Estuary habitat associations for juvenile Pacific salmon and pelagic fish: Implications for coastal planning processes
Bibliographic record
Abstract
Abstract Assessment of risk from industrial developments often relies on simple habitat descriptions for focal species. However, simple habitat metrics may not be accurate predictors of locations that species actually use. Understanding the nature of habitat is particularly pressing for estuaries, as they are among the most degraded ecosystems globally but provide critical rearing habitat for many species, including Pacific salmon. Canadian environmental impact assessment approaches use simple habitat‐type models to assess risk from developments and assume that different species of salmon rely on the same habitat. This study asked what combination of habitat type and biophysical covariates best predicted use of estuary habitat by juvenile salmon and two dominant small pelagic fish. Fish were sampled via purse seine throughout the Skeena River estuary (British Columbia, Canada) for 2 years across different habitat types (eelgrass, open water, sandy banks, and rocky shores). Simple habitat‐type models were compared with models with more complex biophysical variables to predict the variability in relative abundance of juvenile Chinook (Oncorhynchus tshawytscha), coho (Oncorhynchus kisutch), and sockeye (Oncorhynchus nerka) salmon, along with pelagic fish species Pacific herring (Clupea pallasii) and surf smelt (Hypomesus pretiosus). The combination of variables that best predicted abundance differed across fish species. Pelagic fish were associated with near‐shore sites, increased temperature (herring), and increased salinity (smelt). Juvenile coho and sockeye salmon (but not Chinook), were more abundant in higher turbid waters. Chinook and sockeye salmon used eelgrass habitat more frequently than other habitat types, whereas coho salmon were more abundant in areas with high macroalgae cover. Models with these variables had greater predictive power than those using habitat type alone for juvenile salmon. Simple classifications of estuary habitat currently used in environmental risk assessment may not reflect the complex nature of fish–habitat associations. Understanding biophysical factors associated with estuary fish abundance can inform management of estuary habitat to support their nursery function for important fish.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".