Attracting Lake Sturgeon back to a historical spawning location following a 20-year hiatus using effluent from tanks holding gravid broodstock
Bibliographic record
Abstract
ABSTRACT Objective Spawning habitat enhancement for sturgeon species has often resulted in limited long-term success, raising questions about the process of spawning site selection. Acknowledging that Lake Sturgeon Acipenser fulvescens have well-developed chemoreceptors and the general potential for learned behavior in fish, we investigated whether Lake Sturgeon spawning could be reinduced at a habitat not contemporarily utilized by the species. Methods The study was conducted where the Landing River empties into the Nelson River main stem in northern Manitoba, Canada. Effluent from streamside holding tanks containing gravid broodstock, presumably rich in hormones or pheromones, was discharged by hose into the Landing River during spring from 2019 to 2024. The attraction of Lake Sturgeon into the Landing River was then monitored using visual methods. Results After a nearly 20-year absence, Lake Sturgeon were successfully attracted back into the Landing River and spawning behavior was observed. A maximum concurrent count of 30 Lake Sturgeon was observed via aerial drone in 2024. The first individuals observed in the Landing River each year often closely followed the timeline of luteinizing hormone releasing hormone injections into the broodstock and/or egg releases in the tanks, suggesting an acute attraction response related to hormones or pheromones associated with spawning that were contained in the effluent released into the Landing River. In three different observation years, the draw of the effluent appeared to be eventually superseded by the draw of other fish and/or spawning activity within the river. Conclusions Lake Sturgeon spawning site selection appears to be strongly influenced by the presence of conspecifics. In the future, it may be possible to produce synthetic Lake Sturgeon hormones or pheromones and release them into the water to guide Lake Sturgeon into habitats that are conducive to successful egg hatch and subsequent recruitment.
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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 | 0.000 | 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) | 0.001 | 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".