The relative abundance of three plant functional types in temperate grasslands and shrublands of North and South America: effects of projected climate change
Bibliographic record
Abstract
Aim Use a regression model that relates climatic variables to the relative abundances of shrubs, C 4 and C 3 grasses to project the plant functional type composition of temperate grasslands and shrublands within North and South America in response to climate change. Location The temperate zone grassland and shrubland regions of North and South America. Methods We used a regression model to project changes in the relative abundances of shrubs, C 4 and C 3 grasses under three general circulation model (GFDL, GISS, UKMO) climate change scenarios. The three climate change scenarios were applied to a global data set of mean monthly temperatures and precipitation. The regression model, which incorporates mean annual temperature, mean annual precipitation and seasonality of precipitation as input variables, was used to project plant functional type changes. Spatial patterns of change were analysed using a geographical information system. Results Relative abundance of C 4 grasses were projected to increase >10% throughout most of the study region at the expense of C 3 grasses. There were essentially no areas where C 4 grasses decreased in abundance, and the areas with no change were largely the southern Great Plains and the Intermountain Basin and Range of North America. C 3 grasses declined throughout with the exception of the north‐western Great Plains of the US and Canada, and north central Argentina. Changes in shrub abundance were mixed with some increases in Patagonia and the desert regions of the south‐western US; there were also some projected decreases, however, the locations varied across models. Main conclusions The projections made by our regression model were consistent with those of other more complex vegetation dynamics models. Changes in plant community composition in response to climate change may be substantial in certain areas and will probably lead to changes in water and nutrient cycling.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".