Using structural equation modelling to test the passenger, driver and opportunist concepts in a <i>Poa pratensis</i> invasion
Bibliographic record
Abstract
The passenger, driver, and opportunist models are conceptual models of the invasion process used to describe alternative invasion scenarios. In the passenger model, both the invasive species and native community respond independently to environmental changes. In the driver model, changes to the native community are driven by the invasive species, while in the opportunist model invasion occurs in response to changes in the native community. In any given invasion scenario, however, it is possible that the relationships between the invasive, the native community, and the environment correspond to some combination of these invasion models acting simultaneously. We study invasion by Poa pratensis in a grassland in Alberta, Canada. Poa pratensis is a non‐native plant implicated with loss of plant diversity in the region. In a three year field experiment, we manipulate the environment though defoliation, water addition, and nitrogen addition, and measure responses of P. pratensis cover, and cover and richness of the native community. We use structural equation modelling to describe the relationships between the invasive, the native community, and the environmental changes, and then interpret these relationships using the three invasion models. We found that P. pratensis predominantly invaded via the driver model, with subsequent reductions in native plant cover, but not in species richness. Positive effects of the environmental changes on P. pratensis also aided its ability to drive native cover. As well, we found some involvement of the opportunist model, through a negative relationship between the native community and the invasive. As invasion mainly proceeded via the driver model, management actions to limit invasion should focus on efforts to control abundance of P. pratensis itself.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".