Changing Sensitivity of Diverse Tropical Biomes to Precipitation Consistent with the Expected Carbon Dioxide Fertilization Effect
Bibliographic record
Abstract
Abstract Global environmental changes have implications for the terrestrial ecosystem functioning, but disentangling individual effects remains elusive. The impact of vegetation responses to increasing atmospheric CO 2 concentrations is particularly poorly understood. As the atmospheric CO 2 concentration increases, the CO 2 acts as a fertilizer for plant growth. An increase in atmospheric CO 2 reduces the amount of water needed to produce an equivalent amount of biomass due to closing or a narrowing of the stomata that reduces the amount of water that is transpired by plants. To study the impacts of climate change and CO 2 fertilization on plant growth, we analyzed the growing season sensitivity of plant growth to climatic forcing from alpine to semi-desert eco-climatic zones of Ethiopia for various plant functional types over the period of 1982–2011. Growing season 3 rd generation Normalized Difference Vegetation Index of Global Inventory Modeling and Mapping Studies (NDVI) was used as a proxy of plant growth, while mean growing season precipitation (prec), temperature (temp), and solar radiation (sr) as the climate forcing. The sensitivities of plant growth are calculated as a partial correlation, and a derivative of NDVI with respect to prec, temp and sr for earliest and recent 15-year periods of the satellite records, and using a moving window of 15-year. Our results show increasing trends of plant growth that are not explained by any climate variables. We also find that an equivalent increase in prec leads to a larger increase in NDVI since the 1980s. This result implies a given amount of prec has sustained greater amounts of plant foliage materials over time due to decreasing transpiration with increasing CO 2 concentration as expected from the CO 2 fertilization effect on water use efficiency and plant growth. Increasing trends of growth in shallow-rooted vegetation tend to be associated with woody vegetation encroachment.
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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 | 0.001 |
| 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 | 0.000 | 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 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".