Reintroduction of fishes in Canada: a review of research progress for SARA-listed species
Bibliographic record
Abstract
Fishes are among the most threatened taxa in Canada with over 70 species, subspecies, and (or) designatable units presently listed for protection under the Species at Risk Act (SARA). Protecting these species requires a diverse set of strategies based on the best-available data and information. One approach identified under SARA and in Canadian federal recovery strategies for improving the status of SARA-listed fishes is species reintroduction, which involves the release of individuals into areas from which they have been extirpated with the goal of re-establishing self-sustaining populations. The success of reintroduction relies on a comprehensive understanding of species ecology and life history, with considerations around population genetics and genomics. However, SARA-listed species are some of the most poorly known species in Canada due to their rarity and relative lack of research investment prior to the enactment of SARA. As a result, SARA-listed species have the most to lose if reintroduction activities are not carefully researched, planned, and executed. Therefore, the purpose of this review is to present an accessible summary on the state of reintroduction science for SARA-listed fishes in Canada with the hope of motivating future research to support reintroduction activities. We focus our review on 14 SARA-listed freshwater or anadromous fishes identified as candidates for reintroduction in federal recovery strategies. We follow the species-specific summaries with guidance on how basic research questions in population ecology, habitat science, and threat science provide a critical foundation for addressing knowledge gaps in reintroduction science. Subsequently, we identify the importance of genetic and genomic techniques for informing future research on the reintroduction of SARA-listed species. We conclude with recommendations for active, experimental approaches for moving reintroduction efforts forward to recover Canadian fishes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".