Spotty distributions: Spotted Gar (Lepisosteus oculatus) and Spotted Sucker (Minytrema melanops) range expansion in eastern Lake Erie
Bibliographic record
Abstract
Natural range expansions in warm-water freshwater fishes are currently not well understood, but shifts in native species distributions can be influenced by many factors, including habitat restoration or degradation and climate change. Here, we provide empirical evidence of range expansions observed in two native freshwater fish species in Lake Erie: Spotted Gar (Lepisosteus oculatus) and Spotted Sucker (Minytrema melanops). We confirmed our field identifications of L. oculatus and M. melanops using mtDNA barcoding. Maximum likelihood phylogenetic analyses reveal that our samples confidently resolve in the L. oculatus and M. melanops clades respectively, with additional identification support from BLAST searches. Notably, we found no correlation between the increased detection rate of both species and an increase in sampling effort when compared to previous records. Historically, eastern Lake Erie experienced habitat degradation through channelization, siltation, dredging, and toxification of sediments. We hypothesize that recent habitat remediation efforts have provided suitable habitat for both species to recolonize shallow waters with densely vegetated habitat (>90% substrate coverage). Both species are likely to continue their northern expansion as habitats are restored and climatic changes favor warm-water fishes.
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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 | 0.000 | 0.000 |
| 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".