Environmental influences on the size and recruitment of inland Cisco populations in three Minnesota Sentinel lakes
Bibliographic record
Abstract
Abstract Objective Cisco Coregonus artedi are a pelagic coldwater fish that are widely distributed throughout many lakes across Canada and the northern Midwest and play an important role as forage for large piscivores. Cisco are sensitive to oxythermal stress caused by a combination of warm epilimnetic temperatures and hypolimnetic hypoxia during peak stratification periods and are at risk from stressors such as climate change and land use changes. We hypothesized that interannual variability in the amount of oxythermal habitat and prey resources were associated with observed differences in size structure and recruitment success of different Cisco populations. This study evaluated the relationship between characteristics of three inland (non-Laurentian Great Lakes) Cisco populations, including maximum size and age-0 density, with environmental and biological factors such as pelagic oxythermal habitat, zooplankton availability, and Cisco density and biomass over a 7-year time series. Methods Targeted, standardized, annual pelagic sampling was conducted from 2013 to 2019 using hydroacoustic sonar and vertical gill nets to sample Cisco, vertical net tows to sample zooplankton, and temperature and dissolved oxygen profiles to measure oxythermal habitat. Linear regression and mixed-effect models were developed for two selected response variables: upper 95th percent total length (mm) and standardized age-0 density (fish/ha-m3). Result Cyclopoid copepod densities explained the most variability in the observed size differences, while age-1+ Cisco biomass best explained the variability in the age-0 density response variable. Additionally, the number of growing degree-days at dissolved oxygen of 3.0 mg/L explained variability in both response variables. Conclusion Results from this study document the importance of zooplankton prey, oxythermal habitat, and internal population dynamics on Cisco size and recruitment. This information provides fisheries managers with insights on the characteristics of inland lake systems that influence variability in Cisco populations and how this effects the vulnerability of this relatively important forage species on gape-limited predatory fish.
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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".