Grassland Phenology Response to Drought in the Canadian Prairies
Bibliographic record
Abstract
Drought is a significant climatic disturbance in grasslands, yet the impact drought caused by global warming has on grassland phenology is still unclear. Our research investigates the long-term variability of grassland phenology in relation to drought in the Canadian prairies from 1982 to 2014. Based on the start of growing season (SOG) and the end of growing season (EOG) derived from Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g datasets, we found that grasslands demonstrated complex phenology trends over our study period. We retrieved the drought conditions of the prairie ecozone at multiple time scales from the 1- to 12-month Standardized Precipitation Evapotranspiration Index (SPEI). We evaluated the correlations between the detrended time series of phenological metrics and SPEIs through Pearson correlation analysis and identified the dominant drought where the maximum correlations were found for each ecozone and each phenological metric. The dominant drought over preceding months account for 14–33% and 26–44% of the year-to-year variability of SOG and EOG, respectively, and fewer water deficits would favor an earlier SOG and delayed EOG. The drought-induced shifts in SOG and EOG were determined based on the correlation between the dominant drought and the year-to-year variability using ordinary least square (OLS) method. Our research also quantifies the correlation between precipitation and the evolution of the dominant droughts and the drought-induced shifts in grassland phenology. Every millimeter (mm) increase in precipitation accumulated over the dominant periods would cause SOG to occur 0.06–0.21 days earlier, and EOG to occur 0.23–0.45 days later. Our research reveals a complex phenology response in relation to drought in the Canadian prairie grasslands and demonstrates that drought is a significant factor in the timing of both SOG and EOG. Thus, it is necessary to include drought-related climatic variables when predicting grassland phenology response to climate change and variability.
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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".