Global distribution pattern in characteristics of gross primary productivity response to soil water availability
Bibliographic record
Abstract
Understanding how carbon assimilation rates respond to water availability is crucial for diagnosing global carbon and water cycles. This study aims to investigate characteristics and drivers of gross primary productivity (GPP) responses to soil water availability using three parameters from a light-use-efficiency (LUE) model: W I , k W and α W , representing the inflection point, slope and lag effect of GPP response to soil water availability changes, respectively. We followed a hybrid modeling approach coupling an artificial neural network with the LUE model to derive model parameters and examine intricate relationships between these parameters and features characterizing climate, vegetation, nutrient deposition, soil properties and elevation across 196 eddy covariance sites. Relationships between the LUE model parameters and observed ecosystem properties were analyzed using partial dependence plots and Shapley additive explanation dependence plots. Our results revealed significant statistical differences in parameters across plant functional types. Specifically, forests exhibited lower inflection points, responding more steeply and immediately to water availability changes, contrasting with smoother and lagged responses from open shrubs. Vegetation seasonality, represented by variability of enhanced vegetation index (EVI) and seasonal EVI, was the most influential noncategorical factor, followed by soil properties. Notably, the relationships were predominantly nonlinear. Additionally, older forest ecosystems generally showed lower vulnerability while responding more steeply to relative soil water availability changes than younger forests. While aridity was less influential on parameter variability than anticipated, aridity seasonality was a primary driver for the inflection point. High temperatures and substantial diurnal and annual temperature ranges were linked to pronounced lag effects. Despite these findings, challenges remain regarding model accuracy on annual scales, parameter uncertainties and interactions between features. Overall, this study underscores the spatial heterogeneity of GPP responses to soil water availability and highlights the importance of considering variability in model parameters and GPP sensitivities across space and time.
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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 | 0.001 | 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 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".