National screening-level risk assessment (SLRA) of Goldfish, Prussian Carp, Chain Pickerel, and Black Crappie in Canada
Bibliographic record
Abstract
Aquatic invasive species (AIS) are species introduced or spread to ecosystems beyond their natural range that threaten biodiversity, economy, and society. Recently, four freshwater fishes were identified as being of concern for Canadian freshwaters: Goldfish (Carassius auratus), Prussian Carp (Carassius gibelio), Black Crappie (Pomoxis nigromaculatus) and Chain Pickerel (Esox niger). The latter two species are native in some regions of Canada, while the carps are strictly non-indigenous. All four species currently have at least one non-indigenous population established in Canada and are undergoing range expansions. A screening-level risk assessment (SLRA) was performed using an adaptation of the Canadian Marine Invasive Screening Tool (CMIST) to identify the level of risk (high, moderate, or low) of these four species across freshwater ecoregions in Canada. SLRAs help decision-makers identify which species pose substantial threats to native species/ecosystems and which may consequently require detailed-level risk assessments. Of the four species assessed, Goldfish presented the highest invasion risk across Canada, especially in southern ecoregions. Prussian carp was a high-risk species in ecoregions within Western Canada and invasion risk of both carps was moderate in all remaining ecoregions. Chain Pickerel was of high risk in New Brunswick and Nova Scotia (and moderate elsewhere, but low in the Arctic), while Black Crappie was of moderate risk everywhere except the Arctic (low risk). High frequency of arrival and a strong potential for dispersal via anthropogenic mechanisms can be correlated to the presence of established AIS populations within an ecoregion (or adjacent ecoregions), because established AIS populations increase future introduction potential. These three correlated factors were the primary likelihood of invasion drivers underpinning risk predictions in this work. Evidence in the literature of impacts on populations, communities, ecosystem functioning, and habitat in their native or invaded range were the primary impact of invasion factors which contributed to elevated final risk levels. A lack of information on the impacts of Black Crappie invasion lowered its risk levels and associated certainty, showing that the availability of information (e.g., on impacts and dispersal mechanisms) played an important role in final determinations of risk. For all four fishes across Canadian ecoregions, some uncertainty in the data means that assessments of moderate risk may reflect the middle point of two potential extremes (i.e. high and low), but true risk may be anywhere from high to low. Low risk is unlikely to be no risk, but nor is it likely to be high risk. Consequently, heatmaps showing risk levels should be used in conjunction with biplots of likelihood and impact scores in order to understand the range in scoring and the certainty associated with likelihood and impact of invasion scores underpinning risk for each fish in each ecoregion. High uncertainty around anthropogenic introductions and activities and the spatial scales over which they operate contribute to greater uncertainty of the risk level for all fishes. Detailed-level risk assessments at finer spatial scales may be pertinent to complete for each species and ecoregion identified here as at high risk for invasion.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".