ARTICLE Exotic Plant Colonization and Occupancy Within Riparian Areas of the Interior Columbia River and Upper Missouri River Basins, USA
Bibliographic record
Abstract
Abstract Exotic plant invasions into riparia often result in shifts in vegetative composition, altered stream function, and cascading effects to biota at multiple scales. Characterizing the distribution patterns of exotic plants is an important step in directing targeted research to identify mechanisms of invasion and potential management strategies. In this study, we employed occupancy models to examine the associations of landscape, climate, and disturbance attributes with the colonization and occupancy patterns for spotted knapweed (Centaurea stoebe L.), Canada thistle (Cirsium arvense L., Scop.), and cheatgrass (Bromus tectorum L.) in the riparia of headwater streams (n=1,091) in the Interior Columbia River and Upper Missouri River Basins. We found relatively low occupancy rates for cheatgrass (0.06, SE=0.02) and spotted knapweed (0.04, SE=0.01), but moderate occupancy of Canada thistle (0.28, SE=0.05); colonization rates were low across all species (<0.01). We found the distributions of spotted knapweed, Canada thistle, and cheatgrass to exhibit significant associations with both ambient climate conditions and anthropogenic and natural disturbances. We attribute the low to moderate occupancy and colonization rates to the relatively remote locations of our sample sites within headwater streams and urge consideration of means to prevent further invasions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".