Great Lakes Science Center

Abstract Objective Test whether dietary differences among hatchery strains of Lake Trout Salvelinus namaycush were associated with variation in egg thiamine concentrations and assess the potential for using strain-specific traits to mitigate thiamine deficiency complex (TDC) in Lake Ontario restoration efforts. Methods Stomach content analysis and stable isotope mixing models were used to compare diets among four hatchery strains of Lake Trout, representing lean and humper morphotypes. Egg thiamine concentrations were measured to test whether dietary differences correlated with susceptibility to TDC. Results Significant differences in diet and egg thiamine concentrations were observed among Lake Trout strains. A humper-like morphotype strain exhibited elevated thiamine levels, which were associated with reduced consumption of prey known to degrade thiamine levels when consumed, Alewife Alosa pseudoharengus and Rainbow Smelt Osmerus mordax, and increased reliance on Round Goby Neogobius melanostomus or sculpin species (Slimy Sculpin Cottus cognatus and Deepwater Sculpin Myoxocephalus thompsonii). These findings support the link between the consumption of Alewife and Rainbow Smelt and thiamine deficiency. Conclusions Our findings support a link between strain-specific dietary preferences and susceptibility to TDC in Lake Trout. Preferential stocking of strains less prone to TDC, such as the strain derived from a humper morph from Lake Superior, could enhance restoration success in Lake Ontario. This strategy may be especially effective given the continued dominance of Alewife in the lake's prey base and the current management priorities supporting fisheries for Pacific salmon Oncorhynchus spp.

Upstream migrating fish must balance energetic costs with environmental and ecological constraints. Theoretical models predict that fish can conserve energy by adjusting swim speed in response to changing water velocity, but detailed field tests of these predictions remain rare. We used fine-scale acoustic telemetry and hydrodynamic modeling to track adult sea lamprey (Petromyzon marinus) migrating through a river and evaluated how movement speeds varied with environmental conditions and individual traits. Swim speed increased with faster water velocities, while ground speed remained constant, supporting the hypothesis that lampreys regulate swim effort to maintain a fixed migration pace. The average ground speed aligned with a predicted optimum of ~1 body length per second. Both swim and ground speeds declined slightly in deeper water, suggesting a trade-off between movement efficiency and predator avoidance. These findings provide field-based support for a theoretical model of energy optimization and underscore how migratory fish adaptively adjust behavior to local hydrodynamic conditions. Our results highlight the value of using high-resolution tracking and environmental modeling to reveal behavioral tactics that underlie efficient migration strategies.

Little is understood of lake browning (due to increased dissolved organic carbon; DOC) in large lakes such as the Laurentian Great Lakes. Lake browning can alter whole lake ecosystems, including decreasing exposure to damaging ultraviolet radiation (UV-B) which is strongly and selectively attenuated by DOC more so than photosynthetically active radiation (PAR). We compared the changes in UV-B and PAR transparency to DOC data collected during the ice-free seasons from 62 nearshore sites in four of the five Great Lakes from 2002 to 2022 using linear mixed effects regression models based on backwards selected Bayesian information criteria. Regionally, DOC significantly increased from 2002 to 2022 by 0.5% per year on average. DOC strongly and inversely explained the variability of UV-B and PAR transparencies, as did seasons and offshore influence on these habitats. We provide regional evidence of lake browning within the nearshore habitats of the Great Lakes as a strong contrast to the well-documented increased offshore water transparency associated with the spread of invasive dreissenid mussels.

ABSTRACT Reservoir morphology influences a waterbody’s response to temperature and corresponding effects on stratification, water quality, and ecosystem health. Because local reservoirs may respond differently to the same climate forcing, understanding shape-driven influences is critical to optimised reservoir management, particularly under future climate scenarios. For this study, 5 archetypical hypothetical reservoir morphologies (shapes) were created based on constant surface area and maximum depth from a previously modelled polymictic reservoir, Blagdon Lake in southwest England, to establish how bathymetry alters stratification regimes. The shapes were modelled with the Aquatic Ecosystem Model 3D (AEM3D) and then forced with multiple future climate scenarios based on UK Climate Projections (UKCP18). Two stratification predictors, lake geometry ratio (GR) and Osgood index (OI), were used to characterise stratification in the differently shaped reservoirs. Model results show that all reservoirs stratified with similar thermocline depths during simulated summers, with some variations in water-column stability and hypolimnion volume. Morphologies with larger OI values tended to have increased strength, areal extent, and duration of stratification. Results highlight the importance of taking reservoir shape into account when planning management strategies and optimising reservoir design under future climates.

Benthic mapping relies on a range of methods to ground truth remotely sensed data to map physical and biotic features. Traditional methods are labor-intensive and time-consuming with limited scalability. Autonomous underwater vehicles (AUVs) and remote operated vehicles (ROVs) can collect high volumes of quality data but require automated processing routines and pose other logistic challenges. This paper explores the automated use of multiple machine learning (ML) models to detect and delineate benthic habitat components from structure-from-motion (SfM) orthomosaics. Overlapping AUV photos were processed into high-resolution, seamless orthomosaics for a portion of the Lake Michigan lakebed. ML models were trained to delineate rocks and live mussels and subsequently applied to the SfM orthomosaics. Results demonstrate that a robust pipeline with SfM and ML models is an effective approach to efficiently, accurately, and comprehensively map benthic habitats.

