibit a trend of forest degradation. This study creatively separated the GPP of the four PFTs within YJSE and revealed their future trends under various scenarios. This provides new insights into the responses of photosynthesis in complex ecosystems, such as savannas, to future climate changes.
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 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".