THE IMPACT OF INVASIVE GRASSES ON THE POPULATION GROWTH OF<i>ANEMONE PATENS</i>, A LONG-LIVED NATIVE FORB
Bibliographic record
Abstract
Negative impacts of invasive plants on natives have been well documented, but much less is known about whether invasive plants can cause population level declines. We used demographic models to investigate the effects of two invasive grasses on the demography and population growth of Anemone patens, a long-lived native perennial of North American grasslands. Demographic data of A. patens growing in patches characterized by Bromus inermis, Poa pratensis, or native grasses were used to parameterize integral projection models. Models based on both average conditions and those allowing for environmental stochasticity indicate that A. patens is slowly increasing in patches of native grass (lambda = 1.02) and declining in patches of invasive grasses, particularly those dominated by B. inermis (lambda = 0.93). Extinction probabilities indicate that A. patens should persist in native grass patches, but has a much higher probability of extinction in Bromus patches compared to Poa patches. While sensitivity analyses showed that survival had the biggest effect on population growth rates in all habitats, results of a Life Table Response Experiment (LTRE) revealed that slower individual growth rates in patches of invasive grasses contributed the most to the observed reduction in population growth. These results suggest that invasive grasses may cause slow declines in A. patens, despite short-term coexistence, and that controlling B. inermis only would not be sufficient to ensure A. patens persistence.
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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 | 0.000 | 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".