Can Invasive Species Enhance Competitive Ability and Restoration Potential in Native Grass Populations?
Bibliographic record
Abstract
Native plant individuals often persist within communities dominated by exotics but the influence of this exposure on native populations is poorly understood. Selection for traits contributing to competitive ability may lead to native plant populations that are more tolerant of the presence of exotic invaders. In this way, long‐term coexistence with an exotic may confer competitive advantages to remnant (experienced) native populations and be potentially beneficial to restoration. In past studies we have documented genetic differentiation within native grass populations exposed to the exotic invader Russian knapweed (Acroptilon repens). Here, we examine populations of a cool‐season grass, needle‐and‐thread (Hesperostipa comata [Trin. & Rupr.]) and a warm season, alkali sacaton (Sporobolus airoides [Torr.]) collected from Russian knapweed‐invaded sites and adjacent noninvaded sites to assess their relative competitive ability against a novel exotic neighbor, Canada thistle (Cirsium arvense). Experienced S. airoides (from within A. repens invasions) appear to better tolerate (accumulate biomass, leaf nitrogen content, and to initiate new tillers) the presence of a novel competitor (C. arvense). Experienced and inexperienced H. comata genets differ in their response to the presence of C. arvense. Relative neighbor effects of native grasses on C. arvense were generally greater from experienced grasses. The ability to compete with novel neighbors may be driven by general competitive traits rather than species‐specific coevolutionary trajectories. Irrespective of competitive mechanisms, the conservation of native species populations within weed invasions may provide an important restoration tool by retaining unique components of native gene pools selected by competitive interactions with exotics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".