Productivity of North American grasslands is increased under future climate scenarios despite rising aridity (vol 29, pg 1, 2016)
Bibliographic record
Abstract
© 2016 Macmillan Publishers Limited. All rights reserved. Grassland productivity is regulated by both temperature and the amount and timing of precipitation. Future climate change is therefore expected to influence grassland phenology and growth, with consequences for ecosystems and economies. However, the interacting effects of major shifts in temperature and precipitation on grasslands remain poorly understood and existing modelling approaches, although typically complex, do not extrapolate or generalize well and tend to disagree under future scenarios. Here we explore the potential responses of North American grasslands to climate change using a new, data-informed vegetation-hydrological model, a network of high-frequency ground observations across a wide range of grassland ecosystems and CMIP5 climate projections. Our results suggest widespread and consistent increases in vegetation fractional cover for the current range of grassland ecosystems throughout most of North America, despite the increase in aridity projected across most of our study area. Our analysis indicates a likely future shift of vegetation growth towards both earlier spring emergence and delayed autumn senescence, which would compensate for drought-induced reductions in summer fractional cover and productivity. However, because our model does not include the effects of rising atmospheric CO 2 on photosynthesis and water use efficiency, climate change impacts on grassland productivity may be even larger than our results suggest. Increases in the productivity of North American grasslands over this coming century have implications for agriculture, carbon cycling and vegetation feedbacks to the atmosphere.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".