Increasing global aridity destabilizes shrub facilitation of exotic but not native plant species
Bibliographic record
Abstract
Earth’s dryland (hyper-arid, arid, semi-arid, and dry sub-humid) ecosystems face increasing aridity and invasion by exotic plant species. In concert, these global changes threaten the biodiversity, ecosystem functioning, and economic viability of drylands worldwide, with critical implications for environmental quality and human wellbeing. Positive interactions (facilitation) from shrubs can buffer native plant communities against increasing aridity, but this could backfire if exotic species are facilitated more than natives. Thus, understanding how native and exotic plant species respond to shrub facilitation along aridity gradients is essential for predicting the ecological consequences of concomitant aridification and exotic plant invasion in changing drylands. Here, we performed meta-analyses using 152 independent studies to compare the positive effects of shrubs on native vs. exotic plant species across Earth’s dryland ecosystems that vary in aridity. Globally, shrubs facilitate the abundance, diversity, reproduction, and survival of native plant species but do not consistently facilitate any measure of exotic plant performance. As aridity increases, shrub effects on native species do not change, but shrub effects on exotic species become more negative. Thus, across dryland ecosystems globally, shrubs facilitate more measures of native plant performance than exotic plant performance, and as aridity increases, shrub facilitation remains stable for native species but transitions towards resistance for exotic species. At the global scale, dryland aridification may pose a greater threat to exotic species than native species, inasmuch as shrubs and their interactions remain intact.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.007 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".