Incorporating lakes in stream fish habitat models: are we missing a key landscape attribute?
Bibliographic record
Abstract
Although lakes and rivers are intimately connected, more effort is needed to develop conceptual approaches accounting for lake–stream interactions within the drainage network. Lakes can buffer the impacts of environmental variability in streams and facilitate stream fish recolonization processes. However, lakes have rarely been incorporated in habitat models for stream fish. We examine whether including the presence of lakes in habitat models can improve our understanding of brook trout (Salvelinus fontinalis) abundance in streams. We quantified brook trout relative abundance in 36 streams over 3 consecutive years by single-pass electrofishing. Relative abundance of brook trout in streams was greatest when lakes were present in the stream network. Lakes had greater influence on relative abundance in headwater streams than in larger streams. These results emphasize the importance of considering lakes as a critical attribute in landscape fish habitat models, many of which focus on terrestrial landscape variables. We discuss potential gains from incorporating the presence of lakes in (i) multiscale habitat models, (ii) analyses of spatiotemporal distribution of thermal refuges, and (iii) metrics of habitat connectivity in lake–stream networks.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".