Invasion and drought alter phenological sensitivity and synergistically lower ecosystem production
Bibliographic record
Abstract
Climate change and shifting species composition have influenced ecosystem-scale phenology worldwide. For instance, invasive plant species have greater vegetation phenological sensitivity to climate change than native plant species in some regions, and hence invasion could modify how ecosystem carbon gain responds to increased drought frequencies expected with climate change. Results from a 4-yr drought experiment show that invasion reduced ecosystem potential for carbon gain via increased sensitivity to reduced rainfall. Using canopy greenness (Normalized Difference Vegetation Index, NDVI) as a proxy for potential ecosystem carbon gain, we show that areas invaded by herbaceous species had up to a 70% reduction in maximum NDVI under severe drought conditions as compared to areas dominated by native shrubs. Phenological differences between herbaceous- and shrub-dominated vegetation contributed to this reduction in potential ecosystem carbon gain because invaded areas had delayed green-up, especially under drought conditions, and shrub senescence was accelerated by drought. Hence, invasion by herbaceous species and increased drought frequencies are likely to act synergistically to reduce ecosystem capacity for carbon gain in this system. Our findings suggest that predicting ecosystem responses to future climate change could be improved by projecting of the spread of invasive species and accounting for phenological variation between native and invading species.
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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 | 0.000 |
| 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 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".