Juvenile salmonid growth, survival, and production in a large river floodplain modified by beavers (<i>Castor canadensis</i>)
Bibliographic record
Abstract
Beavers (Castor canadensis) may strongly influence juvenile salmon production by damming spring brooks that are primary rearing habitats on expansive floodplains of large Pacific Rim salmon rivers. We studied three floodplain rearing habitats in the Kwethluk River, Alaska: free-flowing (beaver-free, n = 3) and beaver-influenced (below beaver dams, n = 4) spring brooks and early-successional beaver ponds (n = 4). We analyzed juvenile coho (Oncorhynchus kisutch) and Chinook (Oncorhynchus tshawytschwa) salmon movement, survival, densities, and growth using a multistate robust capture–mark–recapture design. Survival (46% to 80%) and densities (0.9 fish·m−2) were highest in beaver-free spring brooks. Ponds had lower salmon densities, producing less biomass per unit area than beaver-influenced or beaver-free spring brooks (1.87 ± 0.57 g·m−2 vs. 2.98 ± 1.22 and 3.23 ± 0.73 g·m−2). However, ponds covered 2× greater area than either type of spring brook and therefore produced more salmon biomass at the floodplain scale than either type of spring brook (175 kg vs. 149 kg in beaver-influenced spring brooks and 140 kg in beaver-free spring brooks). We conclude that beaver damming of floodplain spring brooks produces bigger juveniles and more total biomass, but spring brooks produce significantly more, albeit smaller, coho and Chinook juveniles. Thus, the presence of beavers on the floodplain increases habitat variation, which provides a larger range of growth opportunities for juvenile salmon.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".