Above- and below-ground responses of native and invasive prairie grasses to future climate scenarios
Bibliographic record
Abstract
More intense and frequent droughts, coupled with elevated temperatures, are projected for grasslands worldwide. Although it has been suggested that alterations in temperature and precipitation will increase the success of biological invasions, studies that combine these climate change scenarios are limited. These changes in climate may increase the success of non-native, invasive plant species directly, as these species often possess traits that are favored by variable climates, or indirectly through negative impacts on native vegetation or alterations in soil microbial communities, such as arbuscular mycorrhizal (AM) fungi. The goal of our research was to assess above- and below-ground production and mycorrhizal fungal abundance of functionally similar native and invasive non-native grass species under projected climate-change scenarios. We assessed plant biomass, intra-radical AM root colonization, and relative abundance of extra-radical fungal biomass of two native (Schizachyrium scoparium (Michx.) Nash, Pascopyrum smithii (Rydb.) Á.Löve) and two invasive (Bothriochloa ischaemum (L.) Keng, Bromus inermis Leyss.) grass species subjected to increased temperature and decreased soil water availability. Regardless of temperature or soil moisture, the invasive grasses in our study displayed greater seedling growth as compared with paired native species. Invasive grasses were also generally characterized by greater intra-radical colonization by AM fungi, compared with native species. Our data suggest that invasive grasses will continue to be problematic and successfully out-compete native grasses following increased temperatures and reduced water availability, as projected by climate-change models.
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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".