Using phenology to unravel differential soil water use and productivity in a semiarid savanna
Bibliographic record
Abstract
Abstract Savannas are water‐limited ecosystems characterized by two dominant plant types: trees and an understory primarily made up grass. Different phenology and root structures of these plant types complicate how savanna primary productivity responds to changes in water availability. We tested the hypothesis that productivity in savannas is controlled by the temporal and vertical distribution of soil water content (SWC) and differences in growing season length of understory and tree plant functional types. To quantify the relationship between tree, understory, and savanna‐wide phenology and productivity, we used PhenoCam and satellite observations surrounding an eddy covariance tower at a semiarid savanna site in Arizona, USA. We distinguished between SWC across two different depth intervals (shallow, <0–30 cm and deep, >30–100 cm). We found that tree greenness increased with SWC at both depths, while understory greenness was only sensitive to the shallower SWC measurements. Onset of ecosystem dormancy, estimated from satellite observations close to the eddy covariance tower, explained more variability in annual gross primary productivity (GPP) than in other phenometrics. Higher SWC led to an extended growing season, caused by delayed dormancy in trees, but the understory showed no evidence of delayed dormancy in wetter periods. We infer that the timing of ecosystem scale dormancy, driven by trees, is important in understanding changes in a savanna's GPP. These findings highlight the important effects of rainfall during the winter. These findings suggest that savanna GPP is conditional on different responses to moisture availability in each of the dominant vegetation components.
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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) | 0.001 | 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 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".