A dominance‐based approach to map risks of ecological invasions in the presence of severe uncertainty
Bibliographic record
Abstract
Abstract Aim Uncertainty has been widely recognized as one of the most critical issues in predicting the expansion of ecological invasions. The uncertainty associated with the introduction and spread of invasive organisms influences how pest management decision makers respond to expanding incursions. We present a model‐based approach to map risk of ecological invasions that combines two potentially conflicting goals: (1) estimating the likelihood of a new organism being established at a given locale and (2) quantifying the uncertainty of that prediction. Location Eastern and central Canada. Methods Our methodology focuses on the potential for long‐distance, human‐assisted spread of invasive organisms. First, we used a spatial simulation model to generate distributions of plausible invasion outcomes over a target geographical region. We then used second‐degree stochastic dominance (SSD) criteria to rank all geographical locations in the target region based on these distributions. We applied the approach to analyze pathways of human‐assisted spread (i.e., with commercially transported goods) of the emerald ash borer (EAB) ( Agrilus planipennis Fairmaire), a major pest of ash trees in North America. Results The projected potential of the pest to establish at remote locations is significantly shaped by the amount of epistemic uncertainty in the model‐based forecasts. The estimates based on the SSD ranking identified major ‘crossroads’ through which the movement of the EAB with commercial transport is most likely to occur. The system of major expressways in Ontario and Quebec was confirmed as the primary gateway of the pest’s expansion throughout the Canadian landscape. Main conclusions Overall, the new approach generates more realistic predictions of long‐distance introductions than models that do not account for severe uncertainties and thus can help design more effective pest surveillance programmes. The modelling technique is generic and can be applied to assess other environmental phenomena when the level of epistemic uncertainty is high.
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 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".