National Aquatic Invasive Species Risk Assessment for Zebra Mussel and Quagga Mussel, April 2022
Bibliographic record
Abstract
Since being introduced into the Laurentian Great Lakes region in the 1980s, Zebra (Dreissena polymorpha) and Quagga (Dreissena rostriformis bugensis) Mussels have spread throughout North America and have had significant ecological impacts to freshwater ecosystems. In Canada, Zebra Mussels have subsequently spread to certain waterbodies in Manitoba and southern Quebec, while Quagga Mussels have only spread within the Laurentian Great Lakes region. Compared to the 2012 risk assessment, this new assessment was conducted at a higher spatial resolution. It included all provinces and territories, new and updated environmental data and species occurrence information, and two different habitat suitability models. The ecological risk for both Zebra and Quagga Mussels was assessed by integrating metrics related to introduction, establishment, and ecological impact. This new assessment did not evaluate the risk to individual waterbodies. Rather, it provided an ecological risk assessment at 9,260 m x 9,260 m grid cell resolution across Canada. As a result, risk and impacts may differ at smaller spatial scales where conditions may be more or less favourable. Introduction was assessed based on a proxy of human activity (Human Footprint Index) and proximity to invaded waterbodies (connectivity metric). Establishment was assessed using two modeling approaches to characterize habitat suitability (Calcium-based model and MaxEnt-based model). Since Zebra and Quagga Mussel have significant and well documented negative ecological impacts, the impact to Canadian freshwater aquatic ecosystems was determined to be very high. For both Zebra and Quagga Mussel, all provinces and territories contain watersheds that have Moderate or High Ecological Risk. Higher ecological risk areas are distributed along the southern reaches of Canada ranging from Nova Scotia to southern Alberta and British Columbia. The ecological risk presented here represents current conditions. As Zebra and Quagga Mussel invasions continue and environmental drivers change, the risk to Canadian freshwater aquatic ecosystems may change and will need to be reassessed. Data limitations resulted in a number of uncertainties related to the characterisation of risk in this assessment. Improved and expanded geospatial data (e.g., environmental, species, and introduction vectors) will improve future risk assessments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".