Foraging behaviour and optimal microhabitat selection in Yukon River Basin nonanadromous Dolly Varden Charr (<i>Salvelinus malma</i>)
Bibliographic record
Abstract
Abstract Species conservation requires understanding the mechanistic processes of habitat selection and their effects on fitness. Nonetheless, there are few fitness‐based habitat selection models for aquatic organisms. We examined multiple aspects of foraging behaviour of nonanadromous Dolly Varden Charr ( Salvelinus malma ) in Panguingue Creek, Alaska, USA and applied these data to test a fitness‐based microhabitat selection model. Velocity negatively affected prey capture success, positively affected holding velocity, and had no effect on reactive distance. Dominance was a better predictor of prey capture success than length difference between competitors, but there was no relationship between these variables and holding velocity or reactive distance. We used the velocity–prey capture success relationship to parameterise the microhabitat habitat selection model and compared the predicted optimal holding velocity to the 95% confidence interval (24.9–29.3 cm/s) of holding velocities occupied by Dolly Varden ( N = 29) in Panguingue Creek. The prediction of 24.0 cm/s fell just slightly (0.9 cm/s) outside the lower limit of the confidence interval; the model barely failed to predict holding velocity for this species in Panguingue Creek. Although this discrepancy fell within measurement error, model failure also may have been due to influence of high turbulence on fish holding velocities in the creek, low sample sizes imposed by permitting limitations, or field logistical issues. The relationship between velocity and prey capture success is an important aspect of drift feeder habitat selection. Our optimal holding velocity prediction for Dolly Varden should aid in the management and conservation of this species.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.002 | 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 teacher head, 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".