Declines in lake whitefish larval densities after dreissenid mussel establishment in Lake Huron
Bibliographic record
Abstract
Lake whitefish (Coregonus clupeaformis) is an ecologically and commercially significant species across the Laurentian Great Lakes. Over the past 20 years, lake whitefish population abundance has substantially declined across lakes Huron and Michigan, being driven by reduced recruitment of juvenile fish into the population. However, the life stage at which the recruitment bottleneck is occurring and what factors are contributing to the declines remain unknown. One hypothesis is that dreissenid mussels reduced zooplankton availability to larval lake whitefish, leading to poor growth and survival of this critical life stage. Here, we present results of a larval fish survey conducted at the Fishing Islands spawning region in Lake Huron and examine whether declines in juvenile recruitment are linked to reduced larval fish density. Larval fish were collected annually during two time periods: (1) a historical time period before dreissenid mussel establishment (1976–1986); and (2) a contemporary time period after dreissenid mussels became established (2017–2019, 2021). We found significant declines in larval densities and growth between historical and contemporary time periods. Following dreissenid establishment, larval densities and growth were on average only 23% and 55% of historical values, respectively. Moreover, year class strength at the juvenile stage (age 4) was positively related to larval density. Several explanatory variables contributed to annual variation in larval densities, with dreissenid mussels and water levels having the most consistent effect. Our results suggest that juvenile recruitment is being limited at the larval stage, owing to overall lower larval production and potentially exacerbated by slower growth.
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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) | 0.001 | 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".