Comparison of diets for Largemouth and Smallmouth Bass in Eastern Lake Ontario using DNA barcoding and stable isotope analysis
Bibliographic record
Abstract
Largemouth (LMB: Micropterus salmoides) and Smallmouth Bass (SMB: Micropterus dolomieu) are important species in the recreational fisheries of the Laurentian Great Lakes. The invasion of the Round Goby (Neogobius melanostomus) into these lakes has changed several facets of black bass biology, but there is still much to learn about the relationship between these species. Previous dietary analyses have shown Round Goby to be important prey for bass, but have been limited by low visual identification rates of dissected stomach items. Within the present study, DNA barcoding and stable isotope analysis improve prey identification and provide a more quantitative dietary analysis of adult black bass in Lake Ontario, comparing the importance of Round Goby as prey between these two species. Eighty-four LMB (406mm fork length ±4mm SEM) and two hundred sixty-four SMB (422mm ±2mm) obtained as tournament mortalities had prey identified using DNA-based methods. Round Goby was the most prevalent prey species for both predators. The diet of LMB was three times more diverse than that of SMB, which almost entirely consists of Round Goby. Our results provide further support that recent increases in the size of Lake Ontario bass are a result of Round Goby consumption, and that the effects of this dietary shift on body condition are greater for SMB. Techniques developed in this study include reverse-oriented dual priming oligonucleotides used as blocking primers for predator DNA, and an automated design approach of restriction fragment length polymorphism tests for identifying prey DNA barcodes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".