Potential impacts of climate change on the distributions of several common and rare freshwater fishes in Canada
Bibliographic record
Abstract
ABSTRACT Climate change will ultimately affect the supply and quality of freshwater lakes and rivers throughout the world. This study examines the potential impacts of climate change on freshwater fish distributions in Canada. Climate normals data (means from 1961 to 1990) from Environment Canada were used to map current climate found throughout the tertiary watersheds of Canada. Logistic regressions based on these climate data were used to develop predictive presence‐absence equations for (a) common commercially and recreationally important species and (b) an Arctic freshwater species and a freshwater fish species of conservation significance listed by the Committee on the Status of Endangered Wildlife (COSEWIC). The Canadian Centre for Climate Modelling and Analysis Global Coupled Model 2(IS92a) provided forecasts of Canada's climate in 2020 and 2050. The data from this scenario and the logistic regressions provided a ready framework for predicting the potential distributions of the fishes. Physical and ecological barriers would have to be overcome for the distribution of these species to actually change in response to climate change. Generally, coldwater species may be extirpated from much of their present range while cool and warm‐water species may expand northward. Species that are limited to the most southern regions of the country may expand northwards. A conceptual framework for assessing potential climate change impacts on fishes and the variety of management strategies required to deal with these impacts are discussed. Our forecasts demonstrate the need for climate change assessments in species at risk as well as for common species.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".