Freshwater Fisheries in Canada: Historical and Contemporary Perspectives on the Resources and Their Management
Bibliographic record
Abstract
Abstract.—Canada has 215 native species of freshwater fishes, many of which are subdivided into ecologically and genetically distinct populations and population assemblages. Several systems are used to assess the conservation status of these species and population assemblages, the most extensive of which is that used by the Committee on the Status of Endangered Wildlife in Canada (COSEWIC) under the purview of Canada’s Species at Risk Act (SARA). Regular evaluation of the status of Canada’s freshwater fish fauna is important so we can assess how we are meeting Canada’s legal obligations to protect species at risk. Here, we review the importance of conservation of freshwater fishes, the process by which Canada’s freshwater fishes are assessed by COSEWIC, and how these assessments relate to the subsequent legal listing and recovery processes under SARA. We also summarize the numbers of species assessed as at risk (i.e., extirpated, endangered, threatened, or special concern), examine why they are at risk, discuss the major limitations to effective conservation of Canada’s freshwater fishes, and make recommendations to improve the performance of the assessment and listing processes in Canada. A total of 260 freshwater and diadromous fish “wildlife species” (which may comprise a species, subspecies, variety, or geographically or genetically distinct population or set of populations) have been assessed as at some level of risk and 13 have been declared extinct and two extirpated from Canada. Habitat loss and degradation as well as invasive species are the most important factors driving at risk status. South-central British Columbia, southwestern Ontario, and southwestern Québec have the greatest number of freshwater fish wildlife species assessed as at risk by COSEWIC, but there are many cases where these fishes have not yet been legally listed for protection under SARA by the federal government. Weakened federal environmental legislation, limitations of the SARA process itself, lack of full implementation of SARA provisions and related environmental policies, and continued lack of understanding of critical aspects of the biology and demography of freshwater fishes all hinder effective protection. We strongly recommend immediate increased investment in research concerning the biology of Canada’s freshwater fish fauna, continued evaluation of the COSEWIC assessment and SARA listing processes, immediate adoption of formal definitions and quantitative benchmarks for recovery, and full and timely implementation of SARA provisions and related aspects of federal biodiversity policies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".