Sea lamprey (Petromyzon marinus) control in the Laurentian Great Lakes of North America is among the largest and most successful control programs of an invasive species anywhere on the planet. The effort began more than 75 years ago; it unites multiple nations, states, and provinces with the common goal of controlling this invasive species and protecting a valuable fishery. The science-based control program is administered by the Great Lakes Fishery Commission (GLFC), a body arising from a treaty signed by the United States and Canada. In the present article, we share 10 lessons learned from decades of successful sea lamprey control with the hopes of informing ongoing and future control programs targeting biological invasions. The 10 lessons we identified are to act boldly in times of crisis, to maintain the social license, to invest in capacity building, to break down the silos, to support fundamental science, to diversify your portfolio of control measures, to strive for continuous improvement, to confront the trade-off between information and action, to keep your foot on the gas, and to keep your eyes on the prize. The GLFC has long fostered a framework that uses some military strategy and verbiage that extends across the lessons (e.g., know your enemy). Other lessons are more nascent as the GLFC reenvisions its relationship with Indigenous peoples and governments in a path to reconciliation where two-eyed seeing is being embraced. Through adaptive management, horizon scanning methods, and embracing implementation science, the lessons learned about sea lamprey control will continue to evolve, which is itself a lesson. We submit that the lessons shared in the present article will help guide invasive species control programs spanning taxa, ecosystems, and regions.

The use of acoustic telemetry is steadily expanding to help answer questions related to habitat use, movement, and behavior of fishes. Significant time and resources are invested to start acoustic telemetry studies; therefore, careful planning is needed to limit post-release mortality of tagged individuals. Deep, cold-water species present additional challenges to acoustic tagging because of changes in temperature and pressure experienced during capture. The objective of our study was to determine if capture method, surface water temperature, water depth, or fish size influenced short-term post-release survival of a deep, cold-water species, Lake Trout Salvelinus namaycush . In 2023, 299 Lake Trout were captured with angling or gillnets across Lake Ontario (Laurentian Great Lake – U.S. & CAN) and surgically implanted with acoustic transmitters. We estimated 30-day post-release mortality and 24-h post-release distance traveled for tagged Lake Trout. We used Cox proportional hazards models to identify factors affecting survival probability and multiple linear regression to identify factors affecting post-release distance traveled. Thirty-day post-release mortality was minimal (9.03 %, 27/299 Lake Trout); however, mortality was 6.37 times more likely for Lake Trout captured in gillnets compare to angling ( p = 0.003). Lake Trout length had a marginally significant effect on mortality ( p = 0.052) but capture depth and temperature did not ( p > 0.05). Lake Trout post-release distance traveled was not significantly influenced by capture gear, depth, temperature, or Lake Trout length ( p = 0.61). Our results indicate that tagging-induced post-release mortality is minimal for Lake Trout tagged in the spring, but survival can be increased by avoiding use of gillnets.

Growing concerns about heat in urban areas paired with the sparsity of weather stations have resulted in individuals drawing on data from citizen science sensor networks to fill in data gaps. In the past decade, a proliferation of crowd-sourced sensors has provided low-cost local air quality and temperature, with one brand having over 14,000 sensors deployed in the United States between 1 January 2017 and 20 July 2021. Although the air quality data from PurpleAir sensors have been widely studied, less attention has been paid to reported temperature. Gridded modeled temperature datasets are widely used in epidemiologic studies. The spatial granularity of the crowd-sourced sensor data captures local temperature variation which existing gridded datasets cannot, and can potentially be used to generate exposure assessments for health research. We compare temperature metrics reported by the dominantly used crowd-sourced sensor in the United States with a gridded temperature product, the North American Land Data Assimilation System (NLDAS)-2, which although not a gold-standard measure of temperature, is widely used in epidemiologic research. We evaluate the lag between indoor and outdoor sensor temperatures. We report associations of the difference between outdoor sensor temperatures and NLDAS-2 temperatures, an indicator of degradation, and the duration of sensor operation. Finally, based on the temperature range recorded by the outdoor sensors vis-a-vis NLDAS-2 temperatures, we provide a list of 271 (2.5%) sensors potentially misclassified as outdoor and likely located indoors. We observed that the outdoor sensors agreed well with NLDAS-2 (R2 > 0.82). This association broke down under warm conditions (daily average NLDAS ≥21.1oC). Our comparison suggests that a radiative-correction needs to be applied to use crowd-sourced data reliably. However, the spatial granularity of the continental sensor network can reduce the measurement error in exposure assignment compared to the NLDAS-2. Indoor sensor temperatures lagged hourly NLDAS temperatures by 2 hours across almost all climate zones. The mean difference in hourly sensor and NLDAS-2 temperatures increased by 0.57oC for every operational year, suggesting that careful attention must be paid to degradation. Overall, we found that researchers should be aware of the limitations in crowd-sourced sensor air temperatures when examining extreme heat, or when aggregating sensor data across multiple years.