Preliminary assessment of the State of Fish and Fish Habitat in Fisheries and Oceans Canada’s Ontario and Prairie Region
Bibliographic record
Abstract
With the modernization of the Fisheries Act, DFO committed to producing ‘State of Fish and Fish Habitat’ (SOFFH) reports for Canada’s freshwater ecosystems. As part of this initiative, DFO’s Ontario and Prairie (O&P) Region selected the Lake Erie and Lake Ontario drainage basins (Lower Great Lakes Area; LGLA) and the Alberta East Slopes Area (AESA) as focal areas for reporting on in 2023. A Canadian Science Advisory Secretariat meeting was held June 29‒30, 2021, to elicit input from academic, environmental practitioners, FFHPP, and DFO Science on the appropriate indicators, metrics, and data that could be used for the O&P SOFFH report. The five indicators selected by DFO O&P were: Biodiversity, Water Quality, Connectivity, Land Use and Land Cover, and Climate Change. Data for up to six metrics per indicator were summarized for each of the reporting areas. The findings indicated that LGLA has high fish species richness. However, a number of fishes and mussel species have been listed as species at risk. Water quality parameters often exceeded thresholds in areas with the greatest urban and agricultural development, and there was also an absence of natural riparian cover in those areas. Ninety-two per cent of the barriers within the LGLA are known to prevent fish movement. Forward and backward bioclimatic velocities were found to be highest in the assessment units surrounding the Greater Toronto Area and assessment units in the Lake Ontario basin. Flood forecasts showed variable changes in the location and heights of 100-yr floods with climate change. The AESA has lower fish species richness relative to the LGLA and a correspondingly, lower number of species at risk. Water quality parameters were often consistent with guidelines for the protection of aquatic life and connectivity varied amongst watercourses in the area. Land use and land cover in the AESA showed high spatial variance, with rangeland and crops in the southeast and trees and snow/ice in the western and northeastern regions. Due to the presence of large national and provincial parks, entire assessment units were protected in the mountainous regions of AESA. Forward bioclimatic velocities were two times faster and flood heights were also higher in the AESA compared to LGLA. This report provides insight into the SOFFH within the AESA and LGLA. However, limited data were available for some metrics, resulting in high uncertainty related to the SOFFH in some assessment units. As such, we identified key data gaps and limitations of the selected indicators and metrics. This information could be used to prioritize spatial extents and items for future research and monitoring projects. The process outlined in this report demonstrates how a quantitative approach to reporting on the SOFFH could be applied by DFO in other regions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".