Recruitment dynamics of non-native largemouth bass within the Sacramento–San Joaquin Delta
Bibliographic record
Abstract
Largemouth bass (LMB; Micropterus salmoides) recruitment is limited by a critical developmental period during early life stages, but this mechanism may be less important within non-native habitats. We conducted boat electrofishing surveys in four tidal lakes of California’s Sacramento–San Joaquin Delta (SSJD) from 2010 to 2011 to describe introduced LMB recruitment dynamics. We evaluated growth, proximate composition, and health indices of young-of-the-year (YOY) LMB among tidal lakes and developed an integrated count model to determine how factors known to affect LMB recruitment shape SSJD population structure. Our results show a mismatch among growth, nutrition, and YOY abundance, where the tidal lake with the most abundant and fastest-growing LMB had the poorest nutritional status. The warm winter water temperatures and lack of a hatching-cohort growth advantage suggests overwinter starvation plays a less important role in SSJD LMB recruitment than in many native LMB habitats. Collectively, our results suggest that habitat characteristics (submerged aquatic vegetation) and not overwinter mortality shapes SSJD LMB population structure, a mechanism consistent with contemporary hypotheses about the altered fish community structure of the SSJD.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".