Modeling the risks and damages from a “potential” invasive plant species: yellow starthistle in British Columbia
Bibliographic record
Abstract
Yellow Starthistle (Centaurea Solstitialis) is an annual invasive weed introduced to Western United States from the Mediterranean region. It favours sunny areas and responds aggressively to human disturbances such as road development, firebreaks and animal grazing. It also benefits from longer growing seasons and increased levels of CO2 disproportionately more than native plants. Yellow starthistle (YST) is not yet known to occur in Canada but has been sighted in Washington and northern Idaho. I use a bioeconomic model to produce five study cases of the effects of YST on ranching in BC: (i) a baseline scenario without YST; (ii) a counterfactual scenario where YST is allowed to invade unimpeded; (iii) with the stimulating effects of climate change; (iv) a case where the model is augmented by a hazard function to mimic YST’s invasion risk, (v) and the same scenario augmented by climate change. I use an exponential probability distribution for invasion that has been derived from statistical analyses of YST biological characteristics and time to invasion of a representative sample of herbaceous invasives in North America. A representative ranching operation is used as a study site with rangelands being the dominant type of land-use. Producers are assumed to maximise their profit subject to the function of YST spread and the probability of a YST invasion. I found that YST could have significant impacts on ranch operations: severe reductions in yearly profits (-62%) in case of unimpeded invasion, -80% with the climate change catalysis. I found that persistent populations occupying between 19% and 25% of a representative ranch could be expected. Hazard-augmented model showed that the risk of invasion could be internalised through relatively moderate reductions in stocking rate (-19%) and more significant reductions stocking rate in case of climate change-catalysed invasion (-51%) from the business as usual scenario. I analyse these numbers in more detail through sensitivity studies by concentrating on long-term profitability. I conclude with a discussion of the policy implications of our research for addressing invading species risks prior to invasion, beginning with the cost-effectiveness advantages of early detection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".