Interactive effects of global change factors on terrestrial net primary productivity are treatment length and intensity dependent
Bibliographic record
Abstract
Abstract Individual effects of co‐occurring global change factors on net primary productivity (NPP) have been widely studied; however, their interactive effects remain highly debated. Here, we conducted a global meta‐analysis based on 919 multifactor observations from 120 published studies to examine the interactive effects on NPP of global change factors including elevated [CO 2 ], warming, nitrogen addition, irrigation, drought and changes in species diversity. On average, of the factors studied, six pairs of factors had additive and two pairs had synergistic interactions. Importantly, some of those interaction types changed over time and with treatment intensity. The synergistic interaction between elevated [CO 2 ] and nitrogen addition became additive at high nitrogen addition rates, whereas the synergistic interaction between irrigation and warming diminished at higher temperatures. Over time, the additive effect between elevated [CO 2 ] and increased species richness switched to synergistic. Other global change factor pairs—including elevated [CO 2 ] and warming, nitrogen addition and increased richness, irrigation and N addition, as well as drought and increased richness—remained additive regardless of their treatment intensity or experimental duration. Interaction types of those global change factor pairs did not vary with ecosystem types assessed in our study. Synthesis . Our results suggest that the assumptions of static effects through time or ignoring treatment intensity effects will provide inaccurate predictions of the interactive effects of global change factors on terrestrial NPP. Understanding the context‐dependent nature of interactive effects is crucial for validating Earth system models and predicting future NPP responses to co‐occurring global change drivers.
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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 | 0.023 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.019 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".