Science for community fisheries: Population assessment and climate impact monitoring for Heiltsuk-led salmon stewardship
Bibliographic record
Abstract
Small scale fisheries support the livelihoods of more than 20 million people and provide food security for millions more around the world, yet science has been slow to embrace the challenge of managing these fisheries. Salmon are foundational for the ecosystems and economies of coastal British Columbia, supporting food, social and ceremonial (FSC) fisheries for 196 First Nations. Despite their cultural and ecological importance, and their vulnerability to ongoing anthropogenic change, we lack the data necessary for management and conservation of wild salmon in much of BC, particularly the remote north and central coast (NCC). Juvenile sockeye rear in lakes for one or two years, so population sizes are often limited by the size and productivity of rearing lakes. Using limnological data collected by Fisheries and Oceans Canada, we built a landscape model of sockeye lake productivity and predicted population capacity for 157 lakes on the NCC. We used these predictions of capacity as priors in a hierarchical-Bayesian stock-recruit model, to estimate productivity, capacity, and conservation benchmarks for 70 sockeye populations. Sockeye are particularly vulnerable to changes in climate, with elevated rates of pre-spawn mortality among migrating adult sockeye at high temperature. Working with the Heiltsuk First Nation, QQs Projects Society, and the Hakai Institute, we established a community-based population monitoring program using a traditional-style salmon weir to capture and tag fish for mark-recapture and telemetry-based estimates of annual population size and temperature-mediated mortality among migrating adult sockeye in the Koeye River. We found rapid declines in survival to spawning when temperatures exceeded 15 °C. Furthermore, river entry measured by the number of fish tagged each day, ceased when the river level dropped below 0.4 m. When water levels are low, migrating sockeye may experience prolonged delays in marine waters, increasing vulnerability to fisheries and predators. Climate impacts on coastal sockeye may therefore be driven by the dual effects of warming temperature and low-water delays. This work will support the development of a Heiltsuk sockeye management plan, establishing management goals and conservation strategies across a territory spanning 15,000 km2 and more than 20 sockeye populations on the NCC.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".