Ecological differences between two closely related morphologically similar benthic whitefish<i>(Prosopium spilonotus</i>and<i>Prosopium abyssicola</i>) in an endemic whitefish complex
Bibliographic record
Abstract
Identifying the differences in ecology between closely related species occupying the same environment contributes to our understanding of community diversity, ecosystem structure, and species conservation. Endemic Bear Lake whitefish (Prosopium abyssicola) and Bonneville whitefish (Prosopium spilonotus) are benthic, morphologically similar, and closely related, yet the extent of differential resource use remains poorly understood. To determine the ecological differences between these two species, we studied their seasonal distribution and diet in Bear Lake, Utah–Idaho. We used bottom-set gill nets to examine how catch of each species of whitefish varied in relation to depth and season (spring and summer). In both spring and summer, Bonneville whitefish dominated the shallower depths (5–30 m), whereas Bear Lake whitefish dominated the deeper depths (45–55 m). Bonneville whitefish ate a variety of benthic invertebrates, but mostly Chironomidae, whereas Bear Lake whitefish fed mostly on Ostracoda. These data describe a closely related morphologically similar, yet ecologically distinct group of whitefish in an ecoregion completely different from those studied before. These results indicated that each species has a very different role in the Bear Lake ecosystem. To conserve this unique fish assemblage, both shallow and deepwater habitats need to be protected.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".