Using systematic conservation planning to inform restoration of freshwater habitat and connectivity for salmon
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Instream barriers remain ubiquitous threats to freshwater species and their habitats. Decisions regarding barrier removal are often aimed at maximizing habitat area and connectivity for freshwater fish; yet can be challenging due to the sheer number of barriers, uncertainty in species presence, abundance, and habitat quality, as well as limited budgets alongside high costs of restoration. Here, we apply systematic conservation planning to prioritize in‐stream barrier removal aimed at restoring habitat connectivity for 14 populations of wild Pacific salmon in the lower Fraser River, Canada's most productive salmon‐bearing river. To understand how priorities change when stream quality is considered, we contrast scenarios that maximize habitat extent with scenarios that include four indicators of habitat quality. Region‐wide, approximately 64% of naturally accessible stream length is currently blocked by barriers. We estimate approximately 75% of this alienated habitat (over 1600 km of stream), could have full access restored with an investment of $200 million CAD, whereas 60% could be restored for half this amount. When stream quality was considered within the optimization, priorities for barriers removal shifted away from urbanized floodplain valleys towards less developed areas. The spatial shift in priorities meant that species like chum salmon ( Oncorhynchus keta ) would see less restored habitat. To inform barrier removal strategies using these model scenarios, an iterative and adaptive approach will be required that includes the values and priorities of rights and titleholders. Continuous improvement in data quality, accuracy, and feedback from monitoring as barriers are restored is also crucial.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it