A new tool for setting biodiversity management priorities adapted from aquatic invasive species management: A pilot using Atlantic salmon ( <i>Salmo salar</i> ) in <scp>NS</scp> , Canada
Bibliographic record
Abstract
Abstract Aquatic invasive species (AIS) are a leading cause of global loss of biodiversity. However, the relationships between AIS and vulnerable species (e.g. species at risk and endemic species) are not well‐documented and few studies have combined risk of AIS invasion with species distribution modelling of vulnerable species. An integrated management approach was developed and applied to assess the effects and risks of AIS on Atlantic salmon ( Salmo salar ) in Nova Scotia (NS), Canada. A semi‐quantitative risk assessment tool was used to evaluate the risk of introduction, establishment and ecological impact of eight AIS currently found across NS. A suitable habitat for Atlantic salmon was predicted using a random forest model and identified watersheds of high conservation value. A vector‐based screening‐level risk assessment was developed to determine the relative risk of potential vectors introducing AIS into each primary watershed in NS, alongside other anthropogenic pressures. Finally, a matrix was developed to provide recommendations to AIS managers based on species invasion stage and invasion risk score. Results from this study showed that ecosystem engineer species (e.g. crayfish and invasive plants) were more likely to affect invaded ecosystems, but were less widely reported in NS While invasive piscivores (e.g. chain pickerel [ Esox niger ], smallmouth bass [ Micropterus dolomieu ]) had less potential ecosystem impacts, they were more widely reported in the assessment area. Hitchhiking on watercraft and fishing gear were the riskiest vectors for continued spread of AIS in NS. The AIS Management Matrix supports recommendations of scenarios in which AIS could be eradicated or where response plans may be developed to control, contain and respond to new introductions. The Herring Cove Medway and the Salmon Mira were the top two watersheds recommended for Atlantic salmon conservation and AIS control or prevention based on habitat conservation value. Water temperature and physical changes to freshwater habitat (especially adjacent land use) were the model variables that were most important to predict Atlantic salmon habitat suitability. At a time of heightened global biodiversity loss, but limited dedicated conservation resources, the integrated management approach developed in this study can be applied to recommend geographically specific actions to managers for strategic vulnerable species conservation planning and AIS management.
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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 | 0.001 | 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".