Intraspecific variation in stable isotopes provides insight into adfluvial migrations and ecology of brook trout in Lake Superior tributaries
Bibliographic record
Abstract
Identifying streams hosting salmonids with poorly understood adfluvial life histories, such as coaster brook trout, is challenging due to the lack of inexpensive, non-lethal techniques for confirming lake to stream movements for stream-captured fish. In this study, we used stable isotope data from 589 brook trout collected throughout the Lake Superior basin to characterize stream versus Lake Superior foraging. We observed strong isotopic separation in δ 13 C between brook trout inhabiting Lake Superior and lake-inaccessible stream reaches (i.e., those lacking Lake Superior access). Using these data, we developed a linear discriminant function (LDF) which assigned brook trout to Lake Superior or stream habitats with over 97 % accuracy. LDF and Bayesian stable isotope mixing models were then used to estimate stream and lake energy use by brook trout collected from lake-accessible reaches. Brook trout caught in lake-accessible reaches had isotope signatures and sizes that were intermediate to fish from lake-inaccessible reaches and Lake Superior, potentially indicative of Lake Superior to stream migrations or possibly an energy subsidy from adfluvial migrants in streams. The LDF was used to estimate the probability that recently grown fin tissue from brook trout collected in lake-accessible reaches resulted from foraging in Lake Superior. We identified tributaries hosting “likely” coaster brook trout using a fish’s length and LDF probability value. Our findings show the potential and limitations of this approach for confirming adfluvial migrations of brook trout.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".