Lake Sturgeon population trends in the St. Clair–Detroit River system, 2001–2019
Bibliographic record
Abstract
Abstract Objective The Lake Sturgeon Acipenser fulvescens is listed as threatened or endangered in 15 states or provinces within the species' native range. Accordingly, investments in habitat and population restoration for this species have increased throughout the Great Lakes. To aid in the evaluation of restoration efficacy, robust population parameters are needed to inform management decisions. The St. Clair–Detroit River system (SCDRS) contains one of the largest self-sustaining Lake Sturgeon populations in the Great Lakes; however, recent estimates of population abundance and growth parameters have not been assessed. Methods Our study used baited setline and mark–recapture data collected between 2001 and 2019 to estimate whether the number of Lake Sturgeon captured varied annually and/or with water temperature and whether population abundance and the population growth rate (λ) varied among three subpopulations located in the SCDRS. Result Trends in the number of Lake Sturgeon captured on setlines varied among subpopulations and by life stage. Annual trends in the number of Lake Sturgeon captured remained consistent over time in the upper St. Clair River, decreased for adults and increased for subadults in the lower St. Clair River, and increased in the Detroit River. With subpopulation abundances of 20,184 (95% confidence interval [CI] = 12,533–27,816) in the upper St. Clair River/southern Lake Huron, 6523 (95% CI = 5720–7327) in the lower St. Clair River, and 6416 (95% CI = 4065–8767) in the Detroit River, our study confirms that the SCDRS contains the largest Lake Sturgeon population with unimpeded access to the Great Lakes. The geometric mean λ for all subpopulations indicated stable populations and ranged from 1.00 to 1.16. Conclusion Our study provides an updated assessment of Lake Sturgeon population parameters that serve as a baseline to evaluate habitat restoration efforts and to inform management of the SCDRS recreational Lake Sturgeon fishery.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".