Diversity in spawning habitat use among Great Lakes Cisco populations
Bibliographic record
Abstract
Abstract Cisco (Coregonus artedi) once dominated fish communities in the Laurentian Great Lakes. Restoring the abundance and distribution of this species has emerged as a management priority, yet our understanding of Cisco spawning habitat use is insufficient to characterise habitat needs for these populations and assess whether availability of suitable spawning habitat could be a constraint to recovery. We characterised the distribution of incubating Cisco eggs in situ across gradients of depth and substrate types to describe the spawning habitat used by three Great Lakes populations. In Chaumont Bay, Lake Ontario, eggs were concentrated on shallow bedrock shoals and not found on deeper silt or sand substrate. In contrast, eggs in Thunder Bay, Lake Superior, and Elk Rapids, Lake Michigan, were found on deeper fine grain sediments with low utilisation of shallow rocky and cobble habitats. These patterns of egg incubation habitat use suggest a broad spawning habitat niche at the species level but distinct spawning habitat preferences at the population level. While our results indicate some historical diversity in spawning habitat use has been maintained across the species’ range in the Great Lakes, comparisons of contemporary spawning habitat utilisation against historical accounts raise questions as to whether some spawning habitat use behaviours may no longer be prevalent within specific lakes. Thus, characterising the portfolio of spawning strategies remaining within lakes may improve our understanding of habitat needs and identify opportunities to maintain population diversity while supporting Cisco rehabilitation.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".