Landscape geomorphology and local‐riverine features influence Broad Whitefish (<i>Coregonus nasus</i>) spawning habitat suitability in Arctic Alaska
Bibliographic record
Abstract
Abstract Landscape‐level geomorphic processes influence the spatial and temporal arrangement of fish habitats in freshwater ecosystems and fishes move across riverscapes, selecting a suite of habitats to maximise fitness. Here, we explore the influence of geomorphology on stream channel attributes and assess Broad Whitefish (Coregonus nasus) spawning habitat potential in the Colville River in Arctic Alaska. Using high‐resolution digital surface models (5 m2), we quantified the stream network extent and summarised channel habitat attributes continuously across the drainage network. Next, we developed an intrinsic potential (IP) model for Broad Whitefish by using geomorphic channel parameters previously understood to be associated with spawning habitats (channel width, median substrate size and channel braiding) to estimate the potential of streams across the Colville River watershed to provide spawning habitat. Our model results show the majority of habitat with high IP (≥0.6) was located within the braided sections of the main channel, which encompass >1548 km, but only 2% of the total channel network. The IP model was tested by tracking radio‐tagged Broad Whitefish using aerial surveys. Prespawn fish moved into the watershed starting mid‐July and mostly used habitat with moderate to very high IP in the middle and lower watershed. Several individuals were relocated in smaller multichannels with vegetated bars that contained very low IP (≤0.2), suggesting that other factors, such as hyporheic flow, may also influence spawning habitat selection. Our study demonstrates that IP modelling offers a useful method to quantify spawning habitat potential in data‐poor riverscapes, providing useful information for managers to assess potential anthropogenic impacts and develop conservation plans to protect essential Broad Whitefish habitat.
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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 | 0.001 | 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".