The risk of aquatic invasive species and their vectors to a national protected area in the Canadian Rocky Mountains
Bibliographic record
Abstract
Risk assessment is a cornerstone of aquatic invasive species (AIS) management, helping prioritise limited resources for prevention and control.However, national or regional-scale AIS risk assessments often lack the spatial resolution to reflect localised conditions, such as those in national parks.We conducted climate matching and semi-quantitative screening-level risk assessments (SLRAs) to evaluate the potential for introduction and impact of 159 aquatic non-native species in the montane and lower subalpine ecoregions of Banff National Park (BNP), a 6,641 km 2 protected area receiving over 4 million visitors annually.To assess vector importance, we tallied the number of top-scoring species linked to each AIS introduction pathway present in BNP.Climate matching identified 110 fish and aquatic invertebrates with potential climate compatibility in the park.Of 55 species assessed via SLRA, 13 were classified as moderate risk and none were high risk, largely due to regulatory protections such as bans on bait use, restricted watercraft access, and absence of fish stocking or trade.Additional biophysical barriers-including cold stream temperatures and steep gradients-further reduce establishment likelihood.Nevertheless, cumulative risk from multiple moderate-risk species remains a concern.Montane lakes, which are warmer and more heavily used for recreation, exhibit only moderate compliance with boating regulations.Four moderate-risk species are desiccation-tolerant, highlighting watercraft as a key vector, while nine are linked to illegal deliberate release, including two present in downstream connected waters without upstream dispersal barriers.These findings emphasise the value of sitespecific AIS risk assessments in informing targeted prevention efforts and support the need for a suite of risk management activities to protect BNP and other mountain protected areas globally.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".