Browsing by Subject "Cisco"

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    Comparing Ship-based to Multi-Directional Sled-based Acoustic Estimates of Pelagic Fishes in Lake Superior
    (2019-05) Grow, Ryan
    Ship-based down-looking acoustic surveys are commonly used to determine the biomass and population density of commercially important fish species for resource managers and scientists, particularly in the Great Lakes and marine systems. However, there are some limitations and biases inherent in traditional down-looking surveys. I examined the use of multi-directional sled mounted acoustics equipped with up, side, and down-looking capabilities to overcome these limitations while examining the Lake Superior pelagic fish community. In the western arm of Lake Superior, I concurrently deployed the sled mounted acoustics during traditional down-looking surveys to directly compare the fish densities obtained from each gear, which I then followed with a mid-water trawl to inform my acoustic data with species composition. My findings from a two-way ANOVA showed a significant difference between fish densities detected by the sled-based survey and the ship-based down-looking survey indicating 60% of the pelagic fish community was missed by the traditional down-looking survey. This study also sought to provide a baseline for future research looking to discover which species in aquatic systems are most effected by traditional survey biases, as well as future work into using alternate forms of acoustic sampling to inform fisheries management and research.
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    Investigating drivers of Cisco (Coregonus artedi) recruitment in Lake Superior
    (2024-12) Nyffeler, Olivia
    Cisco (Coregonus artedi) are a pelagic freshwater fish native to the Laurentian Great Lakes Region. Successful year classes rarely occur, and recruitment is typically poor. Despite many studies investigating recruitment, researchers do not fully understand their erratic recruitment patterns. Cisco populations collapsed in the mid-1960s due to overharvest, climate change, and invasive species introductions. While Lake Superior’s cisco populations are recovering, recruitment remains unpredictable. In this study I analyzed a 43-year dataset of age-1 cisco catches from the United States Geological Survey (USGS) Lake Superior Biological Research Station and evaluated two statistical methods to predict poor recruitment using hypothesized drivers. Poor recruitment was defined as any year with fewer than 35 fish per ha. I used two statistical methods to identify drivers of poor recruitment and evaluated relationships with air temperatures, wind speeds, ice cover and an index of Rainbow Smelt (Osmerus mordax). Drivers were lagged according to the timing of hypothesized environmental drivers of early life survival of cisco prior to capture in bottom trawl surveys. The first model applied the Poor-Recruitment Paradigm to predict poor recruitment using extreme environmental thresholds one covariate at a time. The second model used a Random Forest approach to identify key covariates influencing recruitment from a multiple covariate approach. The first model’s findings indicate that a high ice cover during egg deposition, high May and June air temperatures, and low August and September air temperatures during the larval stage link to poor recruitment. These covariates predicted poor recruitment with 100% accuracy, with no instances of good recruitment occurring beyond the identified thresholds for these specific months. The second model identified December wind speeds, March, August and September temperatures and April wind speeds as some of the most influential factors based on the mean decrease in Gini Index over 100 instances of running the model with the Synthetic Minority Oversampling Technique (SMOTE). Analysis revealed important interactions between September air temperatures and December wind speeds, as well as March air temperatures and April wind speeds. This ability to predict poor Cisco recruitment using environmental covariates provides valuable insights for managers. By pinpointing specific environmental extremes that adversely affect recruitment, this approach improves forecasting of which year classes are likely to success or fail in reaching age-1.