Quantifying lost and inaccessible habitat for Pacific salmon in Canada’s Lower Fraser River
Bibliographic record
Abstract
Abstract Loss of connectivity caused by anthropogenic barriers is a key threat for migratory freshwater species. The anadromous life history of salmonids means that barriers on streams can decrease the amount of habitat available for spawning and rearing. To set appropriate targets for restoration, it is important to know how different populations have been impacted in terms of the location and extent of historically available habitat that has been lost or has become inaccessible. Using mapped and predicted barriers to fish passage in streams and diking infrastructure, the amount of both floodplain and linear stream habitat that remains accessible today was estimated for 14 populations of salmon in the Lower Fraser River, British Columbia, Canada’s most productive salmon river. To place these estimates within a historical context, the floodplain area was estimated using vegetation records from the 1850s, and lost streams were estimated using a digital elevation model‐derived stream network. To bolster areas where little mapping has been done, current barrier data were used to predict locations likely to have barriers. Accessibility to floodplain was poor across the entire region with only 15% of the historical floodplain remaining accessible. Linear stream habitat ranged in accessibility from 28% to 99% across populations based on mapped barriers. Inclusion of predicted barriers revealed an additional 33 km of potentially inaccessible stream habitat and the modeled stream network located approximately 1700 km of stream length that has been completely lost. Comparing habitat accessibility and barrier density against the assessed status of populations revealed insights useful for understanding the impact of barriers on spawning and rearing and guiding the allocation of restoration effort. Applying methods for addressing missing data, such as lost streams and unmapped barriers, was essential for estimating the accessibility of habitat within a historical context. While much emphasis has been placed on the role of marine conditions in wild Pacific salmon recovery, the magnitude of habitat loss in the Fraser cannot be ignored and suggests it is a major driver of observed salmon declines.
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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 | 0.001 | 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".